ACKNOWLEDGEMENT We express our sincere gratitude to Dr.S.K. Laroiya for giving us the opportunity to undergo this project. We further thank him for lending a helping hand when it came to solving our problem related to the project. This project would not have been possible without his valuable time and support. We also thank Amity Business School for an opportunity to undertake a soft skill project at this crucial time in our life in MBA which helped us to understand the topics deeply which were untouched before. Any suggestions to improve are always welcomed.
To Whom It May Concern: I, Dr. S.K. Laroiya, hereby authorize the following students, to conduct a comparative study on detergents. They are authorized to act on my behalf in all manners relating to conducting of this study. Any and all acts carried out by them on my behalf shall have the same affect as acts of mine. Name of Students: 1. 2. 3. 4. 5. 6.
Siddharth Saraswat Saurabh Kumar Ankush Garg Mohd. Haris Khan Abhilash Mishra Getanjali
This authorization is valid until further written notice from me.
Sincerely,
Dr. S.K. Laroiya (H.O.D. Economics Dept. ABS)
ABSTRACT With the increase in per capita income and wide range of choices being available, consumers are main focus for many detergent producing organizations. With competition at its all time peak and with changing trends in demand the companies are finding it hard to survive or to retain their market share. In order to lure the consumers, companies study the quantity being purchased by consumers and at what price. We here try to find out how these factors, confining ourselves to detergent market of India, and many other factors effect the demand of consumers for detergents.
Executive Summary This is based on our research work on Detergents, being FMCG, it made us go to households and interact to find out the consumer’s buying behavior. Our objectives were to find out what are the main features consumers look in detergents while buying, brand loyalty towards a particular brand, major reasons of switching from one brand to the other. To gather the data we used the questionnaires method. This data was fed in a data analysis tool SPSS. With the help of which we analysed and interpreted the data gathered, pertaining the buying behavior of consumers. Along with questionnaires, we also used Internet to find out about the detergent industry and the various brands available. There are more than 10 brands available in the Indian market, but we have chosen 6 major brands. The Indian laundry market is Rs 5000 crore, with HUL enjoying highest 38% of share, followed by others like P&G, Nirma, Ghari etc. Detergent bar comprises of 43% of market share and powder enjoying the rest 57%. The brands which we tapped are Nirma, Ariel, Surf, Tide, Wheel, Surf Excel and leaving others as option. Competition in this market is really high with HUL, P&G, Nirma etc strategizing and innovating to capture the market. The research design used in our research was descriptive incorporating knowledge from secondary information analysis, qualitative research, methodology selection, question measurement & scale selection, questionnaire design and sample design to be used. And simple random sampling was done. Target customers were mainly housewives, bachelors and others who are using detergents. The age group was not defined. Area where research is done is UP, Delhi & NCR because of the convenience factor. Marjory Quantitative Techniques like frequency distribution and cross tabulation to make interpretations
Findings … ka 1 para likhna padega… after u write recommendations……likhna hai..
Introduction to Project
Detergent Detergent is a material intended to assist cleaning. The term is sometimes used to differentiate between soap and other surfactants used for cleaning. As an adjective pertaining to a substance, it (or "detersive") means "cleaning" or "having cleaning properties"; "detergency" indicates presence or degree of cleaning property. The term detergent by itself is sometimes used to refer specifically to clothing detergent, as opposed to hand soap or other types of cleaning agents. Plain water, if used for cleaning, is a detergent. Probably the most widely used detergents other than water are soaps or mixtures composed chiefly of soaps. However, not all soaps have significant detergency and, although the words "detergent" and "soap" are sometimes used interchangeably, not every detergent is a soap. The term detergent is sometimes used to refer to any surfactant, even when it is not used for cleaning. This terminology should be avoided as long as the term surfactant itself is available.
Components Detergents, especially those made for use with water, often include different components such as: •
Surfactants to 'cut' (Emulsify) grease and to wet surfaces
•
Abrasive to scour
•
Substances to modify pH or to affect performance or stability of other ingredients, acids for descaling or caustics to break down organic compounds
• Water softeners to counteract the effect of "hardness" ions on other ingredients •
oxidants (oxidizers) for bleaching, disinfection, and breaking down organic compounds
•
Non-surfactant materials that keep dirt in suspension
•
Enzymes to digest proteins, fats, or carbohydrates in stains or to modifyfabric feel
•
Ingredients that modify the foaming properties of the cleaning surfactants, to either stabilize or counteract foam
•
Ingredients to increase or decrease the viscosity of the solution, or to keep other ingredients in solution, in a detergent supplied as a water solution or gel
•
Ingredients that affect aesthetic properties of the item to be cleaned, or of the detergent itself before or during use, such as optical brighteners, fabric softeners, colors, perfumes, etc.
•
Ingredients such as corrosion inhibitors to counteract damage to equipment with which the detergent is used
•
Ingredients to reduce harm or produce benefits to skin, when the detergent is used by bare hand on inanimate objects or used to clean skin
•
Preservatives to prevent spoilage of other ingredients Sometimes materials more complicated than mere mixtures of compounds are said to be detergent. For instance, certain foods such as celery are said to be detergent or detersive to teeth.
Types There are several factors that dictate what compositions of detergent should be used, including the material to be cleaned, the apparatus to be used, and tolerance for and type of dirt. For instance, all of the following are used to clean glass. The sheer range of different detergents that can be used demonstrates the importance of context in the selection of an appropriate glass-cleaning agent: •
a chromic acid solution—to get glass very clean for certain precision demanding purposes such as analytical chemistry
•
a high-foaming mixture of surfactants with low skin irritation—for hand washing of dishware in a sink or dishpan
•
any of various non-foaming compositions—for dishware in a dishwashing machine
•
other surfactant-based compositions—for washing windows with a squeegee, followed by rinsing
•
an ammonia-containing solution—for cleaning windows with no additional dilution and no rinsing
•
ethano l or methanol in windshield washer fluid—used for a vehicle in motion, with no additional dilution
•
glass contact lens cleaning solutions, which must clean and disinfect without leaving any eyeharming material that would not be easily rinsed
History of Detergent The earliest detergent substance was undoubtedly water; after that, oils, abrasives such as wet sand, and
wet clay. The oldest known detergent for wool-washing is stale (putrescent) urine.
Other detergent surfactants came from saponin sand ox bile. The detergent effects of certain synthetic surfactants were noted in 1913 by A. Reychler, a Belgian chemist. The first commercially available detergent taking advantage of those observations was Nekal, sold in Germany in 1917, to alleviate World War I soap shortages. Detergents were mainly used in industry until World War II. By then new developments and the later conversion of USA aviation fuel plants to produce tetrapropylene, used in household detergents, caused a fast growth of household use, in the late 1940s. In the late 1960s biological detergents, containing enzymes, better suited to dissolve protein stains, such as egg stains, were introduced in the USA by Procter & Gamble.
Indian detergent market The first companies to manufacture detergents in India were HLL and Swastik. HLL test marketed Surf between 1956 and 1958 and began manufacturing it from 1959. Swastik launched Det, a white detergent powder, in 1957. By 1960, Det had made rapid inroads in eastern India. Surf, a blue detergent powder, became the national
market
leader
with
dominant
positions
in
the
west,
north
and
south.
In the early 1960s, the total volume of detergents manufactured in India grew from around 1600 tonnes to 8000 tonnes. HLL dominated the market with a share of almost 70 % compared to Det's
25%. In 1966, another player entered the fray. Tata Oil Mills Company (TOMCO) 2 launched its detergent
powder
'Magic'.
In 1973, TOMCO introduced 'Tata's Tej' in the low-priced segment. TOMCO unveiled another economy detergent powder called OK in 1977. Important inventions over the years of the history of detergents 1950s Liquid laundry, hand dishwashing and all-purpose cleaning products •
Automatic dishwasher powders
•
Detergent with oxygen bleach
•
Fabric softeners (rinse-cycle added)
1960s •
Laundry powders with enzymes
•
Prewash soil and stain removers
•
Enzyme presoaks
1970s •
Fabric softeners (sheets and wash-cycle added)
•
Multifunctional products (e.g., detergent with fabric softener)
•
Liquid hand soaps
1980s •
Automatic dishwasher liquids
•
Detergents for cooler water washing
•
Concentrated laundry powders
1990s
•
Ultra (super concentrated) powder and liquid detergents
•
Automatic dishwasher gels
•
Ultra fabric softeners
•
Laundry and cleaning product refills
Indian Market •
The Indian laundry market is estimated to be Rs 5,000 crore in size
•
Making India world’s third largest detergents market.
•
Detergent bars comprise 43 per cent of the total market and detergent powders comprise the balance 57 per cent.
However, the detergent bar market is shrinking in India
Detergent Brands NIRMA Various Products offered by Nirma are: Nirma Washing Powder This product created a marketing miracle, when introduced in the domestic marketplace. In 1969, when the detergents were priced so exorbitantly that for most of the Indians, it was a luxury item. Nirma envisioned the vast Fabric Wash market segment and sensed a tremendous potential therein. This product was priced at almost one third to that of the competitor brands, resulting into instant trial by the consumers. Owing to its unique environment-friendly, phosphate-free formulation, the consumers became loyal to this brand, helping it to over-take the decades’ old brands, in terms of volumes. This brand had been ranked as the “Most widely distributed detergent powder brand in India” as per All India Census of Retail Outlets carried out in 435 urban towns by the AIMS (Asian Information Marketing & Social) Research agency [Brand Equity - The Economic Times, March 11, 1997]. As per the ORG-MARG Rural Consumer Panel [December 1998] survey, Nirma brand has been ranked as highest in terms of penetration in washing powder category [BT Rural Market Watch, Business Today, June 22, 1999].
Super Nirma Washing Powder Exploding the myth that ‘better quality always demands higher price”, Nirma introduced a spraydried blue coloured washing powder in the premium segment, in 1996. Available in 25g, 500g and 1000g packs, this product out-classed its competitor brands. Though, priced almost 40 % lesser, thus providing a very attractive ‘value-for-money’ proposition. This brand, within a short span of two years, had cornered substantial market share in the premium detergent segment and continues to perform well. Nirma Popular Detergent Powder To cater to the needs of the specific target audience, Nirma launched a good quality product at a very affordable price. The objective is to convert the non-users of detergents into users and also prevent the competitors and local manufacturers to lure away the prospective Nirma consumers by sub-standard products. This product has created a loyal consumer base of its own and has established substantial amount of volumes. It is available in pack sizes of 500g and 1000g pack sizes. Nirma Detergent Cake Deriving inspiration from its success in the Detergent Powder market, Nirma expanded its product portfolio by introducing the “Nirma detergent cake” in 1987. Here again, the excellent pricequality equation tempted the consumers to try the product. Available in 125g and 250g pack sizes, this brand has done exceptionally well. AIMS survey ranked Nirma detergent cake as “The Most widely distributed detergent cake brand”. Due to its unique formulation, this product offers benefits like less melting in water, better stability, and therefore lasts longer. As per the ORGMARG Rural Consumer Panel[December 1998] survey, Nirma brand is ranked highest in terms of penetration in washing cakes / bars category [BT Rural Market Watch, Business Today, June 22, 1999]. Super Nirma Detergent Cake To meet the growing aspirations of consumers and to offer them value-chain product portfolio, Nirma introduced Super Nirma Detergent Cake, in 1992. Available in 125g and 250g pack sizes, this product, within a short span, convinced the consumers of competitor brands to switch their
loyalty towards Super Nirma detergent cake. With a high detergency value, this product offers quality wash to their consumers. Super Nirma Detergent Cake was ranked as the fastest Climber for the year 1997-98 in the detergent cake/ bars category [BUSINESS TODAY, Octobers 22, 1998]. Nirma Popular Detergent Cake The positioning of Nirma Popular Detergent Cake is similar to that of Nirma Popular Detergent Powder. This product is available in 125g and 250g pack sizes, targeted to first-time detergent cake user segment.
WHEEL Wheel - your smart laundry choice The largest laundry brand in Bangladesh, Wheel has always been focused in making laundry a pleasurable and delightful experience for the housewives. Based on its years of understanding of its consumers and huge experience in laundry, Wheel has been continually improving its formulation and form to suit the modern day users. Different formats and pack sizes of Wheel has been designed to cater to the requirements of users with different family sizes, laundry requirements and income groups. Wheel Laundry Soap Wheel Laundry Soap has a perfect formulation that not only gives great clean, but also is gentle to both hand and cloth. The soap comes in individual shrink wrap designed to ensure that the consumers receive a fresh soap with great lemon fragrance. The improved formulation of Wheel Laundry Soap also helps the users to wash more number of clothes than the traditional ball soap. Wheel Washing Powder A dominant market leader in the detergent segment, Wheel Washing Powder is known for its great cleaning ability with minimum effort. The superior formulation of Wheel Washing Powder is enhanced with the power of lemon, which not only removes the tough dirt in your cloth, but also leaves a pleasant lemon fresh fragrance well after washing. The convenience provided by Wheel Washing Powder has relieved many housewives from the laborious laundry process of the tradional Ball Soaps.
ARIEL Ariel is a marketing line of laundry detergents made by Procter & Gamble. It is the flagship brand in
Procter
&
Gamble's European,Mexican, Japanese, Brazilian, Peruvian, Turkish, Filipino,
and Venezuelan portfolios. Ariel first appeared on the UK market circa 1968 and was the first detergent with stain-removing enzymes. It was a high-sudsing powder designed for twin-tub and top-loading washing machines. With the rise in popularity of automatic front-loading washing machines, a suitable low-suds variant was launched in the early 1970s. The mid-eighties saw the range expanding to encompass liquid detergent and compact powder. The compact powder was originally known as "Ariel Ultra"; and was subsequently reformulated into the nineties as "Ariel Futur". This was possibly in response to Unilever's launch of the ultimately doomed "Persil Power", which was seen to damage clothes. Compact powders never proved popular in the UK; so when the tablet variant appeared in July 1999, the compact version disappeared.
In 2003, Ariel brought out its quickwash action to its detergents, to allow consumers to be able to do their laundry on a quickwash cycle. In 2006, Ariel started its "turn to 30" campaign to inspire consumers to wash in cool water so that energy can be saved. Ariel launched a concentrated version of their liquid detergents named Ariel Power in the spring of 2008. In October 2008, Ariel launched their new Excel Gel product which can be used in temperatures as low as 15 degrees celsius. This product was launched under Ariel's "cold is the new hot" campaign.
SURF EXCEL
•
Launched in 1959 & first in Indian detergent powder mkt.
•
It was the first Fast Moving Consumer Goods (FMCG) for Detergent.
•
Surf was the first brand of detergent that was advertised on TV. It is advertised on more than 300 channels across the globe .
•
Introduced the concept of bucket wash to housewives who up till now used to washing clothes with laundry soap bars.
•
Brand to set up a one-stop shop - called Care line - for people seeking solutions to their varied laundry problems.
•
Surf Excel, launched in 1954, is one of the oldest detergent powders in India. Initially, the brand was positioned on the clear proposition of “washes whitest”.
•
Surf Excel underwent various changes in its Brand Communication; from ‘Lalitaji' to 'dhoondte reh jaaoge' to 'jaise bhi daag ho, surf excel hai na', and is today communicated on the platform of 'Dhaag achcha hai'.
•
2006 saw a unique marketing move from HLL.
•
Rin Supreme bar is being migrated to Surf Excel.
•
Right from ‘Lalitaji’, representative of the true-blue cost-conscious Indian woman, till the inspiring storyboards of today, Surf Excel has done it all and in style!
•
HLL to revise Surf Excel pricing - A change in the pricing strategy for HLL Surf Excel brand, which dominates the Rs 5,000 crore detergent powder market, seems to be on the cards.
•
HLL is now reworking the Surf Excel strategy by moving away from positioning the brand on functional benefits, to building an emotional connect
Price Index
Size
Market growth
TO NEAREST COMPETITOR Unilever brand
Relative share
PREMIUM
15%
++
Surf Excel
2.4
MID-PRICED
25%
++
RIN
1.8
MASS
60%
+
Wheel
1.4
TIDE •
Tide is the name of a popular laundry detergent in the market of Canada, the United States and other countries.
•
It is manufactured by Procter & Gamble.
•
First introduced in test marketed in 1946 with national distribution reached in 1949
•
Tide is the World’s Oldest & Most Trusted Detergent brand and is the Market Leader in 23 Countries around the world.
•
The brand regularly introduces new products and technologies to beat the laundry blues
•
Launched in India in mid-2000
•
It gives outstanding whiteness due to its anti redeposition global technology
•
Anti-redeposition Agents help keep soils from re-settling on clothes after they have been removed during the wash itself
•
It offers solution to virtually any stain
•
The brand in India being a relatively new entry has only two types of products namely Tide detergent and Tide bar
•
Tide detergent is available in India in packs of 200 gm, 500 gm, 1 kg, 2 kg and 20 gm single use sachet.
•
Tide bar is available in 75gm,125gm,200gm bars.
Fighting Competition
•
The latest move comes in the wake of the high profile launch of Tide detergent bar.
•
Tide and Ariel always created problems for Surf and Rin. The migration of Rin Supreme bar to Surf Excel bar is aimed at countering Tide.
•
HLL has announced a drastic reduction in price by Rs 20 per kilo on Surf Excel, its premium detergent brand, making it cheaper than competing brand Ariel from Procter & Gamble (P&G). price cut, from Rs 155 to Rs 135 per kg.
Research Objective
Objectives
The following project has been given to us in order to make us understand the real environment of the market in which research is conducted. Marketing research, being a very important field of study in management can only be learned through practically working in the markets. The subject of our study being an FMCG product made us go and interact with the households and know their buying behaviour, preferences and expectations from the detergents they use. In our study we defined our research objectives as follows:•
To find the customer preference in the forms of detergents
•
To find the customer frequency of use of detergents/ number of times they purchase a product in a month
•
To find the various ways by which the customers wash their clothes/ dishes
•
To find the brand loyalty of the customers
•
To find the qualities they look for while buying a detergent
•
To study the reasons that made the customer switch from their previous brands
•
To find the mode of communication through which they came to know about the qualities/ features of their present brand
•
To find the number of times the customer switches from one brand to another.
•
To find the role of packaging in the purchase behavior of a product- quantity.
•
Consumers’ awareness about the harmful effects of the detergents.
The objectives hence set paved the way for the exhaustive research that we conducted in the field to elaborate and analyse separately in order to get a complete and a dynamic overview.
Market Research
Market Research Market research is any organized effort to gather information about markets or customers. It is a very important component of business strategy. The term is commonly interchanged with marketing research; however, expert practitioners may wish to draw a distinction, in that marketing
research is concerned specifically about marketing processes, while market research is concerned specifically with markets. Market research as defined by the International Code on Market and Social Research, includes social and opinion research is the systematic gathering and interpretation of information about individuals or organizations using statistical and analytical methods and techniques of the applied social sciences to gain insight or support decision making.
TYPES OF MARKETING RESEARCH Quantitative marketing research Quantitative marketing research is the application of quantitative research techniques to the field of marketing. It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion. As a social research method, it typically involves the construction of questionnaires and scales. People who respond (respondents) are asked to complete the survey. Marketers use the information so obtained to understand the needs of individuals in the marketplace, and to create strategies and marketing plans.
Qualitative Market Research Qualitative marketing research is a set of research techniques, used in marketing and the social sciences, in which data is obtained from a relatively small group of respondents and not analyzed with inferential statistics. This differentiates it from quantitative analyzed for statistical significance. Qualitative research tools are used primarily to define a problem and generate hypotheses. They are often used as the prelude to quantitative research in order to identify determinants, and develop quantitative research designs. They can be better than quantitative research at probing below the surface in order to understand what drives and motivates behaviour. Because of the low number of respondents involved and the idiosyncratic nature of some data collection methods findings from qualitative marketing research should be applied to larger
populations with caution. They are however, very valuable for exploring an issue and are used by almost all researchers at various points during large research campaigns.
In short, most businesses use one or more of six basic methods to perform market research: literature, surveys, focus groups, personal interviews, observation and field trials. The type of data you need and how much money you’re willing to spend will determine which techniques you choose for your business 1. Literature search involves reviewing all readily available materials. These materials can include internal company information, relevant trade publications, newspapers, magazines, annual reports, company literature, on-line databases, and any other published materials. It is a very inexpensive method of gathering information, although it generally does not yield timely information. Literature searches take between one and eight weeks. 2. Surveys. Using concise, straightforward questionnaires, you can analyze a sample group that represents your target market. The larger the sample, the more reliable the results In-person surveys are one-on-one interviews typically conducted in high-traffic locations such as shopping malls. They allow you to present people with samples of products, packaging or advertising and gather immediate feedback. In-person surveys can generate response rates of more than 90 percent, but they are costly. With the time and labor involved, the tab for an in-person survey can run as high as $100 per interview. Telephone surveys are less expensive than in-person surveys, but costlier than mail. However, due to consumer resistance to relentless telemarketing, getting people to participate in phone surveys has grown increasingly difficult. Telephone surveys generally yield response rates of 50 percent to 60 percent. Mail surveys are a relatively inexpensive way to reach a broad audience. They're much cheaper than in-person and phone surveys, but they only generate response rates of 3 percent to 15 percent. Despite the low return, mail surveys are still a cost-effective choice for small businesses. Online surveys usually generate unpredictable response rates and unreliable data because you have no control over the pool of respondents. But an online survey is a simple, inexpensive way to collect anecdotal evidence and gather customer opinions and preferences.
3. Focus groups. In focus groups, a moderator uses a scripted series of questions or topics to lead a discussion among a group of people. These sessions take place at neutral locations, usually at facilities with videotaping equipment and an observation room with one-way mirrors. A focus group usually lasts for one to two hours, and it takes at least three groups to get balanced results. 4. Personal interviews. Like focus groups, personal interviews include unstructured, open-ended questions. They usually last for about an hour and are typically recorded. Focus groups and personal interviews provide more subjective data than surveys do. The results are not statistically reliable, which means they usually don't represent a large segment of the population. Nevertheless, focus groups and interviews yield valuable insights into customer attitudes and are excellent ways to uncover issues related to new products or service development. 5. Observation. Individual responses to surveys and focus groups are sometimes at odds with people's actual behavior. When you observe consumers in action by videotaping them in stores, at work or at home, you can observe how they buy or use a product. This gives you a more accurate picture of customers' usage habits and shopping patterns. 6. Field trials. Placing a new product in selected stores to test customer response under real-life selling conditions can help you make product modifications, adjust prices or improve packaging. Small business owners should try to establish rapport with local storeowners and Web sites that can help them test their products. 7. Questionnaires. A questionnaire is research instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. Although they are often designed for statistical analysis of the responses, this is not always the case. The questionnaire was invented by Sir Francis Galton. Questionnaires have advantages over some other types of surveys in that they are cheap, do not require as much effort from the questioner as verbal or telephone surveys, and often have standardized answers that make it simple to compile data.
However, such standardized answers may frustrate users. Questionnaires are also sharply limited by the fact that respondents must be able to read the questions and respond to them. Thus, for some demographic groups conducting a survey by questionnaire may not be practical. Question types
Usually, a questionnaire consists of a number of questions that the respondent has to answer in a set format. A distinction is made between open-ended and closed-ended questions. An open-ended question asks the respondent to formulate his own answer, whereas a closed-ended question has the respondent pick an answer from a given number of options. The response options for a closedended question should be exhaustive and mutually exclusive. Four types of response scales for closed-ended questions are distinguished: _ Dichotomous, where the respondent has two options _ Nominal-polytomous, where the respondent has more than two unordered options _ Ordinal-polytomous, where the respondent has more than two ordered options _ (Bounded)Continuous, where the respondent is presented with a continuous scale A respondent's answer to an open-ended question is coded into a response scale afterwards. An example of an open-ended question is a question where the testee has to complete a sentence (sentence completion item) Question sequence In general, questions should flow logically from one to the next. To achieve the best response rates, questions should flow from the least sensitive to the most sensitive, from the factual and behavioural to the attitudinal, and from the more general to the more specific. Before designing the questionnaire, many decisions have to be made. These decisions affect the questionnaire, and should be part of the draft plan for a survey. The draft plan should address the following issues: Survey objectives and data requirements
In order to address the survey's objectives, you should prepare a document that provides a clear and comprehensive statement of the survey's goals, data requirements, and the analysis plan. This document will determine the variables to be measured, and ultimately, the survey questions and response alternatives. When formulating the questions, consult with subject-matter experts and if possible, members of the target audience. Also, examine questions from other surveys on the same or similar topics. This research will provide you with a useful starting point and will help you create appropriate and informative questions. Make certain that the questions are relevant to the survey objectives and information requirements and ensure that there is an established rationale behind each question. Also, you should explain how the information gathered from these questions will be used and whether they will be good measures of the required data.
• Analysis plan The next step in designing a questionnaire is to create an analysis plan. First, outline the questionnaire's objectives and data requirements. Describe the target audience as clearly as possible. Then, identify the reference period (the time period under construction—in the last year, in the last month etc.). Develop a list of the units to be sampled (e.g., students, houses, teachers, etc.). Decide on the method of data collection to be used (e.g., face-to-face interview, telephone interview, mailed questionnaire, etc.). Explain how the questionnaire content, wording, format and pre-testing process will be developed; as well as the procedures put in place to deal with the interviewer training and non-response results. Also, choose the methods to be used during the data processing (e.g., coding, editing etc.). Some of the other issues that can be analysed during this step include estimation methods, result output tabulations, result reports and the analysis. Finally, the last two important issues to be considered are the time required to complete the entire process and the budget that has been allotted to it.
• Survey target population Often the target population (the population for which information is required) and the survey population (the population actually covered) differ for practical reasons, even though they should, in actuality, be the same. Sometimes, it is necessary to impose geographical limitations excluding certain parts of the target population because they are inaccessible due to difficulty or cost. It is also possible that some of the survey concepts and methods that are used can be considered inappropriate for certain parts of the population. For example, consider a survey of post-secondary graduates where the objective is to determine if the graduates found jobs and, if so, what types of jobs. In this case, you might exclude graduates who specialized in religious seminaries or military schools, as these types of graduates would be reasonably assured of securing employment in their respective fields. Thus, the target population might contain only those graduates who graduated from universities, colleges and trade schools. • Method of data collection This next step in questionnaire design involves developing the methods of data collection. This is important step because you need to consider the costs, physical resources, and time required to conduct the survey. First, select the best method for gathering the required data. Keep in mind that cost and data quality will be directly impacted by the method you choose. There are several options available: face-to-face interviews or computer assisted personal interviewing (CAPI) are two examples. These methods are administered by a trained interviewer and can have either a structured or unstructured line of questioning. There are also two telephone methods available: telephone interviews or computer assisted telephone interviewing (CATI). Both of these methods are also administered by a trained interviewer, but the telephone versions are structured with a more formal interview schedule. Finally, there is also the option of a collecting data through a selfcompleted questionnaire. This method allows the respondent to complete the questionnaire without the aid of an interviewer. It is highly structured and can be returned by mail or through a drop-off system. • Size of the survey
Since each survey is different, there are no hard and fast rules for determining its size. The deciding factors in the scale of the survey operations are time, cost, operational constraints and the desired precision of the results. Evaluate and assess each of these issues and you will be in a better position to decide the sample size. Also, consider what should be the acceptable level of error in the sample. If there is a lot of variability in the population, the sample size will need to be bigger to obtain the specified level of reliability. • Data processing plans This processes the questionnaire responses into output. Coding; data capture; editing; dealing with invalid or missing data; and, if necessary creating derived variables are the tasks that will be completed during data processing. In short, the aim in this step is to produce a file of data that is as free of errors as possible. • Budget Sometimes, questionnaire design is decided upon by the amount of money available to do a specific survey. Costs are one of the main justifications for choosing to conduct sample surveys instead of a census. With surveys, it is possible to obtain reasonable results with a relatively small sample or target population. For example, if you need information on all Canadian citizens over 15 years of age, a survey of a small percentage of these (1,000 or 2,000 depending on the requirements) might provide adequate results. • Time One of the advantages of survey sampling is that it permits investigators to produce the information quickly. It is often the case that survey results are required shortly after the need for information has been identified. For example, if an organization wants to conduct a survey to measure the public awareness of a media advertisement campaign, the survey should be conducted shortly after the campaign is undertaken. Since sampling requires a smaller scale of operation, it reduces the data collection and processing time, while allowing for greater design time and more complex processing programs. • Questionnaire testing
This is a fundamental step in developing a questionnaire. Testing helps discover poor wording or ordering of questions; identify errors in the questionnaire layout and instructions; determine problems caused by the respondent's inability or unwillingness to answer the questions; suggest additional response categories that can be pre-coded on the questionnaire; and provide a preliminary indication of the length of the interview and any refusal problems. Testing can include the entire questionnaire or only a particular portion of it. A questionnaire will at some point in time have to be fully tested. • Data Quality This step identifies errors and verifies results. No matter how much planning and testing goes into a survey, something unexpected will often happen. As a result, no survey is ever perfect. Quality assurance programs such as interview training, information editing, computer program testing, non-respondent follow-ups, and data collection and output spot-checks are required to minimize non-sampling errors introduced during various stages of the survey. Statistical quality-control programs ensure that the specified error levels are controlled to minimum.
Research Methodology
Research Methodology
Meaning of Research • Research is composed of two syllables, a prefix re and a verb search. • Re means again, anew, over again. • Search means to examine closely and carefully, to test and try, to probe. • The two words form a noun to describe a careful and systematic study in some field of knowledge, undertaken to establish facts or principles. • Research is an organized and systematic way of finding answers to questions. Basic Research and Applied Research • Basic research is geared toward advancing our knowledge about human behavior with little concern for any immediate practical benefits that might result. • Applied research is designed with a practical outcome in mind and with the assumption that some group or society as a whole will gain specific benefits from the research. The Wheel of Science • Theory – Hypotheses – Observation – Empirical Generalization Hypothesis and Focused Question
• In deductive research, a hypothesis is focused statement which predicts an answer to your research question. It is based on the findings of previous research (gained from your review of the literature) and perhaps your previous experience with the subject. The ultimate objective of deductive research is to decide whether to accept or reject the hypothesis as stated. When formulating research methods (subjects, data collection instruments, etc.), wise researchers are guided by their hypothesis. In this way, the hypothesis gives direction and focus to the research. • In heuristic research, a hypothesis is not necessary. This type of research employs a "discovery approach." In spite of the fact that this type of research does not use a formal hypothesis, focus and structure is still critical. If the research question is too general, the search to find an answer to it may be futile or fruitless. Therefore, after reviewing the relevant literature, the researcher may arrive at a focused research question.
Research Process • Choosing the research problem • Review of related literature • Collection of data • Interpretation of data • Preparing the research report
Research Methods 1
Action research
Action research is regarded as research that is normally carried out by practitioners (persons that stand in the field of work). It is a method par excel lance for instructors/trainers. It enables the researcher to investigate a specific problem that exists in practice. According to Landman this requires that the researcher should be involved in the actions that take place. A further refinement of this type of research is that the results obtained from the research should be relevant. to the practice. In other words it should
be applicable immediately. This means that the, researcher, as expert, and the person standing in the practice, jointly decide on the formulation of research procedures, allowing the problem to be solved Action research is characterized according to by the following four features: Problem-aimed research focuses on a special situation in practice. Seen in research context, action research is aimed at a specific problem recognizable in practice, and of which the outcome problem solving) is immediately applicable in practice. - Collective participation. A second characteristic is that all participants (for instance the researchers and persons standing in the practice) form an integral part of action research with the exclusive aim to assist in solving the identified problem. - Type of empirical research. Thirdly, action research is characterized as a means to change the practice while the research is going on. Outcome of research can not be generalized. Lastly, action research is characterized by the fact that problem solving, seen as renewed corrective actions, can not be generalized, because it should comply with the criteria set for scientific character.
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Historical research
Historical research, as the term implies, is research based on describing the past. This type of research includes for instance investigations like the recording , analysis and interpretation of events in the past with the purpose of discovering generalizations and deductions that can be useful in understanding the past, the present and to a limited extent, can anticipate the future Historians should consequently aspire to getting to the original events that took place and therefore the researcher is dependent on the availability of documentary sources.
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Descriptive research
The term descriptive is self-explanatory and terminology synonymous to this type of research is: describe, write on, depict. The aim of descriptive research is to verify formulated hypotheses that refer to
the present situation in order to elucidate it. Descriptive research is thus a type of research that is primarily concerned with describing the nature or conditions and degree in detail of the present situation The emphasis is on describe rather than on judge or interpret. According to Klopper researchers who use this method for their research usually aim at: •
demarcating the population (representative of the universum) by means of perceiving accurately research parameters; and
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recording in the form of a written report of that which has been perceived.
The aim of the latter is, that when the total record has been compiled, revision of the documents can occur so that the perceptions derived at can be thoroughly investigated . Because the total population (universum) during a specific investigation can not be contemplated as a whole, researchers make use of the demarcation of the population or of the selection of a representative test sample. Test sampling therefore forms an integral part of descriptive research. In descriptive research the following steps should be included: •
Problem selection and problem formulation. The research problem being tested should be explicitly formulated in the form of a question.
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Literature search. Intensive literature search regarding the formulated problem enables the researcher to divide the problem into smaller units.
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Problem reduction.
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Hypothesis formulation.
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Test sampling. The researcher should determine the size of the test sample.
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Information retrieval. The application of appropriate information retrieval techniques to comply with the criteria set for authenticity and competency, is relevant.
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General planning. Any research requires sound planning.
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Report writing. The report entails the reproduction of factual information, the interpretation of data, conclusions derived from the research and recommendations.
You should make sure that you understand the meaning of the terminology used. Consult the
recommended sources for detailed explanations. However, further reference must be made to aspects related to test sampling. •
Test sampling
As mentioned previously, when descriptive research is exposed, demarcation of the population become unavoidable. Test sampling therefor forms an integra! part of this type of research. Two important questions arise frequently when test sampling is anticipated by researchers, namely: -
How big should the test sample be?
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What is the probability of mistakes occurring in the use of test sampling (instead of the whole
population)? Special care should be taken with the selection of test samples. The results obtained from a survey can never be more authentic than the standard of the population or the representatives of the test sample, according to Klopper The size of the test sample can also be specified by means of statistics. It is important for the researcher to bear in mind that it is desirable that test sampling be made as large as possible. The most important criterium that serves as a guideline here, is the extent to which the test sample corresponds with the qualities and characteristics of the general population being investigated. The next three factors should be taken into consideration before a decision is made with regard to the size of the test sample: -
What is the grade of accuracy expected between the test sample and the general population?
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What is the variability of the population? (This, in general terms, is expressed as the standard
deviation.) -
What methods should be used in test sampling? •
Bias saying
When you attempt descriptive research, you should take care that the test sample reflects the actual population it represents. The following example holds validity for the latter: you cannot make a statement
regarding all first-year students if you do not include all first-year students in your research. If you do make such a statement, you have to select enrolled first-year students at all the tertiary institutions or a balanced proportional manner, and include the latter when you select your test sample for your research. Landman points out that, when a test sample does not truly represent the population (universum) from which it is drawn, the test sample is considered a bias sample. It then becomes virtually impossible to make an accurate statement or to predict about the population.
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Experimental research
This type of research is known in literature by a variety of names. Synonyms are, for instance: the cause and consequence method, before and after design, control group design and the laboratory method. Landman summarises experiential research when he states that it is research designed to study cause and consequence. A clear distinction between the terms experiment and experimental research should be evident. In the former there is normally no question about the interpretation of data in the discovery of new meaning. Experimental research, however, has control as fundamental characteristic. The selection of control groups, based on proportional selection, forms the basis of this type of research. Experimental research is basically the method that can be applied in a research laboratory. The basic structure of this type of research is elementary: two situations (cause and consequence) are assessed in order to make a comparison. Following this, attempts should be made to treat the one situation (cause) from the outside (external variable) to affect change, and then to reevaluate the two situations. The perceivable changes that occurred can then be presumed as caused by external variables. •
Control group
Because: control is a fundamental characteristic of this type of research, control groups are a prerequisite. Control groups are selected from a group of selected persons whose experience corresponds with that of the experimental group. The only difference is that they do not receive the same treatment (Landman 1988: 58). •
Variable
In order to do experimental! research, it is necessary to distinguish clearly between the terms dependent
and independent variables. In experimental research it is a prerequisite that the researcher should be able to manipulate the variable and then to assess what the influence of the manipulation on the variable was. A variable is any characteristic (of man or his environment) that can take on different values. Objects are usually not considered as variables - but their characteristics are. As example the following can be considered: a transparency is not a variable (it is an object). The characteristics of the transparency are variables, for example the colour, design etc. In other words, a transparency as an object can take on different values. •
Independent variable
According to Landman
the independent variable is the circumstances or characteristics which the
researcher can manipulate in his effort to determine what their connection with the observed phenomenon is. This means that the researcher has direct control over the variable. As example of an independent variable, is study methods. •
Dependent variable
The dependent variable, on the other hand, is the circumstances or characteristics that change, disappear or appear when the researcher implements the independent variable. For example, learning content that should be mastered (student performance) is the dependent variable, while the manipulation of study methods by means of different teaching methods, is the independent variable. •
Internal and external validity
The importance of control in conducting experimental research has been pointed out earlier. A further pre-requisite for this type of research is validity.Validity is a term used in research methodology that indicates the extent to which a test complies with the aim it was designed for. Internal validity Internal validity means that the perceived difference in the independent variable (characteristics that change) is a direct result of the manipulation of the obtained research results, and therefore possible to conclude. In experimental design, emphasis is placed on the way in which reference between independent
and dependent variables should not be confused by the presence of uncontrolled variables External validity External validity means that the results of the experimental research should be applied to a similar situation outside the experimental design. The results of the experimental research can then be confirmed in similar situations.
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Ex post facto-research
Experimental research, where the researcher manipulates the independent variable, whilst the dependable variable are controlled with the aim of establishing the effect of the independent variable on the dependable variable, is also applicable. The term ex post facto according to Landman is used to refer to an experiment in which the researcher, rather than creating the treatment, examines the effect of a naturally occurring treatment after it has occurred. In other words it is a study that attempts to discover the pre-existing causal conditions between groups. It should, however, be pointed out that the most serious danger of ex post facto-research is the conclusion that because two factors go together, one is the cause and the other is the effect. Jacobs et al. refers to the following procedures when conducting ex post facto-research: •
The. first step should be to state the problem.
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Following this is the determination of the group to be investigated. Two groups of the population that differ with regard to the variable, should be selected in a proportional manner for the test sample.
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Groups, according to variables, are set equal by means of paring off and statistical techniques of identified independent and dependent variables.
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Data is collected. Techniques like questionnaires, interviews, literature search etc:. are used to determine the differences.
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Next follows the interpretation of the research results. The hypothesis is either
confirmed or rejected. Lastly it should be mentioned that this type of research has shortcomings, and that only partial control is possible.
Other Methods • Case and field method: to study intensively the background, current status, and environmental interactions of a given social unit. • Correlational method: to investigate the extent to which variations in one factor correlate with variations in one or more other factors based on correlation coefficient. • Casual-comparative or “Ex post facto” method: to investigate possible cause-and-effect relationships by observing some existing consequence and looking back through the data for plausible casual factors. • True experimental method: to investigate possible cause-and-effect relationships by exposing one or more experimental groups to one or more treatment conditions and comparing the results to one or more control groups not receiving the treatment, random assignment being essential. • Quasi-experimental method: to investigate the conditions of the true experiment in a setting which does not allow the control or manipulation of all relevant variables. • Action research: to develop skills or new approaches and to solve problems with direct application to the classroom or other applied setting. Parametric Analysis
• Description and examination of relationships between different parameters, such as energy and economic factors. • It is an excellent way to get accurate information about the influence of all parameters on the design objectives, such as system performance with respect to other variables. • Together with sensitivity analysis, it enables the engineer to identify the key parameters and know where the focus should be. Sensitivity Analysis • It is the study of how the variation (uncertainty) in the output of a mathematical model can be apportioned, qualitatively or quantitatively, to different sources of variation in the input of a model. • In more general terms, uncertainty and sensitivity analyses investigate the robustness of a study when the study includes some form of mathematical modeling. While uncertainty analysis studies the overall uncertainty in the conclusions of the study, sensitivity analysis tries to identify what source of uncertainty weights more on the study's conclusions. • It looks at the effect of varying the inputs of a mathematical model on the output of the model itself. Sensitivity tests help people to understand dynamics of a system.
Research Design
Research Design A research design is a framework or blueprint for conducting the marketing research project. It details the procedures necessary for obtaining the information needed to structure or solve marketing research problems. Although a broad approach to the problem has already been developed, the research design specifies the details- the nut and bolts- of implementing that approach. A research design lays the foundation for conducting the project. A good research design will ensure that the marketing research project is conducted effectively and efficiently. A research design involves the following components or tasks: • Design the exploratory, descriptive, and/or causal phases of the research. • Define the information needed • Specify the measurement and scaling procedures • Construct and pretest a questionnaire (interviewing form) or an appropriate form for data collection • Specify the sampling process and sample size • Develop a plan of data analysis Research design is the most encompassing of all the steps of marketing research. Research design includes incorporating knowledge from secondary information analysis, qualitative research, methodology selection, question measurement & scale selection, questionnaire design and sample design to be used. According to the objective of the marketing research, the research design is descriptive. The aspect of what, who, how, when and where are defined in the research. Desriptive research determines the perception of toothpaste characterstics in the minds of respondents. Here, respondents are asked questions their behaviour, intentions, attitude, lifestyle, awareness and buying behaviour towards the toothpaste.
Research Instrument The research instrument used by us is Questionnaire method because it is the most feasible way to interact with the sample organizations and get the relevant data for our market research.Mostly all the questions are in structured form as the questions are multiple choice questions or they are to be answered in yes or no.Scaling techniques used by us in the questionnaire includes:-
CONTINUOUS RATING SCALE Where the respondents rate the object by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other.
DICHOTOMOUS QUESTIONS Which has only two response alternatives: yes or no. The decision of the respondent is guided by whether they approach the issue as a yes or no.
MULTIPLE CHOICE QUESTIONS In which respondents have to choose one answer from many options. These questions gives a wide choice to the respondents so that they can give the most accurate and reliable answer.
LIKERT SCALE Where the respondents are provided with a scale that has a number or brief description associated with each category. This scale is useful as respondents readily understand how to use the scale, making it suitable for almost all kind of surveys.
Data Analysis Techniques
Methods of Data Analysis Qualitative Analysis Suppose that we have carried out an experiment on the effects of noise on learning with three groups of nine participants each. One group was exposed to very loud noise, another group to moderately loud noise, and the third group was not exposed to noise at all. What they had learned from a book chapter was assessed by giving them a set of questions, producing a score between 0 and 20. What is to be done with the raw scores? There are two key types of measures that can be taken whenever we have a set of scores from participants in a given condition. First, there are measures of central tendency, which provide some indication of the size of average or typical scores. Second, there are measures of dispersion, which indicate the extent to which the scores cluster around the average or are spread out. Various measures of central tendency and of dispersion are considered next.
Measures of central tendency Measures of central tendency describe how the data cluster together around a central point. There are three main measures of central tendency: the mean; the median; and the mode.
Mean The mean in each group or condition is calculated by adding up all the scores in a given condition, and then dividing by the number of participants in that condition. Suppose that the scores of the nine participants in the no-noise condition are as follows: 1, 2, 4, 5, 7, 9, 9, 9, 17. The mean is given by the total, which is 63, divided by the number of participants, which is 9. Thus, the mean is 7. The main advantage of the mean is the fact that it takes all the scores into account. This generally makes it a sensitive measure of central tendency, especially if the scores resemble the normal
distribution, which is a bell-shaped distribution in which most scores cluster fairly close to the mean. However, the mean can be very misleading if the distribution differs markedly from the normal and there are one or two extreme scores in one direction. Suppose that eight people complete one lap of a track in go-karts. For seven of them, the times taken (in seconds) are as follows: 25, 28, 29, 29, 34, 36, and 42. The eighth person’s go-kart breaks down, and so the driver has to push it around the track. This person takes 288 seconds to complete the lap. This produces an overall mean of 64 seconds. This is clearly misleading, because no-one else took even close to 64 seconds to complete one lap.
Median Another way of describing the general level of performance in each condition is known as the median. If there is an odd number of scores, then the median is simply the middle score, having an equal number of scores higher and lower than it. In the example with nine scorescin the no-noise condition (1, 2, 4, 5, 7, 9, 9, 9, 17), the median is 7. Matters are slightly more complex if there is an even number of scores. In that case, we work out the mean of the two central values. For example, suppose that we have the following scores in size order: 2, 5, 5, 7, 8, 9. The two central
values are 5 and 7, and so the median is The main advantage of the median is that it is unaffected by a few extreme scores, because it focuses only on scores in the middle of the distribution. It also has the advantage that it tends to be easier than the mean to work out. The main limitation of the median is that it ignores most of the scores, and so it is often less sensitive than the mean. In addition, it is not always representative of the scores obtained, especially if there are only a few scores.
Mode The final measure of central tendency is the mode. This is simply the most frequently occurring score. In the example of the nine scores in the no-noise condition, this is 9. The main advantages of the mode are that it is unaffected by one or two extreme scores, and that it is the easiest measure of central tendency to work out. In addition, it can still be worked out even when some of the extreme scores are not known. However, its limitations generally outweigh these advantages. The greatest limitation is that the mode tends to be unreliable. For example, suppose we have the following scores: 4, 4, 6, 7, 8, 8, 12, 12, 12. The mode of these scores is 12. If just one score changed (a 12 becoming a 4), the mode would change to 4! Another limitation is that information about the exact values of the scores obtained is ignored in working out the mode. This makes it a less sensitive measure than the mean. A final limitation is that it is possible for there to be more than one mode.
Measures of dispersion The mean, median, and mode are all measures of central tendency. It is also useful to work out what are known as measures of dispersion, such as the range, interquartile range, variation ratio, and standard deviation. These measures indicate whether the scores in a given condition are similar to each other or whether they are spread out.
Range The simplest of these measures is the range, which can be defined as the difference between the highest and the lowest score in any condition. In the case of the no-noise group (1, 2, 4, 5, 7, 9, 9, 9, 17), the range is 17 _ 1 _ 16. What has been said so far about the range applies only to whole numbers. Suppose that we measure the time taken to perform a task to the nearest one-tenth of a second, with the fastest time being 21.3 seconds and the slowest time being 36.8 seconds. The figure of 21.3 represents a value between 21.25 and 21.35, and 36.8 represents a value between 36.75 and 36.85. As a result, the range is 36.85 _ 21.25, which is 15.6 seconds. The main advantages of the range as a measure of dispersion are that it is easy to calculate and that it takes full account of extreme values. The main weakness of the range is that it can be greatly influenced by one score which is very different from all of the others. In the example, the inclusion of the participant scoring 17 increases the range from 9 to 17. The other important weakness of the range is that it ignores all but two of the scores, and so is likely to provide an inadequate measure of the general spread or dispersion of the scores around the mean or median.
Standard deviation The most generally useful measure of dispersion is the standard deviation. It is harder to calculate than the range or variation ratio, but generally provides a more accurate measure of the spread of scores. However, you will be pleased to learn that many calculators allow the standard deviation to be worked out rapidly and effortlessly, as in the worked example. number of nonmodal scores total number of scores
DATA PRESENTATION Information about the scores in a sample can be presented in several ways. If it is presented in a graph or chart, this may make it easier for people to understand what has been found, compared to simply presenting information about the central tendency and dispersion. We will shortly consider some examples. The key point to remember is that all graphs and charts should be clearly labelled and presented so that the reader can rapidly make sense of the information contained in them. Suppose that we ask 25 male athletes to run 400 metres as rapidly as possible, and record their times (in seconds).
Frequency polygon One way of summarising these data is in the form of a frequency polygon. This is a simple form of chart in which the scores from low to high are indicated on the x or horizontal axis and the frequencies of the various scores (in terms of the numbers of individuals obtaining each score) are indicated on the y or vertical axis. The points on a frequency polygon should only be joined up when the scores can be ordered from low to high. In order for a frequency polygon to be most useful, it should be constructed so that most of the frequencies are neither very high nor very low. The frequencies will be very high if the width of each class interval (the categories used to summarise frequencies) on the x axis is too broad (e.g. covering 20 seconds), and the frequencies will be very low if each class interval is too narrow (e.g. covering only 1 or 2 seconds). Each point in a frequency polygon should be placed in the middle of its class interval. There is a technical point that needs to be made here (Coolican, 1994). Suppose that we include all times between 53 and 57 seconds in the same class interval. As we have only measured running times to the nearest second, this class interval will cover actual times between 52.5 and 57.5 seconds. In this case, the mid-point of the class interval (55 seconds) is the same whether we take account of the actual measurement interval (52.5–57.5 seconds) or adopt the simpler approach of focusing on the lowest and highest recorded times in the class interval (53–57 seconds, respectively). When the two differ, it is important to use the actual measurement interval. How should we interpret the findings shown in the frequency polygon? It is clear that most of the participants were able to run 400 metres in between about 53 and 67 seconds. Only a few of the athletes were able to better a time of 53 seconds, and there was a small number who took longer than 67 seconds.
Histogram A similar way of describing these data is by means of a histogram. In a histogram, the scores are indicated on the horizontal axis and the frequencies are shown on the vertical axis. In contrast to a frequency polygon, however, the frequencies are indicated by rectangular columns. These columns are all the same width but vary in height in accordance with the corresponding frequencies. As with frequency polygons, it is important to make sure that the class intervals are not too broad or too narrow. All class intervals are represented, even if there are no scores in some of them. Class
intervals are indicated by their mid-point at the centre of the columns. Histograms are clearly rather similar to frequency polygons. However, frequency polygons are sometimes preferable when you want to compare two different frequency distributions. The information contained in a histogram is interpreted in the same way as the information in a frequency polygon. In the present example, the histogram indicates that most of the athletes ran 400 metres fairly quickly. Only a few had extreme times
Bar Chart Frequency polygons and histograms are suitable when the scores obtained by the participants can be ordered from low to high. In more technical terms, the data should be either interval or ratio However, there are many studies in which the scores are in the form of categories rather than ordered scores; in other words, the data are nominal. For example, 50 people might be asked to indicate their favourite leisure activity. Suppose that 15 said going to a party, 12 said going to the pub, 9 said watching television, 8 said playing sport, and 6 said reading a good book.
These data can be displayed in the form of a bar chart. In a bar chart, the categories are shown along the horizontal axis, and the frequencies are indicated on the vertical axis. In contrast to the data contained in histograms, the categories in bar charts cannot be ordered numerically in a meaningful way. However, they can be arranged in ascending (or descending) order of popularity. Another difference from histograms is that the rectangles in a bar chart do not usually touch each other. The scale on the vertical axis of a bar chart normally starts at zero. However, it is sometimes convenient for presentational purposes to have it start at some higher value. If that is done, then it should be made clear in the bar chart that the lower part of the vertical scale is missing. The columns in a bar chart often represent frequencies. However, they can also represent means or percentages for different groups
Terminology
Nominal data: data consisting of the numbers of participants falling into qualitatively different categories. Ordinal data: data that can be ordered from smallest to largest. Interval data: data in which the units of measurement have an invariant or unchanging value. Ratio data: as interval data, but with a meaningful zero point. Parametric tests: statistical tests that require interval or ratio data, normally distributed data, and similar variances in both conditions. Non-parametric tests: statistical tests that do not involve the requirements of parametric tests.
Correlational studies In the case of correlational studies, the data are in the form of two measures of behavior from each member of a single group of participants. What is often done is to present the data in the form of a scattergraph (also known as a scattergram). It is given this name, because it shows the ways in which the scores of individuals are scattered.
Qualitative Analysis of Data There is an important distinction between quantitative research and qualitative research. In quantitative research, the information obtained from the participants is expressed in numerical form. Studies in which we record the number of items recalled, reaction times, or the number of aggressive acts are all examples of quantitative research. In qualitative research, on the other hand, the information obtained from participants is not expressed in numerical form. The emphasis is on the stated experiences of the participants and on the stated meanings they attach to themselves, to other people, and to their environment. Those carrying out qualitative research sometimes make use of direct quotations from their participants, arguing that such quotations are often very revealing. Qualitative analysis is often less influenced than is quantitative analysis by the biases and theoretical assumptions of the investigator. In addition, it offers the prospect of understanding the participants in a study as rounded individuals in a social context. This contrasts with quantitative analysis, in which the focus is often on rather narrow aspects of behaviour. The greatest limitation of the qualitative approach is that the findings that are reported tend to be unreliable and hard to replicate. Why is this so? The qualitative approach is subjective and impressionistic, and so the ways in which the information is categorised and then interpreted often differ considerably from one investigator to another.
Interviews In general terms, unstructured interviews (e.g. non-directive or informal) lend themselves to qualitative analyses, whereas structured interviews lend themselves to quantitative analysis. As Coolican (1994) pointed out, there are various skills that interviewers need in order to obtain valuable data. These skills involve establishing a good understanding with the person being interviewed, adopting a non-judgemental approach, and developing effective listening skills. Evaluation
There are various problems involved in interpreting interview information. First, there is the problem of social desirability bias. Most people want to present themselves in the best possible light, so they may provide socially desirable rather than honest answers to personal questions. This problem can be handled by the interviewer asking additional questions to establish the truth. Second, the data obtained from an interviewer may reveal more about the social interaction processes between the interviewer and the person being interviewed (the interviewee) than about the interviewee’s thought processes and attitudes. Third, account needs to be taken of the self-fulfilling prophecy. This is the tendency for someone’s expectations about another person to lead to the fulfilment of those expectations. For example, suppose that a therapist expects his or her patient to behave very anxiously. This expectation may cause the therapist to treat the patient in such a way that the patient starts to behave in the expected fashion.
Case studies Case studies (intensive investigations of individuals) come in all shapes and sizes. Probably the best-known case studies are those of Freud and others in the field of clinical psychology. However, detailed case studies have also been carried out in personality research and in studies of cognitive functioning in brain-damaged patients. One way in which case studies have been used to study personality involves an approach known as psychobiography. This was defined by McAdams (1988, p. 2) as “the systematic use of psychological (especially personality) theory to transform a life into a coherent and illuminating story.”
Content Analysis Content analysis is used when originally qualitative information is reduced to numerical terms. Content analysis started off as a method for analysing messages in the media, including articles published in newspapers, speeches made by politicians on radio and television, various forms of
propaganda, and health records. More recently, the method of content analysis has been applied more widely to almost any form of communication. One of the types of communication that has often been studied by content analysis is television advertising. For example, McArthur and Resko (1975) carried out a content analysis of American television commercials. They found that 70% of the men in these commercials were shown as experts who knew a lot about the products being sold. In contrast, 86% of the women in the commercials were shown only as product users. There was another interesting gender difference: men who used the products were typically promised improved social and career prospects, whereas women were promised that their family would like them more.
Evaluation One of the greatest strengths of content analysis is that it provides a way of extracting information from a wealth of real-world settings. The media influence the ways we think and feel about issues, and so it is important to analyse media communications in detail. The greatest limitation of content analysis is that it is often very hard to interpret the findings. There are also problems of interpretation with other communications such as personal diaries or essays. Diaries or essays may contain accurate accounts of what an individual does, thinks, and feels. On the other hand, individuals may provide deliberately distorted accounts in order to protect their self-esteem, to make it appear that their lives are more exciting than is actually the case, and so on. Another problem is that the selection and scoring of coding units can be rather subjective. The coding categories that are used need to reflect accurately the content of the communication, and each of the categories must be defined as precisely as possible
Sampling Plan
Sampling Plan Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Researchers rarely survey the entire population for two reasons: (1) The cost is too high. (2) The population is dynamic, i.e., the component of population could change over time. There are three main advantages of sampling: (1) The cost is lower. (2) Data collection is faster (3) It is possible to ensure homogeneity and to improve the accuracy and quality of the data because the data set is smaller.
Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business, sampling is widely used for gathering information about a population.
Process The sampling process comprises several stages: •
Defining the population of concern
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Specifying a sampling frame, a set of items or events possible to measure
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Specifying a sampling method for selecting items or events from the frame
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Determining the sample size
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Implementing the sampling plan
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Sampling and data collecting
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Reviewing the sampling process
Population definition Successful statistical practice is based on focused problem definition. In sampling, this includes defining the population from which our sample is drawn. A population can be defined as including all people or items with the characteristic one wishes to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population. Although the population of interest often consists of physical objects, sometimes we need to sample over time, space, or some combination of these dimensions. For instance, an investigation of supermarket staffing could examine checkout line length at various times, or a study on endangered penguins might aim to understand their usage of various hunting grounds over time. For the time dimension, the focus may be on periods or discrete occasions.
Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups due to frame issues etc Sometimes they may be entirely separate - for instance, we might study rats in order to get a better understanding of human health, or we might study records from people born in 2008 in order to make predictions about people born in 2009.
Sampling frame In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will actually vote at a forthcoming election (in advance of the election). As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. The most straightforward type of frame is a list of elements of the population (preferably the entire population) with appropriate contact information. For example, in an opinion poll, possible sampling frames include: •
Electoral register
•
Telephone directory
The sampling frame must be representative of the population and this is a question outside the scope of statistical theory demanding the judgment of experts in the particular subject matter being studied. All the above frames omit some people who will vote at the next election and contain some people who will not; some frames will contain multiple records for the same person. People not in the frame have no prospect of being sampled. Statistical theory tells us about the uncertainties in extrapolating from a sample to the frame. In extrapolating from frame to population, its role is motivational and suggestive. In defining the frame, practical, economic, ethical, and technical issues need to be addressed. The need to obtain timely results may prevent extending the frame far into the future.
The difficulties can be extreme when the population and frame are disjoint.: Kish posited four basic problems of sampling frames: •
Missing elements: Some members of the population are not included in the frame.
•
Foreign elements: The non-members of the population are included in the frame.
•
Duplicate entries: A member of the population is surveyed more than once.
•
Groups or clusters: The frame lists clusters instead of individuals.
Probability and non probability sampling A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection. Example: We want to estimate the total income of adults living in a given street. We visit each household in that street, identify all adults living there, and randomly select one adult from each household. (For example, we can allocate each person a random number, generated from a uniform distribution between 0 and 1, and select the person with the highest number in each household). We then interview the selected person and find their income. People living on their own are certain to be selected, so we simply add their income to our estimate of the total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person's income twice towards the total. (In effect, the person who is selected from that household is taken as representing the person who isn't selected.) In the above example, not everybody has the same probability of selection; what makes it a probability sample is the fact that each person's probability is known. When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.
Probability sampling includes: Simple Random Sampling, Systematic Sampling, Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling. These various ways of probability sampling have two things in common: •
Every element has a known nonzero probability of being sampled and
•
Involves random selection at some point.
Non probability sampling is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage'/'under covered'), or where the probability of selection can't be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, non probability sampling does not allow the estimation of sampling errors. These conditions place limits on how much information a sample can provide about the population. Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population. Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a non probability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities. Non probability Sampling includes: Accidental Sampling, Quota Sampling and Purposive Sampling. In addition, non response effects may turn any probability design into a non probability design if the characteristics of non response are not well understood, since non response effectively modifies each element's probability of being sampled. Sampling methods Within any of the types of frame identified above, a variety of sampling methods can be employed, individually or in combination. Factors commonly influencing the choice between these designs include:
•
Nature and quality of the frame
•
Availability of auxiliary information about units on the frame
•
Accuracy requirements, and the need to measure accuracy
•
Whether detailed analysis of the sample is expected
•
Cost/operational concerns
Simple random sampling In a simple random sample ('SRS') of a given size, all such subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. This minimises bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. However, SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population. For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to over represent one sex and under represent the other. Systematic and stratified techniques, discussed below, attempt to overcome this problem by using information about the population to choose a more representative sample. SRS may also be cumbersome and tedious when sampling from an unusually large target population. In some cases, investigators are interested in research questions specific to subgroups of the population. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. Convenience sampling Convenience sampling (sometimes known as grab or opportunity sampling) is a type of non probability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a sample population selected because it is readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct such a survey at a shopping center early in the morning on a
given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include: 1. Are there controls within the research design or experiment which can serve to lessen the impact of a non-random, convenience sample whereby ensuring the results will be more representative of the population? 2. Is there good reason to believe that a particular convenience sample would or should respond or behave differently than a random sample from the same population? 3. Is the question being asked by the research one that can adequately be answered using a convenience sample? In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Sample Size The sample size of a statistical sample is the number of observations that constitute it. It is typically denoted n, a positive integer (natural number). Typically, all else being equal, a larger sample size leads to increased precision in estimates of various properties of the population, though the results will become less accurate if there is a systematic error in the experiment. This can be seen in such statistical rules as the law of large numbers and the central limit theorem. Repeated measurements and replication of independent samples are often required in measurement and experiments to reach a desired precision. A typical example would be when a statistician wishes to estimate the arithmetic mean of a quantitative random variable (for example, the height of a person). Assuming that they have a random sample with independent observations, and also that the variability of the population (as measured by the standard deviation σ) is known, then the standard error of the sample mean is given by the formula:
σ/√n
It is easy to show that as n becomes very large, this variability becomes small. This leads to more sensitive hypothesis tests with greater statistical power and smaller confidence intervals.
Errors in Research There are always errors in a research. By sampling, the total errors can be classified into sampling errors and non-sampling errors.
Sampling Error Sampling (1)
errors
Selection
are
caused
error:
by
Incorrect
sampling selection
design.
It
probabilities
includes: are
used.
(2) Estimation error: Biased parameter estimate because of the elements in these samples.
Non-sampling Error Non-sampling errors are caused by the mistakes in data processing. It includes: (1)
Over
coverage:
Inclusion
of
data
from
outside
of
the
population.
(2) Under coverage: Sampling frame does not include elements in the population. (3) (4)
Measurement Processing
error:
The
error:
respondent Mistakes
misunderstand in
the data
question. coding.
(5) Non-response: After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis. A particular problem is that of non-responses. In survey sampling, many of the individuals identified as part of the sample may be unwilling to participate or impossible to contact. In this case, there is a risk of differences, between (say) the willing and unwilling, leading to biased estimates of population parameters. This is often addressed by follow-up studies which make a repeated attempt to contact the unresponsive and to characterize their similarities and differences with the rest of the frame. The effects can also be
mitigated by weighting the data when population benchmarks are available or by imputing data based
on
answers
to
other
questions.
Non response is particularly a problem in internet sampling. One of the main reasons for this problem could be that people may hold multiple e-mail addresses, which they don't use anymore or don't check regularly.
Findings And Interpretation
Q.1 How frequently do you wash your clothes? a).daily b) once a week c) twice a week d) fortnightly e) monthly ANALYSIS AND INTEPRETATION
How frequently do you wash your clothes? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
daily
177
64.1
64.1
64.1
once a week
21
7.6
7.6
71.7
twice a week
78
28.3
28.3
100.0
Total
276
100.0
100.0
This table is showing that how frequently people wash their clothes.so out of 276 respondents 177 respondents wash their clothes daily which results for 64.1 % of total and 21 wash their clothes once a week which result for 7.6 % of total ,78 wash their clothes twice a week which results for 28.3 % of total respondents.
Q.2 How do you wash your clothes? a) hand wash b) washing machine c)dhobi or laundry d) soak it and leave e) any other ANALYSIS AND INTEPRETATION
Do you wash your clothes by hand? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
9
3.3
3.3
3.3
yes
84
30.4
30.4
33.7
no
180
65.2
65.2
98.9
3
3
1.1
1.1
100.0
Total
276
100.0
100.0
As you can see through the table presented above, 65.2% people are not washing their clothes by hand. This shows the shift in the trend of mode of washing clothes. Now more and more people are moving towards other means of washing. This could also work as sign of fall of market share of detergent bar as more and more people are moving away from the hand washing of clothes. Do you wash your clothes in washing machine? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
18
6.5
6.5
6.5
yes
156
56.5
56.5
63.0
no
102
37.0
37.0
100.0
Total
276
100.0
100.0
In the data of 276 people surveyed, we came to see that 156 spoke in the favour of using washing machine for washing the clothes. This shows the growing trend of people moving towards more
automated mode of washing clothes. This signifies that first people are looking for more refined washing of clothes. This means they are ready to pay more in order to have a better wash. Do you give your clothes to dhobi or to a laundry for wasing? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
27
9.8
9.8
9.8
yea
39
14.1
14.1
23.9
no
210
76.1
76.1
100.0
Total
276
100.0
100.0
There is a very nominal percentage of people who are using dhobi or a laundry for washing and cleaning of their clothes. This shows that people are more oriented towards washing their clothes through automated method i.e., washing machine, than getting it through dhobis or laundries.
Do you soak your clothes in detergent and leave it? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
27
9.8
9.8
9.8
yes
3
1.1
1.1
10.9
no
246
89.1
89.1
100.0
Total
276
100.0
100.0
There is a very low percentage of people who take into account this method of washing clothes. As such it could be used as an indicator showing that demand for detergent might be low for this mode of washing.
Q.3) Which according to you is the most appropriate form for washing clothes? a) bar b) powder c) liquid ANALYSIS AND INTEPRETATION
Which is the most appropriate form of detergent for washing clothes Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
Bar
27
9.8
9.8
9.8
Powder
228
82.6
82.6
92.4
Liquid
21
7.6
7.6
100.0
Total
276
100.0
100.0
From the above data it can be concluded without doubt that most people are using powder for their washing of clothes. This data can be used well by detergent producing organization. The data shows that people are using more of powder form detergent which are mostly compatible with washing machine, which presents a big market share which can be satisfied by moving towards powder form of detergent.
Q.4) Do you consider using different products considering seasonal variations?
a) yes b) no ANALYSIS AND INTEPRETATION Do you use different products considering seasonal variations Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
yes
108
39.1
39.6
39.6
no
165
59.8
60.4
100.0
Total
273
98.9
100.0
System
3
1.1
Total
276
100.0
This is an important fact being presented in the above table. The table shows that people are more season conscious while choosing their detergent. This means that people want their detergents to be adaptive to different clothes as the season demands.
Q.5 Which is the detergent you are using now? a) ariel b) surf c) tide d) surf excel e) wheel f) nirma g) other ANALYSIS AND INTEPRETATION Do you use ariel? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
24
8.7
8.7
8.7
yes
15
5.4
5.4
14.1
no
237
85.9
85.9
100.0
Total
276
100.0
100.0
The people who use ariel as their preferred brand are low in number. This data if used to be generalized then we can say that the market share of the product is low. Do you use surf ? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
24
8.7
8.7
8.7
yes
9
3.3
3.3
12.0
no
243
88.0
88.0
100.0
Total
276
100.0
100.0
The data above shows that the market share of surf is very low. We can see that the use of surf as the preferred brand is lower than ariel and if we generalize the data to the real population then we can say that the market share is very low and it is loosing demand as a preference among the consumers.
Do you use tide? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
18
6.5
6.5
6.5
yes
63
22.8
22.8
29.3
no
195
70.7
70.7
100.0
Total
276
100.0
100.0
In the above table it is shown that what percentage of people from our sample use tide as their current brand. As compared to the brands shown above, tide seems into a better position. 22.8% people prefer it as their current brand. Do you use surf excel? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
15
5.4
5.4
5.4
yes
168
60.9
60.9
66.3
no
93
33.7
33.7
100.0
Total
276
100.0
100.0
Surf excel turns out to be the most used brand till now in the analysis. With over 60% of people of our sample using it as their current brand gives it a huge market share if we generalize the data. This data can also be used to denote that demand of this product can be said to be high.
Do you use wheel? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
27
9.8
9.8
9.8
yes
18
6.5
6.5
16.3
no
231
83.7
83.7
100.0
Total
276
100.0
100.0
Wheel is also showing very low demand as the preferred brand. It has only 6.5% people of our sample using it as their current brand, which is low and could translate into low percentage of market share and also demand.
Do you use nirma? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
27
9.8
9.8
9.8
yes
3
1.1
1.1
10.9
no
246
89.1
89.1
100.0
Total
276
100.0
100.0
Nirma till now has shown the least share with 1.1%. This is a very low share for any product till now in our analysis showing that very least number of individuals use it as their current brand. If this is translated to apply to real market it shows that the product is loosing its market share and is also very low in demand.
You use any other other brand not listed above? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
0
27
9.8
9.8
9.8
yes
24
8.7
8.7
18.5
no
225
81.5
81.5
100.0
Total
276
100.0
100.0
According to the data presented above most of the people surveyed have confined themselves only to the brands listed separately above. In the others category, Rin and ghadi detergent came out superior then others in this particular category. Even though as a whole it doesn’t represent any huge percentage of share but then too it is able to move ahead of products such as surf, nirma etc.
$a1 Frequencies Responses N detergent used presentlya
Percent
Percent of Cases
Do you use surf ?
9
3.0%
3.3%
Do you use tide?
63
21.0%
22.8%
Do you use ariel?
15
5.0%
5.4%
168
56.0%
60.9%
Do you use wheel?
18
6.0%
6.5%
Do you use nirma?
3
1.0%
1.1%
24
8.0%
8.7%
300
100.0%
108.7%
Do you use surf excel?
You use any other other brand not listed above? a
Total
a. Dichotomy group tabulated at value 1.
Q.6 For how long you have been using using your present brand? a.) less than 1 yr b)1-2 yrs c)2-3 yrs d) 3-4 yrs e) 4-5 yrs f)more than 5yrs
Statistics For how long you have been using your existing brand? N
Valid
264
Missing
12
Median
5.00
Range
5
For how long you have been using your existing brand? Cumulative
Valid
Missing Total
Frequency
Percent
Valid Percent
Percent
less than 1year
6
2.2
2.3
2.3
1-2 yrs
12
4.3
4.5
6.8
2-3 yrs
39
14.1
14.8
21.6
3-4 yrs
24
8.7
9.1
30.7
4-5 yrs
132
47.8
50.0
80.7
more than 5 yrs
51
18.5
19.3
100.0
Total
264
95.7
100.0
System
12
4.3
276
100.0
From the data above we can interpret, that most of the customer are brand loyal and do not change their brand easily. 70% of the sample says that they are using their present brand since last 4-5 years or more.
Q.7 Which is the previous brand you were using? a) ariel b) surf c) tide d) surf excel e) wheel f) nirma g) others ANALYSIS AND INTEPRETATION
Which is the previous brand you were using? Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
ariel
33
12.0
13.9
13.9
surf
54
19.6
22.8
36.7
tide
33
12.0
13.9
50.6
surf excel
21
7.6
8.9
59.5
wheel
36
13.0
15.2
74.7
nirma
24
8.7
10.1
84.8
others
36
13.0
15.2
100.0
Total
237
85.9
100.0
System
39
14.1
Total
276
100.0
Surf seems to be the brand which has been mostly shifted from by people from the data collected. It might be because of the competition posed by tide or nirma or through the increased share of surf excel. After surf it is wheel from which the sampled units have shifted their preferences. The least shifted product is surf excel with only 7.6% showing that how well it has been able to sustain the brand loyalty.
Q.8 What is the reason behind shifting to your current brand from the previous one? a) price b) packaging c) additional special features d) friendliness e) better cleaning f) availability g) offers associated with the product. ANALYSIS AND INTEPRETATION
What was the reason behind shifting to his brand from the previous one? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
price
18
6.5
7.1
7.1
packaging
3
1.1
1.2
8.3
55.4
60.7
69.0
any
special
features
like 153
usage with rough water
Missing
friendliness
21
7.6
8.3
77.4
better cleaning
54
19.6
21.4
98.8
availability
3
1.1
1.2
100.0
Total
252
91.3
100.0
System
24
8.7
Total
276
100.0
Most of the responses show that the reason behind changing their previous brand was lack of special features. Special features could be like usage with the rough water etc. it has been shown
has the most effective reason for changing of the previous brand. After this has been the better cleaning factor with 19.6%. therefore we can conclude that if a brand was to increase its demand and market share it should concentrate on providing special features.
Q.9 What is the price range(per kg) of the detergent you use? a) 50-75 b) 75-100 c) 100-125 d) 125-150 e) 150+ ANALYSIS AND INTEPRETATION
What is the price range of the detergent you use? Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
50-75
69
25.0
25.3
25.3
75-100
69
25.0
25.3
50.5
100-125
96
34.8
35.2
85.7
125-150
39
14.1
14.3
100.0
Total
273
98.9
100.0
System
3
1.1
Total
276
100.0
This table is showing the price range of the products that the respondents use.so most of the respondents use the 100-125 kg detergent.which is related with the SURF EXCEL which amounts to 34.8 %of the total respondents.and second most popular price range is 50-75 and 75-100 both which includes the TIDE ,GHADI , NIRMA etc which amounts to 25 % each of the total respondents. Q.10 What is the quantity of detergent used? a) less than 500gms. b) 500 -1000 gms c) 1000-2000 gms d) 2000- 3000 gms e) more than 3000gms. ANALYSIS AND INTEPRETATION
Statistics quantity of detergent you use in a month? N
Valid
243
Missing
33
Median
3.00
Range
4
quantity of detergent you use in a month? Cumulative
Valid
Missing Total
Frequency
Percent
Valid Percent
Percent
<500gm
9
3.3
3.7
3.7
1kg
87
31.5
35.8
39.5
2kg
90
32.6
37.0
76.5
3kg
24
8.7
9.9
86.4
>3kg
33
12.0
13.6
100.0
Total
243
88.0
100.0
System
33
12.0
276
100.0
Hence from the table monthly consumption of detergents for the maximum respondents is from 12 kgs
Q.11 How many family members do you have? a) 1
b) 2 c) 3 d) 4 e) 5 f) 5+
ANALYSIS AND INTEPRETATION Statistics How many family members do you have? N
Valid
276
Missing
0
Median
4.00
Range
5
How many family members do you have? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
1
3
1.1
1.1
1.1
2
15
5.4
5.4
6.5
3
27
9.8
9.8
16.3
4
159
57.6
57.6
73.9
5
36
13.0
13.0
87.0
>5
36
13.0
13.0
100.0
Total
276
100.0
100.0
From the table we can see that 57.6% of the population says that there family members are 4.Thus most of the people are using 1-2 kgs of surf for 4 family members which gives us monthly consumption for an individual from 250-500 gms.
Q.11 Generally, in what you like to purchase your detergent? a) 100-250 gms b) 250-500 gms c) 500-1000 gms d) 1000-2000 gms e) 2000 gms+ ANALYSIS AND INTEPRETATION
Generally, in what quantity do you like to purchase the detergents? Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
100-250gm
9
3.3
3.3
3.3
250-500gm
54
19.6
20.0
23.3
500-1000gm
123
44.6
45.6
68.9
1000-1500gm
84
30.4
31.1
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
This table is showing that in what quantity respondents would like to purchase their Detergents.so the most preferable quantity lies between 500-1000gm range which results the 44.6 % of the total respondents.secondly preferable quantity lies between 1000-1500 gm range Which results 30.4 %of the total respondents Thus we can infer that most customers buy detergents in 1kg packet.
Q.12 From where do you learn about the brand you are using currently? a) TV advertisements b) news paper c) radio broadcast d) banners/ hoardings e) mall promotion f) shopkeeper/ frinds advide g) others ANALYSIS AND INTEPRETATION
From where did you learn about the brand you are currently using? Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
Tv advertisements
198
71.7
72.5
72.5
mall promotions
3
1.1
1.1
73.6
shopkeepers' advice
45
16.3
16.5
90.1
any other
27
9.8
9.9
100.0
Total
273
98.9
100.0
System
3
1.1
Total
276
100.0
We can easily conclude from the above table that most people have learned from the means of tv advertisements. This shows us as to how effective is tv advertisements as a means of promotion of products
Q.13 How many detergent brands you have tried in past 2 yrs? a) one b) two to three c) about five d) don’t remember e) no other brand ANALYSIS AND INTEPRETATION How many detergents brand have you tried in the past 2 years? Cumulative
Valid
Frequency
Percent
Valid Percent
Percent
one
147
53.3
53.3
53.3
two or three
90
32.6
32.6
85.9
about five
9
3.3
3.3
89.1
don't remember
27
9.8
9.8
98.9
no other brand
3
1.1
1.1
100.0
Total
276
100.0
100.0
This table again suggests that most of the customers are very brand loyal towards the brand they are using and do not change easily and hesitate to go to a new product.
Q.14 Till now the most satisfying brand has been …. a) ariel b) surf c) tide d) surf excel e) wheel f) nirma g) other ANALYSIS AND INTEPRETATION
Till now the most satisfying brand has been... Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
ariel
12
4.3
4.5
4.5
surf
24
8.7
9.0
13.5
tide
42
15.2
15.7
29.2
surf excel
153
55.4
57.3
86.5
wheel
3
1.1
1.1
87.6
any other
27
9.8
10.1
97.8
11
6
2.2
2.2
100.0
Total
267
96.7
100.0
System
9
3.3
Total
276
100.0
According to the above table, surf excel has been rated as the most satisfying brand as compared to other brands presented and computed in our survey. It has astonishing 55.4% of responses in its favour. It is more than half of our sample size favouring it. Second in this list is tide with 15.7% and third being any other category which involves products like rin, ghadi etc. this data can help us know as to how the people prefer the brands.
Q.15 Importance of following factors in selecting a detergent on a scale of 1-10, 10 being the highest. a) Brand image b) Advertising. c) Recommendations by friends/family members d) Importance of availability e) fragrance f) packaging g) price
Descriptive Statistics N Importance of brand image in 270
Minimum
Maximum
Mean
Std. Deviation
1
10
6.88
2.269
1
10
6.24
2.033
2
10
6.21
1.984
1
10
6.03
1.745
1
9
4.11
2.306
1
10
4.42
1.987
1
10
7.31
1.542
selecting a detergent on a scale of 1-10 Importance of advertisements 270 in selecting a detergent on a scale of 1-10 Importance
of 270
recommendation
by
friends/family
in
members
selecting a detergent on a scale of 1-10 Importance of availability in 270 selecting a detergent on a scale of 1-10 Importance of fragrance in 270 selecting a detergent on a scale of 1-10 Importance of packaging in 270 selecting a detergent on a scale of 1-10 Importance
of
price
in 270
selecting a detergent on a scale of 1-10 Valid N (listwise)
270
This table shows that price is the most important factor in selecting a detergent follwed by brand image and suggestion by friends/ family members.
Frequency Table
Importance of brand image in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
1
3
1.1
1.1
1.1
2
12
4.3
4.4
5.6
3
6
2.2
2.2
7.8
4
36
13.0
13.3
21.1
5
24
8.7
8.9
30.0
6
9
3.3
3.3
33.3
7
36
13.0
13.3
46.7
8
75
27.2
27.8
74.4
9
48
17.4
17.8
92.2
10
21
7.6
7.8
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Importance of advertisements in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
1
3
1.1
1.1
1.1
2
9
3.3
3.3
4.4
3
15
5.4
5.6
10.0
4
24
8.7
8.9
18.9
5
57
20.7
21.1
40.0
6
24
8.7
8.9
48.9
7
54
19.6
20.0
68.9
8
42
15.2
15.6
84.4
9
39
14.1
14.4
98.9
10
3
1.1
1.1
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Importance of recommendation by friends/family members in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
2
6
2.2
2.2
2.2
3
21
7.6
7.8
10.0
4
33
12.0
12.2
22.2
5
39
14.1
14.4
36.7
6
57
20.7
21.1
57.8
7
21
7.6
7.8
65.6
8
57
20.7
21.1
86.7
9
30
10.9
11.1
97.8
10
6
2.2
2.2
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Importance of availability in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
1
9
3.3
3.3
3.3
3
12
4.3
4.4
7.8
4
21
7.6
7.8
15.6
5
51
18.5
18.9
34.4
6
63
22.8
23.3
57.8
7
57
20.7
21.1
78.9
8
48
17.4
17.8
96.7
9
6
2.2
2.2
98.9
10
3
1.1
1.1
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Importance of fragrance in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
1
15
5.4
5.6
5.6
2
96
34.8
35.6
41.1
3
15
5.4
5.6
46.7
4
21
7.6
7.8
54.4
5
57
20.7
21.1
75.6
6
18
6.5
6.7
82.2
7
15
5.4
5.6
87.8
8
21
7.6
7.8
95.6
9
12
4.3
4.4
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Importance of packaging in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
1
12
4.3
4.4
4.4
2
30
10.9
11.1
15.6
3
66
23.9
24.4
40.0
4
21
7.6
7.8
47.8
5
78
28.3
28.9
76.7
6
30
10.9
11.1
87.8
7
12
4.3
4.4
92.2
8
9
3.3
3.3
95.6
9
6
2.2
2.2
97.8
10
6
2.2
2.2
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Importance of price in selecting a detergent on a scale of 1-10 Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
2
3
1.1
1.1
1.1
3
3
1.1
1.1
2.2
4
9
3.3
3.3
5.6
5
12
4.3
4.4
10.0
6
36
13.0
13.3
23.3
7
84
30.4
31.1
54.4
8
63
22.8
23.3
77.8
9
45
16.3
16.7
94.4
10
15
5.4
5.6
100.0
Total
270
97.8
100.0
System
6
2.2
Total
276
100.0
Q.16 When you think of detergents, which advertisements you can recall now…. When you think of detergents , which advertisements you can recall now Cumulative
Valid
Missing
Frequency
Percent
Valid Percent
Percent
ariel
9
3.3
3.9
3.9
surf
3
1.1
1.3
5.3
tide
42
15.2
18.4
23.7
surf excel
102
37.0
44.7
68.4
wheel
6
2.2
2.6
71.1
nirma
51
18.5
22.4
93.4
others
15
5.4
6.6
100.0
Total
228
82.6
100.0
System
48
17.4
Total
276
100.0
The most preferred brand is surf excel and its reason could be well explained with the help of the advertisement recall data. Surf excel tops the list again and with 37%. I think: “Dhag Acche Hai” is the most popular tagline which is associated with detergents. Nirma comes second with its advertisement with tide coming third.
CROSSTAB ASSOCIATION BETWEEN BRAND USED PREVIOUSLY AND NOW A) PEOPLE USING NIRMA
Do you use nirma? * Which is the previous brand you were using? Crosstabulation Count Which is the previous brand you were using?
Do you use nirma?
ariel
surf
tide
surf excel
wheel
nirma
0
3
9
0
6
0
3
yes
3
0
0
0
0
0
no
27
45
33
15
36
21
Total
33
54
33
21
36
24
Do you use nirma? * Which is the previous brand you were using? Crosstabulation Count Which previous
is
the brand
you were using?
Do you use nirma?
others
Total
0
3
24
yes
0
3
no
33
210
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
37.244a
12
.000
Likelihood Ratio
34.801
12
.001
1
.239
Linear-by-Linear Association 1.389 N of Valid Cases
237
a. 13 cells (61.9%) have expected count less than 5. The minimum expected count is .27.
This shows that there is a significant shift in the usage of nirma in mostly urban areas where this research is conducted as there is a sharp decrease in the in the users of nirma from 24 to 3. This also shows that the standard of living has gone up significantly and people are ready to pay more for better quality. 2) PEOPLE USING ARIEL
Case Processing Summary Cases Valid N Do you use ariel? * Which is 237
Missing
Total
Percent
N
Percent
N
Percent
85.9%
39
14.1%
276
100.0%
85.9%
39
14.1%
276
100.0%
85.9%
39
14.1%
276
100.0%
the previous brand you were using? Do you use surf ? * Which is 237 the previous brand you were using? What was the reason behind 237 shifting to his brand from the previous one? * Which is the previous
brand
you
were
using?
Do you use ariel? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using?
Do you use ariel?
ariel
surf
tide
surf excel
wheel
nirma
0
0
9
0
6
0
3
yes
3
0
3
0
0
0
no
30
45
30
15
36
21
Total
33
54
33
21
36
24
Crosstab Count Which previous
is
the brand
you were using?
Do you use ariel?
others
Total
0
3
21
yes
0
6
no
33
210
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
39.352a
12
.000
Likelihood Ratio
43.967
12
.000
1
.652
Linear-by-Linear Association .203 N of Valid Cases
237
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .53.
Ariel has also lost it’s market share due to increasing popularity of surf excel and as per the table and bar chart we can find that instead of 33 were using previously only 15 people replied to presently using.
3) PEOPLE USING SURF
Do you use surf ? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using?
Do you use surf ?
ariel
surf
tide
surf excel
wheel
nirma
0
3
9
0
6
0
3
yes
0
0
0
0
3
0
no
30
45
33
15
33
21
Total
33
54
33
21
36
24
Crosstab Count Which previous
is
the brand
you were using?
Do you use surf ?
others
Total
0
0
21
yes
6
9
no
30
207
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
48.007a
12
.000
Likelihood Ratio
51.623
12
.000
1
.566
Linear-by-Linear Association .330 N of Valid Cases
237
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .80.
Surf also saw a significant dip in th number of users from 54 that were previously using to 9 using presently. 4) PEOPLE USING SURF EXCEL
Do you use surf excel? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using?
Do you use surf excel?
ariel
surf
tide
surf excel
wheel
nirma
0
3
6
0
3
0
0
yes
24
33
21
12
21
15
no
6
15
12
6
15
9
Total
33
54
33
21
36
24
Crosstab Count Which previous
is
the brand
you were using?
Do you use surf excel?
others
Total
0
3
15
yes
18
144
no
15
78
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
16.844a
12
.156
Likelihood Ratio
22.053
12
.037
1
.017
Linear-by-Linear Association 5.703 N of Valid Cases
237
a. 7 cells (33.3%) have expected count less than 5. The minimum expected count is 1.33.
This brand has captured the market share significantly as there is a sharp increase in the number of respondents who were using previously to those using presently from 21 to 168 5) PEOPLE USING TIDE
Do you use tide? * Which is the previous brand you were using? Crosstabulation Count Which is the previous brand you were using?
Do you use tide?
ariel
surf
tide
surf excel
wheel
nirma
0
3
3
0
3
0
3
yes
6
21
6
9
6
3
no
24
30
27
9
30
18
Total
33
54
33
21
36
24
Do you use tide? * Which is the previous brand you were using? Crosstabulation Count Which previous
is
the brand
you were using?
Do you use tide?
others
Total
0
3
15
yes
6
57
no
27
165
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
26.197a
12
.010
Likelihood Ratio
29.175
12
.004
1
.331
Linear-by-Linear Association .945 N of Valid Cases
237
a. 7 cells (33.3%) have expected count less than 5. The minimum expected count is 1.33.
There is a significant increase in the tide users from 33 to 63
6) PEOPLE USING WHEEL
Do you use wheel? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using?
Do you use wheel?
ariel
surf
tide
surf excel
wheel
nirma
0
3
9
0
6
0
3
yes
0
6
0
0
6
0
no
30
39
33
15
30
21
Total
33
54
33
21
36
24
Crosstab Count Which
is
previous
the brand
you were using?
Do you use wheel?
others
Total
0
3
24
yes
6
18
no
27
195
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
Pearson Chi-Square
36.556a
12
.000
Likelihood Ratio
47.085
12
.000
1
.946
Linear-by-Linear Association .005 N of Valid Cases
237
a. 13 cells (61.9%) have expected count less than 5. The minimum expected count is 1.59.
Wheel also lost it’s market and instead of 36 who were using it previously now only 18 are using presently.
CROSSTAB ASSOCIATION BETWEEN BRAND USED PREVIOUSLY AND REASON FOR SHIFTING
Case Processing Summary Cases Valid N Do you use ariel? * Which is
Missing
Percent
N
Total
Percent
N
Percent
237
85.9%
39
14.1%
276
100.0%
237
85.9%
39
14.1%
276
100.0%
237
85.9%
39
14.1%
276
100.0%
the previous brand you were using? Do you use surf ? * Which is the previous brand you were using? What was the reason behind shifting to his brand from the previous one? * Which is the previous brand you were using?
Do you use ariel? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using? ariel Do you use ariel?
surf
tide
surf excel
wheel
nirma
0
0
9
0
6
0
3
yes
3
0
3
0
0
0
no
30
45
30
15
36
21
Total
33
54
33
21
36
24
Crosstab Count Which is the previous brand you were using? others Do you use ariel?
Total
0
3
21
yes
0
6
no
33
210
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
39.352a
12
.000
43.967
12
.000
Linear-by-Linear Association
.203
1
.652
N of Valid Cases
237
Pearson Chi-Square Likelihood Ratio
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .53.
Do you use surf ? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using? ariel Do you use surf ?
surf
tide
surf excel
wheel
nirma
0
3
9
0
6
0
3
yes
0
0
0
0
3
0
no
30
45
33
15
33
21
Total
33
54
33
21
36
24
Crosstab Count Which is the previous brand you were using? others Do you use surf ?
Total
0
0
21
yes
6
9
no
30
207
Total
36
237
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
48.007a
12
.000
51.623
12
.000
Linear-by-Linear Association
.330
1
.566
N of Valid Cases
237
Pearson Chi-Square Likelihood Ratio
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .80.
What was the reason behind shifting to his brand from the previous one? * Which is the previous brand you were using?
Crosstab Count Which is the previous brand you were using? ariel
surf
tide
surf excel
What was the reason behind price
0
6
0
0
shifting to his brand from the packaging previous one? any special features like
0
3
0
0
18
21
21
12
3
3
3
0
12
18
9
9
0
3
0
0
33
54
33
21
usage with rough water friendliness better cleaning availability Total
Crosstab Count Which is the previous brand you were using? wheel What was the reason
price
nirma
others
Total
0
0
0
6
0
0
0
3
30
21
30
153
friendliness
3
3
3
18
better cleaning
3
0
3
54
availability
0
0
0
3
36
24
36
237
behind shifting to his brand packaging from the previous one? any special features like usage with rough water
Total
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
77.040a
30
.000
Likelihood Ratio
79.860
30
.000
Linear-by-Linear Association
10.824
1
.001
Pearson Chi-Square
N of Valid Cases
237
a. 29 cells (69.0%) have expected count less than 5. The minimum expected count is .27.
CROSSTAB ASSOCIATION ADVERTISEMENT RECALL
BETWEEN
BRAND
USED
AND
Case Processing Summary Cases Valid N Do you use ariel? * When you
Missing
Percent
N
Total
Percent
N
Percent
228
82.6%
48
17.4%
276
100.0%
228
82.6%
48
17.4%
276
100.0%
228
82.6%
48
17.4%
276
100.0%
228
82.6%
48
17.4%
276
100.0%
228
82.6%
48
17.4%
276
100.0%
228
82.6%
48
17.4%
276
100.0%
think of detergents , which advertisements you can recall now Do you use surf ? * When you think of detergents , which advertisements you can recall now Do you use tide? * When you think of detergents , which advertisements you can recall now Do you use surf excel? * When you think of detergents , which advertisements you can recall now Do you use wheel? * When you think of detergents , which advertisements you can recall now Do you use nirma? * When you think of detergents , which advertisements you can recall now
Do you use ariel? * When you think of detergents , which advertisements you can recall now
Crosstab Count When you think of detergents , which advertisements you can recall now ariel Do you use ariel?
surf
tide
surf excel
0
3
3
9
0
3
yes
9
0
0
0
0
0
no
0
0
39
93
6
48
Total
9
3
42
102
6
51
Count When you think of detergents , which advertisements you can recall now Others
Total
0
0
18
yes
0
9
no
15
201
Total
15
228
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
265.338a
12
.000
Likelihood Ratio
94.935
12
.000
Linear-by-Linear Association
16.262
1
.000
Pearson Chi-Square
N of Valid Cases
228
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .12.
nirma
0
Crosstab
Do you use ariel?
wheel
Do you use surf ? * When you think of detergents , which advertisements you can recall now
Crosstab Count When you think of detergents , which advertisements you can recall now ariel Do you use surf ?
surf
tide
surf excel
wheel
nirma
0
0
3
3
6
0
3
yes
0
0
0
9
0
0
no
9
0
39
87
6
48
Total
9
3
42
102
6
51
Crosstab Count When you think of detergents , which advertisements you can recall now Others Do you use surf ?
Total
0
0
15
yes
0
9
no
15
204
Total
15
228
Chi-Square Tests Asymp. Sig. (2Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases
df
sided)
56.453a
12
.000
35.388
12
.000
3.006
1
.083
228
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .12.
Do you use tide? * When you think of detergents , which advertisements you can recall now
Crosstab Count When you think of detergents , which advertisements you can recall now ariel Do you use tide?
surf
tide
surf excel
wheel
nirma
0
0
3
0
9
0
3
yes
0
0
24
9
0
9
no
9
0
18
84
6
39
Total
9
3
42
102
6
51
Crosstab Count When you think of detergents , which advertisements you can recall now Others Do you use tide?
Total
0
0
15
yes
6
48
no
9
165
15
228
Total
Chi-Square Tests Asymp. Sig. (2Value Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases
df
sided)
95.861a
12
.000
71.224
12
.000
1.328
1
.249
228
a. 12 cells (57.1%) have expected count less than 5. The minimum expected count is .20.
Do you use surf excel? * When you think of detergents , which advertisements you can recall now
Crosstab Count When you think of detergents , which advertisements you can recall now Ariel Do you use surf excel?
surf
tide
surf excel
wheel
nirma
0
0
0
3
3
0
0
yes
0
3
15
87
0
33
no
9
0
24
12
6
18
Total
9
3
42
102
6
51
Crosstab Count When you think of detergents , which advertisements you can recall now others Do you use surf excel?
Total
0
0
6
yes
6
144
no
9
78
15
228
Total
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
74.245a
12
.000
83.133
12
.000
Linear-by-Linear Association
.108
1
.743
N of Valid Cases
228
Pearson Chi-Square Likelihood Ratio
a. 12 cells (57.1%) have expected count less than 5. The minimum expected count is .08.
Do you use wheel? * When you think of detergents , which advertisements you can recall now
Crosstab Count When you think of detergents , which advertisements you can recall now ariel Do you use wheel?
surf
tide
surf excel
wheel
nirma
0
0
3
3
9
0
3
yes
0
0
3
0
0
12
no
9
0
36
93
6
36
Total
9
3
42
102
6
51
Crosstab Count When you think of detergents , which advertisements you can recall now Others Do you use wheel?
Total
0
0
18
yes
0
15
no
15
195
Total
15
228
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
71.195a
12
.000
53.565
12
.000
Linear-by-Linear Association
.040
1
.842
N of Valid Cases
228
Pearson Chi-Square Likelihood Ratio
a. 13 cells (61.9%) have expected count less than 5. The minimum expected count is .20.
Do you use nirma? * When you think of detergents , which advertisements you can recall now
Crosstab Count When you think of detergents , which advertisements you can recall now ariel Do you use nirma?
surf
tide
surf excel
wheel
nirma
0
0
3
3
9
0
3
yes
0
0
0
0
0
0
no
9
0
39
93
6
48
Total
9
3
42
102
6
51
Crosstab Count When you think of detergents , which advertisements you can recall now Others Do you use nirma?
Total
0
0
18
yes
3
3
no
12
207
Total
15
228
Chi-Square Tests Asymp. Sig. (2Value
df
sided)
80.748a
12
.000
37.064
12
.000
Linear-by-Linear Association
.784
1
.376
N of Valid Cases
228
Pearson Chi-Square Likelihood Ratio
a. 14 cells (66.7%) have expected count less than 5. The minimum expected count is .04.
CORRELATION BETWEEN FREQUENCY OF WASING CLOTHES AND QUANTITY OF DETERGENT USED
Correlations
How frequently do you wash
Pearson Correlation
your clothes?
Sig. (2-tailed) N
quantity of detergent you use Pearson Correlation in a month?
How frequently
quantity of
do you wash
detergent you use
your clothes?
in a month? 1
.006 276
243
-.174**
1
Sig. (2-tailed)
.006
N
243
**. Correlation is significant at the 0.01 level (2-tailed).
-.174**
243
CROSS TAB BETWEEN BRAND USED PREVIOUSLY AND REASON FOR SHIFTING BRAND Case Processing Summary Cases Valid N Which is the previous brand
Missing
Percent 237
N
85.9%
Total
Percent 39
N
14.1%
Percent 276
100.0%
you were using? * What was the reason behind shifting to his brand from the previous one?
Which is the previous brand you were using? * What was the reason behind shifting to his brand from the previous one? Crosstabulation Count What was the reason behind shifting to his brand from the previous one? any special features like usage with rough price
packaging
water
friendliness
Which is the previous brand
ariel
0
0
18
3
you were using?
surf
6
3
21
3
tide
0
0
21
3
surf excel
0
0
12
0
wheel
0
0
30
3
nirma
0
0
21
3
others
0
0
30
3
Total
6
3
153
18
Which is the previous brand you were using? * What was the reason behind shifting to his brand from the previous one? Crosstabulation Count What was the reason behind shifting to his brand from the previous one? better cleaning
availability
Total
Which is the previous brand
ariel
12
0
33
you were using?
surf
18
3
54
tide
9
0
33
surf excel
9
0
21
wheel
3
0
36
nirma
0
0
24
others
3
0
36
Total
54
3
237
Symmetric Measures Value Nominal by Nominal
Approx. Sig.
Phi
.570
.000
Cramer's V
.255
.000
N of Valid Cases
237
200 150 Series1 Series2 Series3 Series4
100 50 0
1
2
3
4
5
6
7
Series1
15
9
63
168
18
3
0
Series2
12
24
42
153
3
0
27
Series3
9
3
42
102
6
51
15
Series4
33
54
33
21
36
24
36
PRESENTLY USING
MOST SATISFYING
ADVERTISEMENT PREVIOUSLY RECALL USING
ARIEL(1)
15
12
9
33
SURF(2)
9
24
3
54
TIDE(3)
63
42
42
33
SURF EXCEL(4)
168
153
102
21
WHEEL(5)
18
3
6
36
NIRMA(6)
3
0
51
24
OTHERS(7)
0
27
15
36
TERMED AS TERMED AS TERMED SERIES 1 SERIES 2 SERIES 3
AS TERMED AS SERIES 4
Detergents in news
P&G drops prices of detergent brands Ariel, Tide TRYING to get further volumes in its detergent business, Procter & Gamble Home Products Ltd has decided to reduce prices of its Tide and Ariel brands by 20-50 per cent. Having reduced prices of the sachets by 50 per cent last year, the extra volumes generated by the company have led it to adopt a similar reduction for its detergent bags as well. At a press conference, Mr Rahul Malhotra, Country Marketing Manager, P&G India Ltd, said, "It was our sachet experience which gave us the confidence to drop prices for the detergent bags as well. Although we do not expect volumes to explode as in the case of sachets, the purpose is to get the masses to experience our products." The price of the one kg pack of Ariel will now be reduced from Rs 135 to Rs 99, while the Tide price has been slashed from Rs 85 to Rs 46 for 1 kg. The prices of the 200 and 500 gm packs have also been reduced to Rs 22 for Ariel and Rs 10 for Tide (earlier Rs 30 and Rs 20 respectively) and Rs 50 for Ariel and Rs 23 for Tide (earlier Rs 70 and Rs 43 respectively). Improving upon the internal efficiencies within the company in areas such as distribution, manufacturing and cost of raw material, the company claims that it is now in a position to pass on these benefits to the consumer. However, the company is unable to comment on how this exercise will shift paradigms in the declining detergent category. Says Mr Malhotra: "We have to wait and see how competition reacts to our move." The Rs 5,000-crore detergent category has been stagnant for the past few years with high saturation levels in the category, which is already well penetrated." "The market is big but people need to wash more often and move to better quality detergents. Hopefully, then, there will be some value growth in the category," he observes. Expecting to upgrade consumers from using bars to detergents and then to more premium detergents, P&G is hoping its price cuts will accelerate this change. To convince the masses to use its products, P&G has roped in well-known faces such as Ms Smriti Irani and Ms Aparna Mehta from the popular soap opera Kyunki Saas Bhi Kabhi Bahu Thi, to endorse its brands. Besides, it will be unleashing new commercials for its brands and there will be significant jumps in its ad budgets.
Without disclosing figures for the projected growth rates for its brands, Mr Malhotra says, "We expect volumes to go higher than the price reductions. So if Tide is being reduced by 50 per cent, we expect its volumes to more than double."
A new price war is bubbling up in the Rs 4,500-crore branded detergents sector. Following the footsteps of its arch-rival Hindu- stan Unilever Ltd (HUL), Procter & Gamble India (P&G) plans to cut by 12-20% the prices of its flagship detergent brands, Tide and Ariel, within weeks. Along with price cuts, P&G is also poised to increase the weights of its detergent brands, to take on market leader HUL, said key industry sources. After playing the ‘who blinks first’ game, Hindustan Unilever took price cuts in select stockkeeping units (SKUs) of its specific laundry brands last month. According to retailers, the SKU price of Surf Excel Blue 200 gm was reduced from Rs 25 to Rs 23 and Surf Excel Blue 500 gm, from Rs 62 to Rs 55. “The SKU price of Rin Powder 1 kg was reduced from Rs 70 to Rs 50, while the grammage for Rin Powder Rs 10 pack was increased,” said a leading retailer in south Mumbai. As a first initiative to counter HUL, P&G has introduced Ariel in a new pack priced at Rs 10. To announce the new launch, P&G is beaming a high-voltage television campaign with a tag line ‘Mehngai Maar Gayee’. This initiative will be followed by future price cuts. After waging an aggressive price war, HUL and P&G are now gearing up to battle for mindshare through high-decibel ads. To start with, HUL is gearing up to launch an aggressive advertising campaign to promote its brand Surf within a month. Ad major Lowe Lintas is fine-tuning HUL’s new campaign for Surf. “As expected, P&G is taking price cuts to counter its rival’s new move. Very soon, HUL & P&G will slug it out to gain mind as well as market share in the branded detergents sector”, said a Mumbai-based analyst. When contacted by FE, a company spokesperson from P&G said: “We do not comment on future business plans.” Currently, HUL leads the pack in the branded detergents sector with an estimated market share of 37%. Other key players in this space are Nirma, Gadhi, and Henkel.
HLL Bets Big On Mass Mart For Future Growth In Detergents Mumbai, March 10: “We’ve got the scores on the board; they’ve yet to bat”. That summed up Hindustan Lever’s (HLL) first official reaction to the latest price war in detergents where the company has a 40 per cent share, while rival Procter & Gamble’s (P&G) share four per cent of the overall market. In an interview with FE, HLL head (marketing and consumer development) Sanjay Dube said: “There’s not going to be any blood-bath. It’s a logical marketing strategy with a longer-term gameplan.” The strategic move to drop prices of Surf Excel to widen the net to the mass consumer is a deliberate and cautious approach, according to Mr Dube. Mr Dube, however, said there is no room for more price cuts on Surf Excel. Further, no price cuts will be taken on its other detergent brands — Rin and Wheel (claimed to be the largest laundry brand) — he added. On the contrary, the price of Rin was increased a few months ago on the brand’s upgradation in quality. P&G announced price cuts on Ariel (500 gm) from Rs 70 to Rs 50 and on Tide (500 gm) from Rs 43 to Rs 23, on March 2. Following this, HLL immediately reduced Surf Excel’s price to match Ariel’s. HLL, which has been quiet on the issue, said its strategy was not a reaction to P&G’s. The company pointed out that it was HLL which initiated such moves by introducing Lux at Rs five sometime back. “We were the first to reduce the price of Surf Excel in January 2003 from Rs 153 to Rs 135. Then we brought down the price of sachets from Rs three to Rs 1.50,” he said. The company got the value equation right for Lux, Lifebuoy and Wheel, he added. “We may or may not apply this strategy across categories,” said Mr Dube. On the reduction of margins on account of cut in Surf Excel price, he said the move would lead to higher profits and sales as volumes were expected to go up substantially. However, HLL did not commit on any volume numbers. Mr Dube said there would be both bottomline and topline growth in detergents, despite the price reductions.
The company is hopeful that the strategy will unlock growth potential for its detergent brands in a now sharper price-point detergent market where the price spectrum has compressed to three levels — premium (Rs 99), mass (Rs 40-45)... Washing and caring for your clothes is a constant challenge. Luckily, Surf excel is always up to the job. Constant improvement Surf excel is the highest selling premium washing powder in Bangladesh. Over the years it has anticipated the changing washing needs of the Bangladeshi homemaker and constantly upgraded itself. Surf excel has been a pioneer in the country in encouraging others to let their children explore and discover the world around them. For any consequential stains there is Surf excel. Surf excel's advanced formula effectively removes dried in stains. 10 Most stain prone areas Kids are so involved in what they do that without noticing it they get dirty in all sorts of areas. The 10 most stain prone areas are grimy collars, greasy cuffs, inky pockets, sweaty underarms, waists and sleevees, muddy socks, trouser falls and knees and paint stained pant bottoms. New Surf excel's powerful 'Blue Energy' effectively penetrates the layers of fabrics and even removes tough stains hidden in the 10 most stain prone areas. Dirt is Good! Although it might sound strange for a leading laundry brand like Surf excel to say this, we believe, like you, that this type of dirt is good: it's an important part of a child's development. It's how kids learn, express their creativity and even bolster their immune systems. At a time when growing numbers of children are leading sedentary lives, often cocooned in the home, glued to the TV and the web, we're not afraid to celebrate this time-honoured truth.
Hindustan Lever gives in, admits Wheel has no lime Our Corporate Bureau NEW DELHI, DEC 22: The `war of lemons' being fought between Hindustan Lever Ltd (HLL) and Fena Ltd has taken a major turn with the former admitting that its leading brand `Wheel' does not contain any lemon. The two had earlier crossed swords over Fena's innovative `think-beyond-nimbu' ad campaign, the transmission of which was allegedly scuttled by HLL. In a reply submitted by HLL with the Monopolies & Restrictive Trade Practices Commission (MRTPC) to a petition filed by Fena, the former admitted that Wheel only contains lemon perfume. However, HLL dismissed that it has misled its consumers. HLL had been challenged by Fena in MRTPC on the grounds of misleading consumers by a false claim in the advertisement campaign of Wheel, which emphasises the cleaning power of lemon, an ingredient closely associated with the brand. Fena had earlier filed a case in the Advertising Standards Council of India (Asci) alleging violation of the council's code of honesty in advertising by HLL by way of makingfalse and misleading statements. In its reply HLL further said that the company has spent over Rs 50 crore on the advertising of Wheel over the last ten years but has never misled the consumer on the product's content. Further hearing in the case has been scheduled for February. The Wheel-Fena detergent war began in October last year when HLL reportedly objected to Fena's new advertisement, which was to be telecasted during the popular television serial Om Namah Shivay on Doordarshan. However, HLL allegedly pressurised the programme producer to drop the Fena ad.
In its submission with the MRTPC, HLL however, has denied that lemon is the main ingredient in Wheel and that claims of lemon being added for better washing action on clothes and stain removal were never made by the company as a strategy to sell the product. HLL clarified that Wheel detergent powder and cake contain only "lemon perfume" from natural lemon oil. The company has pointed out that the packaging of the product does notmention that either the Wheel detergent or, the cake contain lemon, as alleged by Fena. The company has justified the visual depiction of lemon and its reference in Wheel's advertisement and product packaging on the basis of the presence of lemon perfume.
Consumer body digs up dirt on detergents
LALITAJI's clever pitch `Surf ki kharidari me hi samajhdari hain' (buying Surf makes sense) may not wash. And, it could well apply to a host of other detergent brands. Tests done by a consumer organisation suggest that multinational brands such as Surf, Ariel and Henko that rule the roost in the Rs 5,000-crore industry may not offer as much value for money as some of the ordinary brands when it comes to removing dirt.
Consumer Guidance Society of India (CGSI), an independent organisation that fights for consumer rights, has found that while low-end brands such as Sasa Green, Nirma, Wheel Active and Chek cost a rupee per wash, premium brands such as Surf Excel Auto cost Rs 8 per wash and Ariel costs Rs 7 per wash. In terms of soil-removing capacity (detergency), low-end detergent brands' washing efficiency was similar to that of the premium brands, and there is definitely no cost advantage in choosing a well-known brand over a lesser-known brand, says Dr A.R. Shenoy, Chairman, CGSI. Detergency is expressed as the percentage of dirt removed from a fabric, and is measured by determining the `reflectance readings' after washing a standard soiled cloth. The test protocol was as per IS 4955: 2001, Indian Standards prescription for household laundry detergent powders (fourth revision). CGSI tested 19 brands to check their dirt-removing capability. A wider price disparity among these brands did not result in a distinct change in washing benefits, CGSI says in its report titled `Comparative Detergency Values: Which washes cleanest.' Manufacturers of premium detergent brands cry foul over CGSI's contention and question its standards. "Consumer end-point for laundry is a sum total of lots of variables apart from basic detergency like stain removal, whiteness of the garments, extent of damage to colours, extent of damage to garments, damage to hands, odour removal from the fabric and perfume of the fabric. Premium powders deliver highly on all of these apart from basic detergency, while low-end powders have poor or no delivery for most or all of them," said a Hindustan Lever spokesperson in a written response to the test report. Sasa Green, made by Mahila Udyog, the makers of Lijjat Papad, has a detergency of 67 per cent as against Hindustan Lever's Surf Excel, which has a detergency of 74 per cent. On the other hand, the cost per wash for the former is only 90 paise compared to the latter's cost of Rs 5 per wash. Similarly, P&G's Ariel has a detergency of 80 per cent as against Lever's Wheel Active detergency of 74 per cent. Ariel's cost per wash adds up to Rs 7 per wash while Wheel Active's costs only Re 1 per wash.
A P&G research and development spokesperson responded to CGSI's contention claiming that the reports focussed only on one aspect of product performance. P&G, however, chose to put money on its own findings. "Based on our intensive consumer tests across the length and breadth of India and the strong consumer response we have received to the price correction on Ariel and Tide, we know that consumers do recognise and appreciate all the superior benefits that Ariel and Tide provide, which, however, cannot be dimensionalised in a single `test'." Like HLL & P&G, which rebutted the research findings, Henkel Spic also maintained that the findings were uni-dimensional. However, Mr Vijay Subramaniam, General Manager (Marketing), Henkel Spic India, was pleased to note that the CGSI report findings rated its detergent as one of the most effective brands in terms of cost per wash. Mr Subramaniam said, "We are happy to note that among premium brands like Ariel, Surf Excel, Henko's cost per wash is the lowest - this is line with our internal tests and is testimonial to our superior quality." These 19 brands were tested as per BIS protocol on solid white fabric and blocking the extra fluorescence generated by the detergents while measuring `reflectance' after washing as per BIS protocol to do justice to actual performance of the active matter in the detergent formulation. This is the spine of the CGSI study. As regards stain-removing efficiency, both HLL & P&G have their proprietary protocols and CGSI is game to test these samples again for stain-removal efficiency if these companies are willing to share their respective protocols. "We are also keen to test `other performance parameters' as claimed by premium detergent makers," says Dr Shenoy. Detailing the test methodology, he said the samples were purchased from different areas of Mumbai and coded to mask their identity. The testing was done at a laboratory accredited by National Accreditation Board for Testing and Calibration Laboratories (NABL). All samples were
tested and compared against standard Grade I detergent control formulation assigned a value of 70 per cent and put through on a machine wash, as hand wash using a brush could have resulted in variations. Amidst this exchange of views on efficacy and other performance parameters of detergents, the only thing intriguing to consumers would be that the truth still resonates in the classic Alyque Padamsee copy for a leading detergent: Dhoondte Reh Jaoge. Superior cleaning in a choice of Two Fragrances – Ariel Spring Clean & Ariel Fresh Clean Another Breakthrough Innovation from Ariel for the Indian Family Ever wished that your clothes could smell mesmerizing and fragrant like your perfumes? Procter & Gamble, the makers of leading international fragrances such as Hugo Boss, Lacoste, Old Spice and Valentino now make your wish come true, with the launch of Ariel in a choice of two exciting fragrances – Ariel Spring Clean with a floral fragrance, and Ariel Fresh Clean with a refreshing fragrance. The two unique fragrances of Ariel now offers Indian consumers an unbeatable combination of ‘superior cleaning’ and ‘a choice of fragrances’ with the launch of Ariel Spring Clean and Ariel Fresh Clean. Research conducted by Ariel on the Indian laundry market indicates that ‘fragrance’ in detergents is an important factor of delight for the home-maker in her daily laundry chore. Inspired by the roses that bloom in Indian spring time, Ariel Spring Clean has a flowery fragrance. And inspired by the subtle refreshing scent of the jasmine flower, Ariel Fresh Clean has a fresh fragrance catering to the Indian consumers’ special fondness for mogra. Ariel is the world’s leading detergent and epitomizes ‘stain removal’. Introduced in India in 1991, Ariel has continuously led other detergents in product innovation. It was the first to bring the ‘compact detergent’ technology in India; the enzyme technology for superior and safe stainremoving power; the proprietary ‘smart eyes’ technology which helps detect and remove stains better than any other detergent; and now for the first time superior cleaning in a choice of fragrances. Over the years, the brand has enjoyed endorsement from celebrities such as actress & social worker Shabana Azmi, Begum of Pataudi, successful actress and homemaker of the Pataudi family Sharmila Tagore, TV’s most popular ‘saas-bahu’ duo Smriti Iraani and Apara Mehta, and lakhs
of
homemakers
in
India.
Ariel contains safe ingredients for normal fabrics and skin under recommended usage conditions for laundry, and meets the Company’s stringent human and environmental safety standards. Ariel in its choice of two fragrances – Spring Clean and Fresh Clean, is available at all leading general and chemist stores and the price remains unchanged at Rs. 145 for 1.5kg; Rs. 99 for 1kg; Rs. 50 for 500gm; Rs. 22 for 200gm and Rs. 2 for a 20gm sachet. NIRMA'S
DOWNTURN
IN
THE
DETERGENT
MARKET
Interview with Azhar Kazmi
Nirma, one of the leading names in the Indian detergent market, has been going through a rough time of late, after giving big names like HLL a run for their money. It seems the big players have cracked the mystery, and are now finding it easy to turn the table on Nirma. This interview probes the reasons behind this sudden reversal of fortunes.
Do you think continuous innovation of products plays a crucial part in a company's long term sustainability
in
the
market?
Let me start by saying that despite all the glamour associated with innovation, I feel it remains an enigmatic human phenomenon: At the same time that it is invigorating, it is energy sapping too. Corporate innovation is like that. To resolve this dilemma, I believe the better approach is to consider innovation as an integral part of the company's product/service mission. This means selective
innovation.
Having said that let me accept that there is no doubt that continuous innovation of products/services plays a crucial part in any company's long-term sustainability in the market. However, there are certain caveats to this proposition. Innovation of products/services must be in response to emerging customer needs and preferences. It should be tailored to the market demands, and at the same time fit in with the positioning of the company's products/services in the market. Any innovation that is just done to 'keep up with the Joneses' or emerges out of a "me-too" strategy
does not really serve the purpose. Witness the very low percentage of product/service ideas that reach the commercialization stage, and you would know the high mortality rate of ideas. A genuine innovation, in my view, is necessarily not a technological marvel; it could be a unique way to produce and market a product or service. For instance, it could be a new way to distribute a product or service that no one in the industry thought of earlier. Alternatively, it could be a novel way to provide after-sale service. Therefore, product innovation is just a sub-set of a larger canvas of innovations in all the value chain activities of a company. Besides that, we must not be ignoring the potential of managerial innovation: Innovations that bring out novel ways of managing within organizations. These types of innovations cut across the product/market boundaries, and help the company apply newer techniques to a wide variety of product/market combinations. What according to you is/are the reason(s) for Nirma's downturn in the detergent segment? At the immediate level, Nirma's downturn is a combination of offensive and defensive strategies adopted by its rivals in the detergent segment of the industry. Having taken an initial beating, Hindustan Levers Ltd (HLL) has reacted to the low-cost provider strategies of Nirma in so many different ways. Besides its Project Millennium and continual efforts at restructuring, HLL has been alive to the market needs. HLL, despite being a bulky and bureaucratic corporate entity, has always been a practitioner of innovative techniques, especially in its strong area of rural marketing. HLL has undertaken several actions, and among these we have the offensive strategies of price-cutting and defensive strategies of coming up with matching products with some differentiated elements such as in the case of 'Wheel' detergent. Besides these, there are other deeper reasons too that I reflect on in my answers to the other questions. David can attack Goliath'-how relevant do you think is this in the case of Nirma? David attacked Goliath and emerged as a winner vanquishing his much stronger opponent. However, in the case of Nirma versus Hindustan Lever it is a continuing warfare between the two. Goliath is yet to be vanquished and is making it difficult for David to win. An allegory that seems more relevant to me in the Nirma-HLL case is a fight between two lions vying for hunting
space
with
each
jealously
guarding
its
own
turf.
Going back to the heady days of 1990s when Nirma was making rapid forays into the markets and challenging HLL, one can recall the euphoria that Nirma generated not only in the markets and industry, but also in the academic circles. Business cases were developed illustrating how a homegrown, entrepreneurial venture could take up the cudgel against a well-established multinational corporation. These ideas were also picked up by MNC-haters around the world, and Nirma soon became an icon of the small, nimble local entity fighting valiantly against the big, bad MNC
wolves.
Behind the initial success of Nirma, in my opinion, was the down-to-earth approach of its founder, Karsanbhai Patel. How Patel could translate his keen understanding of the production process and marketing of detergents on an industrial scale, despite his modest background, is now a part of the corporate folklore in India. That this happened in Gujarat-the home of entrepreneurs-is nothing surprising. Gujarat showed the way earlier in the white revolution that took place in India. From a strategic point of view, everything that could be done by Nirma to create a low-cost product was in fact done: A high level of backward integration leading to captive supply sources, in-house manufacturing and packaging design, direct distribution channels, personal and trustbased relationship with distributors, and sustained, enduring, and lowkey promotion. In retrospection, one could discern an integrated approach in the panorama of activities that Nirma performed.
Such
a
strategic
approach
yielded
handsome
results.
As it is said, within the success lies the seed of failure and vice versa. I sometimes feel that too much of a good thing may turn out to be bad: Nirma is facing its present troubles owing to its initial
successes.
Drucker talked about implicit and explicit theories that organizations develop for their business wherein they incorporate assumptions about the environment, specifically the markets, customers, and important technologies they deal with; the mission and purpose of business; and the core competencies
required
to
fulfill
the
mission.
Karsanbhai's implicit and explicit theories related to Nirma served it well until the initial
assumptions changed as they inevitably had to. I cannot say that Nirma did nothing to respond to those changing assumptions; it did a lot. Nevertheless, it seems these are now proving to be inadequate. Take the case of its legendary lowcost leadership strategies. I think they outlived their utility long back to the type of business environment that is emerging. Yet Nirma continued to insist on replicating its proven formula for success. The marketing environment was changing yet Nirma did not react fully to it and did not change its assumptions. Is the customer, who relished buying Nirma's product in the 1990s the same as today's customer? I do not think so. Therefore, a mismatch has occurred in what Nirma offers and what the customer really wants. Obviously, the customer is now demanding enough to expect not only low price but better quality too - an area where Nirma, owing to its misdirected attitude to cost cutting, is not able to satisfy. Any other thoughts/views you would like to share with our readers? What according to you are the strategies Nirma should adopt to retain its customers' loyalty and regain its No.2 position in the
market?
Conventional wisdom could say that Nirma can embark on a series of short-term measures to tinker with its strategy and manage to keep afloat. However, that is the worst thing that can happen, though I feel it might also be the most likely thing to happen since that seems to be the obvious solution. In my opinion, ideally what is required is a fundamental shift in its thinking, whereby it revises its assumptions that makes it rethink its implicit and explicit theories about its business. Going back to the heady days of 1990s when Nirma was making rapid forays into the markets and challenging HLL, one can recall the euphoria that Nirma generated not only in the markets and industry,
but
also
in
the
academic
circles
Take for instance the case of customer loyalty. Just like employee loyalty, customer loyalty is a chimera. I think any company trying to gain or regain customer loyalty is living in a fool's paradise. The Indian market-as is the case with Indian society - is fast becoming a transactional market. Customers do not buy a product because it is from company X or Y. They buy it, since it
satisfies their needs and wants. So who is Nirma's customer? Is she/he the same person who was the customer earlier in the 1990s? Or is she/he a different person? If yes, then how should the realignment in the strategy take place to make her continue buying Nirma's product? That is the route to rethinking about the assumptions that govern business. That is the type of rethinking that I am
proposing
for
Nirma.
Corporate affairs are too complex to capture by means of a single or a few strategies, or some programs and plans. I will just take up a few issues and write out my thoughts about Nirma. First issue that I would like to write on is the size of the company. Nirma has undoubtedly grown into a big corporation, and has ventured into so many different areas by means of a variety of strategies like integration and diversification. With size, arise the challenges of organization and control. Nirma needs to focus on these challenges and see how it can retain some of its nimbleness and
yet
reap
the
advantages
of
size,
such
as
the
scale
economies.
Secondly, Nirma has reached a stage in its evolution, where it seriously needs to rethink its vision and mission. Here, I do not mean that it should rewrite its mission statement in more beautiful words. I mean that it should think seriously, what has made it reach the stage where it is. Nirma focused on the customer whose basic requirement was an acceptable quality product available at an affordable price. This customer still demands that. But, a slow change has been taking place all these years especially in post-1991 period in India. The indices capture much of that change, yet some escape attention. The customer, in the segments that Nirma served, no longer wants just a cheap product. She/he demands quality too, and is willing to pay a bit higher for that. That means there is a requirement for the best value product: A product that offers quality that would be an 'aha'
experience
at
that
price.
Nirma needs to resolve this dilemma: Does it want to serve its original customer segment that still wants a cheap product and may be even ready to compromise on quality; or does it want to move up serving the value-conscious customer (that was its customer in the 1990s)? If I were asked which direction Nirma should go in resolving its dilemma, I would unreservedly say it should
remain in the direction of its original customer segment. Why? Because, the value-conscious customer-that Nirma struggles to serve, and that it can't do as well as its rivals can do-has a choice. Leaving Nirma aside, she/he can move to Hindustan Lever or Proctor & Gamble or Wipro. That is what has been happening. In the segments that Nirma has moved up, owing to the initial momentum of following its customer, it has too much competition to handle and is clearly not up to
the
task.
By bowing down to serve its original customer, Nirma would be creating a space for itself, where many will not be willing to come. This customer is the poor customer who needs to eke out a living and buy the necessities at a low price to make both ends meet. She/he lives in the vast expanses of India where life is a daily experience of struggle to somehow keep body and soul together. She/he is a part of a 600+ million customers market. To do this means that a company like Nirma needs to leverage on two things: Scale economies and low-cost operations. Nirma has accumulated enough learning to undertake low-cost operations. What it needs is to build scale economies
with
a
sharp
focus
on
a
limited
range
of
complementary
products.
If I would frame a vision for Nirma, it would go something like this: "A big company dedicated to serving
continually
the
daily
needs
of
the
small
woman
and
man."
What I am proposing in not an original idea. C K Prahalad has been recently writing about the fortune that lies at the bottom of the pyramid. The pyramid is the distribution of income among the people of the world. Very few people are at the top echelons of the pyramid, while a disproportionately large number lie at the bottom layers. Prahalad's prescriptions to focus on the poor as a potential market are mainly directed at the MNCs. What I propose is that a local Indian company can do that as well, maybe even better.
