Chapter 6 Marketing Research and Information Systems

1. Chapter 6 Marketing Research and Information Systems

2. Avoid ambiguity Problem Definition Be specific but not too rigid Research Objective Measurable Watch for symptoms Specific Research Design Exploratory Descriptive census Secondary Causative Source of Data Sample Primary Observation Data Collection Techniques Experimentation Tools Survey Focus Group Data Analysis (Primary, secondary and advanced) Questionnaire Interview schedule Association and Projection test (TAT) Report and presentation 5.2

3. Marketing Research problem is information oriented, narrowly and precisely defined. • Response to following three key questions can help sharply define marketing research problem: • Purpose of information being sought • Whether the information already exist; & • Whether the question posed by decision maker can be researched 5.4

4. SOURCES OF DATA 1. Primary Data 2. Secondary Data 5.5

5. Sources of Secondary Data 1. Organizations, journals and newspapers 2. Company’s Internal Database • The most common source of internal database is the sales call report, sales territory information system and brand score cards. • The fastest growing usage of an internal database is database marketing. • database marketing can assist in: 1. Identification of most and least profitable customers. 2. Identification of attractive market segments and target efforts with greater efficiency and effectiveness. 3. Evaluation of sales territories 4. Identification of new market opportunities. 5. Evaluation or modification of marketing programmes. 5.6

6. Data Collection • A Researcher needs to be more clear on • Procedure of Data Collection • Tools for Data Collection • Data can be collected through any or combinations of following techniques : • Observation – Observing what a customer is buying and doing in the store.Involves how he behaves in the shopping area,how he or she dresses up, & what the customer says when he or she sees the product. • Experimentation- Experimenting with new product ideas,advertising copies and campaigns, sales promotion ideas,pricing and distribution strategies with the target segment. • Uncontrolled Experimentation- Test Marketing the new product and market strategy in the market environment with all uncontrollable elements playing the role.

7. Data Collection • Controlled & Simulated Experimentation- Here the external environment variables (specially competition) are controlled and customer feedback monitored. Pre testing advertising campaigns or with a select customer groups by showing the product & competition advertisements of together are examples of controlled experimentation. • Survey – Carrying out opinion polls involving customers, sales persons, dealers, traders and experts. Customer and trade surveys are also very common. • Focus Groups – This helps researchers to understand the subconscious self of the customer • Tools of data collection • The tools are – • Questionnaire- used for the survey method • Interview Schedule – used mainly for exploratory research • Association Tests – primarily used in qualitative research.

8. Preliminary issues like research objectives, target response, etc. Decision on issues to be probed/asked Decision on response format, I.e whether close-ended or open-ended response Wording/style of the questions and what to avoid Sequencing the questions Conditions of questions Pre-test, revise(if need be) and finalise Steps in Questionnaire Design 5.3

9. Questionnaire Design • Close-ended v/s Open-ended Questions – • A close ended question is one where the respondent has to select a response from one among the multiple choices offered to him or her. • Such a questionnaire can be easily tabulated and analyzed. But can be used only when the researcher understands customer behavior well. • If the behavior is unknown and researcher wishes to probe,open ended questions will deliver results. • Measurement of Attitudes- Most research exercises measure attitudes of individuals. The attitude of a consumer towards a particular brand of a product or service is a function of – • The number of consumers of that brand. • At a specific time & in a given geographical area,who are • Personally interviewed, using a • Specified attitudinal scale,to obtain • The response information provided by the attitude scale. Consumers attitude may be understood as numerical ratings on a like and dislike scale.

10. Scales employed in Measuring Attitude • Scales are of four types – • Nominal Scale – This permits the most elementary mathematical analysis.Here numbers are used only for identification purposes. • Ordinal Scale – These are ranking scales. Requires the customers ability to distinguish between the elements according to a single attribute and direction. • Interval scales – These scales allow an individual to make meaningful statements about differences separating two objects. A typical example of interval example is the preference for a brand of perfume exhibited by a consumer on the scale a) I like it the most b) I like it c) I neither like it or dislike it d) I dislike it e) I dislike it the most Responses measured on these scales can be analyzed using statistical tools like mean, standard deviation and correlation of co-efficient.

11. Scales employed in Measuring Attitude • Ratio Scales – It possesses a unique zero point and all arithmetical operations are possible here. • Now three types of scaling can be identified- • Respondent Centered Approach- Researcher examines systematic variation across respondents. • Stimulus Centered Approach – Focus is on studying the variations across different brands (stimulus) of detergents on the “gentleness on the hands” (attributes). • Response Approach – Researcher examines both (a) and (b) • The Semantic Differential is a type of quantitative judgement that results in scales that are often further analyzed by such techniques as factor analysis.It enables the researcher to probe both the direction & the intensity of respondents attitudes towards such concepts as corporate image, advertising image & brand image.

12. Data Analysis • Raw data has no usage in marketing research.Most elementary method is arithmetical analysis using percentile and ratios. • Statistical analysis like mean, median, mode, percentages,standard deviation and coefficient of correlation can be used. • Today increasing use of marketing decision support system (MDSS).It is a system that consists of data collection tools and techniques for analysis with supporting software & hardware. This is used by an organization to gather and interpret relevant information from its environment and convert it into a basis for marketing actions.commonly used decision models used by marketers are: • Markov-process model which helps decision maker to understand the probability of moving from current state to any future state. For example, a brand manager of an FMCG Company can determine switching and retention rate for his / her brand of toilet soap over a period of time and based on it the brand’s ultimate share. • Queuing model: Shows the waiting time and queue length that can be expected in any system, given the arrival & service times & the number of service channels. A retail manager can use this model to plan his customer service and check out counters on week days & weekends and holidays. He can even use this model to plan the parking facilities on weekdays, weekends & holidays. Similarly, marketing manager for petroleum company can use it to plan his logistics.

13. Data Analysis          (c) Brand Aid model: This is a marketing mix model focused on consumer goods industry. The model contains sub models for advertising, pricing & competition. The model is calibrated with a creative blending of judgment, historical analysis, tracking and experimentation. • (d) Mediac: This is a media planning model used by advertisers. This model includes analysis of market segments, sales potential estimation and understanding of issues in consumer learning (like forgetting) timing issues & competitor media schedules. • (c) Sales Response Models: These models estimate functional relations between one or more marketing variables like sales force size and resulting sales & demand level.

14. 3. World wide web • Internet discussion groups and special interest groups as a source of secondary information. • Databases on CD-ROM • Geographic Information Systems Advantages of Secondary Data • Economies of time and cost • Convenience • Data may offer a solution to the research problem 5.7

15. MARKETING INTELLIGENCE SYSTEM • Marketing Research provides information at a specific time on customers, trade.competition and future trends in each of these segments. • Components of Intelligence systems- • Customer Intelligence- Provides useful information on a customers business,preferences or loyalties,personal demographics details etc. • Competitor Intelligence – Gives information on strengths/weaknesses of each territory, the strategy/tactics used by them and how the customer procures competitor brands. 5.8

16. MARKETING INFORMATION SYSTEMS • Order generation, processing, delivery and payment cycle. • Sales management information giving details on firm’s sales, market share, profitability and trends in each market. • Payment history • Orders Lost/Won • Brand Monitors • Distribution Audit Reports • Service Monitor Reports • Product Performance Reports 5.8

17. Typically any such system provides a perspective at three different levels. • Transaction processing / level • Transaction level information system is useful at the sales person level • Managerial and operational level • Reports on performance branch, region and product, sundry debtors status etc. form a part of managerial and operation level information system. • Strategic level • National sales data, relative market, share, shifts in customer preferences, competition in different product / markets, receivables etc are information used at the strategic level i.e. at the top management level. 5.9

18. Decision making is a five step sequence. Source Data Predictions/ Inferences/ Generalizations Action Values & Choice 5.10

19. Data Mining and Warehousing • Data warehouse is an I.T architecture aimed at storing and organising information in a meaningful manner. • Consists of a set of programmes that extract data from the operational environment like reports on sales call, branch and regional performance, product/brand customer complaints, service call etc. • The strategy of data warehousing involves providing data to the users in meaningful manner which can help them to take an operational and strategic decision. 5.11

20. Successful data warehousing involves the following: • Providing information both to the operational and strategic decision-maker • Data warehouse often supports analysis of trends overtime and comparisons of current and historical data. The idea behind data mining, then, is the "non -trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data." 5.12

21. Future Trends : Online Mining (OLM) & Web Mining (WM) Online Data Mining (ODM); also called OLAP mining is among the many different paradigms and architectures for data mining systems. It integrates online analytical processing with data mining and mining knowledge in multi-dimensional databases. 5.13