The Marketing Research Process Chapter 3 Audhesh Paswan, Ph.D.

1. The Marketing Research ProcessChapter 3Audhesh Paswan, Ph.D.

2. INFORMATION • REDUCES UNCERTAINTY • HELPS FOCUS DECISION MAKING

3. Marketing Research can be . . . Accurate Fast Inexpensive Pick two! Can’t have three at the same time!

4. STAGES IN THE RESEARCH PROCESS • PROBLEM DISCOVERY AND PROBLEM DEFINITION • RESEARCH DESIGN • SAMPLING • DATA GATHERING • DATA PROCESSING AND ANALYSIS • CONCLUSIONS AND REPORT

5. Establishing the need for marketing research. 1 • Is it needed? • May not be needed: • information may already be available • not enough time to do study • not enough money • costs may outweigh value of research

6. Marketing Research • Marketing Research Types Problem Identification Problem-Solving * Market Potential Research * Market Share Research * Image Research * Market Characteristics * Sales Analyses Research * Forecasting Research * Business Trend Research * Segmentation Research * Product Research * Pricing Research * Promotion Research * Distribution Research

7. Define the problem 2 • Most important part - everything else is based upon this! • May do “exploratory research” to help define the problem • Think of yourself as a “marketing doctor” • make sure you can tell the symptoms from the problem.. • specify the symptoms > itemizing the possible causes of the symptoms > listing the reasonable alternative course of action.

8. Defining the Problem Results in Clear Cut Research Objectives Symptom Detection Exploratory Research (Optional) Analysis of the Situation Problem Definition Statement of Research Objectives

9. “The formulation of the problem is often more essential than its solution” Albert Einstein

10. The Process of Problem Definition Isolate and identify the problems, not the symptoms. Determine the unit of analysis Understand the background of the problem. Ascertain the decision maker’s objectives. State the research questions and research objectives. Determine the relevant variables

11. Establish research objectives. 3 • What information is needed to solve the problem? • Set objectives associated with this information.

12. I keep six honest serving men, (they taught me all I knew), their names are what, and why, and when, and how, and where and who.” --Rudyard Kipling

13. Determine research design 4 • Exploratory Research • unstructured, informal, and sometimes intuitive • Descriptive Research • very common in marketing research • descriptive in nature • involves communication and/or observation for data collection • lends itself to statistical analysis

14. 4 Determine research design • Causal Research • establish cause and effect relationship • problem: multiple causes and effects • problem: hard to isolate • involves experiments • e.g., pretest, posttest, control groups Education Income Happiness

15. Research Design. . 4 Exploratory Descriptive Causal

16. DEGREE OF PROBLEM DEFINITION Exploratory Research Descriptive Research Causal Research (Unaware of Problem) (Aware of Problem) (Problem Clearly Defined) “Our sales are declining and “What kind of people are buying “Will buyers purchase more of we don’t know why.” our product? Who buys our our products in a new package? competitor’s product?” “Would people be interested “Which of two advertising in our new product idea?” “What features do buyers prefer campaigns is more effective?” in our product?” possible situation

17. Research Design - I 4 • Exploratory Research: Objective: Discovery of ideas and insights. Characteristics: Flexible, Versatile, Unstructured, Often the Front End of total Research Design, Small Non-representative Sample, Analyses typically qualitative. Findings: Tentative, typically followed by further exploratory, descriptive or causal research. Methods: Literature Search, Focus Groups, Experience Surveys, Pilot Surveys, Expert Interviews, Case Studies, Reliance on Secondary Data.

18. Research Design - II 4 • Descriptive Research: Objective: Describe Market Characteristics or Functions, Test Specific Hypotheses. Characteristics: Prior Formulation of Hypotheses, Preplanned, Formal and Structured Design, Information needed is predefined, Sample is Large and Representative, Data Analyses typically Quantitative. Findings: Conclusive, used as input into Decision Making. Methods: Surveys, Panels and Observation (Typically Primary Data).

19. Research Design - III 4 • Causal Research: Objective: Determine Cause and Effect Relationship, Test Specific Hypotheses. Characteristics: Manipulation of Independent Variables, and Control of Other Mediating Variables. Prior Formulation of Hypotheses, Preplanned, Formal and Structured Design, Information needed is predefined, Sample Representative, Data Analyses typically Quantitative. Findings: Conclusive, used as input into Decision Making. Methods: Experiments (Typically Primary Data)

20. IDENTIFYING CAUSALITY A causal relationship is impossible to prove. Evidence of causality: 1. The appropriate causal order of events 2. Concomitant variation--two phenomena vary together 3. An absence of alternative plausible explanations

21. If you do not know where you are going,any road will take you there.

22. RESEARCH DESIGN • MASTER PLAN • FRAMEWORK FOR ACTION • SPECIFIES METHODS AND PROCEDURES

23. 5 Identify information types and sources • Two types of information: • Secondary data • information already collected for some other purpose • internal or external • typically used in exploratory research • some key problems?? • Primary data • information collected to specifically answer the problem • observation or communication method

24. 5 Information - Data • Data - Known facts or things used as basis for inference; information; material to be processed and stored. • Information - what is told, knowledge, news, charge or accusation. Information Data

25. 5 Information Sources • Four major sources. Intuition Experience Decision Making Process Authority Research

26. Secondary Primary Anecdotes Experience Case studies Opinions, etc. Focus groups Interviews projection techniques, etc. Census Syndicated data Journals Magazines, etc. Surveys Observations Experiments Tests, etc. Marketing Research Data 5 • Secondary vs Primary • Qualitative vs Quantitative • Internal vs External Qualitative Quantitative

27. Secondary Data Primary data Descriptive Survey data Observation Experiment Marketing Research Data 5 • Marketing Research Data Qualitative Data Quantitative data Causal

28. Determine methods of accessing data 6 • Depends on what kind of data is needed • Methods different for secondary data collection than for primary data collection, e.g., • Secondary data - library, internet, buy syndicated data, CD-ROM, etc. • Primary data - mail, telephone, mall intercept, door-to-door, etc.

29. Data Collection • Qualitative Research Objective: To gain understanding of the underlying reasons and motives (Exploratory stage). Sample: Small number, nonrepresentative. Method: Unstructured. Analyses: Nonstatistical. Outcome: Develop initial understanding.

30. Data Collection • Quantitative Research Objective: To quantify the data, and generalize the results to the population of interest. Sample: Large numbers, Representative. Method: Structured. Analyses: Statistical. Outcome: Recommend a final course of action.

31. Data Collection - Methods • Qualitative Data Direct (Nondisguised) Indirect (Disguised) Focus Groups Depth Interviews Projective Techniques Association Completion Construction Expressive

32. Data Collection - Methods • Quantitative/ Primary Data Communication Versatility Speed Cost Observation Objectivity Accuracy Relevant for: Demographics, Socioeconomic, Psychological/Lifestyle Characteristics; Attitudes, Opinions, Awareness, Knowledge, Intentions, Motivations, and Behavior.

33. Data Collection - Methods • Communication or Surveys Personal Telephone Mail In Home Mall Intercept CAPI Traditional Telephone Mail Interview CATI Mail panel

34. Data Collection Methods - Comparison Criteria Telephone Personal Mail Flexibility of data collection M H L Diversity of Questions L H M Use of physical stimuli L H M Quantity of data L H/M M/H Response rate M H L/M Speed H M/H L Cost M M/H L Interviewer bias M H No Sample control M/H M/H L/M Field force control M L/M H Sensitive Information H L H L=Low, H=High, M=Medium.

35. Data Collection - Methods • Observation Audit Personal Observation Content Analyses Mechanical Observation Trace Analyses

36. Data Collection - Methods • Experiments - Test Marketing. • Simulated, Controlled, Standard, and National Rollout. Factor Laboratory Field Environment Artificial Realistic Control High Low Reactive Error High Low Demand Artifact High Low Internal Validity High Low External Validity Low High Time Short Long Number of Units Small Large Implementation Ease High Low Cost Low High

37. Design data collection forms 7 • Depends on type of research being conducted • Some key issues: • Structured or unstructured • Disguised or undisguised • Number of questions. • Wording and sequencing of questions.

38. Types of Variables Categorical Continuous Dependent Independent

39. Measurement Instrument • Communication methods typically use a questionnaire as the instrument. • The questionnaire must motivate the respondents to cooperate, become involved, and provide complete and accurate answers.

40. Measurement Instrument 1. Specify the information needed 2. Type of interviewing method 3. Content of individual questions 4. Design the questions to overcome inability and unwillingness 5. Decide on the question structure 6. Determine the question wording 7. Arrange the questions in proper order 8. Identify the form and layout 9. Reproduce the questionnaire 10. Eliminate bugs by pretesting.

41. Measurement Instruments • Marketers want to measure: Demographics/Socioeconomic Characteristics Psychographics and Lifestyles Personality Motivation Consumer knowledge regarding Product - Awareness, Attribute and Price. Purchase - Where and When of purchase. Usage - Usage operations and situations. Past Behavior, Attitudes and Opinions, Behavioral Intentions, etc.

42. Measurement Scales 1. Nominal - identify and classify (Sex, user-nonuser, etc.; Descriptive - percentage and mode; Inferential - Chi-square, binomial tests) 2. Ordinal - relative position but not magnitude of difference (Quality/ preference ranking, market position, etc.; Descriptive - %, median; Inferential - Rank-order Correlation, Friedman ANOVA)

43. Measurement Scales 3. Interval - differences, arbitrary zero point. (Temp, attitudes, opinions, index numbers, etc.; Descriptive - range, mean, standard deviation; Inferential - Correlation, t-tests, ANOVA, regression, and multivariate analyses). 4. Ratio - fixed zero point, ratios. (Length, weight, age, income, sales, market share, etc.; Descriptive - geometric & harmonic mean; Inferential - Coefficient of variation)

44. Measurement Scales • Attitudes, Opinions , Preferences and Perceptions. • “When you can measure what you are speaking about and express it in numbers, you know something about it.” Lord Kelvin. • Scales should be evaluated for reliability and validity.

45. Measurement Scales • Operationalization of scales for measuring Attitudes, Opinions , Preferences and Perceptions. • Usually an adaptation of Interval scale. 1. Continuous rating scale - mark on a continuous line. 2. Itemized Rating Scale, e.g., Likert Scale - five point (Strongly agree to Strongly disagree) scale. Semantic differential scale - seven point scales with bipolar labels. Staple scale - Unipolar ten-point scale, -5 to +5, without a neutral

46. Determine sample plan and size 8 • Who are you going to sample -respondent? • How many are you going to sample - sample size? • Depends upon • Time • Money • Response rate • Type of data collection form • Some key terms - sample elements, sample frame, sampling plans, sample size.

47. SAMPLING • SUBSET OF POPULATION • WHO IS TO BE SAMPLED • HOW LARGE A SAMPLE • HOW WILL SAMPLE UNITS BE SELECTED

48. Sampling • Sampling Techniques: 1. Nonprobability: Convenience, Judgmental, Quota, and Snowball Sampling. 2. Probability: Simple, Systematic, Stratified (Proportionate, Disproportionate), Cluster (One-stage, Two-stage), and Others.

49. Collect data 9 • Trained interviewers • Questionnaires • Data collection companies • Avoid non-sampling errors

50. Analyze data 10 • Give meaning to raw data - interpretation • Involves - data cleaning, coding, tabulation, cross-tabulation, statistical tests, & interpretation. • Descriptive statistics - frequencies, mean, median, mode, SD, etc. • Statistical analysis • tests of association - cross tabs, correlation, regression, etc. • Test of difference - t-test, f-test, ANOVA etc. • Presentation