1. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-1

2. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-2 MARKETING RESEARCH: FROM INFORMATION TO ACTION C HAPTER

3. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-3 AFTER READING THIS CHAPTER YOU SHOULD BE ABLE TO: 1.Identify the reason for doing marketing research and describe the five-step marketing research approach leading to marketing actions. 2.Describe how secondary and primary data are used in marketing, including the uses of questionnaires, observations, experiments, and panels.

4. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-4 AFTER READING THIS CHAPTER YOU SHOULD BE ABLE TO: 3.Explain how information technology and data mining link massive amounts of marketing information to meaningful marketing actions.

5. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-5 TEST SCREENINGS: LISTENING TO CONSUMERS TO REDUCE MOVIE RISKS What’s in a Movie Name? The Risks in Today’s Blockbuster Movies

6. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-6 TEST SCREENINGS: LISTENING TO CONSUMERS TO REDUCE MOVIE RISKS Using Marketing Research to Reduce Movie Risk  Test Screenings  Tracking Studies

7. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-7 FIGURE 8-1 FIGURE 8-1 Marketing research questions asked in test screenings of movies, and how they are used

8. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin THE ROLE OF MARKETING RESEARCH Slide 8-8 What is Marketing Research?What is Marketing Research?  Decision Decision Why Good Marketing Research is Difficult Five-Step Marketing Research Approach to Make Better Decisions  Decision Making

9. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-10 FIGURE 8-2 FIGURE 8-2 Five-step marketing research approach leading to better marketing actions

10. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 1: DEFINE THE PROBLEM Slide 8-13 Set the Research Objectives Descriptive Research  Objectives  Three Kinds of Research Causal Research Exploratory Research Identify Possible Marketing Actions  Measures of Success Measures of Success

11. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-14 Fisher-Price How do you define the problem?

12. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-15 Fisher-Price How do you discover “hot toys” and why are good forecasts important?

13. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 2: DEVELOP THE RESEARCH PLAN Slide 8-16 Determine How to Collect Data  Methods New-Product Concept  Concepts Sampling Sampling Probability Sampling Probability Sampling Nonprobability Sampling Nonprobability Sampling Statistical Inference Statistical Inference Specify ConstraintsSpecify Constraints Identify Data Needed for Marketing Actions

14. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-20 Data Secondary Data Primary Data

15. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-21 FIGURE 8-3 FIGURE 8-3 Types of marketing information

16. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-22  Internal Secondary Data Census Bureau Secondary Data  External Secondary Data Periodicals/Journals Syndicated Data Services  Advantages and Disadvantages of Secondary Data

17. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-23 WEB LINK Online Databases and Internet Resources Useful for Marketers LexisNexis ProQuest Bloomberg STAT-USA FirstGov Google Wall Street Journal Investor’s Daily

18. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-26  Observational Data Observational Data Meter/Diary Primary Data Mystery Shopper Ethnographic Research

19. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-27 Nielsen Media Research “People Meter” What kind of primary data is collected?

20. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-28 FIGURE 8-4 FIGURE 8-4 Nielsen ratings of the top 10 national television programs from September 27, 2004 through October 3, 2004

21. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-29 FIGURE 8-5 FIGURE 8-5 Nielsen//NetRatings of the top 10 Internet websites for September 2004

22. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-31  Questionnaire Data Questionnaire Data Individual Interviews Primary Data Focus Groups “Cool Hunters”

23. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-33  Questionnaire Data Types of Surveys Primary Data  Personal Interview  Mail  Telephone  E-mail/Fax/Internet  Mall Intercept Interview

24. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-34 FIGURE 8-A FIGURE 8-A Comparison of three kinds of surveys

25. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-35 FIGURE 8-6 FIGURE 8-6 Typical problems in wording questions

26. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-36 Question Formats  Questionnaire Data Primary Data  Open-Ended  Closed-Ended/Fixed Alternative  Dichotomous  Semantic Differential Scale  Likert Scale

27. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-38 FIGURE 8-7A FIGURE 8-7A Sample questions from Wendy’s survey

28. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-39 FIGURE 8-7B FIGURE 8-7B Sample questions from Wendy’s survey

29. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-40  Panels and Experiments Panel Experiment Drivers Test Markets  Advantages and Disadvantages of Primary Data Primary Data

30. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-44  The Marketing Manager’s View of Sales Drivers Data vs. Information Using Information Technology to Trigger Marketing Actions Information Technology Information Technology

31. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin STEP 3: COLLECT RELEVANT INFORMATION Slide 8-46  Key Elements of an Information System Data Warehouse Using Information Technology to Trigger Marketing Actions Sensitivity Analysis  Data Mining: A New Approach to Searching the Data Ocean Data Mining: A New Approach to Searching the Data Ocean

32. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-47 FIGURE 8-9 FIGURE 8-9 How marketing researchers and managers use information technology to turn information into action

33. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-49 STEP 4: DEVELOP FINDINGS Set the Research Objectives  Analyze the Data  Present the Findings

34. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-56 STEP 5: TAKE MARKETING ACTIONS Make Action Recommendations  Evaluating the Decision Itself Implement the Action Recommendations Evaluate the Results  Evaluating the Decision Process Used

35. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-81 Marketing Research Marketing research is the process of defining a marketing problem and opportunity, systematically collecting and analyzing information, and recommending actions.

36. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-82 Decision A decision is a conscious choice from among two or more alternatives.

37. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-83 Measures of Success Measures of success are criteria or standards used in evaluating proposed solutions to a problem.

38. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-84 Constraints Constraints in a decision are the restrictions placed on potential solutions to a problem.

39. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-85 Sampling Sampling involves selecting representative elements from a population.

40. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-86 Probability Sampling Probability sampling involves using precise rules to select the sample such that each element of the population has a specific known chance of being selected.

41. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-87 Nonprobability Sampling Nonprobability sampling involves using arbitrary judgments to select the sample so that the chance of selecting a particular element may be unknown or 0.

42. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-88 Statistical Inference Statistical inference involves drawing conclusions about a population from a sample taken from that population.

43. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-89 Data Data are the facts and figures related to the problem, and are divided into two main parts: secondary data and primary data.

44. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-90 Secondary Data Secondary data are facts and figures that have already been recorded before the project at hand.

45. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-91 Primary Data Primary data are facts and figures that are newly collected for the project.

46. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-92 Observational Data Observational data are the facts and figures obtained by watching, either mechanically or in person, how people actually behave.

47. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-93 Questionnaire Data Questionnaire data are the facts and figures obtained by asking people about their attitudes, awareness, intentions, and behaviors.

48. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-94 Information Technology Information technology involves a computer and communication system to satisfy an organization’s needs for data storage, processing, and access.

49. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/IrwinSlide 8-95 Data Mining Data mining is the extraction of hidden predictive information from large databases.