TEST SCREENINGS: LISTENING TO CONSUMERS TO REDUCE MOVIE RISKS • Using Marketing Research to Reduce Movie Risk Test Screenings Tracking Studies © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-6 FIGURE 8-1 Marketing research questions asked in test screenings of movies, and how they are used © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-7 FIGURE 8-2 Five-step marketing research approach leading to better marketing actions © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-10 STEP 3: COLLECT RELEVANT INFORMATION • Data • Secondary Data • Primary Data © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-20 FIGURE 8-3 Types of marketing information © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-21 STEP 3: COLLECT RELEVANT INFORMATION • Secondary Data Internal Secondary Data External Secondary Data • Census Bureau • Syndicated • Periodicals/Journals • Data Services Advantages and Disadvantages of Secondary Data © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-22 WEB LINK Online Databases and Internet Resources Useful for Marketers LexisNexis Wall Street Journal ProQuest STAT-USA Bloomberg FirstGov Investor’s Daily Google © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-23 STEP 3: COLLECT RELEVANT INFORMATION • Primary Data Observational Data • Meter/Diary • Mystery Shopper • Ethnographic Research © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-26 Nielsen Media Research “People Meter” What kind of primary data is collected? © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-27 FIGURE 8-4 Nielsen ratings of the top 10 national television programs from September 27, 2004 through October 3, 2004 © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-28 FIGURE 8-5 Nielsen//NetRatings of the top 10 Internet websites for September 2004 © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-29 STEP 3: COLLECT RELEVANT INFORMATION • Primary Data Questionnaire Data • Individual Interviews • Focus Groups • “Cool Hunters” © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-31 TRU “Me” Exercise snapshot: Sarah Why collect primary data this way? © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-32 STEP 3: COLLECT RELEVANT INFORMATION • Primary Data Questionnaire Data • Types of Surveys Personal Interview Mail Telephone E-mail/Fax/Internet Mall Intercept Interview © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-33 FIGURE 8-A Comparison of three kinds of surveys © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-34 FIGURE 8-6 Typical problems in wording questions © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-35 STEP 3: COLLECT RELEVANT INFORMATION • Primary Data Questionnaire Data • Question Formats Open-Ended Closed-Ended/Fixed Alternative Dichotomous Semantic Differential Scale Likert Scale © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-36 Wendy’s What questions should be asked in a survey and how would the information collected be used? © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-37 FIGURE 8-7A Sample questions from Wendy’s survey © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-38 FIGURE 8-7B Sample questions from Wendy’s survey © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-39 STEP 3: COLLECT RELEVANT INFORMATION • Primary Data Panels and Experiments • Panel • Experiment • Drivers • Test Markets Advantages and Disadvantages of Primary Data © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-40 STEP 3: COLLECT RELEVANT INFORMATION • Using Information Technology to Trigger Marketing Actions The Marketing Manager’s View of Sales Drivers • Data vs. Information • Information Technology © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-44 FIGURE 8-8 Product and brand drivers: factors that influence sales © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-45 STEP 3: COLLECT RELEVANT INFORMATION • Using Information Technology to Trigger Marketing Actions Key Elements of an Information System • Data Warehouse • Sensitivity Analysis Data Mining: A New Approach to Searching the Data Ocean © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-46 FIGURE 8-9 How marketing researchers and managers use information technology to turn information into action © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-47 Fingerhut Why use data mining? © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-48 STEP 4: DEVELOP FINDINGS • Set the Research Objectives Analyze the Data Present the Findings © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-49 Tony’s Pizza What findings can be developed from data mining? © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-50 FIGURE 8-10 Presenting findings to Tony’s marketing manager that lead to recommendations and actions © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-51 FIGURE 8-10A Presenting findings to Tony’s marketing manager © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-52 FIGURE 8-10B Presenting findings to Tony’s marketing manager © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-53 FIGURE 8-10C Presenting findings to Tony’s marketing manager © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-54 FIGURE 8-10D Presenting findings to Tony’s marketing manager © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-55 STEP 5: TAKE MARKETING ACTIONS • Make Action Recommendations • Implement the Action Recommendations • Evaluate the Results Evaluating the Decision Itself Evaluating the Decision Process Used © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-56 Marketing Research Marketing research is the process of defining a marketing problem and opportunity, systematically collecting and analyzing information, and recommending actions. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-81 Decision A decision is a conscious choice from among two or more alternatives. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-82 Measures of Success Measures of success are criteria or standards used in evaluating proposed solutions to a problem. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-83 Constraints Constraints in a decision are the restrictions placed on potential solutions to a problem. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-84 Sampling Sampling involves selecting representative elements from a population. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-85 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. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-86 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. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-87 Statistical Inference Statistical inference involves drawing conclusions about a population from a sample taken from that population. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-88 Data Data are the facts and figures related to the problem, and are divided into two main parts: secondary data and primary data. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-89 Secondary Data Secondary data are facts and figures that have already been recorded before the project at hand. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-90 Primary Data Primary data are facts and figures that are newly collected for the project. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-91 Observational Data Observational data are the facts and figures obtained by watching, either mechanically or in person, how people actually behave. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-92 Questionnaire Data Questionnaire data are the facts and figures obtained by asking people about their attitudes, awareness, intentions, and behaviors. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-93 Information Technology Information technology involves a computer and communication system to satisfy an organization’s needs for data storage, processing, and access. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-94 Data Mining Data mining is the extraction of hidden predictive information from large databases. © 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin Slide 8-95