Download step 3: collect relevant information

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
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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
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STEP 3: COLLECT
RELEVANT INFORMATION
• Data
• Secondary Data
• Primary Data
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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FIGURE 8-3 Types of marketing information
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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Nielsen Media Research “People Meter”
What kind of primary data is collected?
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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FIGURE 8-5 Nielsen//NetRatings of the top
10 Internet websites for September 2004
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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FIGURE 8-6 Typical problems in wording
questions
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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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
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FIGURE 8-7A Sample questions from
Wendy’s survey
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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FIGURE 8-7B Sample questions from
Wendy’s survey
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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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
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Tony’s Pizza
What findings can be developed from data mining?
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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FIGURE 8-10 Presenting findings to Tony’s
marketing manager that lead to
recommendations and actions
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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FIGURE 8-10C Presenting findings to Tony’s
marketing manager
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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FIGURE 8-10D Presenting findings to Tony’s
marketing manager
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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
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Decision
A decision is a conscious choice from
among two or more alternatives.
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Measures of Success
Measures of success are criteria or
standards used in evaluating proposed
solutions to a problem.
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Constraints
Constraints in a decision are the
restrictions placed on potential solutions
to a problem.
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Sampling
Sampling involves selecting
representative elements from a
population.
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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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.
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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
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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
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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
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Primary Data
Primary data are facts and figures
that are newly collected for the project.
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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Observational Data
Observational data are the facts and
figures obtained by watching, either
mechanically or in person, how people
actually behave.
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Questionnaire Data
Questionnaire data are the facts and
figures obtained by asking people about
their attitudes, awareness, intentions, and
behaviors.
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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
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Data Mining
Data mining is the extraction of hidden
predictive information from large
databases.
© 2006 McGraw-Hill Companies, Inc., McGraw-Hill/Irwin
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