Market Intelligence
Julie Edell Britton
Session 2
August 8, 2009

 Announcements
 Southwestern Conquistador Beer Case
 Backward Market Research
 Secondary data quality
 Measure types
 Hypothesis Testing and Chi-Square

• National Insurance Case for Sat. 8/22
– Download National.sav from platform
– SPSS on machines in MBA PC Lab and see installation direction on the platform on how to install on your machine
– Do tutorial to familiarize with SPSS
– Use handout in course pack to answer questions: 1-6
– Stephen will do a tutorial on Friday, 8/21 from 1:00 -
2:15 in the MBA PC Lab and be available on 8/21 from 7 – 9 pm in the MBA PC Lab to answer questions
– Submit slides by 8:00 am on Sat. 8/22
3

 Feasibility decisions
 Problem formulation, information needs
 Role of secondary data
 Role of research and time budgets
 Quality, cost, speed
4

 What should Mr. Gomez do?
 Consumer behavior?
 What information do we need to make decision?
 Which reports allow that information to be estimated?
 What decision do these reports suggest?
5

 Feasibility studies need data on: industry demand, market share, investment, costs, margins. Break even analysis common.
 Conceptualize data before doing research
 Effort at problem formulation stage reduces later costs of doing research
 Secondary data is the place to start
6

 Cost of information is real; research budget typically constrained
 Cheap info may not be most economical if it is unreliable
 Just because budget has funds does not mean you should conduct extraneous research.
7

 Announcements
 Southwestern Conquistador Beer Case
 Backward Market Research
 Secondary data quality
 Measure types
 Hypothesis Testing and Chi-Square

 Obvious? Psychology of why so hard to do.
 Imagine the end of the process:
 What will the final report look like? DUMMY TABLES
 What decision alternatives might be implemented?
 What analyses can support a choice between alternatives?
 Where to get the data for analysis?
 Do they already exist?
 If not, may need to commission a study.
 Design the study (“n eed” vs. “ nice-to-know ”)
 Analyze data & make recommendation

Table A: National and Oregon Resident Annual Beer
Consumption
Year
1996
1997
Entire
US
Population
Oregon
Over 21 Entire Over 21
Population
1998
Average
Source: Study A
Table B: Population Estimates for Five Oregon Counties in Market
Area
C
D
E
Entire Population
County
A
B
Total
21 and over
County
A
B
C
D
E
Total
Source: Study B
1998
1998
1999
1999
2000
2000
2001
2001
2002
2002 2003
2003

Consumers’
Upbeat
Feelings
Consumers’
Learning of
Ad Claims
Consumers’
Attitude toward the
Ad
Consumers’
Attitude toward the
Brand
Ad A
Ad B
Ad Score = .25 UpF +.20 Claims + .15 AAd + .40 AB
Action Standard - Run the Ad with the Higher Ad Score

 Marketing Planning & Info System.
 Agree on Research Purpose
AmEx
 Research Objectives (hypotheses, bounds)
 Value of Information (the clairvoyant, p. 59)
 Design Research
 Collect Data & Analyze
 Report Results & Make Recommendations

 Marketing Planning & Info System.
 Agree on Research Purpose
AmEx
 Research Objectives (hypotheses, bounds)
 Value of Information (the clairvoyant, p. 59)
 Design Research
 Collect Data & Analyze
 Report Results & Make Recommendations

American Express Marketing Research Brief
(To Be filled out by End User)
 Marketing Background Describe the current information or environment – what are the issues that precipitated the need for the research? What business units will be impacted?
 Business Decisions What decisions will be made and what actions will be taken as a result of the research? (If appropriate, specify alternatives being considered). What other data or business considerations will impact the decision?
 Information Objectives What are the key questions (critical information) that must be answered in order to make the decision?
 Relevant Populations Who do we need to talk to and why?
 Timing When must the research be completed to make the marketing decision?
 Budget –
How much money has been budgeted for this research? To what budget line will it be charged?
 Requested by ________________ Manager
 Requested by ________________ Director
 Requested by ________________ Vice President

American Express Marketing Research Brief
(To Be filled out by Marketing Research)
Job # __ Project Title _________ Budget Line ___ Business Unit___
 Marketing Background
 Business Decisions To Be Made
 Research Objectives
 Research Design
 Action Standards
 Existing Sources of Information Consulted (e.g. syndicated and/or previous research)
 Research Firm
 Timing
 Cost
 Market Research Department Travel Cost
 Approval ________________ Vice President
 Approval ________________ if between $100,000 and $500,000 - Sr. VP
 Approval ________________ if over $500,000 - Exec. Committee Member

American Express Marketing Research Actionability Audit
(To Be filled out by End User)
 Project Name
 End User Name
1.
What Decisions or Actions were taken or are planned as a result of this research? If none, explain why.
2.
Were any Actions Taken or are any actions being considered that are in conflict with the research learning? If so, why?
3.
In retrospect, is there anything that could have been done differently to improve the actionability of the research investment? If so, what?
4.
Relevant Populations Who do we need to talk to and why?

 Marketing Planning & Info System.
 Agree on Research Purpose
AmEx
 Research Objectives (hypotheses, bounds)
 Value of Information (the clairvoyant, p. 59)
 Design Research
 Collect Data & Analyze
 Report Results & Make Recommendations

 Exploratory
 Generate ideas on alternatives & criteria to evaluate the alternatives
 Descriptive
 1-way: frequencies, proportions, means, medians
 2-way: correlations, crosstabs
 Causal
 Assess cause-effect relationships

 Announcements
 Southwestern Conquistador Beer Case
 Backward Market Research
 Secondary data quality
 Measure types
 Hypothesis Testing and Chi-Square

 Backward market research (1, 2)
 Getting data and judging its quality
 Secondary data (2)
 Exploratory research (3)
 Descriptive research (4,5)
 Causal research (6)
 Analysis frameworks for classic marketing problems (7-10)

 Primary -- collected anew for current purposes
 Secondary -- exists already, was collected for some other purpose
 Finding Secondary Data Online @ Fuqua
 http://library.fuqua.duke.edu

 If you can’t find the source of a number, don’t use it. Look for further data.
 Always give sources when writing a report.
 Applies for Focus Group write-ups too
 Be skeptical.

 Advantages
 cheap
 quick
 often sufficient
 Disadvantages
 there is a lot of data out there
 numbers sometimes conflict
 categories may not fit your needs

Internal External
Database:
Can
Slice/Dice; Need more processing
Summary:
Can’t change categories, get new crosstabs
WEMBA_C
Knowledge
Management
*IRI = Information Resources, Inc. (http://us.infores.com/)
IMS Health,
Nielsen, IRI*
Conquistador,
Simmons,
IRI_factbook

KAD p. 120 & “What’s Behind the Numbers?”
 Data consistent with other independent sources?
 What are the classifications? Do they fit needs?
 When were numbers collected? Obsolete?
 Who collected the numbers? Bias, resources?
 Why were the data collected? Self-interest?
 How were the numbers generated? Exter:

Sample size
 Sampling method (Sessions 5&6)

Measure type
 Causality (MBA Marketing Timing & Internship)

Took Core
Marketing
Term 1
Term 3
Got Desired
Marketing
Internship
76%
51%
Did Not Get Desired
Marketing Internship
24%
49%

 If you can’t find the source of a number, don’t use it. Look for further data.
 Always give sources when writing a report.
 Applies for Focus Group write-ups too
 Be skeptical.

MBA’s May Be A Marketing Liability…
“A master of Business Administration degree is not only worthless, it can work against a marketer, according to a survey of marketing executives from 32 consumer-products companies by consulting firm
Ken Coogan & Partners...Marketing executives from 18 underperforming companies – which had sales grow 7% less than their categories on average in the last two years ended August 2005 – were twice as likely to have been recruited out of MBA programs than marketing executives from out-performing companies, which averaged growth 6.2% faster than their categories over the two years.”
Source: AdAge.com, March 21, 2006
Overperformers (n = 9)
Underperformers (n = 18)
Mktg. Executive had an MBA
55.5%
88.9%
Mktg. Executive did not have an MBA
44.5%
11.1%

 Announcements
 Southwestern Conquistador Beer Case
 Secondary data quality
 Measure types
 Hypothesis Testing and Chi-Square

 Nominal : Unordered Categories
 Male=1; Female = 2;
 Ordinal : Ordered Categories, intervals can’t be assumed to be equal.
 I-95 is east of I-85; I-80 is north of I-40; Preference data
 Interval : Equally spaced categories, 0 is arbitrary and units arbitrary.

Fahrenheit temperature – each degree is equal
 Ratio : Equally spaced categories, 0 on scale means 0 of underlying quantity.
 $ , Age

Ratio
Examples Permissible
Transform
Q1 = Bottles of wine Q2 = b*Q1 e.g., cases sold (b = 1/12)
Meaningful
Stats
All below
+ % change
Interval Wine Rating Scale
1 = Very Bad to
20 = Very Good
Ordinal Rank order of wines
1 = favorite
2 = 2 nd
preferred
3 = least preferred
Nominal
1 = Pinot Noir
2 = Merlot
3 = Chardonnay
Att2 = a + (b*Att1) e.g., 81 to 100 (a = 80, b = 1) e.g., 80.5 to 90 (a = 80, b = .5)
Any order preserving
100 = favorite
90 = 2 nd
preferred
0 = least preferred
Any transformation is ok
16 = Pinot Noir
3 = Merlot
13 = Chardonnay
All below
+ mean
All below
+ median
# of cases mode

 The mean is a meaningless statistic when a variable is ordinal or nominal.
 That is because different permissible transformations lead to different conclusions
 Example on next slide: Male and female speed to finish quiz (lower # means faster finish)
 Measure 1 implies males faster, but measure 2 implies females faster.
 In contrast, median is meaningful for ordinal data, because different permissible transformations lead to same conclusion
 Median female faster than median male in measure
1, measure 2, or any permissible transform

M
M
F
F
F
F
M
Gender Measure 1 Measure 2 Means
M 1 1 Measure 1
M
F
2
3
2
3
M=5.4 < F=5.6
Measure 2
M=65.4 > F=25.6
6
7
4
5
8
9
10
4
5
6
107
108
109
110
Medians
Measure 1
M=7 > F=5
Measure 2
M=107 > F=5

Index= 100* (Per Capita Segment i) / (Per Capita Ave)
Age Group Population Units (000) Sales
<25 700 1400 2.00
25-34
(000s)
500
Sales Per Capita Segment
1250 2.50
Index
70
88
35-44
45-54
55 +
Total
300
240
260
2000
900
960
1196
5706
3.00
4.00
4.60
2.85
105
140
161
100

 Announcements
 Southwestern Conquistador Beer Case
 Backward Market Research
 Secondary data quality
 Measure types
 Hypothesis Testing and Chi-Square

A. Raw Frequencies
Accept Reject
M 140 860 1000
F 60
200
740
1600
800
B. Cell Percentages
Accept Reject
M .078 .478 .556
F .033 .411 .444
.111 .889 1.0

C. Row Percentages
Accept
M 140/1000
= .140
F 60/800
=.075
Reject
860/1000
= .860
740/800
= .925
D. Column Percentages
Accept
M 140/200
= .700
F 60/200
=.300
1.00
Reject
860/1600
= .538
740/1600
= .462
1.00
1.00
1.00

 If a potential causal interpretation exists, make numbers add up to 100% at each level of the causal factor.
 Above: it is possible that gender (row) causes or influences acceptance (column), but not that acceptance influences gender. Hence, row percentages (format C) would be desirable.

Hypothesis: What you believe the relationship is between the measures.
Theory
Empirical Evidence
Beliefs
Experience
Here: Believe that acceptance is related to gender
Null Hypothesis: Acceptance is not related to gender
Logic of hypothesis testing: Negative Inference
The null hypothesis will be rejected by showing that a given observation would be quite improbable, if the hypothesis was true.
Want to see if we can reject the null.

1. State the hypothesis in Null and Alternative Form
– Ho: There is no relationship between gender and MBA acceptance
– Ha1: Gender and Acceptance are related
(2-sided)
– Ha2: Fewer Women are Accepted (1-sided)
2. Choose a test statistic
3. Construct a decision rule

 Used for nominal data, to compare the observed frequency of responses to what would be “expected” under some specific null hypothesis.
 Two types of tests
 Contingency (or Relationship) – tests if the variables are independent – i.e., no significant relationship exists between the two variables
 Goodness of fit test – Compare whether the data sampled is proportionate to some standard


2  i k 

1
( O i
O
Observed number in cell i i

E i
)
2
With (r-1)*(c-1) degrees of freedom
E i
E
Expected number in cell i i k number of cells r number of rows c number of columns
E
= Column Proportion * Row Proportion * total number observed i

A. Observed Frequencies
Accept Reject
M 140 860 1000
B. Cell Percentages
Accept Reject
M .078 .478 .556
F .033 .411 .444
F 60 740 800
.111 .889 1.0
200 1600 1800
C. Expected Frequencies
Accept Reject
M .111*.556*1800=111 .889*.556*1800=890
F .111*.444*1800= 89 .889*.444*1800=710


2  i k 

1
( O i

E i
E i
)
2
With (r-1)*(c-1) degrees of freedom

2
=(140-111) 2 /111 + (860-890) 2 /890 + (60-89) 2 /89 + (740-710) 2 /710
= 19.30 So?
3. Construct a decision rule

1. Significance Level -
 
.
05
Probability of rejecting the Null Hypothesis, when it is true
2. Degrees of freedom - number of unconstrained data used in calculating a test statistic - for Chi Square it is (r-1)*(c-1), so here that would be 1. When the number of cells is larger, we need a larger test statistic to reject the null.
3. Two-tailed or One-tailed test – Significance tables are (unless otherwise specified) two tailed tables. Chi-Sq is on pg 517
Ha1: Gender and Acceptance are related (2-sided) Critical Value =
3.84
Ha2: Fewer Women are Accepted (1-sided) Critical Value = 2.71
4.
Decision Rule: Reject the Ho if calculated Chi-sq value (19.3)
> the test critical value (3.84) for Ha1 or (2.71) for Ha2

 Used for nominal data, to compare the observed frequency of responses to what would be “expected” under some specific null hypothesis.
 Two types of tests
 Contingency (or Relationship) – tests if the variables are independent – i.e, no significant relationship exists
 Goodness of fit test – Compare whether the data sampled is proportionate to some standard

Ho: Car Color Preferences have not shifted
Ha: Car color Preferences have shifted
Data Historic Distribution Expected # = Prob*n
Red 680
Green 520
Black 675
White 625
Total(n) 2500
30%
25%
25%
20%
Do we observe what we expected?
750
625
625
500


2  i k 

1
( O i

E i
E i
)
2
With (k-1) degrees of freedom

2
=(680-750) 2 /750 + (520-625) 2 /625 + (675-625) 2 /625 + (625-500) 2 /500
= 59.42
So?
3. Construct a decision rule

1. Significance Level -
 
.
05
Probability of rejecting the Null Hypothesis, when it is true
2. Degrees of freedom - number of unconstrained data used in calculating a test statistic - for Chi Square it is (k-1), so here that would be 3. When the number of cells is larger, we need a larger test statistic to reject the null.
3. Two-tailed or One-tailed test – Significance tables are (unless otherwise specified) two tailed tables. Chi-Sq is on pg 517
Ha: Preference have changed (2-sided) Critical Value = 7.81
4.
Decision Rule: Reject the Ho if calculated Chi-sq value (59.42) > the test critical value (7.81).

 Finding & Evaluating Secondary Data
 Measure Types
 permissible transformations
 Meaningful statistics
 Index #s
 Crosstabs
 Casting right direction
 Chi-square statistic
 Contingency Test
 Goodness of Fit Test