Transcript Slide 1
Southwestern Conquistador Beer,
Secondary Data, Measures,
Hypothesis Formulation, Chi-Square
Market Intelligence
Julie Edell Britton
Session 2
August 8, 2009
Today’s Agenda
Announcements
Southwestern Conquistador Beer Case
Backward Market Research
Secondary data quality
Measure types
Hypothesis Testing and Chi-Square
Announcements
• 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
SWCB Objectives
Feasibility decisions
Problem formulation, information needs
Role of secondary data
Role of research and time budgets
Quality, cost, speed
4
SWCB Questions
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
SWCB Conclusions
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
SWCB Conclusions (cont.)
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
Today’s Agenda
Announcements
Southwestern Conquistador Beer Case
Backward Market Research
Secondary data quality
Measure types
Hypothesis Testing and Chi-Square
Backward Market Research
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 (“need-” vs. “nice-to-know”)
Analyze data & make recommendation
Table A: National and Oregon Resident Annual Beer
Consumption
US
Year
Entire
Population
Over 21
Oregon
Entire
Over 21
Population
1996
1997
1998
Average
Source: Study A
Table B: Population Estimates for Five Oregon Counties in Market
Area
Entire Population
County
1998
A
B
C
D
E
Total
21 and over
County
A
B
C
D
E
Total
Source: Study B
1998
1999
2000
2001
2002
2003
1999
2000
2001
2002
2003
Analysis Dummy Table
Consumers’ Consumers’
Upbeat
Learning of
Feelings
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
Research Process Fig 3-1, p.49
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
Research Process Fig 3-1, p.49
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.
Were any Actions Taken or are any actions being considered
that are in conflict with the research learning? If so, why?
In retrospect, is there anything that could have been done
differently to improve the actionability of the research
investment? If so, what?
Relevant Populations - Who do we need to talk to and why?
2.
3.
4.
Research Process Fig 3-1, p.49
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
Overview of Research Design
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
Today’s Agenda
Announcements
Southwestern Conquistador Beer Case
Backward Market Research
Secondary data quality
Measure types
Hypothesis Testing and Chi-Square
3 Key Skills
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 vs. Secondary Data
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
Primary vs. Secondary Data
Evaluating Sources of
Secondary Data
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.
Secondary Data: Pros & Cons
Advantages
cheap
quick
often sufficient
Disadvantages
there is a lot of data out there
numbers sometimes conflict
categories may not fit your needs
Types of Secondary Data
Database: Can
Slice/Dice; Need
more processing
Summary:
Can’t
change categories,
get new crosstabs
Internal
External
WEMBA_C
IMS Health,
Nielsen, IRI*
Knowledge
Management
Conquistador,
Simmons,
IRI_factbook
*IRI = Information Resources, Inc. (http://us.infores.com/)
Secondary Data Quality:
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)
It is Hard to Infer Causality from
Secondary Data
Took Core
Marketing
Did Not Get Desired
Marketing Internship
Term 1
Got Desired
Marketing
Internship
76%
Term 3
51%
49%
24%
Evaluating Sources of
Secondary Data
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.
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
Mktg. Executive
had an MBA
Mktg. Executive did not
have an MBA
Overperformers (n = 9)
55.5%
44.5%
Underperformers (n = 18)
88.9%
11.1%
Today’s Agenda
Announcements
Southwestern Conquistador Beer Case
Secondary data quality
Measure types
Hypothesis Testing and Chi-Square
Measure Types
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
Meaningful Statistics &
Permissible Transformations
Examples
Permissible
Transform
Meaningful
Stats
Ratio
Q1 = Bottles of wine Q2 = b*Q1
e.g., cases sold (b = 1/12)
All below
+ % change
Interval
Wine Rating Scale
1 = Very Bad to
20 = Very Good
Rank order of wines
1 = favorite
2 = 2nd preferred
3 = least preferred
All below
+ mean
Ordinal
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 = 2nd preferred
0 = least preferred
Any transformation is ok
16 = Pinot Noir
3 = Merlot
13 = Chardonnay
All below
+ median
# of cases
mode
The Interval/Ordinal Distinction
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
Means and Medians with Ordinal Data
Gender
Measure 1 Measure 2 Means
M
1
1
Measure 1
M
2
2
M=5.4 < F=5.6
F
3
3
Measure 2
F
4
4
M=65.4 > F=25.6
F
5
5
F
6
6
Medians
M
7
107
Measure 1
M
8
108
M=7 > F=5
M
9
109
Measure 2
F
10
110
M=107 > F=5
Ratio Scales & Index Numbers
Index= 100* (Per Capita Segment i) / (Per Capita Ave)
(000s)
Sales Per Capita Segment
Age Group Population Units (000) Sales
Index
<25
700
1400
2.00
70
25-34
500
1250
2.50
88
35-44
300
900
3.00
105
45-54
240
960
4.00
140
55 +
260
1196
4.60
161
Total
2000
5706
2.85
100
Today’s Agenda
Announcements
Southwestern Conquistador Beer Case
Backward Market Research
Secondary data quality
Measure types
Hypothesis Testing and Chi-Square
MBA Acceptance Data
A.
Raw Frequencies
Accept
Reject
M
140
860
1000
F
60
740
800
200
1600
B.
Cell Percentages
Accept
Reject
M
.078
.478
.556
F
.033
.411
.444
.111
.889
1.0
C.
M
F
D.
M
F
Row Percentages
Accept
Reject
140/1000
= .140
60/800
=.075
860/1000
= .860
740/800
= .925
Column Percentages
Accept
Reject
140/200
= .700
60/200
=.300
1.00
860/1600
= .538
740/1600
= .462
1.00
1.00
1.00
Rule of Thumb
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
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.
Steps in Hypothesis Testing
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
Chi-Square Test
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
Chi-Square Test
(Oi Ei )
Ei
i 1
k
2
2
With (r-1)*(c-1)
degrees of freedom
number in cell i
Oi Observed number in cell i Ei Expected
under independence
i
k
number of cells
r
number of rows
c
number of columns
Ei = Column Proportion * Row Proportion * total number observed
MBA Acceptance Data Contingency
A.
Observed Frequencies
Accept
Reject
M
140
860
1000
F
60
740
800
200
1600
1800
C.
B.
Cell Percentages
Accept
Reject
M
.078
.478
.556
F
.033
.411
.444
.111
.889
1.0
Expected Frequencies
Accept
Reject
M
.111*.556*1800=111
.889*.556*1800=890
F
.111*.444*1800= 89
.889*.444*1800=710
Chi-Square Test
(Oi Ei )
Ei
i 1
k
2
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?
i
3. Construct a decision rule
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
Chi-Square Table
Chi-Square Test
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
Goodness of fit – Chi-Square
Ho: Car Color Preferences have not shifted
Ha: Car color Preferences have shifted
Data
Red
680
Green 520
Black
675
White
625
Total(n) 2500
Historic Distribution Expected # = Prob*n
30%
25%
25%
20%
Do we observe what we expected?
750
625
625
500
Chi-Square Test
(Oi Ei )
Ei
i 1
k
2
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
i
So?
3. Construct a decision rule
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).
Chi-Square Table
Recap
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