Southwestern Conquistador Beer, Secondary Data, Measures, Hypothesis Formulation, Chi-Square

Southwestern Conquistador Beer, Secondary Data, Measures,

Hypothesis Formulation, Chi-Square

Market IntelligenceJulie Edell Britton

Session 2August 8, 2009

Today’s Agenda

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

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• 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

Announcements

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SWCB Objectives

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

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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?

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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

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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.

Today’s Agenda


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 Oregon

Year Entire Over 21 Entire Over 21

Population Population

1996

1997

1998

Average Source: Study A Table B: Population Estimates for Five Oregon Counties in Market Area Entire Population County 1998 1999 2000 2001 2002 2003 A B C D E Total 21 and over County 1998 1999 2000 2001 2002 2003 A B C D E Total Source: Study B

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

Analysis Dummy Table

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.

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?




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


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

Advantagescheapquickoften sufficient

Disadvantagesthere is a lot of data out therenumbers sometimes conflict categories may not fit your needs

Types of Secondary Data

Internal External

Database: Can Slice/Dice; Need more processing

WEMBA_C IMS Health, Nielsen, IRI*

Summary: Can’t change categories, get new crosstabs

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 sizeSampling method (Sessions 5&6) Measure typeCausality (MBA Marketing Timing & Internship)

It is Hard to Infer Causality from Secondary Data

Took Core Marketing

Got Desired Marketing Internship

Did Not Get Desired Marketing Internship

Term 1 76% 24%

Term 3 51% 49%

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 SkepticalMBA’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 TypesNominal: 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

Att2 = a + (b*Att1) e.g., 81 to 100 (a = 80, b = 1) e.g., 80.5 to 90 (a = 80, b = .5)

All below + mean

Ordinal Rank order of wines 1 = favorite 2 = 2nd preferred 3 = least preferred

Any order preserving 100 = favorite 90 = 2nd preferred 0 = least preferred

All below + median

Nominal 1 = Pinot Noir 2 = Merlot 3 = Chardonnay

Any transformation is ok 16 = Pinot Noir 3 = Merlot 13 = Chardonnay

# 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 conclusionsExample 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 conclusionMedian 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 SegmentAge Group Population Units (000) Sales Index

<25 700 1400 2.00 7025-34 500 1250 2.50 8835-44 300 900 3.00 10545-54 240 960 4.00 14055 + 260 1196 4.60 161Total 2000 5706 2.85 100

Today’s Agenda


MBA Acceptance Data

Accept Reject

M 140 860 1000

F 60 740 800

200 1600

A. Raw Frequencies

Accept Reject

M .078 .478 .556

F .033 .411 .444

.111 .889 1.0

B. Cell Percentages

Accept Reject

M 140/ 1000 = .140

860/ 1000 = .860

1.00

F 60/ 800 =.075

740/ 800 = .925

1.00

C. Row Percentages

D. Column Percentages

Accept Reject

M 140/ 200 = .700

860/ 1600 = .538

F 60/ 200 =.300

740/ 1600 = .462

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.

TheoryEmpirical EvidenceBeliefsExperience

Here: Believe that acceptance is related to gender

Null Hypothesis: Acceptance is not related to gender

Logic of hypothesis testing: Negative InferenceThe 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

k

i i

ii

E

EO

1

22 )( With (r-1)*(c-1)

degrees of freedom

iO Observed number in cell i i

iE Expected number in cell iunder independence

k number of cells r cnumber of rows number of columns

iE = Column Proportion * Row Proportion * total number observed

MBA Acceptance Data Contingency

Accept Reject

M 140 860 1000

F 60 740 800

200 1600 1800

A. Observed Frequencies Accept Reject

M .078 .478 .556

F .033 .411 .444

.111 .889 1.0

B. Cell Percentages

Accept Reject

M .111*.556*1800=111 .889*.556*1800=890

F .111*.444*1800= 89 .889*.444*1800=710

C. Expected Frequencies

Chi-Square Test

k

i i

ii

E

EO

1

22 )(

With (r-1)*(c-1) degrees of freedom

i

2=(140-111)2/111 + (860-890)2/890 + (60-89)2/89 + (740-710)2/710= 19.30 So?


Decision Rule1. Significance Level -

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 517Ha1: 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

05. Probability of rejecting the Null Hypothesis, when it is true

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 shiftedHa: Car color Preferences have shifted

Data Historic Distribution Expected # = Prob*n

Red 680 30% 750Green 520 25% 625Black 675 25% 625White 625 20% 500Total(n) 2500

Do we observe what we expected?

Chi-Square Test

k

i i

ii

E

EO

1

22 )(

With (k-1) degrees of freedom

i

2=(680-750)2/750 + (520-625)2/625 + (675-625)2/625 + (625-500)2/500= 59.42

So?


Decision Rule1. Significance Level -

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).

05. Probability of rejecting the Null Hypothesis, when it is true

Chi-Square Table

RecapFinding & Evaluating Secondary DataMeasure Types

permissible transformationsMeaningful statistics

Index #sCrosstabs

Casting right direction Chi-square statistic

Contingency Test Goodness of Fit Test

Documents

Southwestern Conquistador Beer, Secondary Data, Measures, Hypothesis Formulation, Chi-Square