We Don’t Need No Stinking Statistics

Objectives

• Present three high level examples and applications of where statistics can be used in Marketing

– Chi Square Analysis

– Gage R&R

– Conjoint Analysis

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Case Study 1

• The rapid pace of change in products creates continued challenges to growth

• Consumers' needs and preferences are shifting

• Facing a growing number of competitors and a decline in the business’s core product category

• Customers are looking for help to drive growth

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Challenge

• Increase sales by

–Optimizing product portfolio by

• Looking at consumers in a different way to have

–Better insight into consumer needs

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Objective

• Create sustainable growth by:

– Looking at products through the consumer’s eyes

– Tailoring the brand strategy to meet consumer needs

– Creating commercial tactics to support brand strategy

– Aligning the organization behind a clear set of priorities

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Understanding Consumer Choice

1. Consumer choice is driven by needs

2. The intersection of needs and when they use a product creates “demand spaces”

3. Targeting and fulfilling "Demand Space" needs drives growth

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Let’s Keep It Simple

• Major survey was done of which the core questions were rather simple (22,000 respondents)

• People were asked when did they last use a certain category of product regardless of brand

• The key follow up questions were:– Specifically which type of product?

– How did you feel?

– What time of day did you use it?

– Where were you when you used it?

– Who were you with?

• Chi Square analysis was suggested as a simple first pass

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Chi-square (2) test

• When you want to see association between two categorical variables, a Chi-Square (2) test for association (non-independence) can be used

• This tests if the probabilities of items being classified for one variable depend on the classification of the other variable

• Chi-Square (2) hypothesis test – H0: There is no association between two variables

Ha: There is association between two variables

• Use the p-value to determine if you reject or fail to reject the null hypothesis

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Data table structure

X \ Y 1 … j … c Total

1 O11 … O1j … O1c T1.

… … … … … … …

i Oi1 … Oij … Oic Ti.

… … … … … … …

r Or1 … Orj … Orc Tr.

Total T.1 … T.j … T.c T..

Oij is observed frequency in cell (i, j)

H0: There is no association between two variables

Ha: There is association between two variables

Expected frequency for cell (i, j)

1)1)(c(rfreedom,ofdegreesthewith

E

)E(O

i j ij

2ijij

The total 2 is calculated by

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Eij= (Row i Total) x (Column j Total)Total Number of Observations

Example of a Data Table Structure

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Actual counts of each cell derived from the raw survey values

Mini Commands

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Mini Commands

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Output

Pearson Chi-Square = 2452.491, DF = 64, P-Value = 0.00013

Interpreting Output

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Matching Demand Spaces and Products

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Conclusion

• Chi Square is a simple data based tool for confirming that there was a relationship between category of product and the context of usage by the customer

• This permitted a matching of product portfolio with the biggest opportunities to match consumer needs and drive sales

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Case Study 2

• Marketing incurs $xMM in rework costs from Agencies for creative work submitted

• With marketing costs going up, budgets being frozen or reduced and an increase in the options for placement of creative work, the rework reduces the amount of dollars available for actually marketing product

• A more consistent way of judging creative work must be developed

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Case Study 2

• Multiple people judge the creative work depending on the project

• Two types of creative work were evaluated

– Printed

– Video

• Can we use a simple Gage R&R Study to determine whether the current process is repeatable, reproducible and accurate?

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Case Study 2

• A Gage R & R study was set up with 3 marketing people evaluating 10 creative pieces, 5 which were distinct Print pieces and 5 which were distinct Videos

• Each piece was evaluated two times – crossed study• Each piece was rated on a 1-15 scale with 15 as “love

it” and 1 as “hate it”

• Each piece was rated twice with a one week break between trials

• The senior executive who usually makes the final decision also rated the creative pieces and was considered the “expert” opinion

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Case Study 2

• The pieces were randomized with appropriate matching done transparently during the experiment

• Results would indicate whether the evaluators are consistent both within themselves as well as across themselves and agreed with the ratings assigned by the senior marketing executive who usually makes the final decision

• Inconsistency and inaccuracy could be an explanation for much of the rework as a result of the decision being person dependent rather than the quality of the actual creative piece

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Gage R&R – Data Format

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Mini Commands

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Mini Commands

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Output

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OutputGage R&R Study - ANOVA Method

Two-Way ANOVA Table With Interaction

Source DF SS MS F P

Creative Pie 9 426.340 47.3711 3.0656 0.021

Evaluators 2 16.459 8.2295 0.5326 0.596

Creative Pie * Evaluators 18 278.144 15.4525 61.4412 0.000

Repeatability 30 7.545 0.2515

Total 59 728.489

α to remove interaction term = 0.05

Gage R&R %Contribution

Source VarComp (of VarComp)

Total Gage R&R 7.8520 59.61

Repeatability 0.2515 1.91

Reproducibility 7.6005 57.70

Evaluators 0.0000 0.00

Evaluators*Creative Pie 7.6005 57.70

Part-To-Part 5.3198 40.39

Total Variation 13.1718 100.00

Number of Distinct Categories = 1 25

Output

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Conclusion

• Poor measurement system (repeatability/reproducibility almost 60%)

• Significant reproducibility issues

• Linearity and bias issues

• Too much disagreement between evaluators which can lead to rework

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Case Study 3

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• Company wants to test market a new product • Factors to test are;

• Color theme• Graphics• Package size• Graphics Orientation• Price• Location in the store

• Traditional approach was to select a few “best” combinations determined by the Marketing people and then run some tests in the marketplace

• A decision was made to try a different approach and utilize a conjoint analysis to better understand what factors might be important to the customer, what interactions between factors might exist and which combination may yield the optimal positive customer response

Conjoint Analysis

• Theory and Principles:

– Customers attach different values to different service/process attributes (service features) combinations

– Basic question of conjoint analysis: How will purchase decisions change with different packages or combinations of product/service attributes?

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Conjoint Analysis Approach

• Identify Key Product/Service Attributes

• Identify Possible Levels/Combinations of Attributes

• Present Combinations to Customers

• Have Customers Rate or Rank the Combinations

• Analyze the Resulting Data as a Designed Experiment

• Use the Response Optimizer to make Trade-Offs

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Conjoint Analysis Approach

• Identify Key Product Attributes

– Color Theme – Single Color or Mixed Colors

– Graphics – Traditional or Modern

– Package size – Short or Tall

– Graphic orientation – Horizontal or Angled

– Price - $1.99 or $2.39

– Location in store – Specialty or Traditional

• Identify Possible Levels/Combinations of Attributes

– Six factor, two level, ¼ Fraction giving 16 combinations

– Resolution IV (confounding between Main Effects and 3 way interactions and above as well as confounding between 2 way interactions)

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Conjoint Analysis Approach

• Present Combinations to Customers

– Made mock up boards of the 16 combinations

– Used 25 customers for experiment

• Have Customers Rate or Rank the Combinations

– Customers rank ordered the 16 combinations in order of preference with 1 being most preferred

– Used average of the 25 rankings for analysis

• Analyze the Resulting Data as a Designed Experiment

• Use the Response Optimizer to make Trade-Offs

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Mini Commands

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Mini Commands

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Worksheet

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Output

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Output

* * *

*

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OutputAnalysis of Variance

Source DF Adj SS Adj MS F-Value P-Value

Model 13 324.618 24.971 89.78 0.011

Linear 6 187.639 31.273 112.44 0.009

Color theme 1 21.856 21.856 78.58 0.012

Graphics 1 101.506 101.506 364.96 0.003

Package Size 1 3.331 3.331 11.98 0.074

Graphics Orientation 1 1.051 1.051 3.78 0.191

Price 1 58.141 58.141 209.04 0.005

Location 1 1.756 1.756 6.31 0.129

2-Way Interactions 7 136.979 19.568 70.36 0.014

Color theme*Graphics 1 4.516 4.516 16.24 0.056

Color theme*Package Size 1 58.141 58.141 209.04 0.005

Color theme*Graphics Orientation 1 3.706 3.706 13.32 0.068

Color theme*Price 1 19.141 19.141 68.82 0.014

Color theme*Location 1 33.351 33.351 119.91 0.008

Graphics*Graphics Orientation 1 15.801 15.801 56.81 0.017

Graphics*Location 1 2.326 2.326 8.36 0.102

Error 2 0.556 0.278

Total 15 325.174

Model Summary

S R-sq R-sq(adj) R-sq(pred)

0.527376 99.83% 98.72% 89.05%

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Output

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Output

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Output

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Conclusion

• As a result of the Conjoint Analysis all main effects were explored

• Main effects, although confounded with 3 way interactions, provide good insight into which ones matter

• Although the two way interactions are confounded in a Res IV experiment they do provide food for thought

• Final result was counter intuitive and should be explored further but, if true, then minimal effort will be needed to introduce the new product and margins appear to be higher than first expected

• A few trial and error runs would not have provided as much insight and understanding

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In ClosingHE MEANS STATISTICS NOT BADGES BUT HE IS WRONG.

MARKETING CAN USE STATISTICS TO MAKE BETTER DECISIONS!!

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