market intelligence session 12 contadina, regression, database marketing

Market Intelligence Session 12

Contadina, Regression, Database marketing

Agenda

• Contadina Case• Regression

– Multicollinearity– Database Marketing

Agenda

Contadina STM

• Trial and Repeat – Users and Non-Users– Pizza and Toppings

Contadina Epilogue

What happened?

• Cunliffe decided to launch Contadina Pizza without further testing.

• Sales (volume, trial, & repeat), share, and profit performance were below expectations (& $45 million cut-off):

1991(Q3&Q4) 1992 1993 $7,097 $21,919 $17,187

What went wrong

• Inaccurate inputs– Penetration 10%, not 24%– Market support $11M instead of $18M

• Bad luck: price & promotion war b/w Dominos and Pizza Hut, delivery now same price as Contadina

• Change in product– Topping spoilage 60%, changed product to cheese and

cheese & pepperoni only – not what was rated in BASES!

Agenda

Regression - Basics

• Terminology– In simple regression with a single variable, we get

a zero-order effect (full effect) because we do not control for anything else.

– In multiple regression we technically speak of the coefficients as partial effects because it is the change in Y from a change in X holding everything else in the regression constant

Regression – Homework assignment

Regression• In marketing, approach to regression depends

on what we are trying to do– PROMOTION ANALYSIS, PROFITABILITY ANALYSIS

• Uncover the marginal effects of a marketing action on customer response – e.g., coupons on sales

– DATABASE MARKETING • Forecast customer response – not particularly interested in

the marginal effects of any action but very interested in having an overall model that can predict the dependent variable (Y) very well

Part 1: Regression for promotion analysis

• Run multiple regression with outcome variable (often sales) as Y and marketing actions (e.g., 4 P’s) as predictors

Multicollinearity in Multiple Regression

• Multicollinearity: when 2 or more of your predictor variables are highly correlated with each other

Detecting MC

• Correlation matrix among explanatory variables: values (off-diagonal elements) of > .5 are often interpreted as indicating MC.

• Large changes in the estimated regression coefficients when a predictor variable is added or deleted

• Insignificant regression coefficients for the affected variables, but a significant F-test for overall regression

• Multivariable regression finds an insignificant coefficient of a particular predictor, yet a simple linear regression of the same predictor yields a significant coefficient

• Variance inflation factor (VIF) > 5

Coefficientsa

40.680 1.154 35.266 .000

7.278 1.923 .167 3.786 .000 1.000 1.000

25.288 1.841 13.734 .000

-4.875 2.119 -.112 -2.301 .022 .683 1.465

5.455 .536 .496 10.179 .000 .683 1.465

(Constant)

H=Any Children Under 6?

(Constant)

H=Any Children Under 6?

C=Household Size

Model1

B Std. Error

UnstandardizedCoefficients

Standardized

Coefficients

t Sig. Tolerance VIF

Collinearity Statistics

Dependent Variable: B=Weekly Food Expenditure Dollarsa.

Diagnostics

• Tolerance=

• VIF =Variance Inflation Factor

= 1/Tolerance

Rule of thumb: MC problem if VIF > 5

Remedies for MC• Leave model as is

– Significance of coefficients may be reduced– Can still apply model to new data if predictors follow same pattern of

MC • Drop predictor

– Remaining predictors will be more significant but will also be biased high• Obtain more data

– More data can produce more precise parameter estimates (with lower standard errors)

• Aggregate predictors– Factor analysis or PCA to reduce factors – Create index

Remedies for MC• Leave model as is

– Significance of coefficients may be reduced– Can still apply model to new data if predictors follow same pattern of

MC • Drop predictor

– Remaining predictors will be more significant but will also be biased high• Obtain more data

– More data can produce more precise parameter estimates (with lower standard errors)

• Aggregate predictors– Factor analysis or PCA to reduce factors – Create index

Doritos Example

• Identification of Promotion Effects– Effect of Price Promotions on Sales of XL size

• IRI Dataset (Market Level, Weekly Data)• Sales Models for XL Size

– Effects of own price (own price effect) & price of other sizes (cross price effects) on sales of XL size

– Multicollinearity exists: we’ll look at options

Promotion Example: Symphony-IRI data of Dorito Weekly Sales

SM XL 2XL 3XL

Sizes:

Promotion Example: Symphony-IRI data of Dorito Weekly Sales

SM XL 2XL 3XL

Sizes:

Correlations

1.000 .018 .449** .502** .085

. .854 .000 .000 .391

104 104 104 104 104

.018 1.000 .091 .120 -.801**

.854 . .356 .224 .000

104 104 104 104 104

.449** .091 1.000 .950** .107

.000 .356 . .000 .279

104 104 104 104 104

.502** .120 .950** 1.000 .067

.000 .224 .000 . .500

104 104 104 104 104

.085 -.801** .107 .067 1.000

.391 .000 .279 .500 .

104 104 104 104 104

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Average Price PerPound Small Size

Average Price PerPound XL Size

Average Price PerPound 2XL Size

Lbs Extra LargeSize 9 Oz $2.19

AveragePrice Per

PoundSmall Size

AveragePrice PerPound XL

AveragePrice Per

Pound2XL Size

AveragePrice Per

Pound3XL Size

Lbs ExtraLarge Size9 Oz $2.19

Correlation is s ignificant at the 0.01 level (2-tailed).**.

Correlations

1.000 .018 .449** .502** .085

. .854 .000 .000 .391

104 104 104 104 104

.018 1.000 .091 .120 -.801**

.854 . .356 .224 .000

104 104 104 104 104

.449** .091 1.000 .950** .107

.000 .356 . .000 .279

104 104 104 104 104

.502** .120 .950** 1.000 .067

.000 .224 .000 . .500

104 104 104 104 104

.085 -.801** .107 .067 1.000

.391 .000 .279 .500 .

104 104 104 104 104

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

AveragePrice Per

PoundSmall Size

AveragePrice Per

Pound2XL Size

AveragePrice Per

Pound3XL Size

Doritos Regression Equation

• Dependent Variable = Sales (lbs.) of XL size• Model 1: Overloaded

– Independent Variables: Price XL, Price SM, Price 2XL, Price 3XL

• Model 2: Omitted variable

SalesXLt a b1PSM _ t b2PXL _ t b3P2XL _ t b4P3XL _ t

Which variable to drop?

• Which is more correlated with DV?• Which is more correlated with remaining

predictors?• Which is less important variable to you?

Correlations

1.000 .018 .449** .502** .085

. .854 .000 .000 .391

104 104 104 104 104

.018 1.000 .091 .120 -.801**

.854 . .356 .224 .000

104 104 104 104 104

.449** .091 1.000 .950** .107

.000 .356 . .000 .279

104 104 104 104 104

.502** .120 .950** 1.000 .067

.000 .224 .000 . .500

104 104 104 104 104

.085 -.801** .107 .067 1.000

.391 .000 .279 .500 .

104 104 104 104 104

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

Pearson Correlation

Sig. (2-tailed)

AveragePrice Per

PoundSmall Size

AveragePrice Per

Pound2XL Size

AveragePrice Per

Pound3XL Size

• Model 2: Omitted variable– Independent variables: Price XL, Price SM, Price

2XL (note: NO 3XL) 26

SalesXLt a b1PSM _ t b2PXL _ t b3P2XL _ t

Coefficientsa

316.844 2445.178 .130 .897

253.361 515.280 .033 .492 .624

-1915.729 136.477 -.813 -14.037 .000

3590.806 2495.219 .267 1.439 .153

-1413.885 2574.564 -.106 -.549 .584

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Dependent Variable: Lbs Extra Large Size 9 Oz $2.19a.

MODEL 1: XL SALESDORITOS - PREDICTING XL SALES FROM PRICE OF SM, XL, 2XL, 3XLDoritos - Only Own XL Price Coefficient is Significant

• Model 2: Omitted variable– Independent variables: Price XL, Price SM, Price

2XL (note: NO 3XL)

SalesXLt a b1PSM _ t b2PXL _ t b3P2XL _ t

Coefficientsa

625.958 2371.187 .264 .792

175.976 493.905 .023 .356 .722

-1924.665 135.029 -.817 -14.254 .000

2305.377 861.524 .172 2.676 .009

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

MODEL 2: XL SALESDORITOS - PREDICTING XL SALES FROM PRICE OF SM, XL, 2XL (DROP 3XL)Doritos - Own XL and 2XL Price Coefficients Significant

Coefficientsa

625.958 2371.187 .264 .792

175.976 493.905 .023 .356 .722

-1924.665 135.029 -.817 -14.254 .000

2305.377 861.524 .172 2.676 .009

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Coefficientsa

316.844 2445.178 .130 .897

253.361 515.280 .033 .492 .624

-1915.729 136.477 -.813 -14.037 .000

3590.806 2495.219 .267 1.439 .153

-1413.885 2574.564 -.106 -.549 .584

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Model 1 (Over-loaded)

Model 2 (omitted variable)

Coefficientsa

625.958 2371.187 .264 .792

175.976 493.905 .023 .356 .722

-1924.665 135.029 -.817 -14.254 .000

2305.377 861.524 .172 2.676 .009

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Coefficientsa

316.844 2445.178 .130 .897

253.361 515.280 .033 .492 .624

-1915.729 136.477 -.813 -14.037 .000

3590.806 2495.219 .267 1.439 .153

-1413.885 2574.564 -.106 -.549 .584

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Model 1 (Over-loaded)R2 = .67

Model 2 (omitted variable)R2 = .56

Coefficientsa

625.958 2371.187 .264 .792

175.976 493.905 .023 .356 .722

-1924.665 135.029 -.817 -14.254 .000

2305.377 861.524 .172 2.676 .009

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Coefficientsa

316.844 2445.178 .130 .897

253.361 515.280 .033 .492 .624

-1915.729 136.477 -.813 -14.037 .000

3590.806 2495.219 .267 1.439 .153

-1413.885 2574.564 -.106 -.549 .584

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Coefficientsa

625.958 2371.187 .264 .792

175.976 493.905 .023 .356 .722

-1924.665 135.029 -.817 -14.254 .000

2305.377 861.524 .172 2.676 .009

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Coefficientsa

316.844 2445.178 .130 .897

253.361 515.280 .033 .492 .624

-1915.729 136.477 -.813 -14.037 .000

3590.806 2495.219 .267 1.439 .153

-1413.885 2574.564 -.106 -.549 .584

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Coefficientsa

316.844 2445.178 .130 .897

253.361 515.280 .033 .492 .624

-1915.729 136.477 -.813 -14.037 .000

3590.806 2495.219 .267 1.439 .153

-1413.885 2574.564 -.106 -.549 .584

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Overloaded model: will provide more accurate R2, but individual predictors won’t be as significant due to inflated standard errors. Result: you’ll know how much total variance is accounted for by model but won’t know which predictors have significant effect

Coefficientsa

625.958 2371.187 .264 .792

175.976 493.905 .023 .356 .722

-1924.665 135.029 -.817 -14.254 .000

2305.377 861.524 .172 2.676 .009

(Constant)

Model1

B Std. Error

Standardized

Coefficients

t Sig.

Omitted variable bias: R2 will be reduced so you won’t get as accurate info on total variance accounted for. Individual influential predictors will be significant but they will be biased (inflated) because they also include effect of omitted variable

Summary

• Interpretation of Regression Coefficients• Diagnose potential Multicollinearity and Omitted Variables

issues– Cost of these issues is decreased precision or bias in coefficient

estimates• Know your options. Every situation will be different.

– Are there other, less correlated, variables available as substitutes for the correlated variables?

– For strategy purposes is it acceptable to drop or combine a variable and measure a compound effect such as coupons and promotions?

– Can you create an index or aggregate correlated predictors? 37

Part 2: Regression for Database Marketing

• Form of direct marketing that uses databases of (potential) customers to generate personalized communications in order to promote a product/service• Common in financial services, telecommunications, and retail

• Database: usually name, address, transaction history (internal sales/delivery systems), or compiled list from 3rd party – Sources: charity donation forms, product warranty cards, applications

for free products, subscription forms, credit card applications, etc.• Uses Multiple Regression to predict likelihood of database of

customers purchasing each product/service– scores then used to select customers for communication

Database Marketing: Steps

• Step 1: Calibrate model• Step 2: Customer “scoring”• Step 3: Target new customers

Step 1: Calibrate model• First send an offer to a small subset of the database.

Collect Data and code response to predict response by others in database– Logistic regression model where Dependent Variable is

(0/1) binary (e.g., took offer or not)– Objective is to predict Response well (Y-hat), measured by a

high R-squared, so correlated predictors OK

)...( 2211 ikikiii xbxbxbafy

)...(ˆ 2211 kikiii xbxbxbafy

Step 2: Customer Scoring

• For remaining customers in database: Use the regression results to predict the probability of response to an offer– Predicted Y-hat = predicted probability of taking

Step 3: Target new customers

• Make offer to all customers for whom:– (probability of response * margin) > (cost of

contact)– Anything over 0, although would like to recover

fixed costs as well as variable costs• In the long run, each individual receives

customized treatment (different set of offers)

Database Marketing Example: Rodale International

Step 1: Calibrate model – 1 million customers in database; send offer for

handweights to a subset (5,000)– Response probability for the subset = 1.9%

• Logistic Regression Model:– Likelihood of taking offer =

Function (Age, Previous purchases, Years with Rodale, Runner’s world subscriber, household income, Payment method, Subscription offers sent last year, Sweepstake entrant, Years at current residence, Frequency of purchase, Returns, Net $ amount purchased, Recency of last purchase, etc.).

Database Marketing

1) Use sample (5,000) to build response model using regression 2) Y-hat Prob = f (a + b1(Age) + b2(#PrevBuys) + b3(Years) + b4(Runner) + …

+ bn(HHinc)) within this sample of 5,000

Prospect ID Buy Age #PrevBuys

Years with us

Runner’s World

… HH Inc

1 1 28 2 2 0 … 28k

2 0 51 5 6 1 … 49k

3 0 38 0 0 1 … 89k . . . . . .

5,000 1 71 0 4 0 … 61k

Step 2: Customer scoring• 5000 customers initially contacted are small

subset of total potential customers in database

• We now want to use regression equation to create a “score” for other 995,000 customers: how likely is each to buy handweights?

Database Marketing3) Now predict probability each prospect 5,001 – 1,000,000 in

your database would buy, if sent the offer

Prospect ID Prob Buy

Age #PrevBuys

Years with us

Runner’s World

… HH Inc

5001 .40 38 3 9 1 … 85k

5002 .26 25 0 3 1 … 40k

5003 .13 43 0 5 0 … 25k . . . . . .

1,000,000 .32 25 1 1 0 … 12k

Database Marketing3) Now predict probability each prospect 5,001 – 1,000,000 in

your database would buy, if sent the offer

Age #PrevBuys

Years with us

Runner’s World

… HH Inc

5001 .40 38 3 9 1 … 85k

5002 .26 25 0 3 1 … 40k

5003 .13 43 0 5 0 … 25k . . . . . .

1,000,000 .32 25 1 1 0 … 12k

These are customer “scores”

Step 3: Target new customers• Determine break-even and send offer to

those whose probabilities (scores) are above that

Database Marketing ExampleAssume:• The margin from a prospect who accepts the offer is $2.00• It costs $0.40 to send an offer to one prospect

BEprob =

Age #PrevBuys

Years with us

Runner’s World

… HH Inc

5001 .40 38 3 9 1 … 85k

5002 .26 25 0 3 1 … 40k

5003 .13 43 0 5 0 … 25k . . .

1,000,000 .32 25 1 1 0 … 12k

Database Marketing ExampleAssume:• The margin from a prospect who accepts the offer is $2.00• It costs $0.40 to send an offer to one prospect To determine the break even probability: $0.40 = Prob*$2.00

BEprob = 0.20 Send offer to prospects with scores X higher than .20

Age #PrevBuys

Years with us

Runner’s World

… HH Inc

5001 .40 38 3 9 1 … 85k

5002 .26 25 0 3 1 … 40k

5003 .13 43 0 5 0 … 25k . . .

1,000,000 .32 25 1 1 0 … 12k

Internet and database marketing

• Internet is powerful database marketing vehicle

• 2 examples of changes internet has brought to database marketing– Customized or personalized websites for each

customer – Send offers via web (e.g., email)

Customized websites

• Customize and/or personalize pages to show items of interest

• Easy ROI calculation using experiment with test and control group

• 100,000 customers– 80,000 get personalized web page– 20,000 (control group) gets generic one– Can determine value in about a week

Example of internet marketing experiment

Number Sales Sales/Customer

Test 80,000 $2,272,000 $28.40

Control 20,000 $469,000 $23.45

Total 100,000 $2,741,000 $27.41

Cost of Personalization

($45,000) ($0.45)

Gain from Personalization

Example of internet marketing experiment

Number Sales Sales/Customer

Test 80,000 $2,272,000 $28.40

Control 20,000 $469,000 $23.45

Total 100,000 $2,741,000 $27.41

Cost of Personalization

($45,000) ($0.45)

Gain from Personalization

compare

Sending offers via web• Customer communications: relationship building • Low cost to send offers

– Always include web response option:

$0.00 $0.50 $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 $4.50

Course Wrap Up

Goals of course

• Tools to reduce demand uncertainty

• Learn how to evaluate different types of research – before and after it’s done

• Act as a research provider to become a more sophisticated user

Tools for common marketing decisions

• Competition: Quadrant analysis, disaggregate choice modeling, perceptual mapping, conjoint, cannibalization analyses

• Positioning: disaggregate choice modeling, Quadrant analysis, perceptual maps

• Segmentation and targeting: Index #s, Interactions, a priori v. clustering methods, choosing a basis, 3 criteria, Segments of 1, (index #s, interactions, clustering, 1-to-1

• New Product Development: Focus groups, conjoint analysis, BASES, Disaggregate choice modeling, perceptual mapping

• Pricing: Conjoint analysis & Regression, experiments• Promotion Effects: Multiple regression, experiments• Product Design: Focus Groups, Conjoint• Database Marketing: Regression for customer scoring

(forecasting), customer data (e.g., loyalty) programs 59

Finally, …

• End up regularly using material from this class in your job? Please consider being a future guest speaker.

• Thank you for a great term!

market intelligence session 12 contadina, regression, database marketing

Documents

a comparison of regression and artificial intelligence...

computational intelligence - spsc · • spam filters,...

open doors to data science and artificial intelligence · o...

regression analysis linear regression logistic regression

regression analysis regression models

nestle contadina pasta

gaussian process and prediction. (c) 2001 snu cse artificial...

cs 4100: artificial intelligence...cs 4100: artificial...

regression i: poisson‘sche regression · t. kießling:...

1 curve-fitting spline interpolation. 2 curve fitting...

chapter 7 – classification and regression trees © galit...

applied regression analysis - department of...

chapter 9 – classification and regression trees dm for...

109 ' # '7& *#1 & 8 · linear regression approaches (vale...

2. korrelation, linear regression und multiple...

retailmart supervised artificial intelligence regression

1 curve-fitting polynomial interpolation. 2 curve fitting...

chapter 13: simple linear regression. 2 simple regression...

linear regression: evaluating regression models overview...

multiple regression. example: brain and body size predictive...