Market Intelligence Class 8

Agenda Experimental research – Factorial designs Segmentation – Tactical – Strategic – a priori and clustering approaches

Experiments - Factorial Designs 2 or more independent variables (manipulated and/or measured), each with two or more levels. – Type 1: 2 marketing mix variables Both variables manipulated Important for determining whether you need to coordinate marketing actions – Type 2: “tactical segmentation” (1 segment responds differently to a marketing mix variable than another segment) Segmenting variable is measured, marketing action is manipulated Important for determining whether you should segment for that particular marketing action 3

Experiments - Factorial Designs What to look for in factorial designs – Is there a main effect of A? – Is there a main effect of B? – Is there an interaction between A and B? (interaction: effect of one IV on DV depends on level of another IV) Analysis 4

Type 1: 2 marketing mix variables You manage the Oreo account for Kroger – One person on your team manages advertising, another manages in store promotions – You run an experiment to evaluate whether ads and/or end-of-aisle display increase sales You manipulate ads and display between store locations in one city

Oreo Promotion Experiment Factor A: Ads in local paper a1 = no ads a2 = ad in Thursday local paper Factor B: Display location b1 = regular shelf b2 = end aisle Store 1: a1, b1 Store 2: a1, b2 Store 3: a2, b1 Store 4: a2, b2 6 Dependent variable: expenditures per customer over subsequent 2 weeks

OREO PROMOTION EXPERIMENT Scenario 1 (EXPENDITURES/CUSTOMER/2 WKS) 7

8 Main effect of A? Main effect of B?

OREO PROMOTION EXPERIMENT Scenario 1 (EXPENDITURES/CUSTOMER/2 WKS) 9 Interaction? this diff v. this diff

OREO PROMOTION EXPERIMENT Scenario 1 (EXPENDITURES/CUSTOMER/2 WKS) 10 Interaction? this diff vs. this diff

SALES OF OREOS 11

SALES OF OREOS 12 2 main effects, no interaction

Oreo Promotion Experiment Scenario 2 (Expenditures/customer/2 wks) Main effect of A? Main effect of B?

Oreo Promotion Experiment Scenario 2 (Expenditures/customer/2 wks) Interaction? 0.50

15 “Cross-over” interaction SALES OF OREOS (Expenditures/customer/2 wks)

Oreo Promotion Experiment Scenario 3 (Expenditures/customer/2 wks)

Oreo Promotion Experiment Scenario 3 (Expenditures/customer/2 wks) Main effect of A? Main effect of B?

Oreo Promotion Experiment Scenario 3 (Expenditures/customer/2 wks) 18 Interaction? 1.30

SALES OF OREOS (Expenditures/customer/2 wks) 19 “fan effect” interaction

How to analyze in SPSS: ANOVA If both IVs are manipulated between subjects – Analyze – General linear model – Univariate – Bring DV to Dependent variable, IVs to “Fixed factors” – Options -- estimated marginal means – bring main effects and interactions to right side

Data in SPSS

Look for significance of 2 main effects and interaction

Look for means of main effects and interaction

Oreo Example No A x B interaction – Effect of changing A (Ads) is independent of level of B (Display Location). – Implies that Ad & Display decisions can be decoupled…they influence sales additively A x B interaction – Effect of changing A (ads) depends on level of B (display location), and/or vice-versa – Cannot decouple variables 25

Type 2: Tactical Segmentation Should groups be treated same or differently with respect to specific marketing decision variable? A is a controllable decision variable and B is a potential segmentation variable – Interaction means that segments respond differently to this marketing lever – Example: coupon x urban/suburban Question: does marketing mix variable have bigger effect for segment A or B? Is coupon more effective in urban or suburban neighborhoods? 26

Interactions and segmentation 27

Interactions and segmentation 28 Pending ANOVA results, coupons have a bigger effect in the suburbs

Tactical Segmentation Example - Dog Food 1 potential segmentation variable (Size of Dog) 2 decisions – Price: Hi v. Lo – Ad Theme: Love of Master v. Dog’s Active Life 29

Segmentation Example: Dog Food I (rated on 10 pt scale) Price Advertising 30

Segmentation Example: Dog Food I Price Advertising 31

32

Implications of Contrast A variable that is an excellent basis for segmentation with respect to one decision about a marketing mix element may be a poor basis for segmentation with respect to another mix element For any given mix element decision, when evaluating alternative bases for segmentation, look for ones with big differences in sensitivity to mix variable. 33

Tactical Segmentation II Example - Dog Food 1 decision: Price (hi vs. lo) 2 potential segmentation variables – Size of Dog – Income of owner 34

Segmentation Example: Dog Food II 35 How to compare? ANOVA

Segmentation Example: Dog Food II 36 How to compare? Can also look at “difference of differences”

Segmentation Example: Dog Food II 37

How to analyze in SPSS: ANOVA If both IVs are manipulated between subjects – Analyze – General linear model – Univariate – Bring DV to Dependent variable, IVs to “Fixed factors” – Options -- estimated marginal means – bring main effects and interactions to right side If 1 IV is between subjects and the other is within subjects – Analyze – General linear model – repeated measures – Name within-Ss variable and enter # levels, choose define, drag variables to right – Bring between-Ss IV to “between subject” box on right – Options – estimated marginal means – bring main effects and interactions to right side

Let’s analyze this in SPSS: same subjects rated both ads 39

Data in SPSS

Look for sign. of within-Ss main effect, between-Ss main effect, and interaction within within x between between

Look at means for main effects and interaction

Strategic v. Tactical Segmentation Tactical Segmentation – Should groups be treated same or differently with respect to specific marketing decision variable? – Evaluate interactions Strategic segmentation – What product markets to serve? – A priori vs. clustering (analytic) approaches in segmentation 43

Strategic: Choosing a Basis for Segmentation 3 Criteria for useful segments Different options or “levels” for segmenting 44

Criteria for “Useful” Segments 1.Homogeneity within segment, heterogeneity between 2.Systematic differences in behaviors 3.Marketing Mix efficiency potential – Make more money treating as segments than if we treated market as unsegmented whole 45

Potential bases for segmentation 46 Demographics/psychographics Lifestyle/Usage situation Product benefits desired Brand beliefs, perceptions, and preferences Purchase intentions Purchase behavior

Choosing how to segment When would you want to choose variables from one level vs. another? – What information is Available – Is a segmentation Actionable – Strength of correlation with Purchase Behavior 47

Potential bases for segmentation 48 Demographics/psychographics Lifestyle/Usage situation Product benefits desired Brand beliefs, perceptions, and preferences Purchase intentions Purchase behavior Less correlated with behavior More correlated with behavior

Choosing how to segment When would you want to choose variables from one level vs. another? – What information is Available – Is a segmentation Actionable – Strength of correlation with Purchase Behavior – Time frame planned for using segmentation 49

Potential bases for segmentation 50 Demographics/psychographics Lifestyle/Usage situation Product benefits desired Brand beliefs, perceptions, and preferences Purchase intentions Purchase behavior Less correlated with behavior More correlated with behavior Longer time- frame Shorter time- frame

Strategic Segmentation: A Priori A priori -- Segments chosen by the analyst before collecting data Often based on demographics – Example: Golfers, Male, 50-70, income > $50,000 – IBM initial approach for “Rubik’s Cube” segmentation 51

Strategic segmentation: Clustering Clustering-based approaches (a posteriori) – Collect data - ask battery of questions (lifestyle, benefits sought, etc) – Find natural clusters/ segments – Segments do not have labels - describe segments by their mean answers – IBM prometheus used this – Let’s try it with Kerlander soup

Kerlander Soup Raw Data Kerlander'sFisherman'sKerlander'sCapeKerlander's SubjectRegularDelightCreamyCodExtraCreamy

Clustering We want to run analyses that reveal which of the Ps cluster with other Ps and get a feel for how many segments might exist Our segmentation basis here is each subject’s taste rankings for the five brands. Do subjects segment in a systematic way based on their tastes? Use the Kerlander Soup Data for the 20 respondents.

Types of cluster analysis Hierarchical – Quantifies how far apart/close together 2 cases are, then forms groups – After running, you determine how many clusters you need/want K-means clustering – You select number of clusters – Estimates cluster means and assigns each case to cluster for which its distance to cluster mean is smallest 2 step clustering – First step, cases assigned to “preclusters” – Second step, preclusters are clustered using hierarchical algorithm.

Cluster Analysis Let’s run all 3 versions of cluster analysis What segments are revealed?

Hierarchical Clustering Will start with each individual as segment and begin combining until 1 segment is reached Look at solution at each stage and see what is clustering together and where there is a large “distance” associated with forming the next cluster

Hierarchical Clustering 58

Hierarchical Clustering Will start with each individual as segment and begin combining until 1 segment is reached Look at solution at each stage and see what is clustering together and where there is a large “distance” associated with forming the next cluster In SPSS – Make sure data are sorted by Sub # (helps for later interpretation – May need to standardize variables if on different scales – Choose Analyze – Classify – Hierarchical Cluster Pick Variables (the 5 soups that you have taste rankings for) Statistics: choose agglomeration schedule Choose Plots – Dendogram to get the chart of segments being formed

Output: Agglomeration Schedule

Output – dendogram X-axis is measure of distance – Closer you are to the left side, the smaller the distance between the objects that were combined. As you get big leaps in distance between the combined objects, you question whether you should be combining these segments Compare dendogram to original data. – Why were 10, 19, 4, 5, and 8 immediately combined into a segment? – What about 15, 16, 1, 9, and 3? – Based on this chart, how many segments?

Cluster SPSS – Kerlander Soup

Kerlander Soup Raw Data Kerlander'sFisherman'sKerlander'sCapeKerlander's SubjectRegularDelightCreamyCodExtraCreamy

Output – dendogram X-axis is measure of distance – Closer you are to the left side, the smaller the distance between the objects that were combined. As you get big leaps in distance between the combined objects, you question whether you should be combining these segments Compare dendogram to original data. – Why were 10, 19, 4, 5, and 8 immediately combined into a segment? – What about 15, 16, 1, 9, and 3? – Based on this chart, how many segments?

Cluster SPSS – Kerlander Soup If we went to two segments we would combine 10, 19, …, 14, 6 in a single segment At this stage, 10,19,4,5 and 8 will join with 13 and 17

Cluster SPSS – Kerlander Soup If we went to two segments we would combine 10, 19, …, 14, 6 in a single segment At this stage, 10,19,4,5 and 8 will join with 13 and 17

Now re-run analysis Choose Save – Single solution – 3 In data set: new variable representing cluster membership Use new variable to examine differences in segments – Descriptive statistics -- Cross-tabs – membership on row, preferences on columns

This will also be new variable in data set

Now re-run analysis Choose Save – Single solution – 3 In data set: new variable representing cluster membership Use new variable to examine differences in segments – Descriptive statistics -- Cross-tabs – membership on row, preferences on columns

K-Means Clustering Choose Analyze – Classify -- K-Means cluster – Pick Variables – Pick Number of Clusters (3 based on other cluster analysis) – Choose Save – Cluster Membership – Choose Save – Distance from Cluster Center

K-Means Cluster Analysis SPSS will save variables in your data that reflect segment membership as well as the distance an object (a taste tester here) is from the center of the cluster. – Ideally: all members close to the center of the assigned cluster. – Tradeoff: the more clusters you have, the closer each member will be to the center of the cluster, but the less interpretable / practical Check the segment assignment. Compare it to the hierarchical approach we took.

2-step cluster analysis Analyze – classify – 2 step cluster Put variables into categorical or continuous Distance measure: Euclidean Options – standardize if necessary Output – create cluster membership variable Results in this case?

Which one to use? Hierarchical – Good for small data sets – Can easily examine solutions with increasing numbers of clusters K-means clustering – If you know how many clusters you want – Good for moderately sized data sets 2 step clustering – For large data files (>1,000 cases) – Mixture of continuous and categorical variables