Segmentation and Targeting

Outline The segmentation-targeting-positioning (STP) framework Segmentation The concept of market segmentation Managing the segmentation process Deriving market segments and describing the segments Cluster analysis Discriminant analysis Targeting

Marketing mix Segmentation and targeting STP Segmentation, Targeting, Positioning Product All consumers in the market Price Communication Target marketing and positioning Target market segment(s) Distribution Marketing strategies of competitors

How STP creates value More focused marketing efforts can better meet customer needs Customers develop preferences for offerings that deliver greater value and satisfaction Customers become loyal to the brand and the firm if the brand/firm provides value and satisfaction Loyalty leads to greater market share and insulates the firm against competition Profitability increases

Motivation for market segmentation One size fits all usually doesn t work (all potential customers are not created equal) Segment-of-one marketing is often not feasible (costs outweigh the benefits) Compromise: Market segmentation

Market segmentation Partitioning a market that is characterized by heterogeneity in customers response to the marketing mix into more homogeneous submarkets.

Segmentation bases Observable General Observable features of the physical and social environment (esp. demographics) Product-specific Behavioral characteristics (user status, loyalty status, usage rate) Usage situations Latent Values, lifestyles and psychographics, personality variables Awareness Product attributes and benefits Willingness to buy

Problems with many segmentations Markets can be segmented on the basis of lots of different variables, but it s unlikely that many of these variables capture differences in response to the marketing mix; Product-specific segmentation bases are usually better indicators of differences in customer response than general segmentation bases; Particularly motivational variables (purchase motivations, customer needs, benefits sought) are important for segmentation; However, they are not directly observable, so they have to be supplemented with managerially useful descriptors that characterize the segments;

Segmentation criteria The essence of market segmentation: market response is homogeneous within segments and heterogeneous between segments (differentiability) individuals can be assigned to a segment based on a meaningful profile of segment characteristics (identifiability) Additional requirements: the size and purchasing power of relevant segments can be determined (measurability) the company is able to develop a marketing mix that will appeal to the members of a given segment (actionability) members of a segment can be reached with the appropriate marketing mix (accessibility) segments and segment membership do not change in the short run (stability)

Differences in customer response Response Who s this? Segment B B 2 A 2 A 1 B 1 x 1 x 2 Who s this? Segment A marketing variable

Segmentation bases (cont d) Use product-specific segmentation bases to derive segments (segmentation variables): difference in response is key Use general segmentation bases to profile the segments (discriminant variables): identifiability is key

Managing the segmentation process Define the segmentation problem Objectives, resources, and constraints Identify data needs Primary vs. secondary data Sample definition (category users, existing customers, heavy vs. light users, loyals vs. switchers) Segmentation and discriminant variables (based on available data and/or qualitative research) Conduct the segmentation study and analyze the data Step 1: Derive the market segments (cluster analysis) Step 2: Describe the market segments (discriminant analysis) Implement the results

Step 1: Deriving market segments The idea is to group (potential) customers who are similar in their response to some element of the marketing mix (e.g., response to different product features, including price; response to advertising or promotions; response to different distribution channels) Choose segmentation variables that capture relevant response differences, which can eventually be used to position the firm s offering to the right customers; Assume that we have data for a relevant sample of customers on a set of segmentation variables of interest; how can we do a segmentation analysis?

A simple segmentation example: Preferences of 6 consumers for 2 attributes of beer Observations / Segmentation Variables R1 10 10 R2 8 9 R3 5 6 R4 6 5 R5 3 3 R6 1 2

A simple segmentation example: Preferences of 6 consumers for 2 attributes of beer

Actual segments in the beer market (based on Consumer Reports) Craft ales Craft lagers Imported lagers N.A. beer less Regular and ice beer Light beers bitter more

Segmentation in the real world In practice, we have Many potential customers Many segmentation variables What to do? Custer analysis to the rescue!

Cluster analysis Basic question: How can objects (customers, brands, stores, etc.) be grouped such that objects within the same cluster are similar and objects in different clusters are dissimilar? In segmentation, the objects of interest are customers and similarity is assessed in terms of relevant segmentation variables; Issues in cluster analysis: How is similarity measured? How are clusters formed? How many clusters should be distinguished? How should the clusters be interpreted?

How is similarity measured? Overall measures of similarity [not relevant here] Direct measures of overall similarity Indirect measures of overall similarity (e.g., switching data) Derived measures of similarity (e.g., based on preferences for certain benefits) Metric data Correlational measures (e.g., similarity in the profile of ratings across certain benefits) Distance measures (Euclidean, city-block) Non-metric data Matching coefficients (i.e., extent to which customers want the same features in a product)

Euclidean distance R1 R6 d R1 R 6 = (X 2 X 1 ) 2 +(Y 2 Y 1 ) 2

Similarity data as input to cluster analysis R1 R2 R3 R4 R5 R6 R1 -- R2 S 21 -- R3 S 31 S 32 -- R4 S 41 S 42 S 43 -- R5 S 51 S 53 S 53 S 54 -- R6 12.04 S 63 S 63 S 64 S 65 --

How are clusters formed? Hierarchical cluster procedures: result in a tree-like (nested) structure that can be represented in a dendrogram; Agglomerative (bottom-up) methods: initially there are as many clusters as objects and then objects are combined; Single linkage Complete linkage Average linkage Centroid method Ward s method Divisive (top-down) methods: initially there is one large cluster that is subsequently divided into smaller clusters; Non-hierarchical cluster (partitioning) procedures: K-means clustering: an initial partition into G groups is chosen and objects are reassigned if the total error can be reduced; solutions for different G are analyzed;

Agglomerative methods Single linkage: similarity is based on the shortest distance between any two points in two clusters (nearest-neighbor approach); at each step, the most similar clusters are joined; Complete linkage: similarity is based on the largest distance between any two points in two clusters (farthest-neighbor approach); Average linkage: similarity is based on the square root of the average of the squared distances of all objects in two clusters; Centroid method: similarity is based on the distance between the centroids of the clusters; Ward s method: clusters are formed such that the increase in within-group variability is minimized;

Hierarchical agglomerative methods Which two of these three clusters should be joined in the next step based on single linkage? Complete linkage? Average linkage? The centroid method? Ward s method?

Non-hierarchical clustering For this three-cluster solution, can the total error be reduced by reassigning a respondent to a different cluster?

How many clusters should be formed? No generally accepted stopping rule is available; In a hierarchical cluster solution, inspect the dendrogram (tree graph), which shows the distance (dissimilarity) at which two clusters are joined; Look for the point in the dendrogram where combining two clusters results in a large increase in the within-cluster heterogeneity; Ultimately, a cluster solution should be practically useful; try out different solutions and choose the one that is most interpretable and yields the most actionable insights.

Dendrogram

How should the clusters be interpreted Compute the average score of the cluster members on the clustering variables used to compute the similarity measure. Name the clusters! If additional variables not used during clustering are available for each of the objects, use these variables to further profile and differentiate the clusters.

Cluster averages for maltiness and bitterness: Name the clusters! Cluster 1 Cluster 2 Cluster 3 Maltiness 2.0 5.5 9.0 Bitterness 2.5 5.5 9.5

Special problems in cluster analysis Clustering variables: The final cluster solution depends strongly on the variables that were included in the cluster analysis. Clustering variables have to be chosen carefully. If clustering variables are very similar, this may exaggerate the influence of the underlying common factor. If some variables are highly correlated, it may be better to combine these variables prior to clustering. Outliers: Unusual observations can greatly distort the final solution obtained in the analysis. Check for outliers before doing the analysis. Outliers can also be detected in the dendrogram. Standardizing the data: Variables with large variances have a disproportionate influence on similarity. If the clustering variables are measured on different scales, standardize the data (usually by variable, but possibly by observation).

Office Star data 40 respondents rated the importance of 6 attributes when choosing an office supply store: variety of choice, (availability of) electronics, (availability of) furniture, quality of service, low prices, and return policy; Importance was rated on a scale from 0 (not at all important) to 10 (extremely important); Data on three descriptor variables are also available: whether or not the respondent is a professional, the respondent s income, and the respondent s age; Data on these three descriptor variables are also available for an additional 300 respondents for whom no segmentation data were collected;

Distance Segmentation and targeting Using ME for segmentation: Office Star data with 9 clusters 537.17 d 348.59 c 36.19 25.07 21.52 18.33 18.08 16.51 b a 1 7 4 8 2 9 5 3 6 Cluster ID

3-cluster solution for Office Star data Cluster Sizes The following table lists the size of the population and of each segment, in both absolute and relative terms. Size / Cluster Overall Cluster 1 Cluster 2 Cluster 3 Number of observations 40 18 14 8 Proportion 1 0.45 0.35 0.2 Segmentation Variables Means of each segmentation variable for each segment. Segmentation variable / Cluster Overall Cluster 1 Cluster 2 Cluster 3 Variety of choice 7.53 9.11 6.93 5.00 Electronics 4.57 6.06 2.79 4.38 Furniture 3.45 5.78 1.43 1.75 Quality of service 4.00 2.39 3.50 8.50 Low prices 5.05 3.67 8.29 2.50 Return policy 4.50 3.17 6.29 4.38

10 Means of segmentation variables by cluster and overall 9 8 7 6 5 4 3 2 1 0 Variety of choice Electronics Furniture Quality of service Low prices Return policy Overall Cluster 1 Cluster 2 Cluster 3

Assignment for next week LRB Chapter 3 Segmentation and Classification Tutorial (ME) GE Tutorial (ME) Office Star examples

Recap: Cluster analysis (1) Calculate similarities (or differences) between objects (2) Derive clusters Step 1: (R1&R2) vs. R3 vs. R4 Step 2: (R1&R2) vs. (R3&R4) Step 3: (R1&R2) & (R3&R4) Step 1: (R1&R2) vs. R3 vs. R4 Step 2: (R1&R2) vs. (R3&R4) Step 3: (R1&R2) & (R3&R4)

Recap: Cluster analysis (cont d) (3) Choose the number of clusters based on the dendrogram (4) Interpret the clusters Seg 1 Seg 2 Tartar control 9.5 2.5 Whitening 1.5 10.0 Seg 1 Seg 2 Seg 3 Tartar control 9.5 9.0 1.0 Whitening 1.5 10.0 10.0

Step 2: Describing market segments In order to make the segmentation actionable, the market segments have to be profiled (particularly if the segmentation variables are not directly observable); The segmentation study should include readily observable variables that can be used to characterize the segments; The goal is to find actionable variables that are useful for predicting customers segment membership; One technique for doing this is discriminant analysis;

Discriminant analysis Basic question: How can we explain or predict the group (segment) membership of an object (customer) based on certain (metric) independent variables (classification), and how can we determine which variables differentiate between the groups (profiling)? Issues in discriminant analysis: How can groups (segments) be differentiated based on many variables? How can we assess the overall quality of discrimination? Which variables are most effective in discriminating between the groups (segments)?

Two-group discriminant analysis Two equivalent approaches: Find a linear combination of the independent (discriminant) variables such that the resulting discriminant scores t i are maximally different across the two groups: t i = c 1 x 1i +c 2 x 2i + +c p x pi Find the locus of points that are equidistant from the centroid (mean) of the two groups; Assign a customer to the group to which it s closest;

Discriminant scores based on x 1 only Cluster 2 Cluster 1

Discriminant scores based on x 1 and x 2 Cluster 2 Cluster 1

Equidistant points Cluster 2 Cluster 1

Two-group discriminant analysis (cont d) To assess the overall quality of discrimination we can use a hits-and-misses table (confusion matrix): Actual Predicted Group1 Group2 Group1 Correct Incorrect Group2 Incorrect Correct To assess classification accuracy, we need a benchmark for chance prediction: The proportional chance criterion: p 2 + (1 p) 2 [where p is the proportion of observations in group 1]

Example of two-group discriminant analysis with two classification variables Discrimination Data Data used for discrimination Variables / Observations Cluster x1 x2 Age group Level of education (younger to older) (low to high) 1 1 1 3 2 1 1 5 3 1 2 4 4 1 5 2 5 2 2 8 6 2 4 8 7 2 5 6 8 2 6 4 9 2 7 7 10 2 8 5

Assessing the quality of discrimination Cluster Sizes The following table lists the size of the population and of each segment, in both absolute and relative terms. Confusion Matrix Size / Cluster Overall Cluster 1 Cluster 2 Number of observations 10 4 6 Proportion 1 0.4 0.6 Comparison of cluster membership predictions based on discriminant data and actual cluster memberships. High values in the diagonal of the confusion matrix (in bold) indicate that discriminant data is good at predicting cluster membership. Actual / Predicted cluster Cluster 1 Cluster 2 Cluster 1 4 0 Cluster 2 0 6 Actual / Predicted cluster Cluster 1 Cluster 2 Cluster 1 100.00% 00.00% Cluster 2 00.00% 100.00% Hit Rate (percent of total cases correctly classified) 100.00% [ Proportional chance criterion = 52% ]

Two-group discriminant analysis (cont d) Assessing the importance of individual predictor variables: Check whether the discriminant function is significant and if so, how strongly each independent (discriminant) variable is correlated with the discriminant function scores; Variables with larger (absolute) correlations are more useful for discriminating between the groups; The means of the variables that are important for discrimination can then be compared across groups in order to profile the segments;

Discriminant Function Assessing the importance of predictor variables Correlation of variables with each significant discriminant function. (Significance level < 0.05). Discriminant variable / Function Function 1 x2 (Education) -0.779 x1 (Age) -0.689 Variance explained 100 Cumulative variance explained 100 Significance level 0.001 Classification Coefficients Coefficients are from each variable in the discrimination function. This matrix was used internally, and will be required to run further discriminant analysis (i.e., classification) on external data. Discriminant Variables / Functions Function 1 x1 (Age) -0.242 x2 (Education) -0.345

Describing the segments Discriminant Variables Means of each discriminant variable for each segment. Discriminant variable / Cluster Overall Cluster 1 Cluster 2 x1 (Age) 4.1 2.25 5.333 x2 (Education) 5.2 3.5 6.333

Discriminant analysis for more than two groups For G groups, (G-1) discriminant functions are estimated (assuming we have at least G-1 independent variables); different discriminant functions usually separate different sets of groups based on different variables; the discriminant functions are used for deciding which variables discriminate effectively between groups; For purposes of classification, observations are assigned to the group to which they are closest; The quality of discrimination can be assessed with a hitsand-misses table as before, but the proportional chance criterion becomes p 2 i, where the p i are the prior probabilities of group membership;

Hits-and-misses table for Office Star data Confusion Matrix Comparison of cluster membership predictions based on discriminant data and actual cluster memberships. High values in the diagonal of the confusion matrix (in bold) indicate that discriminant data is good at predicting cluster membership. Actual / Predicted cluster Cluster 1 Cluster 2 Cluster 3 Cluster 1 10 3 5 Cluster 2 0 13 1 Cluster 3 2 2 4 Actual / Predicted cluster Cluster 1 Cluster 2 Cluster 3 Cluster 1 55.60% 16.70% 27.80% Cluster 2 00.00% 92.90% 07.10% Cluster 3 25.00% 25.00% 50.00% Overall hit rate = 67.5%, proportional chance criterion = 36.5%

Discriminant analysis of Office Star data Discriminant Function Correlation of variables with each significant discriminant function (significance level < 0.05). Discriminant variable / Function Function 1 Function 2 Age 0.91 0.013 Income (000's) 0.696 0.336 Professional 0.068-0.771 Variance explained 71.36 28.64 Cumulative variance explained 71.36 100 Significance level 0 0.042 Discriminant Variables Means of each discriminant variable for each segment. Discriminant variable / Cluster Overall Cluster 1 Cluster 2 Cluster 3 Age 40.525 44.222 30.929 49.0 Income (000's) 42.500 48.333 32.143 47.5 Professional 0.475 0.333 0.500 0.75

Classification results for 300 additional respondents Respondents / Discriminant variables and predicted cluster Professional Income (000's) Age Predicted Cluster Customer 1 1 45 30 2 Customer 2 0 55 50 1 Customer 3 1 20 56 3 Customer 4 0 45 23 2 Customer 5 1 55 56 3 Customer 6 0 20 31 2 Customer 7 0 15 58 3 Customer 8 0 20 44 2 Customer 9 0 20 44 2 Customer 10 1 35 28 2 Etc. Row Labels (Cluster) Count of Predicted Cluster Average of Age Average of Income (000's) Average of Professional 1 86 46 53 0.19 2 132 30 32 0.55 3 82 53 45 0.73 Grand Total 300 41 42 0.50

Issues in discriminant analysis Technically, the IV s should be multivariate normal and the covariance matrices should be equal across groups. Larger samples are needed when many independent variables are included in the analysis (e.g., 20 observations per IV). The selection of relevant IV s is crucial, and the IV s should not be too highly correlated. Outliers can negatively influence the results. When the hit rate is calculated for the sample for which the discriminant function was estimated, it will be biased upward.

Recap: Discriminant analysis Choose discriminant variables that can be expected to be predictive of segment membership; Run the discriminant analysis and assess the overall quality of the discrimination based on the confusion matrix (hits-and-misses table) and the proportional chance criterion; Assess the usefulness of individual discriminant variables based on the magnitude of their correlation with significant discriminant functions and compare the means of important discriminant variables across segments; Classify new customers into segments based on their scores on the discriminant variables;

Target marketing evaluation of the attractiveness of each market segment and selection of target segments; evaluation of market segments based on market segment characteristics (attractiveness) company objectives and resources (competitive position) selection of target segments can result in undifferentiated (mass) marketing differentiated marketing concentrated marketing

Portfolio analysis portfolio models are tools to allocate scarce resources to different businesses (e.g., product markets) in a multi-business firm; steps in portfolio analysis: identify strategic business units (or SBUs); rate each SBU in terms of market attractiveness and competitive position; decide whether to build, maintain, harvest, or divest a business; the goal is to have a balanced portfolio of businesses which will ensure profitability and growth in the long run;

BCG growth-share matrix 20%? market growth rate 10% maintain leadership and build future cash cow build share or divest 0% harvest and manage for maximum profitability divest 10x 1x.1x relative market share

Steps in constructing a market attractiveness/competitive position matrix for selecting target markets List the segments to be evaluated and estimate their size Identify the key factors determining market attractiveness (e.g., size, growth, margins, current competition) and competitive position (e.g., product fit, access, brand reputation, current penetration) Assign weights to each factor (e.g., 1=least important, 5=most important) Rate each segment on the factors (e.g., 1=worst, 5=best) Calculate each segment s market attractiveness and competitive position score Plot each segment in the matrix

Office Star data [made up not in ME] Horizontal Axis (ratings, weights) On a scale from 1 to 5, rate Products on each factor, and weight the importance of each factor. Competitive Position Cluster 1 Cluster 2 Cluster 3 Weights Product Fit 4 1 3 3 Brand Reputation 4 2 3 4 Market Share 3 1 2 3 Competitive Advantage 3 1 3 2 Vertical Axis (ratings, weights) On a scale from 1 to 5, rate Products on each factor, and weight the importance of each factor. Market Attractiveness Cluster 1 Cluster 2 Cluster 3 Weights Overall Market Size 5 4 2 2 Annual Market Growth Rate 2 4 2 2 Competitive Intensity 3 5 2 4 Historical Margins 3 2 4 3 Market Size On a scale from 1 to 20, please enter market size for each item. Cluster 1 Cluster 2 Cluster 3 Market Size 9 7 4

Market Attractiveness Segmentation and targeting Office Star data Cluster 2 Cluster 1 Cluster 3 Competitive Position

Market attractiveness low medium high Segmentation and targeting Market Attractiveness/ Competitive Position Matrix Opportunities investment Build strength or exit Invest to challenge leader Maximum investment Consolidate position Harvest or divest Cautious investment Selective investment Build on strengths Harvest or divest Harvest or divest Protect position Manage for cash generation low medium high Competitive position

Assignment for next week Downloads the overheads (Positioning.pdf) LRB Chapter 4 Positioning Tutorial (ME) Office Star examples