Database Marketing

Database Marketing Cluster Analysis N. Kumar, Asst. Professor of Marketing

Agenda • Discussion of the first Assignment • Motivation for conducting Cluster Analysis • Benefit Segmentation • Cluster Analysis • Basic Concepts • Hierarchical/Non- Hierarchical Clustering • Implementation in SAS and interpreting the output

Voter Profiling • What are the different voting segments out there? What do they want to hear i.e. issues they care about? • What should I say? N. Kumar, Asst. Professor of Marketing

Ad Campaign • How many customer segments are there? • How many do I want to target? • How should I target – what message should I communicate to each segment? N. Kumar, Asst. Professor of Marketing

Promotional Strategies • Coupon Drops – who should they be targeted at? • Catalog Example – should the catalog be accompanied with a $5 coupon or a $10 coupon or no coupon? N. Kumar, Asst. Professor of Marketing

What is Cluster Analysis? • Cluster Analysis is a technique for combining observations into groups or clusters such that: • Each group is homogenous with respect to certain characteristics (that you specify) • Each group is different from the other groups with respect to the same characteristics N. Kumar, Asst. Professor of Marketing

Data N. Kumar, Asst. Professor of Marketing

Geometrical View of Cluster Analysis Education Income N. Kumar, Asst. Professor of Marketing

Similarity Measures • Why are consumers 1 and 2 similar? • Distance(1,2) = (5-6)2 + (5-6)2 • More generally, if there are p variables: • Distance(i,j) =  (xik - xjk)2 N. Kumar, Asst. Professor of Marketing

Similarity Matrix N. Kumar, Asst. Professor of Marketing

Clustering Techniques • Hierarchical Clustering • Non-Hierarchical Clustering N. Kumar, Asst. Professor of Marketing

Hierarchical Clustering • Distance(1,2) = 2 = Distance(3,4) • Say, we group 1 and 2 together and leave the others as is • How do we compute the distance between a group that has two (or more) members and the others? N. Kumar, Asst. Professor of Marketing

Hierarchical Clustering Algorithms • Centroid Method • Nearest-Neighbor or Single-Linkage • Farthest-Neighbor or Complete-Linkage • Average-Linkage • Ward’s Method N. Kumar, Asst. Professor of Marketing

Centroid Method • Each group is replaced by an average consumer • Cluster 1 – average income = 5.5 and average education = 5.5 N. Kumar, Asst. Professor of Marketing

Data for Five Clusters N. Kumar, Asst. Professor of Marketing

Data for Four Clusters N. Kumar, Asst. Professor of Marketing

Data for Three Clusters N. Kumar, Asst. Professor of Marketing

Dendogram for the Data C1 C2 C3 C4 C5 C6 N. Kumar, Asst. Professor of Marketing

Single Linkage • First Cluster is formed in the same fashion • Distance between Cluster 1 comprising of customers 1 and 2 and customer 3 is the minimum of Distance(1,3) = 181 and Distance(2,3) = 145 N. Kumar, Asst. Professor of Marketing

Complete Linkage • Distance between Cluster 1 comprising of customers 1 and 2 and customer 3 is the maximum of Distance(1,3) = 181 and Distance(2,3) = 145 N. Kumar, Asst. Professor of Marketing

Average Linkage • Distance between Cluster 1 comprising of customers 1 and 2 and customer 3 is the average of Distance(1,3) = 181 and Distance(2,3) = 145 N. Kumar, Asst. Professor of Marketing

Ward’s Method • Does not compute distance between clusters • Forms clusters by maximizing within-cluster homogeneity or minimizing error sum of squares (ESS) • ESS for cluster with two observations (say, C1 and C2) = (5-5.5)2 + (6-5.5)2 + (5-5.5)2 + (6-5.5)2 N. Kumar, Asst. Professor of Marketing

Ward’s Method N. Kumar, Asst. Professor of Marketing

Non-Hierarchical Clustering • Data are grouped into K clusters • Requires a priori knowledge of K N. Kumar, Asst. Professor of Marketing

Basic Steps in Non-Hierarchical Clustering • Select K initial cluster centroids • Assign each observation to the cluster to which it is closest • Reassign or reallocate each observation to one of the K clusters according to a pre-determined stopping rule • Stop if there is no reallocation • Approaches differ in Step 1 and/or step 3 N. Kumar, Asst. Professor of Marketing

Algorithm I • Selects first K observations as cluster centers N. Kumar, Asst. Professor of Marketing

Initial Cluster Centroids N. Kumar, Asst. Professor of Marketing

Initial Assignment N. Kumar, Asst. Professor of Marketing

New Cluster Centroids N. Kumar, Asst. Professor of Marketing

Distance Matrix N. Kumar, Asst. Professor of Marketing

Algorithm II • Differs from Algorithm I in how the initial seeds are modified • As before first K observations are selected as the initial cluster seeds • A seed that is a candidate for replacement is from one of the two seeds that are closest to each other • An observation qualifies to replace one of the two candidates if the distance between the seeds is less than the distance between the observation and the closest seed N. Kumar, Asst. Professor of Marketing

Algorithm II …contd. • C1, C2 and C3 are the initial seeds • The smallest distance between the seeds is between C1 and C2 • Observation C4 does not qualify as a replacement as Distance(C1,C2) > Distance(C4 and the nearest seed C3) • Observation C5 does qualify as a replacement as Distance(C1,C2) < Distance(C5 and the nearest seed C3): replace C2 with C5 N. Kumar, Asst. Professor of Marketing

Initial Assignment N. Kumar, Asst. Professor of Marketing

New Cluster Centroids N. Kumar, Asst. Professor of Marketing

Distance Matrix N. Kumar, Asst. Professor of Marketing

Hierarchical vs. Non-Hierarchical Clustering • Hierarchical clustering does not require a priori knowledge of the number of clusters • Assignments are static • Use hierarchical clustering for exploratory purposes • Non-Hierarchical Methods can be viewed as a complementary rather than a competing method N. Kumar, Asst. Professor of Marketing

Voter Profiling • Survey of voters concerns may help us group customers with similar concerns – perhaps they all live in a certain area? • Target ads/mailings with customized messages N. Kumar, Asst. Professor of Marketing

Ad Campaign • Use attitudinal data to segment customers • Target message appropriately N. Kumar, Asst. Professor of Marketing

Promotional Strategies • Use transaction data to group customers into those that are more prone to purchasing the product on deal • Give a stronger incentive to the price sensitive segment N. Kumar, Asst. Professor of Marketing

Database Marketing