Srikumar and bhasker Inputs: TgtP, Customer DB, SimU, minconf, minsupport, N, S L. Split the CustomerDB into Train DB and Testl 2. For every customer in TestDB a. Compute similarity values with every other user in Train DB b, Sort the customers on non-increasing order of similarity values c. Identify SimU users d. Store the similar users in Collab UserDB e. Store the prediction for the user as alue of Tgp in TrainDB for the users identified in step d above f. Extract the purchase details from Train DB for the customers identified g. Mine association rules with the following constraints Rule consequent is TgtP, Rule consequent has single item and Maximum number of rules s maxrules h. Extract the antecedents of the rules and score them using rule scores i. Sort the above products in descending order of their scores j. Select Top-N products and compute the cumulative scores k. Response prediction for the customer= cumulative score in step j 3. Sort the customers in TestDB on decreasing order of their response prediction 4. Select the target customers based on campaign size, S 5. Return <set of targeted customers> Figure 2 Pseudo-code for target selection mpaign size (S)set by the marketer, train and test database in the ratio of targets can be selected (say, Top-10 per 50: 50 and the methodology evaluated or cent or Top-20 per cent of the wo commonly used metrics viz hit customers are sclected as targets probability chart and gain charts. Hit probability charts show the percentage of targeted customers who will respond EXPERIMENTAL RESULT positively to the campaign given the The complete methodology for target percentage of customers targeted. Gain selection discussed in the section above is charts show the gains to be expected built using C++ on a Pentium-llI PC when the target selection model is running Red Hat Linux 7.2 applied, over the gains usually obtained when the targets are selected at randor Experimental design and metrics For the experimentation real-life data were gathered fro one of the leading Figures 3 and 4 show the hit probability online retailers in India. The collected and gain charts for the experimentation data have customer purchase details of carried out on the real life data set. A 359 customers on 105 product categories cursory look at Figure 3 reveals that, as with average transaction size of 7.51 the campaign size is increased, the The ratio of non-zero entries to the total percentage of response falls and drops to number of entries in the customer a level when all the targets are selected product matrix is 7. 13 per cent (ie at random. For the real-life experiments density of the data set is 7. 13 per cent). performed, a response rate of around 28 The customer database was split into per cent was derived cven when the 66 Journal of Targeting, Measurement and Analysis for Marketing Vol. 13, 1. 61-69 Henry Stewart Publications 0967-323 Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission