Hypermarts frequently use promotions via mail-in-rebate coupons, bulk buy discount offers etc. to influence customers to purchase greater number of products from their stores. Keeping this in mind, the potential benefit to the Hypermart can be significantly increased if the right promotions are targeted to the right customers - more specifically, identifying a new customer as a potential high margin customer and targeting him/her with promotions related to high margin products for greater sales turnover of such products.

In a busy supermarket the number of checkout lanes is constant. Not all customers buy in large volumes. Some buy in small quantity but are forced to wait in long queues at the checkout counter. Our objective is to demarcate a few checkout lanes as "fast checkout lanes" which would exclusively serve these customers (who shop in low quantity), thereby lowering their waiting time. The challenge however is to predict an optimum number of fast checkout lanes, such that on one hand they are able to process these customers fast and on the other they do not remain empty.

We have been hired by our client, a reputed FMCG conglomerate, Unilever as data mining consultants. Our client has a range of products in the Personal Care Category that comprises of soaps etc. One of the brands that our client happens to own is the Dove brand of soap. For the first time the client is formulating a Valentine mai-in-coupon scheme to be rolled out in the month of February (next year). The scheme has the following business objectives:
1) Understand the customer profile of those customers who buy Dove soap.

This project involves data mining on mobile users’ survey data generated as part of the Marketing Research course at ISB. The goal is to predict the mobile carrier preference of a customer based on mobile survey data. The purpose of this prediction is to enable the Retail store to use a predictive model to offer a mobile carrier service to the Customer along with his phone purchase.