Our business objective is to develop a model to profile the customers based on their usage patterns, the activities they indulge in and the preferred handsets in order to design a mobile handset based contract with service plans that will cater to their areas of interest. To realize our business objective, we need to apply unsupervised data mining techniques on given data to bring out clusters of people with similar data-usage attributes and mobile preferences. The clusters, so identified can then be targeted by service providers with customized data plans and contracts.
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.
Targeted advertising can be a big challenge for businesses especially retailers. For any business, advertising is a necessary evil. They need it to retain the customer, but they have to pay the heavy costs of advertising and irritating customers by promoting them products they might not need. Targeted advertising, which ensures that customers get advertisements / promotions for only those products they need or are most likely to buy, helps businesses on two ways.
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.
Retailers spend a considerable amount of time, effort, and money to acquire a new customer. However once a customer has been acquired, the maximum value can only be derived if the customer becomes a repetitive buyer and his/her purchase amounts increase with time. Identifying which customer will qualify for a promotion is a key to this problem and our study makes an attempt to solve this issue. Our model predicts the future shopping basket value of a customer.
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.
With more than 25 Mobile service providers in India, all the service providers want to increase their market share as quickly as possible. Few techniques to improve market share can be by good marketing campaigns or by providing customized connection plans and excellent customer service when the customer walks in to the store. In most of the cases, the customer’s interaction with the representative from service provider will determine whether the customer will continue service with that provider or not.
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.
A software company moved to a new Sales Operations Portal in 2004 to replace an existing system.Design as a The portal is a single source for remote access to all sales related activities: Account management, calendar, tasks, opportunities.
The project goal is to predict the likelihood of open opportunities becoming won or lost deals.
Vliegtarieven.nl is an online booking agency for air tickets. The project goal is to determine the factors affecting online customers' intention of returning to Vliegtarieven.nl to purchase airline tickets, based on a large survey of their customers (we thank Prof Otto Koppius from RSM Erasmus for sharing the data and problem)