The Indian Telecom industry is amongst the most fiercely fought services market in India with more than 10 large-scale operators providing voice and data services at highly competitive prices. The Industry has witnessed significant reduction in profit margins in recent years, with the average revenues per user from voice telephony being amongst the lowest in the world. On the other hand, margins from value added services are largely seen as the next source of growth for mobile operators.
The Indian mobile telephony market is a classic example of a volume based strategy. Average revenue per user (ARPU) is one of the lowest, while the subscriber figures are second only to China. The market itself is highly fragmented with more than 5 players holding less than 90% of the market. In this scenario, identifying new sources of revenues is critical for survival.
One of the most critical factors in Customer Relationship Management that can make or break
a company’s long-term profitability is churn. If a company can predict whether a customer is
likely to churn, it can take a more targeted approach to running promotions to reduce churn.
This is a sophisticated evolution from the traditional approach to incentivize all customers
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.
Currently Mom & Pop Stores do not have much insight into the price changes of electronic
products. This information is useful for the stores to plan for inventory purchases prior to the
price increases and generate profits on the products due to the increased price. Currently, this
decision to buy is based on intuition and there is no real science or reasoning behind it.
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.
The goal of this project is to understand profiles of disconnected customers and analyze churn rates in different geographic regions. This can then serve to develop marketing programs to attract new
customers and add services for existing customers
An airline recently offered promotion to members of its frequent flyer club via direct mail. The response rate was less than desired, and the airline would like to target consumers most likely to accept offer.
The project objective is to create a model that classifies frequent flier club members based on their likelihood of accepting the promotion.