Financial Institutions offer various products to its customers such as personal loans, home loans, savings account, credit cards and so on..
It is very essential for a financial company to know, which user to target for what product and when. The goal of data analytics and data mining department is to arrive at accurate answers to these 3 questions, (Which User, What Product, When), trust me it is a rewarding exercise for the bank, users and definitely the analytics manager.
The problem is very simple. A customer may have purchased a single product or multiple products from a bank. The company would ideally like if all its customers bought all of its products. This possibility however does not exist, therefore without any targeting model; the company would be wasting money, time and resources by randomly targeting customers for its products.
Using a little sophistication of data mining techniques, we would be able to build a targeting model or a response model on customer base. A response model is a predictive model which estimates the future behavior of a given customer even before it occurs. Even with a fair amount of accuracy it can be very useful for company in saving majority of costs in various departments/channels. There are various types of data available for analysis like demographic, behavioral, psychographic / attitudinal. The above data can be very useful in data mining methods, however the cost, usefulness and expertise to store / retrieve data has to be assessed and judged.
Giving an example: A customer has savings account in a bank, based on the information given to the bank along with account status and history, we could target her as a prospect for a personal loan product based on a logic / algorithm.
The logic / algorithm could be as simple as if a customer from metro with account balance of Rs 1,00,000 or more for over 6 months should be a prospect for home loans Rs 20,00,000 or personal loans over Rs 3,00,000. This is a simple example; however it could be a complicated neural network or logistic regression model.
Cross Sell Models: A customer of a product A, is a likely prospect for product B if his account activity positively predicts likelihood of purchase. Target model has to predict purchase of product B.
Up Sell Models: A customer can be targeted for higher segment product in the same category based on his account status and demographics. Normal saving accounts customers can be sold for Premium segments customers, if his account balance is over a period of time.
Deep Sell Models: A customer can be sold more of same product based on his need. Target model has to predict his need.
These target models are also called response models that only predict the purchase behavior of a customer for a given product. This model is ineffective as the cost of randomly targeting a customer for product is LESS THAN cost of targeting using predictive response model.
The main reason is because of below limitations of traditional response model
A) All customers behave in two ways: Purchase or DO NOT Purchase
B) Behavior of customers during absence of any marketing activity.
C) Maximum lifetime value to be derived from customer is unkown, this will result in underselling or selling lower value products when higher products could have been sold.
IF these issues are taken care of by strong statistical modeling then loss making response model can be made profitable.
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Disclaimer: These are my personal views and not a view of any organization or body.
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Extremely useful site for the marketing and sales content.. good work keep it up..
brilliant article good article on marketing strategies for retail and banking.