AV JanataHack Cross-Sell Prediction

Context

Cross-selling identifies products or services that satisfy additional, complementary needs that are unfulfilled by the original product that a customer possesses. As an example, a mouse could be cross-sold to a customer purchasing a keyboard. Oftentimes, cross-selling points users to products they would have purchased anyway; by showing them at the right time, a store ensures they make the sale.
Cross-selling is prevalent in various domains and industries including banks. For example, credit cards are cross-sold to people registering a savings account. In e-commerce, cross-selling is often utilized on product pages, during the checkout process, and in lifecycle campaigns. It is a highly-effective tactic for generating repeat purchases, demonstrating the breadth of a catalog to customers. Cross-selling can alert users to products they didn't previously know you offered, further earning their confidence as the best retailer to satisfy a particular need.

Content

Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from the past year will also be interested in Vehicle Insurance provided by the company.

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if God forbid, you fall ill and need to be hospitalized in that year, the insurance provider company will bear the cost of hospitalization, etc. for up to Rs. 200,000. Now if you are wondering how can the company bear such high hospitalization cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalized that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of a certain amount to the insurance provider company so that in case of an unfortunate accident by the vehicle, the insurance provider company will provide compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel), etc.

Acknowledgements

Thanks Analytics Vidhya for providing yet another exciting dataset for the weekend hackathon.