Customer Lifetime Value Prediction

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Customer Lifetime Value (CLV) measures all the future profits a given customer
will generate in total for a company. It helps the companies to differentiate between customers and determine how much can they reasonably spend to acquire or retain a customer and on which groups of customers should they focus the most to maximize their returns.

Pareto principle given by the Italian economist Vilfredo Pareto, specifies that 80% of the effect comes from 20% of the causes, which means 20% of the customers create 80% of the revenue.

About The Client

A large children’s education and play toys company.

Source: amazon.co.uk

Business Problem

In the consumer products industry, customer loyalty is a key driver of long term profitability. It is much more expensive to acquire new customers than to retain the existing ones. Competitors are constantly trying to make customers jump ship by offering attractive incentives.

With businesses constantly trying to steal share from competitors, it is the key for businesses to know who their most valuable customers are. Their primary focus should be on keeping them satisfied and making appropriate interventions when there are indications that the customer may be changing loyalties. It would be foolish to try to retain customers who are not very profitable as the cost of the intervention may not justify the return.

The difficult part is to be able to distinguish customers who are going to be valuable in the long term (say 2 years) from those whose profitability over two years would not justify attempting to retain them.

The concept of customer lifetime value (CLV) is intended to capture the long term profitability of a customer.  For customers with higher CLV, a higher amount of spending on loyalty programs and interventions is justified.

Estimating CLV of a customer is non-trivial as the data that is available is recent spending history (i.e. their transactions). However, particularly for new customers, recent spending history alone may not indicate their long term spending potential. In the case of education and play toys, the ability to provide age-appropriate toys from infancy to adolescent years of a child means that loyalty can translate to significant profitability.  Here, the customer is the parent and the arrival of a second or subsequent child multiplies the profit potential.

A key to estimating customer lifetime value is to identify features from transaction data that are key indicators of a customer’s long term spending potential.

Our Approach

Our approach involved going beyond the industry-standard general purpose features of Recency-Frequency-Monetary value. This helped to identify the additional features that matter in the play toys category and are indicative of long term spending potential. For this, we used customer data spanning multiple years to predict long term spend.

We built a state of the art machine learning model that bucketed customer’s into seven loyalty categories.

The key steps involved were:

  1. Working with business to identify potential features.
  2. Feature refinement to finalise key features that are strong predictors of long-term customer spending.
  3. Building an ML model to perform the prediction.
  4. Integrating it into their Big Data environment and Tableau visualization engine.

Deployment

  • Delivered and deployed an E2E system with a visualization layer.
  • Also generated rules out of the ML model which provided extremely interesting insights to the business.

Results

Some really interesting features were designed.  Some of them were game changers and increased accuracy substantially.

INSOFE was able to design a framework of feature engineering based on the learnings of this project which it now teaches to the business managers. 🙂 

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