An e-commerce client had no visibility on how to target higher-value customers who had a higher chance of purchasing from them.
This led to a lot of one-and-done customers and low-value customers.
There was no targeting of customers based on predictive analytics, nor was there a data-driven understanding of which factors impact a customer's probability of purchasing more than once.
The Solution.
Designed and built a propensity to purchase model to identify customers with a high probability of purchasing within the next 30 days every month.
Derived feature importance to identify which factors impact a customer's probability to purchase.
Segmented the customer base each month into low, medium and high probability of purchasing to customize targeting.
The Results.
List of customers with probability to purchase was used by the marketing team to target a subset of the customer base each month and optimize the marketing budget.
Targeted campaigns led to up-selling of customers with medium and high probability of purchasing.
Understanding the factors that influence purchasing behaviour led to better profiling of customers and increased customer loyalty.