Historical data on customers are divided into 2 groups viz:
1) Customer who have purchased multiple products
2) Customers who have not purchased multiple products
These 2 groups of leads are statistically compared using ML algorithms across multiple data dimensions
The algorithms detects micro patterns which acts as a trigger for a cross sell / repeat purchase
The output is a propensity score tagged against every customer indicating their likelihood to buy another product or service
• Increase cross sell rates by sending personalised offers to every customer
• Optimise campaign costs by differentiated marketing efforts on customers most likely to convert
