Kurvv's LTV based customer segmentation models predict how valuable new customers and leads will be to your business over their lifetime, and group them into low, medium and high value categories.
This is done by analyzing historical data to identify which attributes are most often associated with high-value customers (customer who are high paying). Then, when you get new customers and leads which share those "high-value" attributes, we can predict their likelihood to purchase again in the future. More specifically, our solution uses either KNN (K-nearest Neighbor) or Naïve Bayes as the Machine Learning algorithm on which one best works for your business.
The resulting output of the ML model is a list of your customers along with their segment (i.e. Customer 1 = High Value). There are almost infinite ways to utilize these results but to list a few. You can more precisely target your marketing towards higher-value customers and reduce spend on lower-value customers. You can use it to seed lookalike (similar users) audiences with advertising partners. You could identify which customers are more likely to be responsive to cross-sell promotions.
Sample Output...
