Predict what your customers will buy, before even they know it

There’s usually no way around it: to increase your share of wallet, you need each customer to buy more of what you’ve got to sell. This means, for starters, having the right products in stock or in your product portfolio. But it also means being able to predict which products customer are most likely to buy, so you promote exactly the right product to each customer. Data science makes this possible, by combining customers’ online behavior with historic purchase data to determine exactly the right product/customer pairing – making product recommendations and other promotional strategies more effective.

Stronger product recommendation

Promoted products are more relevant and interesting when they’re developed based on rich data and cutting-edge analytics. Move beyond pairing products by intuition or user logic.

Increase conversion and basket size

Make customers more likely to purchase by showing them products they’re interested in. They’ll also add more to the cart before checking out.

Improve customer experience

The more relevant each promoted product, the more these recommendations seem like a service, not a sales tactic.

Better strategic planning

Understand the customer-product propensity matrix and factor these insights into your go-to-market strategies.

Get started on your product propensity project today!

View Other Use Cases

Churn Prevention

Identify customers likely to leave, take preventative action.

Customer Lifetime Value

Distinguish between customers based on business value.

Customer Segmentation

Create meaningful customer groups for more relevant interactions.

Demand Forecasting

Know what volumes to expect to improve planning.

Fraud Detection

Identify fraudulent activity quickly, and end it.

Next Best Action

The right action at the right time for the right customer.

Predictive Maintenance

Predict equipment failure, plan cost-effective maintenance.

Price Optimization

Set prices that balance demand, profit, and risk.

Quality Assurance

Resolve quality issues before they become a problem.

Risk Management

Understand risk to manage it.

Text Mining

Extract insight from unstructured content.

Up- and Cross-Selling

Convince customers to buy more.