Purchase propensity is a prediction that indicates how likely a customer is to make a new purchase within a given period. It is calculated using a predictive model that analyzes the customer’s history: when they made a purchase, how often they do so, how much they spend, and how they interact with your brand. It’s the flip side of churn probability: one looks at who’s leaving, the other at who’s ready to come back.

Why does it matter?

Not all customers are at the same stage. Some are ready to buy even if you don't send them anything; others need a little nudge; others aren't going to buy just yet. Knowing this helps you invest more effectively:

• Fewer unnecessary discounts: For those who were already planning to buy, a reminder or a new product is enough.
• Better use of the budget: You focus incentives on those with an average propensity.
• A better experience: fewer irrelevant messages and a lower risk of unsubscribes.

Story mode

Before a sales event, an online electronics store sorts its customer base by purchase likelihood. It sends an exclusive preview of the deals to those with a high likelihood of purchasing. For those in the middle range, it adds a coupon. For example, Andrés, who bought a laptop a year ago and has been looking at accessories, falls into the first group and receives the preview the day before.

How to Use It with Data4Sales

• Data4Sales calculates the probability of a repeat purchase (propensity) for each customer within its predictive models.
• You can cross-reference it with product affinity to decide what to recommend and with the number of days until the next purchase to decide when.
• Create audiences and activate them via WhatsApp, email, or SMS, or sync them with Meta Ads and Google Ads.
• Orbis Creative creates a unique message for each client based on their information.

Related terms

• Predictive model
• Buyback
• Days until the next purchase
• Hearings