Cancellation Prediction for a Real Estate–Financial Company

administracion@infomedia.com.mx (Infomedia) — Tue, 11 Aug 2026 06:57:14 GMT

The Challenge

The company faced high cancellation levels in the early stages of the customer lifecycle, with an average impact close to 45% of the contracted amount.

The analysis relied only on traditional financial variables, with no real ability to anticipate cancellations.

The Solution

Infomedia implemented a predictive model that integrated:

Machine learning
Financial and operational variables
A geostatistical component to infer socioeconomic level
Actionable risk segmentation

In just 12 weeks, the organization moved from uniform strategies to targeted interventions by risk level.

Results

Customers with a probability ≤ 0.4 represented only 12% of the risk
Customer prioritization by economic impact
Greater efficiency in resource allocation
The operation evolved from reactive to preventive.

Is your company making decisions based on real economic impact?

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Cancellation Prediction for a Real Estate–Financial Company