Coinmama uses Pecan to foresee customer behavior

Use Case

Customer behavior predictions

To reduce chargebacks and identify fraudulent transactions, Coinmama needs reliable, accurate methods of analyzing its data. Using data to dig into customer behavior can be a complex, inefficient project — until you find the right tools.

Industry: Cryptocurrency exchange

Company Size: Over 3.5 million customers in 200 countries with annual sales over $130 million

Solution: Fraud detection, payments review

Platform Use Case: Customer behavior predictions

Data Stack: Snowflake, Tableau

140 hours

Saving an estimated 140 hours per month of analysts’ time in the review process


Generating accurate predictions for thousands of transactions per week


Reducing the number of transactions requiring manual review by two-thirds

What Coinmama Says About Pecan

We’ve reduced our rate of false positives by about two-thirds and are now manually reviewing a much smaller list of transactions. Pecan gives us the ability to prioritize that list as well, so we can tend to the riskiest customers first.
Tammy Rotem
Data Analyst

It’s time to refine your outreach with customer foresight

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