Customer & Risk Intelligence

DataWise builds systems that spot customer risk while there is still time to act: churn analysis that explains attrition instead of announcing it, receivables anomaly detection that flags overdue-payment patterns weekly, and daily “who didn’t order today” monitoring. Everything runs on your real transaction data and integrates with the ERP and ordering systems you already use.

How does DataWise detect customer churn before it happens?#

By treating churn as a pattern problem, not a report. In our three-year flagship engagement for a US-market industrial manufacturer, customer attrition had been discovered only after the fact; DataWise built churn analysis that detected attrition patterns and matched them against field intelligence, so sales leadership could explain and act on what was changing, alongside customer-behavior analytics for periods of demand uncertainty and customer clustering that shipped as real deliverables. We extended the same discipline to a global water-technology manufacturer, adding customer churn analysis and geographic sales analytics on top of multi-region demand forecasting. The pattern that matters in B2B is rarely a cancellation; it is a quiet fade, shrinking order sizes, stretching intervals, a narrowing product mix, and models catch that fade earlier than quarterly reviews do. This is now a named enterprise pattern: SAP markets AI that flags when “the customer’s spend declined in recent months”, per Forbes; we build the same signal for a 1,000-customer operation rather than a Fortune 500 ERP.

What does receivables and ordering anomaly detection look like in practice?#

Concrete and unglamorous, which is why it works. For a produce wholesaler serving over 1,000 customers, DataWise designed automatic detection of overdue-payment anomalies with weekly alerting, plus daily “who didn’t order today” monitoring, integrated with the company’s ERP and ordering systems. The design principle: alerts must arrive inside the workflow where collections and sales people already work, and they must be few enough to be trusted. A weekly list of ten customers genuinely behaving out of pattern beats a daily dump of two hundred false alarms, because false positives, not misses, are what kill adoption of operational alerting.

Which customer-risk problems fit this practice?#

• Churn and attrition analysis: detect fading accounts early, explain them with pattern evidence, and match against what the field already suspects.
• Receivables risk: customers drifting out of their own payment pattern, flagged weekly before the debt ages.
• Ordering anomalies: daily monitoring of who stopped ordering, at item or account level.
• Behavioral prediction at scale: DataWise built engaged-user and high-value-player classifiers over hundreds of thousands of betting events, with disciplined evaluation, leakage-controlled train/validation/test splits and full precision/recall reporting, the same rigor we bring to any behavioral model.
• Customer clustering: grouping customers by actual buying behavior so risk and attention are allocated by evidence.