Customer Analytics

Yes, that's usually the first phase of the project — most clients have customer data fragmented across exactly those systems, and we build the integration layer that unifies it into a single customer profile before any scoring or segmentation happens. This phase takes real engineering time, so we scope it explicitly rather than folding it invisibly into the analytics timeline.

We validate every model by backtesting it against your actual historical outcomes — which customers really churned or upgraded — before it goes live, and we report the real accuracy numbers rather than a vendor benchmark from a different industry. Accuracy varies by business, and we're upfront if your data isn't yet rich enough to support high-confidence scoring.

That's the intent — we build the scores to surface directly inside your CRM or support tool as a field or flag your teams already look at, rather than a separate dashboard nobody checks. If a workflow needs an alert or automated trigger off a score, we wire that in too.

Built-in CRM segmentation usually relies on simple rules like purchase count or recency, while our models learn from patterns across your full customer history — support interactions, behavior, purchase timing together — to predict what a customer is likely to do next, not just categorize what they've already done. That prediction layer is what most CRMs don't natively provide.