Customer Data Platform Unified Customer Profiles & Analytics

Built a customer data platform (CDP) unifying fragmented customer data across e-commerce, CRM, mobile apps, and support systems into rich 360-degree profiles. Before CDP, each system had its own customer view making it impossible to understand the complete customer journey or deliver personalized experiences.

Implemented real-time data pipeline monitoring ingesting purchase history, browsing behavior, support tickets, and product reviews. Identity resolution matched records from different systems (cross-device tracking). Segment definitions enabled marketing campaigns targeting high-value customers or churned users. Predictive models identified customers likely to buy specific products or churn. Analytics dashboards showed customer lifetime value, cohort retention, and behavioral trends.

Identity resolution was the technical foundation everything else depended on. A customer buying on mobile, browsing on desktop, and calling support was generating data in three systems with three different identifiers. Without a unified identity, personalization is impossible and analytics are misleading. We built a deterministic-first matching strategy: exact email matches created definitive links; then probabilistic matching (device fingerprint, IP, purchase pattern) for cases where emails differed. Our merge confidence score determined whether matches were auto-applied or required human review.

The segment builder became the most-used feature by non-technical stakeholders. Marketing managers needed to define audience segments (customers who bought in the last 30 days but haven't engaged with an email campaign) without writing SQL. We built a visual query builder with instant count estimates. The key insight was that business users think in terms of customer behaviors, not database transaction patterns schemas. The interface presented behavioral concepts ("purchased," "clicked," "went dormant") rather than table joins and WHERE clauses. Adoption among marketing staff was 5x higher than prior tools that required SQL.

Churn prediction created an organizational capability that hadn't existed before. Before CDP, "we're losing customers" was anecdotal. After, we could identify at-risk customers by behavioral signals (last login 45+ days ago, declined renewal notification opened but not actioned) and route them to re-engagement campaigns before they churned. In the first quarter post-launch, targeted retention campaigns generated $1.8M in saved ARR by engaging predicted churners before their contracts expired.