Customer Insights – Data explained
Microsoft's customer data platform — ingestion, identity resolution, unified profiles, segments, and measures.
Customer Insights – Data is Microsoft's customer data platform (CDP). It exists to solve a problem nearly every mid-to-large customer-facing business has: the same customer is represented dozens of times across different systems, in different shapes, with overlapping but partial data. CI–Data ingests it all, resolves the identities, and produces a single unified customer profile downstream apps can use.
Ingestion. Connectors pull data from Dynamics 365 (Sales, Service, Finance, Commerce, Business Central), Microsoft 365, Azure Data Lake, Synapse, Snowflake, Salesforce, Adobe, and dozens of other systems — or through Dataverse for native sources. Source systems can be polled on a schedule or streamed via APIs.
Unification. The core engine. CI–Data takes multiple customer-shaped tables (each with names, addresses, emails, phone numbers) and runs identity resolution — rule-based, ML-assisted matching of records likely to be the same person or organisation. Output is a unified customer profile that links back to all the source records.
Enrichment. Once unified, profiles can be enriched with first-party derived attributes (lifetime value, engagement score, churn risk) and third-party data (Microsoft's marketplace of providers — Acxiom, Experian, LinkedIn, weather, geographic). Enrichments are scheduled refresh jobs.
Segments and measures. Segments are query-built dynamic groups of unified customers ("high-value Swedish customers active in the last 30 days"). Measures are aggregate calculations ("total revenue per customer this quarter") attached to profiles. Both update as data flows through.
AI predictions. Out-of-the-box AI models predict churn, lifetime value, and product recommendations. Custom models can be brought in from Azure ML.
Outputs. Profiles, segments, and measures export to Customer Insights – Journeys (for marketing campaigns), Sales (as opportunity insights), Customer Service (for prioritisation and routing), advertising platforms (Meta, Google), and back to Dataverse for downstream automation.
Real-time. CI–Data supports both batch unification (the original mode, scheduled daily/hourly) and real-time signals (events streaming through as they happen, with profile updates within seconds).
Where it fits. CI–Data shines when a business has multiple customer-facing systems whose data isn't natively joined. It is overkill for a single-system shop. Implementation is non-trivial — identity resolution rules need iteration to land cleanly — but the resulting unified profile transforms downstream segmentation and personalisation.
Related guides
- Data unification in Customer Insights — Data — a deep diveHow Customer Insights — Data unifies customer records across sources — match rules, merge logic, golden record creation, and the operational rhythm of identity resolution.
- Measures and attributes in Customer Insights — DataHow to define measures and computed attributes in Customer Insights — Data — calculation logic, dependencies, refresh, and the patterns that turn unified profiles into actionable signals.
- Customer Data Platform vs Data WarehouseHow a CDP like Customer Insights — Data differs from a traditional data warehouse — purpose, structure, activation, and when each fits.
- Customer Insights – Journeys explainedMicrosoft's marketing automation product — segmentation, journeys, email, events, lead scoring, and the Copilot for marketing layer.
- Customer Insights segmentation — a deep diveHow segmentation works in Dynamics 365 Customer Insights — Data and Journeys segments, ML-driven segments, refresh patterns, and the operational discipline that turns segments into revenue.