Customer data in Salesforce, Zendesk, Stripe, and your product but no single view.Unified customer data today.

Customer data unification starts with identity resolution: establishing the single identifier that connects the same customer across different systems. Often, the same person exists as a Lead in Salesforce, a User in your product, a Customer in Stripe, and a Requester in Zendesk — all with slightly different email formats or names. The specialist implements a master customer ID strategy and builds the ETL (extract, transform, load) pipelines that pull data from each source system, resolve identity, and write to a central customer data store. The unified record includes: account details, subscription status, support ticket history, product usage metrics, and payment history — all queryable from one place.

A single customer view (SCV) is a consolidated record of everything known about a customer — their demographics, account status, purchase history, product usage, support interactions, and communication history — accessible from one system. Different teams use different parts: sales uses it to see a prospect's full engagement history before an outbound call, support uses it to see a customer's subscription tier and recent product activity before responding to a ticket, customer success uses it to track health scores and usage trends to identify expansion or churn risk, and finance uses it to reconcile customer identity across billing and CRM systems. The specialist designs the SCV data model to serve all these use cases from a single schema.

For most mid-size businesses, the specialist implements a data pipeline using modern data stack tools: a data warehouse (BigQuery, Snowflake, or Redshift) as the unified storage layer, dbt for data transformation and identity resolution logic, and Fivetran or Airbyte for source connectors that pull data from CRM, support, and billing systems. The transformed unified customer table is served to a BI tool (Metabase, Looker, or Tableau) for reporting. For smaller teams that need something simpler, the specialist builds a direct integration pipeline in Python or Node.js that syncs the relevant data into a PostgreSQL database without the full data warehouse infrastructure. The recommendation depends on your data volume, team technical capability, and budget.

Not all customer data sources have clean APIs. The specialist handles common difficult sources: legacy CRM systems with SOAP APIs or flat-file exports are connected via scheduled data extracts with transformation scripts; spreadsheets and CSV exports are ingested via a scheduled upload process; on-premise systems are connected via a secure agent that extracts data without opening a public API endpoint; and third-party data providers (credit risk, firmographic data) are integrated via their API with rate limit handling and refresh logic. The pipeline architecture includes error handling and alerting so your team knows if a source stops syncing before the stale data causes a customer-facing problem.

A customer health score combines signals from multiple data sources into a single metric that predicts churn or expansion risk. The specialist implements a health score model using the unified customer data: product engagement signals (login frequency, feature adoption breadth, active users versus contracted seats), support signals (open critical tickets, ticket volume trend, resolution time), commercial signals (days since last renewal, invoice payment status, plan tier), and success milestones (onboarding checklist completion, key activation events, executive sponsor engagement). Each signal is weighted based on its correlation with churn in your historical data. The score is calculated nightly and surfaced in your CS team dashboard with trend arrows so they can prioritise outreach to customers whose health is declining.