Customer 360 Template for Product Analytics

A CRM record (Salesforce, HubSpot) captures sales and relationship data: deal size, contacts, notes, pipeline stage. A customer 360 adds product usage data, support interactions, billing history, NPS feedback, and feature requests. The CRM is one data source that feeds the 360 view. Most CRM records do not include product analytics, which is often the most predictive data for retention and expansion.

For small teams (<100 accounts): a shared spreadsheet or Notion database works. For mid-size teams: customer success platforms like Gainsight, Vitally, or Planhat aggregate data from multiple sources. For large teams: a data warehouse (Snowflake, BigQuery) with dbt models and a BI tool (Looker, Metabase) for visualization. The tool matters less than the process. Start with the template structure and automate incrementally.

Product usage metrics should refresh daily or weekly. Support data should sync in real-time or daily. Financial data updates on billing events. Relationship fields (executive sponsor, last QBR) are updated manually after each interaction. The overall health score should recalculate weekly. If you cannot automate all updates, prioritize usage and support data because those change most frequently and are most predictive of churn.

Yes. Churned customer profiles are valuable for post-mortem analysis: they reveal common patterns that preceded churn. Tag churned accounts clearly and analyze them quarterly. The [churn prediction template](/templates/churn-prediction-template) uses historical churn data to build predictive models. Archive churned profiles after 12 months but keep them queryable.

Start by identifying the most common data quality problems: duplicate accounts, missing fields, stale records, and conflicting values across systems. Designate one system as the "source of truth" for each field (e.g., Stripe for MRR, Salesforce for account owner). Build validation rules that flag inconsistencies. Dedicate 30 minutes per week to data hygiene. Perfect data quality is unrealistic; aim for "good enough to make decisions" and improve iteratively.