Glossary
Customer journey analytics
Analyzing a customer's touchpoints across channels and time to understand and improve the full path to purchase and beyond.
Customer journey analytics stitches together the individual touchpoints a customer has with a company, marketing emails, website visits, sales calls, support tickets, purchases, across channels and over time, into a connected sequence, then analyzes that sequence for patterns that predict conversion, retention or churn.
This differs from customer journey mapping, which is a largely qualitative, workshop-driven exercise to document an idealized or typical journey; journey analytics works from actual event-level data at scale, closer to funnel analysis but not limited to a single linear funnel, since real customers move between channels and stages non-sequentially and often re-enter a journey after a gap.
Teams use journey analytics to find where customers stall or drop off, to see which touchpoint sequences correlate with higher lifetime value, and to feed attribution modeling with a fuller picture of influence than last-touch data alone provides. It also increasingly feeds customer health score models with behavioral sequence data, not just point-in-time usage snapshots. A common pitfall is assuming correlation between a touchpoint and good outcomes means causation, when both may simply reflect an already high-intent customer.
Last reviewed September 22, 2026