Kissmetrics
Person-level event analytics, funnels and cohorts for product and growth teams.
What it does
Kissmetrics is an analytics platform that records and analyzes user events resolved to people and accounts so product, marketing and growth teams can investigate journeys, measure retention, and evaluate changes over time. Person-level event tracking and People search Kissmetrics captures events tied to individual people or accounts across devices using client-side JavaScript or server-side ingestion. Teams can search and filter by person or account attributes to inspect specific user journeys, troubleshoot customer issues, and build segments based on identity-linked behavior. Funnels and Paths Build step-by-step funnels to see where users drop off and use Paths reports to visualize the common routes people take through a site or app. These reports support forward and backward path analysis and are aimed at diagnosing onboarding friction and prioritizing product fixes. Cohorts and Retention reporting Create cohorts based on signup dates, behaviors, or custom properties and track retention and engagement over time. Cohort analysis is useful for comparing groups after releases, measuring lifecycle changes, and segmenting A/B test outcomes by user group. Activity, metrics and product-usage dashboards Kissmetrics provides time-series and comparative event reports, plus prebuilt metric dashboards and feature-usage comparisons to monitor launches and spot regressions. These reports surface which events and features correlate with higher engagement among your best users. A/B test reporting and revenue attribution The platform includes A/B test reporting designed to show experiment lift and connects behavioral events to revenue metrics for subscription or recurring models. This lets teams link product changes and experiments to monetary outcomes where instrumentation captures revenue events. Implementation requirement Kissmetrics requires installing its tracking code or using its event ingestion APIs; accurate person-level analytics depend on correct event and identity instrumentation and consistent event/property naming.
