Early risk signals identified so your team can act before users leave
Lifecycle logic designed to move users toward long-term value
Drop-off diagnosed, CRM interventions defined for your team to test
Expansion and renewal signals surfaced, segmentation logic defined for your CS team
Retention problems are solved by the whole team — product, engineering, CRM, and CS. My role is to provide the diagnosis, the logic, and the CRM layer so every team knows where to focus and what to act on next.
Find early signals — inactivity patterns, support spikes, drop-off events — and explain which users to prioritise and when.
Find where users go cold, then design the trigger logic and message frameworks for your team to implement.
Find where users stall and deliver a prioritised hypothesis list with CRM-side interventions your team can test.
Find expansion and renewal signals in your CRM and product data. Define the segmentation logic your sales or CS team needs to act at the right moment.
Map product analytics, CRM history, and support signals into one unified view so every team works from the same data, not siloed snapshots.
Day-1, Day-7, Day-30 retention curves. Activation rate. Time to first value. Churn risk scoring defined by rules your team can track inside your CRM.
Retention analysis connects user behaviour, CRM data, and lifecycle signals to show where users drop off and why. It is the foundation of any strategy to reduce churn, improve engagement, and grow revenue from your existing base.
The same failure points appear across products and markets. These are the moments retention analysis is designed to catch.
Sign up, explore briefly, leave before the product clicks. The activation gap and the missing communication window.
The D1–D7 cliff. Users return once or twice, then go silent. Cohort analysis shows when and who.
Add to cart, start a trial, reach pricing, then disappear. Analysis separates CRM failures from product issues.
Inactivity and support spikes are visible 7–14 days before a user leaves. The window to intervene exists, if you see it.
From data audit to lifecycle strategy and CRM logic — the analytics and CRM layer your team needs to act.
Day-1, Day-7, Day-30 cohort curves. Churn rate, retention rate, and benchmark comparison using publicly available B2B SaaS benchmarks by category and price point.
Find whether the drop is driven by onboarding gaps, communication timing, or missing lifecycle triggers. Explain where a CRM intervention can move the needle and where the hypothesis belongs to product or engineering.
Design onboarding sequences, engagement nudges, and win-back flows across email and CRM channels. Define trigger logic, segmentation rules, and A/B test hypotheses for your team to implement.
Define audience segments and lifecycle communication logic across email and CRM channels. Push and in-app are designed and handed off to your product or engineering team where needed.
Four phases, each with a clear input, a clear output, and a defined handoff to your team.
You provide read-only access to your analytics and CRM systems. I review and validate the data: what events are tracked, how complete the funnel coverage is, what's missing or inconsistent.
Read-only access or CSV exports. No integrations needed. Original data is never modified.
I set up a custom AI analytical layer adapted to your data model, event naming, and business context. This is not a generic connector. The agent is configured to understand your funnel, your segments, and your retention logic specifically.
Data model context, dimension mapping, retention event definitions, segment logic
Generic dashboards miss patterns that only emerge when the AI understands your specific data structure
This retention analysis maps your full user journey, calculates Day-1/7/30 retention curves by cohort, channel, and plan, and identifies where users drop off and why — including silent churners, activation gaps, and at-risk segments your dashboards don't surface.
Funnel drop-off map, cohort retention curves, churn impact by segment, at-risk user list
Report with prioritised findings and hypothesis list, what to test first and why
Based on the retention analysis findings, I design the lifecycle journeys and define trigger logic, segmentation rules, and lifecycle flows for your team to implement.
Trigger logic, segmentation rules, email sequence structure and message frameworks, churn risk scoring logic for your CRM
Start with a call, share your stack, and I will outline where the highest-impact opportunities are.