PostHog
Analytics
Raw product events sitting in an analytics tool tell a rep nothing until someone connects them to an account. PostHog tracks usage and behaviour across self-serve and PLG motions, and Revenue Labs reads those events into account-level signal, flagging what was used and when activity dropped.
Website
Adoption·8 adoption reads this week
Reading Ravensmoor Analytics… ›
TriggerProduct usage · Ravensmoor Analytics - reporting workspace down this week · Same week as last quarter's seasonal dip
Deploy agentDeploying Adoption agent on the account
Use skillLoading skill · Adoption Analysis · Value Realisation Assessment · Customer Health Assessment
Reference memoryReading memory · Adoption · Products · Customer Outcomes
ReasoningRavensmoor Analytics' usage dipped the same week it did last quarter - seasonal on its own, except this time the stakeholder who drove it, Wren Halloway, left the company two weeks ago; this one gets flagged, last quarter's didn't
ActionCompose adoption read → surface inside Customer Meeting Preparation ahead of the call
✓Read in 6 seconds ›
Surfaces inCustomer Meeting PreparationviaSlackTeams
Revenue LabsAPP1:37 PM
LIVE
CUSTOMER MEETING PREP: Ravensmoor Analytics | Check-in Call
WHAT'S CHANGED
Reporting workspace usage dipped the same week it did last quarter, seasonal on its own
Wren Halloway, who drove that usage, left the company two weeks ago
STAKEHOLDERS
Gideon Park · Ops Director · [Primary contact]
ADOPTION & VALUEADOPTION
4 use cases assessed this cycle · 1 gap flagged for the call
Reporting workspace behind expected pace since Wren Halloway left; confirm who owns it now
RECOMMENDED DISCUSSION
Confirm the new workspace owner and reset the adoption pace with Gideon Park
Join MeetingView AccountView Adoption Detail
The tool alone > AI bolted on > AI-native
What changes when PostHog feeds a system that learns
Three jobs PostHog already does, and what each becomes when its evidence joins live company, contact and deal state.
01 | The tool on its own
02 | With AI bolted on
03 | AI-native
Product analytics
Events become insights, funnels, and trends.
PostHog records product events and turns them into insights, funnels, and trends. Product analysts still choose the event definition and commercial question.
PostHog AI answers product questions.
PostHog AI answers questions from PostHog data. The result stays in PostHog and does not attach the signal to the company or deal outside that stack.
Events enter account state.
PostHog events and insights enter the account record with call, ticket, and stage context.
Session replays
A replay shows what happened before a problem.
Session Replay shows real user interactions, errors, and the surrounding context. A product manager still finds the relevant session and decides what it means.
PostHog AI finds and summarises replays.
Replay summaries help investigate a product issue. They do not attach replay evidence to a company or deal, or update the next deal action.
Replay evidence reaches the deal.
Repeated errors and abandoned paths are resolved to the account, where every capability can read the product signal.
Feature flags and experiments
Flags and experiments control product changes.
PostHog feature flags and experiments let teams target releases and compare results. Product teams still decide which result matters to revenue.
PostHog AI answers experiment questions.
PostHog AI answers questions from PostHog data. The result stays in PostHog and does not compare the experiment with renewal, expansion, or loss outcomes.
Commercial outcomes teach product signal.
Closed outcomes reveal which product changes and behaviours preceded them. Future reviews use the product pattern learned from those outcomes.
Runs on it
Capabilities that act on connected evidence
Customer Success
Adoption
Active
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Adoption gaps are named by use case
Usage and stakeholder behaviour are compared with the customer's goals and use case.
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Early usage is read against its stage
Lifecycle requirements separate a normal ramp from adoption that is already slipping.
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The QBR starts with an adoption picture
Use-case and stakeholder gaps, with likely causes, are assembled before the meeting.
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Usage dips are weighed against the customer
The customer's adoption pattern and goals distinguish disengagement from a familiar lull.
The CSM receives traceable adoption gaps so effort reaches customers falling short.
Customer Success
Customer Health
Active
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The health read stays live
Customer Health Memory is applied to Customer State as new evidence arrives.
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Conflicting signals are weighed together
Product, support and relationship evidence explains a move from Healthy to Watch.
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The current read is ready for review
Health, evidence and confidence are attached to the account before the CSM prepares.
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Managers see only changed exceptions
Managers see which accounts changed and why instead of scanning the whole base.
Reconciled health evidence lowers churn and reduces manual portfolio inspection.
Customer Success
Expansion Identification
Active
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The candidate starts with an evidenced thesis.
It checks the use case against expansion, product and value logic.
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The threshold is checked against fit and goals.
It tests product fit, customer goals and commercial logic.
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The stakeholder map shows what changed.
It shows whether the new person adds authority, budget or urgency.
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The review opens with ranked candidates.
Each candidate carries evidence and confidence for CSM review.
Humans decide whether to introduce the commercial conversation.
BUILT FOR SECURITY, CONTROL AND SCALE
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The GTM teams that learn fastest will win.
Connect your stack to a system that learns.