PostHog Handbook Library / Growth

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# Product Intelligence

Auto TL;DR

## At a Glance

This long page covers these main areas. The list is generated from the article headings, so it updates with every handbook rebuild.

1. What is the job to be done?
2. What PostHog products are relevant?
3. A note on self driving for this use case
4. Adoption path and expansion path
5. Entry point
6. Primary expansion path
7. Alternate expansion paths
8. Business impact of solving the problem

### What is the job to be done?

"Help me understand what users do, why they do it, and what to build next."

- Understand how users navigate your product and where they get stuck
- Identify which features drive retention and which are ignored
- Get qualitative context for quantitative patterns (the "why" behind the numbers)
- Validate product hypotheses with experiments before committing engineering resources
- Collect direct user feedback at key moments in the product experience
- Track business outcomes (revenue, expansion) tied to product usage
- Act on insights by automating onboarding and feature adoption messaging based on user behavior

This is our bread and butter. Most accounts start here. The risk is they stay here as a single product analytics customer and never expand. The opportunity is that Product Intelligence naturally creates demand for the other use cases once teams start acting on what they learn.

### What PostHog products are relevant?

- Product Analytics (core) — Funnels, retention, cohorts, lifecycle analysis, trends, user paths. The quantitative foundation for understanding what users do.
- Session Replay — The qualitative layer. Watch what users actually do when the numbers say they're dropping off. Bridges the gap between "40% drop at step 3" and "oh, the button doesn't render on mobile Safari."
- Surveys — Direct feedback loop at key moments. NPS after onboarding, CSAT after support, "why did you cancel?" on churn. Ties qualitative signal to quantitative behavior data.
- Experiments — Validate hypotheses with statistical rigor before committing to a full build. A/B test changes against real metrics, not gut feel. Requires Feature Flags for implementation.
- Workflows — Onboarding sequences, activation nudges, lifecycle engagement. The action layer: once you've identified a drop-off in analytics, Workflows lets you do something about it automatically. (Email drip campaigns)
- AI Evals — For products with AI features: proactively surface where users are struggling based on AI output quality. This is product intelligence driven by AI observability. A bridge product to the AI/LLM Observability use case.
- PostHog AI — Natural language querying and insight discovery. Lowers the bar for non-technical product stakeholders to self-serve. A PM asks "why did retention drop last week?" instead of building a custom query. (Example prompts)
- Replay Vision — AI scanners that watch session recordings for you and turn what they see into queryable events. Point a scanner at a set of recordings and it flags dead ends, scores frustration, tags intent, or summarizes what happened — with citations that jump to the exact moment. The output lands as a `$recording_observed` event, so a finding becomes a funnel breakdown or a cohort, not a note in a vendor's AI panel. See the caveats under pain points before promising it. (Scanner types · Observations)
- Heatmaps — Clickmaps, scrollmaps, and rageclick detection via the toolbar. Available on every plan. The thing UX teams ask for by name when comparing us to Hotjar.
- self-driving — Session replay and product analytics are signal sources for the self-driving loop, so a friction pattern can arrive as an investigated report with a pull request attached rather than a ticket for someone to write. Be careful how you frame this one for a product audience — see the self-driving section below.

#### A note on self-driving for this use case

self-driving cuts across all seven use cases, but it lands differently here than it does in Observability or Release Engineering. An error has an obvious right answer, so it converts into a merged pull request quickly. A product-intelligence finding — "users hesitate on the plan picker" — usually needs a human judgment call about what the product should do, so it more often arrives as a needs input report than as code.

That's not a weakness, and reports are free (you're only billed $15 per pull request, first three each month free). To a PM, self-driving's value here is a prioritized queue of investigated problems with the evidence attached, not an agent that redesigns your onboarding. Full guidance in how to pitch self-driving.

Write it lowercase and hyphenated, and keep the customer's product as the subject — we make their product self-driving. It's a capability, not a SKU you can add to an order form.

### Adoption path and expansion path

#### Entry point

Usually Product Analytics. Customer starts tracking events, builds dashboards, creates their first funnel. Then they hit the ceiling of quantitative data alone: "I can see that users drop off, but not why."

#### Primary expansion path

Product Analytics → + Session Replay → + Surveys → + Experiments → + Workflows

The logic of each step:

- Product Analytics → Session Replay: They know what is happening (40% drop at step 3). They need to see why. Session Replay gives them the qualitative context that numbers can't.
- Session Replay → Surveys: They're watching replays and forming hypotheses about why users struggle. Surveys let them ask users directly at the moment of friction, then tie responses back to behavior data.
- Surveys → Experiments: They've identified the problem through analytics, replay, and feedback. Now they want to test a fix. Experiments require Feature Flags, which gets engineering involved (multithreading moment).
- Experiments → Workflows: They've validated what drives value. Now they want to guide users toward those high-value behaviors automatically, through behavior-triggered engagement sequences.

#### Alternate expansion paths

B2B accounts with Group Analytics: B2B SaaS companies almost always need company-level analytics alongside user-level. If they're B2B and not using Group Analytics, that's a significant upsell opportunity. Group Analytics lets them answer "which companies are most engaged" not just "which users."

Starting from Session Replay: Some accounts come in through Session Replay first (debugging, QA, customer support use cases). They realize they need Product Analytics to quantify what they're seeing qualitatively. The expansion path reverses: Replay → Analytics → Surveys → Experiments.

Product teams that ship AI features: If the product has AI components, AI Evals can proactively surface where users are struggling based on output quality. This bridges Product Intelligence into AI/LLM Observability.

### Business impact of solving the problem

This is the use case with the largest existing install base. Most PostHog accounts start with Product Analytics. The expansion opportunity isn't convincing them to adopt PostHog. It's convincing them to go beyond a single product and use the full Product Intelligence stack.

Workflows close the loop from insight to action. You identify a drop-off point (analytics), you understand why users leave (session replay, surveys), and now you can re-engage users when they disengage (workflows). That's a complete insight-to-action cycle that no competitor offers in one platform.

Product Intelligence creates demand for other use cases. Once the product team is deep in PostHog, they pull in the growth team (Growth & Marketing use case) for acquisition and activation. Once they're running experiments, engineering gets involved in rollouts (Release Engineering). This is the gateway use case.

### Personas to target

| Persona | Role Examples | What They Care About | How They Evaluate | |---|---|---|---| | Product Manager | PM, Senior PM, Head of Product | Feature adoption, retention, user journeys, proving impact to leadership | "Can I see which features drive retention and prove ROI to my VP?" | | Product Engineer | Full-stack eng on a product team | Fast instrumentation, reliable data, not maintaining a data pipeline | "How fast can I instrument this and how reliable is the data?" | | UX Researcher | UX Researcher, Design Lead | User behavior patterns, qualitative + quantitative, session-level detail | "Can I watch real user sessions filtered by the cohort I'm studying?" | | Designer | Product Designer, UX Designer | How users interact with new designs, A/B testing UI changes | "Can I see the before/after impact of my design changes?" | | Founder (early stage) | Founder, CTO at seed/Series A | All of the above. Finding product-market fit. Speed. | "Does this help me figure out what to build next?" |

### Signals in Vitally & PostHog

#### Vitally indicators this use case is relevant

| Signal | Where to Find It | What It Means | |---|---|---| | Product Analytics is the only paid product | Product spend breakdown | Classic single-product account. Full expansion path available. | | High insight/dashboard creation per active user | Engagement metrics | Product team is actively using PostHog for analysis. They're ready for deeper tools. | | Session Replay is free-tier only or not used | Product usage data | They're doing quantitative analysis without qualitative context. Session Replay is the obvious next step. | | B2B company without Group Analytics | Company type + product spend | Major upsell opportunity. B2B companies need company-level analytics. | | Multiple PM or design roles in the user list | User list in Vitally | Product team is in PostHog, not just engineering. This use case is live. |

#### PostHog usage signals

| Signal | How to Check | What It Means | |---|---|---| | Funnels and retention insights being created regularly | Saved insights | Product team is actively measuring conversion and retention. Ripe for Experiments. | | Session Replay enabled but low viewing rate | Replay settings vs. replay views | They've turned it on but aren't using it. Needs onboarding or a nudge to connect it to their analytics workflow. | | No experiments running despite active analytics | Experiments list | They're identifying problems but not testing solutions. Experiments is the next conversation. | | Dashboards shared across multiple users | Dashboard sharing settings | They're collaborating on insights. Good health signal and potential for team expansion. | | High event volume, low survey usage | Product usage metrics | They have the traffic to run surveys but haven't started. Low-hanging cross-sell. |

### Command of the Message

#### Discovery questions

- How does your product team decide what to build next? What data informs that decision?
- When you see a drop-off in a funnel, how do you figure out why users are leaving?
- How do you measure whether a new feature is successful after launch?
- Do you collect direct user feedback inside the product? How is that connected to your analytics?
- When you have a hypothesis about user behavior, how do you validate it? Do you run experiments?
- How do you prove to leadership that a product investment drove business results (revenue, retention)?
- How many tools does your product team use to understand users? (Analytics, replay, surveys, experiments — how many vendors?)
- Can your PMs answer their own questions, or do they depend on data/engineering for every query?

#### Negative consequences (of not solving this)

- Product decisions are based on gut feel or incomplete data because the team can't connect behavior to outcomes
- PMs can see that users drop off but not why, leading to guesswork about what to fix
- Experiments are rare or nonexistent because the tooling is disconnected from analytics, so every test requires a separate setup
- User feedback (surveys) lives in a separate tool, disconnected from behavior data, so you can't answer "what happened right before this user gave us a 3/10?"
- Product team can't prove business impact, making it hard to justify investment or prioritize
- Insights are identified but never acted on because there's no automation layer to guide users or re-engage them

#### Desired state

- One platform for the full cycle: measure behavior → watch sessions → collect feedback → test changes → measure revenue impact → act on insights
- PMs can self-serve answers without waiting for a data team
- Every product change is measured against real retention and revenue metrics
- Onboarding and feature adoption are guided automatically based on user behavior
- Product team and engineering share the same platform, reducing tool fragmentation

#### Positive outcomes

- Faster product decisions: cycle time from "we see a problem" to "we've validated a fix" drops significantly
- Higher retention from catching and addressing drop-off points systematically
- Better resource allocation: experiments prove what works before engineering commits to a full build
- Product team can demonstrate revenue impact to leadership, strengthening their influence
- Tool consolidation: replace separate analytics + replay + survey + experimentation vendors with one platform

#### Success metrics

Customer-facing:

- Feature adoption rates improve for targeted features
- Retention curves flatten or improve for key cohorts
- Experiment velocity increases (more hypotheses tested per quarter)
- Time from insight to action decreases

TAM-facing:

- Customer expands from Product Analytics-only to multi-product (Session Replay, Surveys, Experiments)
- Multiple product team members (PMs, designers) are active
- Experiment usage grows (indicates the product team is using PostHog for decisions, not just reporting)
- Workflow usage starts (they're closing the insight-to-action loop)

### Competitive positioning

#### Our positioning

- Quantitative + qualitative in one platform. Product Analytics and Session Replay together. No switching between Amplitude and Hotjar. Filter replays by funnel drop-off, cohort, or event.
- Insight you can act on. Most analytics tools stop at the dashboard. PostHog lets you act on what you find with Workflows and Experiments.
- Experiments built into the analytics workflow. See a drop-off in a funnel, right-click to create an experiment, measure the result in the same tool. No separate experimentation platform.
- PostHog AI makes analytics accessible. PMs who aren't comfortable with SQL can ask questions in plain English.
- AI on replay that produces queryable data. Most competitors now ship some flavor of AI-over-session-replay. The differentiator is that Replay Vision emits a normal PostHog event, so "sessions where the user hit a dead end" becomes a funnel breakdown, a cohort you can survey, or an experiment exclusion. Everyone else's AI findings stay inside their AI panel.

#### Competitor quick reference

| Competitor | What They Do | Our Advantage | Their Advantage | |---|---|---|---| | Amplitude | Product analytics, cohorts, experiments | Broader platform (replay, flags, surveys, workflows); better pricing; open source | More mature ML features (predictions, audiences); larger enterprise install base | | Mixpanel | Product analytics, funnels, retention | Broader platform; no sampling; replay + surveys + flags included | Some teams prefer the UX; strong mobile analytics | | Hotjar | Session replay, heatmaps, basic surveys | Engineering-grade analytics alongside replay; experiments; flags; we ship heatmaps too | Simpler UX for non-technical users; purpose-built for UX research | | Heap | Auto-capture product analytics, session replay | Also auto-capture, plus flags, experiments, surveys, workflows | Retroactive analytics (virtual events) is a strong pitch | | Pendo | Product analytics + in-app guides | Deeper analytics; experiments; open source; better pricing | More mature in-app guides; stronger enterprise PM workflow features | | Fullstory | Autocapture, replay, StoryAI (session summaries, friction "Opportunities") | AI findings become queryable events, not panel-only insights; flags and experiments in the same tool | More mature enterprise DXA motion; StoryAI has been GA longer and is more established in enterprise deals | | Contentsquare / Heap | Behavioral analytics, Sense Analyst (autonomous analysis agent) | One platform through to shipping the fix; better pricing | Sense Analyst is further along than our replay AI; deep enterprise CX tooling | | LogRocket | Replay, product analytics, Galileo AI (issue severity, Ask Galileo) | Broader platform; our signals feed an agent that opens PRs in your repo | Galileo is GA and dispatches Cursor agents today; strong frontend debugging |

Where PostHog stands: Our strongest position is the breadth of the platform. No competitor offers analytics + replay + heatmaps + surveys + experiments + workflows in one tool. We're weaker against Amplitude in very large enterprises where their ML features and enterprise sales motion are more mature. We're weaker against Hotjar/Pendo for non-technical product teams who want a simpler, more opinionated UX.

On AI specifically, be careful not to oversell: nearly every competitor shipped an AI analyst in the last year, several of them GA for longer than Replay Vision, and a customer who has seen Fullstory's or LogRocket's demo will not be impressed by "we have AI too." The defensible claims are narrower and better: the AI output is a first-class event you can join to the rest of your data, and the loop doesn't stop at an insight — it continues into a pull request. Our sweet spot is technical product teams at companies with engineers who value depth, flexibility, and not paying for 5 separate tools.

### Pain points & known limitations

| Pain Point | Impact | Workaround / Solution | |---|---|---| | No native in-app guides or tooltips | Teams with complex in-app onboarding needs may hit walls | For tooltip/modal UX, keep a dedicated tool (Appcues, Pendo) and use PostHog for analytics + experimentation. | | Workflows is new, less mature than dedicated engagement tools | Teams expecting Braze-level email sequencing will find gaps | Position as behavior-driven automation, not a full lifecycle marketing replacement. Complement with existing tools via Data Pipelines. | | Replay Vision is quota-limited | A broad scanner can burn the monthly credit budget fast | Usage is metered in credits against a monthly budget per organization, scanners sample by default, and it's Gemini-only. Scope scanners narrowly to one flow while tuning — see quota and limits. | | Replay Vision output is a model's judgment, not ground truth | A PM who treats one observation as fact will get burned | Every observation carries a confidence score and citations that link to the exact moment — coach customers to spot-check before acting, and to look for a pattern across sessions rather than trusting a single scan. | | Learning curve for non-technical PMs | PMs used to Amplitude's guided UX may find PostHog's flexibility overwhelming initially | Lead with PostHog AI for querying. Build pre-configured dashboards during onboarding. Start with simple funnels and retention, not HogQL. |

### Getting a customer started

#### What does an evaluation look like?

- Scope: Instrument their core product flow: signup → key activation event → retention-defining action → conversion/upgrade. Build the primary funnel and retention analysis. Enable Session Replay on key flows.
- Timeline: 1 to 2 weeks to see value from analytics and replay. Experiments need enough traffic for statistical significance, so timeline varies.
- Success criteria: Can you answer: "Where do users drop off in our core flow, and why?" Can you see the full retention curve by cohort? Can you watch a replay of a user who dropped off?
- PostHog investment: Product Analytics free tier covers 1M events. Session Replay free tier covers 5K recordings. Surveys, Experiments have generous free tiers.

#### Onboarding checklist

- [ ] Install PostHog SDK with autocapture enabled (getting started)
- [ ] Define and instrument core conversion events (signup, activation, key feature usage, upgrade)
- [ ] Build the primary conversion funnel
- [ ] Set up retention analysis for key activation events
- [ ] Enable Session Replay on production (configure sampling if needed)
- [ ] Watch 5+ replays filtered by funnel drop-off to get qualitative context
- [ ] Build a "Product Health" dashboard for the product team (funnels, retention, trends)
- [ ] Introduce PostHog AI to PMs for self-serve querying
- [ ] Set up one Survey at a key friction point (post-signup NPS, feature feedback)
- [ ] Plan first Experiment targeting a known drop-off point
- [ ] Show heatmaps on their highest-traffic page
- [ ] If they're a fit for Replay Vision, get a first scanner running on a flow they care about

### Cross-sell pathways from this use case

| If Using... | They Might Need... | Why | Conversation Starter | |---|---|---|---| | Product Analytics only | Session Replay | They see the numbers but not the why | "You can see 40% drop off at step 3. Want to watch what's actually happening?" | | Product Analytics + Session Replay | Surveys | They're forming hypotheses from replays and want direct user input | "You're watching sessions and seeing confusion. Want to ask users directly what's tripping them up?" | | High Session Replay volume, low viewing rate | Replay Vision | They're paying to record sessions nobody has time to watch | "You recorded 40,000 sessions last month. How many did anyone actually watch? What if a scanner read all of them and tagged the ones where people got stuck?" | | Watching replays and filing tickets manually | self-driving | The triage between "we saw the problem" and "someone fixed it" is all manual | "You've found the friction. Who writes the fix, and how long does it sit in the backlog first?" | | Product Analytics + Surveys | Experiments | They've identified problems and want to validate fixes | "You know the problem. Let's test whether your proposed fix actually works before building it fully." | | Analytics + Experiments mature | Workflows | They know what works and want to operationalize it | "You proved the new onboarding flow works. Now let's automatically nudge every new user toward it." | | Product team in PostHog | Growth & Marketing (for the growth team) | Product team is in PostHog. Growth team should be too. | "Your PMs are using PostHog for product decisions. Has the growth team seen what they can do with funnels and experiments for conversion optimization?" | | B2B account, no Group Analytics | Group Analytics add-on | B2B companies need company-level analytics | "You're tracking individual users. But do you know which companies are most engaged and which are at risk?" | | Product team using flags for experiments | Release Engineering (for the eng team) | Engineering is implementing flags for experiments. They can use them for releases too. | "Your engineers are already deploying feature flags for experiments. Have they considered using the same infrastructure for all their releases?" |

### Internal resources

- Product Analytics docs: Funnels · Retention · Lifecycle · Cohorts · User paths · SQL
- Session Replay docs: Session Replay
- Surveys docs: Creating surveys
- Experiments docs: Experiments · Exposures
- Workflows docs: Getting started · Email drip campaigns
- Feature Flags docs: Getting started
- PostHog AI docs: Enable PostHog AI · Example prompts
- Group Analytics docs: Group Analytics
- Heatmaps docs: Heatmaps
- Replay Vision docs: Overview · Scanner types · Creating scanners · Observations · Quota and limits
- self-driving: How to pitch self-driving · Docs · Pricing

### Appendix: Company archetype considerations

| Archetype + Stage | Framing | Key Products | Buyer | |---|---|---|---| | AI Native — Early | Product Intelligence looks different here. There's no UX researcher. A GTM engineer or founding PM is looking at funnels, activation rates, and conversion. Frame it as "understand what makes users stick" not "deep behavioral research." | Product Analytics (funnels, retention), Session Replay, Experiments, PostHog AI | Founder, founding PM, GTM engineer | | AI Native — Scaled | Starting to formalize the product function. May have a dedicated PM. AI Evals becomes relevant as a bridge: evaluating AI output quality is product intelligence for AI products. | Product Analytics, Session Replay, Surveys, Experiments, AI Evals | PM, Head of Product, AI Product Lead | | Cloud Native — Early | First real analytics investment. They need to find product-market fit. Speed matters. Don't overwhelm with features. Start with funnels and retention, add replay and surveys as they mature. | Product Analytics, Session Replay, PostHog AI | Founder, first PM, product engineer | | Cloud Native — Scaled | Dedicated product team with PMs, designers, maybe UX researchers. They want depth: cohort analysis, retention by feature, experiment velocity. Workflows becomes relevant for operationalizing insights. | Full Product Intelligence stack. Group Analytics if B2B. | Head of Product, VP Product, UX Research Lead | | Cloud Native — Enterprise | Multiple product teams, multiple workloads. The play is expanding PostHog from one team to many. Standardization and governance matter. RBAC (Enterprise package) becomes relevant. | Full stack + Group Analytics + Enterprise package | VP Product, CPO, product ops |

Canonical URL: https://posthog.com/handbook/growth/use-case-selling/product-intelligence

GitHub source: contents/handbook/growth/use-case-selling/product-intelligence.md

Content hash: `8371a45152fb2a0f`