Use-case selling
Contents
We sell products. Customers buy solutions.
When we pitch "add Surveys," it sounds like we're trying to increase their bill. When we pitch "here's how to close the loop on why users drop off," it sounds like we're solving their problem. Same product. Different framing. Very different conversion rate.
Use cases are how we sell. Products are how we bill. A use case is a discrete problem a team is trying to solve, supported by a combination of PostHog products. Billing, metering, and packaging don't change. What changes is how we talk about it, how we organize around it, and how we measure adoption.
Each use case has a full playbook with discovery questions, competitive positioning, expansion paths, objection handling, and onboarding checklists.
The seven use cases
| Use case | Job to be done | Core buyer |
|---|---|---|
| Product Intelligence | "Help me understand what users do, why they do it, and what to build next." | PMs, designers, product engineers, founders |
| Release Engineering | "Help me ship faster without breaking things." | Engineering managers, platform teams, developers |
| Observability | "Help me know when things break, understand why, and fix them fast." | SREs, platform engineers, DevOps |
| Growth & Marketing | "Help me understand what drives acquisition, conversion, and revenue." | Growth engineers, marketing leads, CRO, GTM engineers |
| AI/LLM Observability | "Help me understand how my AI features perform, what they cost, and how users interact with them." | AI/ML engineers, AI PMs, AI founders |
| Data Infrastructure | "Help me unify product data with business data and get it where it needs to go." | Data engineers, analytics engineers, product ops |
| Customer Experience | "Help me quickly understand what happened, identify the problem, and verify a fix." | Support leaders, engineering leads, CS leaders |
Product coverage matrix
| Product | Primary use case | Secondary use cases |
|---|---|---|
| Product Analytics | Product Intelligence | Growth & Marketing, AI/LLM Obs, Customer Experience |
| Session Replay | Product Intelligence | Release Engineering, Observability, AI/LLM Obs, Customer Experience |
| Feature Flags | Release Engineering | |
| Experiments | Release Engineering | Product Intelligence, AI/LLM Obs, Growth & Marketing, Customer Experience |
| Error Tracking | Observability | AI/LLM Obs, Customer Experience |
| Surveys | Product Intelligence | Growth & Marketing, Customer Experience |
| Web Analytics | Growth & Marketing | |
| Marketing Analytics beta | Growth & Marketing | |
| Revenue Analytics | Growth & Marketing | Product Intelligence |
| Workflows | Growth & Marketing | Product Intelligence |
| Product Tours beta | Growth & Marketing | Product Intelligence |
| LLM Observability | AI/LLM Obs | Customer Experience |
| AI Evals | AI/LLM Obs | Product Intelligence, Release Engineering |
| Data Warehouse | Data Infrastructure | |
| Data Pipelines / Batch Exports | Data Infrastructure | Growth & Marketing |
| PostHog AI | Horizontal (all) | |
| Logging beta | Observability | Customer Experience |
Playbook structure
Every use case playbook follows the same sections, so TAMs know where to find what they need:
- Job to be done
- Relevant PostHog products (with doc links)
- Adoption and expansion paths
- Business impact
- Personas to target
- Signals in Vitally & PostHog
- Command of the Message (discovery, negative consequences, desired state, outcomes, metrics)
- Competitive positioning
- Pain points & known limitations
- Getting a customer started (evaluation scope, onboarding checklist)
- Objection handling
- Cross-sell pathways to other use cases
- Internal resources
- Company archetype considerations