Roadmap

Goals

Q2 2026 objectives

Signals, Skills & MCP

The whole team will contribute to making Web Analytics a first-class citizen in PostHog's AI and automation ecosystem.

Motivation: PostHog is going all-in on AI agents and MCP. Web Analytics needs to ensure questions are routed to the right insight types, signals are comprehensive, and the team is dogfooding MCP daily.

• Route questions to web analytics insight types and queries natively via skills
• Double down on having all web analytics signals (web vitals, heatmaps, bot activity, anomalies)
• Have an MCP hero on the team who uses it daily and dogfoods the integration
• Ship Marketing Analytics skills so PostHog AI can answer marketing questions natively

We'll know we're successful when: PostHog AI reliably answers web analytics questions, and the team is using MCP in daily workflows.

Agent & Bot Analytics + AEO

Lucas RicoyLucas Ricoy will be focusing on launching agent analytics and building some AEO (Answer Engine Optimization) and SEO tooling.

Motivation: Users want to distinguish and understand what bot/agent traffic is driving on their sites. Some because they need to prevent unwanted bots from scraping site content, others want to ensure their pages are seen by the right bots.

• Launch agent/bot analytics with full segmentation across web analytics, product analytics, SQL/HogQL and MCP.
• Build AEO tooling that works for founders
• Ship activity signals: anomaly detection for drops/spikes on tracked pages (bots or not)

We'll know we're successful when: customers can segment agent vs human traffic end-to-end, and the AEO dashboard is adopted by early-access users.

Marketing Analytics

Javier BahamondesJavier Bahamondes will be focusing on finding Marketing Analytics' product-market fit and expanding beyond paid ads.

Motivation: We're getting many feature requests for Marketing Analytics: a direct revenue signal. Expanding beyond paid ads to social, SEO, and other channels makes PostHog more central to how technical marketing teams work. Adding actionability (pausing campaigns, changing budgets) turns MA into a control surface, not just a BI tool. Integrating with Workflows for campaign management increases stickiness and daily usage.

• Expand channel support beyond paid ads: organic, social, referral, email
• Explore workflow actions for closing the loop (campaign actions, budget controls, Workflows integration)
• Run competitor research and customer interviews to define the MA differentiator

We'll know we're successful when: Marketing Analytics supports the top organic/social channels and we have a clear, validated positioning for what makes MA different from competitors.

Live View & Heatmaps

Jordan MryyanJordan Mryyan will be focusing on making live view a compelling real-time experience and improving heatmaps.

Motivation: Live view can become a flywheel for PostHog: connecting it to experiments, error tracking, and session replays gives users reasons to come more often. Not to mention that the dopamine hit is so good.

• Improve live view and ship stable and feature complete real-time dashboard with use cases in mind (campaign launches, AB test tracking, milestone celebrations)
• Cross-product integration: connect live view to experiments, error tracking, and session replay
• Integrate real time capabilities into heatmaps
• (Strecth) Fix heatmaps reliability and UX issues, define pricing strategy for premium features (comparisons, time snapshots)

We'll know we're successful when: live view is stable and integrated with other PostHog products, and heatmaps reliability issues are resolved.

Activation

The whole team will contribute on reversing the decline in Web Analytics activation.

Motivation: Web Analytics activation has been declining and we don't fully understand why. Reversing this trend is critical to growth since users who don't activate never retain.

• Investigate activation metric decline and identify what drives retention
• Ship improved onboarding flows to get new users to value faster

We'll know we're successful when: we've identified the root causes of activation decline and shipped onboarding changes that stabilize or reverse the trend.

Stretch

• Synthetic monitoring: revive the hackathon project for uptime and performance monitoring
• Globe visualization: ship the beta globe view as a polished live view feature
• Comparison date ranges: period vs period and YoY picker across web analytics

Q1 2026 recap

• Pre-aggregated tables shipped to 50% of teams
• Session explorer performance improvements
• Filters and UX revamp across Web Analytics
• Heatmaps inherited and stabilized with many fixes
• Marketing Analytics: improved ROAS, added new sources, ongoing support post-GA
• Agent/bot basic segmentation shipped, browser detection deferred to Q2
• Installation Health v2 partially shipped

Q4 2025 recap

• Marketing Analytics launched as GA with strong user feedback
• Web Analytics integrated with Posthog AI
• Shipped Web Analytics components as insights
• Pre-aggregated tables made available for selected teams
• Query concurrency and performance improved
• Many bug fixes and UX improvements across Web Analytics

Handbook

Who are we building for?

Personas

• Primary Personas:

  • Technical Founder
    • First technical person at the company, responsible for setting up the initial landing page.
    • At an early stage, wants tools that initially cost little to nothing, but won't need to be replaced as the company grows.
  • Product engineer
    • These are the engineers building the product. Normally full-stack engineers skewing frontend or frontend engineers.
    • Product engineers have more limited time. Need to quickly get high-quality insights to inform what they are building and assess what they've shipped.

• Not a focus, but should be usable by:

  • Other engineers
  • Marketing
  • Everyone in the product team (PMs, designers)
  • Non-technical leadership

What types of companies?

• We are building web analytics for high-growth startups that have PMF. Our product should be useful for them right from the start.

Jobs to be done

Web analytics is an opinionated tool, that will help people:

• Measure and improve the effectiveness of their acquisition strategy
• Measure and improve the effectiveness of their conversion funnels
• Understand who is using their website
• Understand how their website is being used
• Create simple-to-medium complexity combinations of these (e.g. how likely are people who came in from this ad to convert, compared to everyone else)

Additionally, it should be:

• Easy to set up
• Easy to use
• It's ok if that means we don't cover every use case, as people who need more can use product analytics

Roadmap

5-year vision

Web analytics as the command center Web analytics becomes the central command hub for everything that happens on the web. It unites acquisition, performance, and behavior data in one intelligent view. It is the 80/20 dashboard for all related PostHog products, connecting directly to experiments, sessions, and insights. No longer a passive reporting tool, it’s where teams take action.

From reactive to proactive Analysis turns into action. Web analytics doesn’t just highlight regressions; it solves them. When conversion rates drop or core web vitals slip, it can open a GitHub PR, create a task, or launch a follow-up experiment automatically.

A self-optimizing web Everything related to the web lives here: marketing spend, attribution, churn risk, SEO/AEO/GEO, and performance metrics, all in one continuous feedback loop. PostHog AI connects the dots across marketing, performance, and behavior, finding opportunities for improvement, generating new assets or variants, and running experiments autonomously.

From chart-drilling to conversations Instead of drilling into dashboards, users simply chat with web analytics. They ask questions, get clear explanations, and receive ready-to-apply actions, all in natural language. The focus shifts from just analyzing data to acting on insights.

Analytics for the agent era As AI agents and LLMs become real web users, PostHog helps teams measure, understand, and optimize for them. Web analytics will distinguish between agent traffic and human traffic, paving the way for the area and empowering teams to tailor their experiences for both human and AI audiences.