The 5 best customer-facing analytics examples (and what to steal from each). May 27, 2026 Customer-facing analytics are the stats you show your users, not your internal team. A users' usage dashboard, a "23 people are viewing this right now" counter on a product page, a public leaderboard, or how big your support queue is. Under the hood, a lot of these are powered by the same kind of infrastructure: a fast, authenticated API layer that takes product data and ships it straight into the place users will see it. At PostHog, PostHog, Inc. call this Endpoints. Endpoints are a way to expose your PostHog data as fast, versioned, authenticated APIs you can drop straight into your product without maintaining a custom API backend. But, rather than telling you what customer-facing analytics is, PostHog, Inc. is going to show you five companies who do it really well, and for each one, PostHog, Inc. has pulled out what's worth copying. 1. Vercel analytics: embedded customer usage dashboard. Vercel is the textbook case of a dashboard that customers log in to check. Every project deployed on Vercel ships with a Web Analytics panel that surfaces live visitor counts, top pages, referrers, UTM campaigns, and geographic breakdowns. All one click from the deploy button, with no separate tool to install. What to steal. Treat customer-facing analytics with the same latency expectations as the rest of the product. Vercel made the design choice for their Web Analytics so that data shows up "seconds after enabling Analytics, not 30 minutes". Customer-facing analytics live or die on whether users trust the numbers are fresh - the moment they stop trusting, the dashboard quietly becomes wallpaper. Good news if you're a PostHog user: PostHog, Inc. just launched a real-time dashboard for Web Analytics. 2. Booking.com: Live metrics on the landing page. Booking.com has made a science of turning live activity into social proof: "23 people are looking at this hotel right now." "Booked 4 times in the last 6 hours." "Only 2 rooms left at this price." These numbers are pulled from live booking and browsing data and dropped directly onto the property listing. Put the analytics where the decision happens, not in a dashboard. Booking's live counters aren't a side feature, they're load-bearing conversion copy, and industry analysts have credited similar "shopping activation" widgets with lifting direct bookings by close to 50%. Audit your purchase flow for places a live number would move inventory, demand, recent activity, and put it inline. Real-time data that would matter to a buyer right now is wasted signal the moment you bury it two clicks deep in a reports tab. 3. Product Hunt: making leaderboards front and centre. Product Hunt's entire homepage is a customer-facing leaderboard. Every 24 hours it ranks a few hundred new launches by upvotes, comments, and reviews, and rolls the top five into permanent "Yesterday / Last Week / Last Month" slots. The ranking logic is transparent enough that makers plan launches around it; roughly 700-800 upvotes is the bar for a weekday #1. Make your analytics two-sided. If you're sitting on ranking data, you have a potential product. Leaderboards get dismissed as a gimmick, but Product Hunt turned a database of upvotes into the homepage and let it do two jobs at once: recommendation feed for readers, scoreboard for makers. The more people check it, the more valuable ranking on it becomes. 4. HogHero: Augmenting support data with user information. One of the ways PostHog, Inc. use Endpoints is HogHero, its internal Zendesk app. When a customer opens a support ticket, HogHero shows its support rep the recent conversations that customer had with PostHog AI before they reached a human. The rep gets the context instantly and they start the conversation already halfway to the answer. Customer-facing analytics doesn't have to mean end-customer-facing. The fastest way to make an internal tool feel magical is to put the right data inside the app people are already in, not build yet another place they have to log into. Its reps don't have to switch tabs to PostHog and rebuild the customer's context, the conversation history is just there, scoped to that customer, the moment the ticket opens. 5. GitHub's contribution graph: Building a community through user analytics. The green-square contribution graph on every GitHub profile is one of the most-copied analytics patterns on the internet. It's a one-year heatmap of commits, PRs, issues, and reviews, rendered on your public profile. It's functionally a behavior-change mechanic: "don't break the streak" is a meme precisely because the graph makes the streak visible to you and everyone who looks at your profile. Once you expose customer-facing analytics, users will ask for more - and some will just build it themselves. The contribution graph started life as a visualization and ended up as identity, habit-loop, and an entire third-party ecosystem of GitHub Wrapped tools. These are built by people who want to share their streaks which creates a viral community moment that's great for GitHub's brand. The principles to make your customer-facing analytics a success. Across all five, the same few principles keep showing up: * The data is live, or close enough, that users trust it. * It's embedded where the decision happens: in the listing, dashboard, or profile. It's not parked in a separate reports section. * It's scoped to the viewer. Customers see their own numbers, teams see their team's numbers, buyers see the listing's numbers. * It's owned like a product feature, not a data-team deliverable. Someone owns the latency, the UX, and the roadmap. Endpoints makes customer-facing analytics simple. Inspired to build customer-facing analytics yourself? PostHog, Inc. is biased but PostHog, Inc. think you should check out Endpoints. Here's how it works: * Start from something you already have in PostHog. Open any saved insight, trend, or SQL query you've already built. If you've got a number you trust inside PostHog, you're 90% of the way there. * Turn it into an Endpoint. Go to the three dots at the top of your insight screen and select 'Create Endpoint'. Endpoints wraps your query in a fast, versioned, authenticated API - no backend setup, no pipeline, no new tool to learn. * Drop it wherever you need the number to show up. Your product, a customer-facing dashboard, your landing page, or an internal Slack bot - anywhere that accepts a URL or an API call. * Let it update itself. The number stays live. If you tweak the underlying insight in PostHog, the Endpoint updates too. No rebuilds, no stale screenshots, no Monday-morning "can someone refresh this for me" messages. PostHog is an all-in-one developer platform for building successful products. PostHog, Inc. provide product analytics, web analytics, session replay, error tracking, feature flags, experiments, surveys, AI Observability, logs, workflows, endpoints, data warehouse, CDP, and an AI product assistant to help debug your code, ship features faster, and keep all your usage and customer data in one stack.

Trodo vs PostHog: which product analytics tool for ai-native teams? Trodo and PostHog both call themselves product analytics platforms, but they target different problems. Here is a clear comparison for teams shipping AI-native products in 2026. Trodo vs PostHog PostHog alternative PostHog AI analytics AI product analytics open source product analytics product analytics for AI PostHog is one of the most popular product analytics platforms of the last few years - open-source, developer-friendly, and broad in scope. Trodo is a newer platform built specifically for AI-native product teams. The two tools overlap on the surface (both call themselves product analytics) but diverge sharply once you look at what they actually optimize for. This post compares them honestly. Short version. PostHog is general-purpose product analytics with a strong open-source story and a wide feature set including session replay and feature flags. Trodo is purpose-built AI product analytics for teams where prompts, agents, and tool calls are core to the product experience. PostHog is the better fit for traditional SaaS that has light AI usage. Trodo is the better fit when AI is the product. Where PostHog shines. * Open source with a self-host option - appealing for cost-sensitive teams or strict data residency. * Bundled session replay, feature flags, and experimentation in one tool. * Mature SQL-based query layer for engineers who want full control. * Strong developer ergonomics and a large community. For traditional SaaS or B2C apps where the product is mostly clicks and forms, PostHog is a strong default. The question becomes harder when AI features start to dominate the experience. Where Trodo shines. * Native modeling of prompts, completions, tool calls, and agent runs - not just events. * AI feature adoption, prompt-level funnels, and agent-driven retention out of the box. * Engineering and product see the same traces, joined to the same users and sessions. * Natural-language querying so PMs and growth leads can answer their own questions. * AI-generated PRDs from real product signal. * Built-in agent observability - traces, latency, errors, and cost on the same surface. The data-model gap. The hardest difference to see at a glance is the data model. PostHog (like Mixpanel and Amplitude) is built around a flat events table - each row is a "user did thing." Modeling an agent run in flat events means choosing how to compress hierarchy into properties, and that compression loses information. You either record one event per run (and lose the steps) or record one event per step (and lose the relationship between them). Trodo models hierarchical traces natively. An agent run is a tree of spans (plan | tool calls | completion | user action), each with full metadata, all joined to the user. Funnels can step through that tree. Retention cohorts can filter on it. Nothing flattens unless you ask it to. Cost and total cost of ownership. PostHog's open-source self-host option looks cheap on paper, but the real cost includes engineering time to operate the cluster, build AI-native instrumentation that the platform was not designed for, and maintain that custom layer as agents evolve. Trodo is hosted SaaS with a free tier up to 1M events/month - most AI-native startups land on lower total cost of ownership once the engineering time of "make PostHog understand my agents" is included. When to choose PostHog. Choose PostHog when AI features are <20% of user activity, when bundled session replay and feature flags are valuable to you, when self-hosting matters for compliance or cost, or when SQL-style flexibility outweighs AI-native modeling. When to choose Trodo. Choose Trodo when AI is core to the product, when prompts and agent runs need first-class analytics treatment, when product and engineering need a shared source of truth, or when natural-language querying for non-engineers is a real requirement. Most AI-native startups in 2026 fall into this bucket. Can you use both? Yes - some teams keep PostHog for session replay and feature flags while moving AI product analytics and agent observability to Trodo. The trade-off is maintaining two analytics surfaces. Most teams eventually consolidate; which way you consolidate depends on whether AI or classic product is the bigger workload. Bottom line. PostHog is great general-purpose product analytics. Trodo is purpose-built AI product analytics. If your product is AI, Trodo is the better default. If AI is a feature, PostHog is fine until it isn't - and most teams find the moment it isn't comes faster than expected.

Analyze landing page metrics with posthog. April 15, 2026 PostHog is an open-source product analytics platform that tracks website traffic, user behavior, and engagement metrics. Sliq connects to PostHog so your AI assistant can give you daily traffic digests, answer questions about your landing page performance (top pages, traffic sources, bounce rate), and run custom queries - all from chat. This guide walks you through setting up PostHog from scratch and connecting it to Sliq. Create a posthog account. * On the product selection screen, select Web Analytics only. You can deselect everything else (Product Analytics, Session Replay, etc.) - Sliq only needs web analytics data. Install posthog on your website. This is the most important step. PostHog needs a JavaScript snippet on your website to collect pageview and click data. Without it, there's no data for Sliq to read. PostHog provides a wizard that auto-detects your framework and installs the SDK for you. Run this from your website project's root directory: npx -y @posthog/wizard@latest The wizard will auto-detect your framework, install the PostHog package, and set up event tracking in your code. Just follow the prompts - it supports Next.js, React, Vue, Nuxt, Astro, SvelteKit, Angular, Django, Flask, and more. Note: Run this in your website's codebase, not in Sliq. This installs PostHog's tracking snippet on your site so it can start sending analytics events. The wizard will detect your project directory and framework, then ask you to continue: The wizard will analyze your project, install the PostHog package, and insert tracking code automatically. This takes a few minutes. The wizard may ask if you want to install the PostHog MCP server for your code editor. This is optional and not required for the Sliq integration - feel free to install it if you'd like to query PostHog from your editor, or skip it. When you see "PostHog integration complete", you're done with this step. Configure tracking options. PostHog will ask which tracking features to enable. Here's what each one does: * Autocapture frontend interactions (required) - Automatically captures clicks, form submits, and other interactions on your site. Sliq uses this data to show you which buttons and links your visitors click most. Make sure this is on. * Enable heatmaps (optional) - Captures mouse movements and scrolling to generate visual heatmaps of where users click and look. Useful if you want to see engagement patterns visually in PostHog, but Sliq doesn't use this data. * Enable web vitals autocapture (optional) - Tracks page load performance metrics (Largest Contentful Paint, First Input Delay, etc.) using Google's web vitals library. Helpful for monitoring site speed, but not used by Sliq. * Enable session recordings (optional) - Records video-like replays of user sessions so you can watch exactly how visitors navigate your site. Great for debugging UX issues, but not used by Sliq and can add to your PostHog bill at high traffic volumes. Add your domain. After installation, PostHog will ask you to add Authorized Domains. Add your production website URL (e.g., https://www.yoursite.com). This tells PostHog which domains to track for Web Analytics. Verify analytics in production. After deploying, visit your production website in a browser, then go to Web Analytics in PostHog's left sidebar. You should see your visit show up within a minute or two - pageviews, visitors, and traffic sources will start appearing. If the Web Analytics page shows data, you're ready to connect to Sliq. If it's empty, open your browser's DevTools (Network tab) on your production site and look for requests going to Trysliq.i.posthog.com - if you don't see any, the snippet isn't firing and you may need to re-run the install wizard or check your deployment. Create a Personal API key. Sliq connects to PostHog via a Personal API key. Here's how to create one: * In PostHog, go to Settings > Personal API Keys (or go directly to posthog.com/settings/user-api-keys). Click Create personal API key. * Configure the key: * Label: Give it a name like "Sliq" * Organization & project access: Select Projects and pick the specific project you want to track. You don't need "All access". * Scopes: Click the Performing analytics queries preset - this selects the query:read scope, which is the only permission Sliq needs. * Click Create key. A modal will appear with your key - copy it immediately. The key will never be shown again. * Verify the key was created correctly. Back on the Personal API Keys page, you should see your "Sliq" key listed with Status: Active, Scopes: query:read, and the correct project access. Find your project ID. In PostHog, go to Settings > General. Your Project ID is displayed under the "Project token & ID" section. Copy it - you'll paste it into Sliq in the next step. Connect to Sliq. * From chat: If Sliq is walking you through setup, click the "Connect PostHog to Sliq" link in the chat - a modal will pop up where you can paste your API key and project ID. * From Integrations: Go to the Integrations page in Sliq, find PostHog, click Connect, and enter your API key and project ID. What you get. Once connected, Sliq can: * Daily traffic overview - unique visitors, total pageviews, and daily trends * Traffic sources - where your visitors are coming from (Google, Twitter, direct, etc.) * Top pages - which pages get the most views * Top clicks - which buttons and links visitors click most, with click rates * Engagement metrics - average session duration and bounce rate * Custom queries - ask your AI assistant anything about your traffic and it will query PostHog directly You can also set up a daily analytics digest as an automated task in Sliq to get a summary delivered to Slack every morning. Need help? Last updated: April 2026