What is product analytics and how is it different from web analytics?
Web analytics (like GA4) focuses on marketing performance — traffic sources, campaign attribution, and conversion events. Product analytics (Amplitude, Mixpanel, PostHog, Heap) focuses on user behaviour within your product — feature usage, activation funnels, retention cohorts, and individual user journeys. Product analytics platforms are built around identified users (tied to a user ID), making it possible to track the same user's actions across multiple sessions and devices over their entire product lifetime.
Which product analytics platform should I choose — Amplitude, Mixpanel, PostHog, or Heap?
Amplitude is best for mature product teams that need powerful behavioural analysis, strong experimentation features, and enterprise integrations. Mixpanel is excellent for teams that want deep funnel and retention analysis with a lower learning curve. PostHog is ideal for teams that want open-source, self-hosted product analytics with built-in session replay and feature flags. Heap auto-captures all user interactions without requiring predefined event tracking, making it good for retrospective analysis. We help you evaluate and select the right platform for your team size, technical capabilities, and analytical needs.
What is an event taxonomy and why is it important?
An event taxonomy is the structured naming system and specification for all the user actions you track in your product analytics platform. It defines event names (e.g., "signup_completed", "feature_activated", "subscription_upgraded"), the properties each event carries (plan_type, user_id, feature_name), and how events relate to business milestones. A good taxonomy makes all analyses consistent and comparable; a bad one creates duplicate events, inconsistent naming, and data that no one trusts. We design your event taxonomy before writing a single line of tracking code.
What is a product funnel and how do I analyse it?
A product funnel tracks the step-by-step sequence of actions a user completes on the path to a desired outcome — typically activation (reaching the core "aha moment"), conversion (upgrading to paid), or a key feature adoption milestone. Funnel analysis shows you the conversion rate between each step and where users drop off. Product analytics platforms provide dedicated funnel tools that let you define any sequence of events as a funnel and analyse it by user segment, time period, and cohort.
What is cohort analysis and why does it matter for retention?
Cohort analysis groups users by a shared characteristic — typically the week or month they signed up — and tracks their behaviour over time. A retention cohort grid shows you what percentage of each signup cohort is still active (returning and using the product) at day 7, day 14, day 30, day 60, and day 90. This reveals whether your retention is improving over time (product improvements showing up in newer cohorts) and identifies the benchmarks your product needs to hit to be sustainable.
Can product analytics track mobile app users as well as web users?
Yes. Amplitude, Mixpanel, PostHog, and Heap all provide SDKs for iOS (Swift/Objective-C), Android (Kotlin/Java), React Native, and Flutter, as well as web (JavaScript) and server-side (Node.js, Python, Ruby, Go, Java) SDKs. Cross-platform user stitching — connecting the same user's actions across web and mobile app — requires implementing user identification correctly at account creation and login. We handle this as part of our implementation.
How does product analytics help with feature adoption?
Feature adoption analysis uses product analytics to measure how many users discover and use a new feature, how quickly they adopt it after release, and how usage evolves over time. You can compare feature usage between user segments (power users vs. casual users, paid vs. free tier), identify users who have not discovered a feature (for targeted in-app or email outreach), and correlate feature usage with retention and upgrade rates to understand which features drive the most long-term value.
What is user identification and why is it critical in product analytics?
User identification (calling the analytics SDK's "identify" method with your internal user ID) links anonymous tracking events to a specific user record, enabling cross-session and cross-device tracking. Without it, every session appears as a new anonymous user — making retention analysis, user journey mapping, and personalisation impossible. We implement user identification at the correct points in your application's authentication flow (signup, login, token refresh) as a core part of every product analytics setup.
Can I migrate from one product analytics platform to another?
Migrating product analytics platforms is possible but requires careful planning. Your historical event data can often be exported and imported into the new platform (with varying fidelity depending on the platforms involved). More importantly, the new platform needs a complete event specification, instrumentation update, and validation period before you can decommission the old one. We manage end-to-end migrations, including parallel running periods to validate data consistency before full cutover.
How long does a product analytics implementation take?
A basic single-platform implementation (SDK integration, core event tracking, initial dashboards) for a web application typically takes 2–4 weeks. This includes event taxonomy design (1 week), development implementation by your engineering team (1–2 weeks, with our specifications and QA support), and dashboard setup and training (1 week). Mobile app implementations or those requiring complex server-side tracking typically add 1–2 weeks.
