You cannot ship good products without understanding how people use them. The analytics tool you choose determines what questions you can answer, how fast you can answer them, and whether your whole team can self-serve or bottleneck through a data analyst.
I tested five product analytics platforms on the criteria that matter most to PMs: ease of event setup, funnel and retention analysis, cohort capabilities, and how quickly a non-technical PM can get to an answer.
The tools below are ranked by overall value for a mid-stage SaaS product team. Your best pick depends on budget, engineering bandwidth, and whether you need analytics-only or analytics-plus-guidance. Here is where they landed.
What Makes a Good Product Analytics Tool
Before diving into individual tools, here is what separates a useful analytics platform from one that collects dust:
With those criteria in mind, here is how the five platforms compare.
The tools below are ordered from strongest overall analytics capability to most specialized. Every tool on this list has a free tier or free option worth trying.
Amplitude
Amplitude is the strongest general-purpose product analytics platform for mid-size to large SaaS teams. Its analysis depth and collaboration features set it apart.
Strengths:
Weaknesses:
Pricing: Free up to 50K MTU. Custom pricing above that. Expect significant costs once you are past 100K MTU, so get a quote early.
Best for: B2B SaaS teams with 50K+ users that need deep behavioral analysis, cohort comparisons, and cross-functional analytics access. If your product team has 5+ PMs who need to self-serve analytics without a dedicated data analyst, Amplitude's collaboration features (shared notebooks, team dashboards, governed event catalog) are what make the difference.
Mixpanel
Mixpanel matches Amplitude in core capabilities and beats it on pricing transparency. If cost predictability matters, Mixpanel's event-based model is easier to budget.
Strengths:
Weaknesses:
Pricing: Free up to 20M events/month. Paid plans start at $25/month. The free tier is the most generous of any dedicated product analytics platform.
Best for: Early-to-mid-stage products that want strong analytics without the enterprise price tag. Mixpanel is particularly strong for B2B SaaS with its group analytics feature, which lets you track account-level behavior alongside individual user metrics. See Mixpanel vs Amplitude for a detailed side-by-side.
Heap
Heap takes a fundamentally different approach: it captures everything automatically. Instead of planning which events to track in advance, Heap records all user interactions and lets you define events retroactively.
Strengths:
Weaknesses:
Pricing: Free tier available. Custom pricing for paid plans. Heap tends to be more expensive than Amplitude and Mixpanel at comparable usage levels because of the autocapture data volume.
Best for: Teams that want fast time-to-value and cannot afford engineering cycles for a full tracking plan. Heap works especially well as a complement to a manually instrumented tool: use Heap for exploration and discovery, use Amplitude or Mixpanel for your core KPIs and dashboards. Compare: Amplitude vs Heap.
Pendo
Pendo combines product analytics with in-app guides and feedback collection. If you want to measure feature usage and nudge users toward adoption in the same tool, Pendo does both.
Strengths:
Weaknesses:
Pricing: Free tier (up to 500 MAU). Custom pricing for paid plans.
Best for: Product teams that want analytics and user guidance in a single platform, especially those focused on feature adoption and onboarding. See Amplitude vs Pendo and Pendo vs Heap.
Google Analytics
Google Analytics (GA4) is the default choice for marketing-focused measurement and a viable starting point for early-stage products that are not ready to invest in a dedicated product analytics tool.
Strengths:
Weaknesses:
Pricing: Free. GA4 360 (enterprise) starts at $50K+/year.
Best for: Marketing-led products, content sites, and early-stage teams that need basic analytics without a budget. Not a substitute for Amplitude or Mixpanel once your product requires behavioral depth. Most teams keep GA4 running alongside their product analytics tool to cover acquisition and marketing attribution.
Comparison Table
| Tool | Starting Price | Free Tier | Event Model | Session Replay | Best For |
|---|---|---|---|---|---|
| Amplitude | Custom | 50K MTU | Manual | Yes | Deep behavioral analysis |
| Mixpanel | $25/mo | 20M events | Manual | No | Cost-effective analytics |
| Heap | Custom | Yes | Autocapture | Yes | Low-engineering setup |
| Pendo | Custom | 500 MAU | Tag-based | No | Analytics + in-app guides |
| Google Analytics | Free | Unlimited | Hybrid | No | Marketing-focused measurement |
How to Choose
Three questions narrow the field:
For teams serious about feature adoption and retention analysis, Amplitude or Mixpanel is the right investment. Google Analytics works as a complement for acquisition data but should not be your only analytics tool once you have a product people are using.
Setting Up Analytics Right
Whichever tool you pick, the implementation approach matters more than the tool itself. Here are the steps that prevent the most common mistakes:
Define your event taxonomy before writing any code. Create a shared document listing every event name, its properties, and when it fires. Use a consistent naming convention (noun_verb or object_action). This takes a day of PM work and saves months of confusion.
Instrument your core funnel first. Do not try to track everything on day one. Start with signup, activation (the "aha moment"), and the core action your product exists for. Add more events once these are reliable.
Validate data accuracy in the first week. Compare event counts against your database or server logs. A 5-10% discrepancy is normal (ad blockers, network issues). A 30%+ discrepancy means your implementation has bugs. Fix them before anyone starts making decisions on the data.
Set up key dashboards for your team. Create 3-5 shared dashboards that answer the questions your team asks weekly: activation rate, daily active users, feature adoption for your current release, and funnel conversion. If people have to build their own analysis from scratch every time, they will not use the tool.
Monitor your North Star Metric. Whichever tool you choose, make your North Star the most visible number in the system. Pin it to the default dashboard. Set up alerts for significant changes. If your analytics tool does not make it easy to check your most important metric in under 10 seconds, your setup needs work.
Common Mistakes to Avoid
Tracking everything from day one. More events does not mean better insights. Start with 15-25 events covering your core user journey, then expand based on actual questions your team asks. Teams that instrument 200 events on launch day end up with a noisy event catalog that nobody trusts.
Ignoring data quality. The most common issue is duplicate events or missing properties. Run a data audit every quarter. Check that key events fire exactly once per action, that user IDs are consistent across platforms, and that required properties are never null. One PM should own data quality, even part-time.
Using vanity metrics. Total signups, page views, and "users" (without defining what counts as a user) look good in reports but do not drive decisions. Focus your dashboards on activation rate, retention by cohort, and feature adoption rate. These metrics tell you whether your product is actually working.
Not sharing insights. Analytics data that stays in the PM team's dashboards is wasted. Set up a weekly email or Slack digest with your top 3-5 metrics. Share interesting findings in all-hands meetings. The more your company sees product data, the more data-informed decisions happen across every function.
Skipping the connection to business metrics. Product analytics shows user behavior. But if you cannot connect DAU/MAU ratio to revenue retention or churn rate, your analytics exist in a vacuum.
Set up at least one dashboard that ties product usage to revenue outcomes. This is the dashboard your CEO and board will actually look at.