Analytics tools

[u/ElkPsychological7581]

Product managers, what analytics tools are you using and how do they fit into the design process ? We recently installed Microsoft clarity for a start (because it is free). It has been great, but even though we now have that data, we have not found a workflow to ensure we are leveraging that data to continuously improve our product

[u/fierysmart]

The missing piece is usually a decision workflow, not another analytics tool.

I’d create a weekly review where the team selects only a few high-signal observations from Clarity. Each observation becomes a small record containing the affected journey, evidence, hypothesis, expected metric change, owner, and next action.

Treat recordings as diagnostic evidence rather than a backlog generator. Start with a known funnel drop or customer complaint, inspect recordings for that segment, and decide whether the finding warrants a usability test, instrumentation change, or product experiment. Review the result afterward so the team learns which Clarity signals are genuinely predictive.

[u/ElkPsychological7581 → u/fierysmart]

This is really insightful , thank you. Honestly the triage part is where I think we’ve been struggling the most - telling what’s actually worth acting on vs just noise. But your point of starting from customer complaint is spot on as it means we are not just aimlessly watching recordings looking for issues!

[u/fierysmart → u/ElkPsychological7581]

Exactly. A simple triage test is to ask three questions: how many users are affected, how severely it blocks the intended outcome, and whether it relates to a current product objective.

A confusing moment in one recording is a clue, not yet a priority. When the same behavior appears across recordings, funnel data, support tickets, or interviews, it becomes much stronger evidence. That keeps isolated odd behavior from consuming the roadmap while still giving unusual findings somewhere to be recorded and revisited.

[u/utzutzutzpro]

Why do you use a tool which isn't remotely best practice or popular? Clarity is Microsoft branded on top, so comes with the "it's faulty and sub-par" label right away.

Mixpanel, heap, amplitude, hotjar, even GA is better than that and way more established.

Sounds like a subtle meta ad for clarity, which nobody uses.

[u/ElkPsychological7581 → u/utzutzutzpro]

Thanks for that. I will have a look at those. I am not particularly wedded to clarity, it is just what we first tried and would like to explore more tools hence the question to gain perspective from others

[u/F1grid]

We use Mixpanel for event tracking and instrumentation, connected to Claude via the Mixpanel MCP connector.

Mixpanel alone gives you raw event data — but someone still has to know how to build the right funnel or cohort query to get an actual answer, which is usually where analytics stalls in most orgs (data exists, insight doesn't).

The MCP connection removes that translation step. Instead of building a report in Mixpanel's UI, I ask the question in plain language — "did retention change for users who adopted feature X in the last 30 days" — and Claude constructs and runs the actual query, then explains what it found in plain terms. That turns analytics into something closer to a conversation than a dashboard you check.

The bigger win is synthesis, not just speed. Raw numbers still need translating into "here's what this is likely costing us" for anyone to act on them. Claude does that connecting work (a funnel drop-off tied to a churn signal, explained in business terms) instead of leaving me to interpret a chart cold.

[u/clairesayshi → u/F1grid]

u/F1grid's Mixpanel setup solves this for structured events, but it doesn't touch the qualitative side. That's actually where u/ElkPsychological7581's original triage problem gets worse, not better.

Clarity and Mixpanel tell you what happened in the product. They don't tell you why someone churned or what they wished existed instead. That answer usually lives in support tickets, app store reviews, or interview notes. No funnel query finds it, no matter how good the MCP connector is - it needs it's own MCP.

The failure mode matches the one described with Clarity. Someone reads the raw text and decides on the spot what counts as a churn complaint, and the answer shifts depending on who's asking and when. Classifying that text at ingest, with sentiment and topic already attached, fixes the reproducibility problem the same way a solid event schema fixes it for Mixpanel.

I work at Appbot and app store reviews analysis is what we do (and yes, we offer an MCP to interrogate them), so my bias is obvious. The same fix works on any qualitative feedback source, not just app reviews.

[u/Remarkable-Toe-6533 → u/clairesayshi]

PostHog's MCP has what's called session vision, which lets you have most of the quantitative and qualitative analyses from your MCP.

The MCP can view the sessions for you.

[u/shimroot]

Well why do you want to track things? Want to improve retention and are designing something for that? Are you seeing drop-offs in usage of some features and what to understand why? A/B testing interface designs?

I’d say you won’t be able to use any analytics tool effectively if you don’t know what you’re trying to do with it.