Finding Useful Product Metrics Without Drowning in Dashboards

Finding Useful Product Metrics Without Drowning in Dashboards

ByBlogSavvy HQ

Why activation rates usually lie to you

Everybody loves a tidy activation metric. Signup -> Email Confirm -> First Dashboard Load -> Congrats, they’re activated! Except… they’re not. They just tapped a button stack. That’s not activation — that’s item scanning at self-checkout with no groceries.

I made this mistake with a SaaS analytics tool I helped launch. Our early activation rate looked amazing — north of 80%. Turns out, users were clicking through onboarding because it was skippable. Then they’d never connect any data sources. Nobody ever looked at our reporting graphs. Cool!

If you define activation based on time-bound steps with no performance signals, you’re measuring a tutorial completion, not user value realization. Instead, reframe activation backwards: ask, “What do retained users always do within 24 hours?” Then walk from there.

Aha: The real activation moment was when users invited another team member into the dashboard. That was the magic moment tied to 30-day retention.

Most PLG platforms like Mixpanel or Amplitude make you set these events up ahead of time, so if you guessed wrong, you’re blind. Set up wildcard logging and use segment-based retroactive queries if your tool supports it. You’ll thank Past You.

Retention is easier to measure badly

The way most orgs track retention is borderline useless. Here’s what I mean. They install a default 7-day or 30-day cube that checks for activity on Day 0, then again on Day 7. That’s a great way to measure whether the user happened to log in again… not whether they got value.

Good retention metrics rely on defining a meaningful “returning” action. That might be a report exported, a dashboard edited, or just a webhook integration firing. Don’t count logins — count hearts beating in the product.

  • Use event-based recurrence (not session count alone)
  • Slice retention by cohort goals, not signup date only
  • Exclude fake visits like token refresh requests
  • Define a product heartbeat (like “weekly insight check”) and measure decay
  • Use rolling windows for B2B data, not fixed intervals
  • Segment users by employed vs trialing — churn IS behaviorally different

Oh, and watch for ghost activity. I once spent a week debugging a major dip in weekly active users — only to realize our automation testing framework was the top ‘user’.

False positives in conversion metrics (aka the bot problem)

There’s always one exec who wants a conversion funnel spreadsheet stapled to the roadmap. The problem is, your funnel probably includes junk traffic like:
– Internal testers
– Sentry error pings counted as real sessions
– Scripted onboarding completions from your QA automation

Even fancy platforms like Heap or Pendo won’t auto-filter these out unless you dig deep into their source tagging tools. And AdSense? Hah. Good luck telling a ghost click from a real one if your bounce rate is camouflaged by lazy pixels.

Fun one I hit last year:

We were showing a 48% click-to-signup rate. Something felt off. Turned out we’d left a ?ref=autotest tag in the production build for our marketing page. Jenkins was replaying homepage visits every 12 minutes.

Automatic traffic filtering isn’t standard. Always manually tag:

  • Known IPs (QA, office, contractors)
  • Mobile test devices (especially on shared VPNs)
  • Third-party bots like GTMetrix, PageSpeed, etc.

If you’re using something like Segment, build a is_internal_traffic boolean early. Trust me. Retroactive cleaning is horrible.

North star metrics always start messy

The first time someone on the team mentions a “north star metric,” someone else’s Google Sheet dies.

I used to work with a product lead who swore our NSM was “shares per user per week.” Except, we’d never instrumented shares. It was a Phantom Metric. By the time we got around to tracking it, we realized 80% of shares happened outside the logged-in experience (Slack copy-pastes from previews).

So we rebuilt it using a Frankenstein combo of backend logs + browser fingerprinting from our own share widget + a hunk of Postgres text-matching. It worked. Barely. But it gave us an actual measurable thing we could move the needle on.

“North Star” isn’t a formula. It’s a messy, evolving artifact of what your *retained* users love — plus a janky way to count it.

If you’re in a B2B context, this metric might only make sense quarterly. If you’re mobile-first, make sure your Firebase setup isn’t eating 30% of actions under ‘Unassigned’ due to mismatched event names (yes, that happens).

Why time-to-value needs to ignore the clock

Startup people are obsessed with reducing “TTFV” — time to first value. It’s a noble dream. Get them in, get them happy, fast.

But it doesn’t always behave rationally. I was working with an email deliverability tool that had a 15-step onboarding, involving DNS changes, SPF validation, and waiting for Cloudflare TTLs to expire. The average setup time was over 6 days — and churn was low. Like, really low.

The reason? People didn’t need quick D1 value. They needed eventual reliability. Our initial dashboard misled us, showing lower TTFV correlating with lower retention. Classic survivorship bias — the impatient users weren’t our ICP.

Long config flows aren’t always a problem. Just track which steps kill motivation. Instead of tracking time-to-value as a timestamp delta, segment users by the number of meaningful outcomes achieved in sequence. Sometimes a slower user is a stickier user.

Event instrumentation math will gaslight you

There’s something no analytics tool will tell you up front: The way they structure event funnels is prone to inflation and suppression — depending on how the events fire.

Case study: In our product, users customized a dashboard, then saved it to share with their team. We tracked it like this:

{
  event: "Dashboard Customization",
  props: {
    added_modules: 3,
    removed_modules: 2
  }
}

But we forgot to log Dashboard Save. And for two months, we thought conversion was broken.

Even once we fixed it, our event structure still caused problems. Sometimes users hit ‘Save’ before making edits, just to check if they could access share links. The funnel viewed that as a completed interaction. But they never used the dashboard again.

This matters more than you think:
– Platforms like Mixpanel or GA4 group events by session logic you might not control.
– WebView-based apps can drop tracking if lifecycle hooks misfire (seen this with React Native).
– If your event order isn’t linear, conversion analysis gets noisy fast.

Solution? Log context events. Instead of trying to reverse-derive meaning, record intent where you can:

{
  event: "Dashboard Interaction",
  props: {
    intent: "ShareReady",
    module_count: 5
  }
}

Why product analytics dashboards are slow and wrong

Ever open a dashboard and think “huh, that’s not what I expected,” then spend 2 hours re-filtering dimensions? Join the club.

The biggest hidden issue is inconsistent timezones and derived properties. I once found that our monthly signup spike was entirely driven by people signing up in the last hour of the Pacific Time window — but GA4 grouped them into tomorrow in UTC. That one-hour gap created a huge dip on the calendar-logged day.

Also, if your dashboards are slow: it’s likely how your lookups are written. String lookups on segmented properties are 5x slower than numeric indexes. Multiply that across thousands of rows and you get a frozen panel. Use hashed indexes or integer enums everywhere you can. Named strings are beautiful til they burn RAM.

We once rewrote a month’s dashboard logic in plain SQL and sent it to management as a Notion screenshot because the main dashboard literally wouldn’t load over VPN.

Reminder: Dashboards are lies that load slowly. Always debug in raw events first.

Underrated tools that helped me stay sane (most days)

This list changes weekly, but here are a few I wish I’d found earlier. Not endorsements — just tools I’ve duct-taped into too many systems to ignore:

  • RudderStack: Yeah, Segment’s prettier, but Rudder logs the payloads more transparently. Essential when your events mysteriously drop.
  • PostHog: Self-host when your CSO starts sweating over PII in cloud platforms. The event replay and correlation UI is underrated.
  • OpenReplay: For figuring out why your conversion funnel flatlined — it gives session context + DOM state.
  • Cloudflare Workers: Handy for building micro-endpoints that validate events before accepting them (to reduce garbage data).
  • ngrok (paid): better than localhost tunneling nightmares when replicating conversions on mobile during QA.

One undocumented quirk: OpenReplay will silently exclude sessions over a certain memory footprint unless you tweak the sampling config. I didn’t realize that until a full week of rage debugging heatmaps missing crucial rage clicks.

When dashboards say flat, but revenue says up

The weirdest mismatch I hit recently was during a product-led pricing change. We moved from usage-based tiers to feature gates. Product usage looked flat. Revenue jumped overnight. Why?

Because users finally hit a clear paywall. But the core product usage didn’t change. Our dashboards only tracked aggregate actions, not upgrade intent.

If you don’t tag upgrade button clicks, friction points, or failed attempts, your analytics will ghost you during pricing changes. Look at revenue triggers. Often, it’s not volume — it’s path constraints suddenly made visible by a pricing wall.

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