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8 Comments

I Built CheckAnalytic Because Privacy Turned Analytics into a Liability! 🔥

I didn’t start CheckAnalytic.com because I wanted to compete with Google Analytics.

I built it because analytics stopped feeling like a tool — and started feeling like a risk.

Not a theoretical risk.

A practical one.

Analytics used to be simple. Then privacy changed everything.

A few years ago, analytics meant:

• add a script,

• see traffic,

• understand behavior,

• make decisions.

Today, especially in the EU, analytics means:

• consent banners,

• partial data,

• legal uncertainty,

• constant trade-offs.

The problem isn’t that teams don’t care about privacy.

It’s that most analytics tools were not designed for this environment.

Consent banners don’t solve the real problem

I implemented consent banners like everyone else.

What I got:

• fewer usable metrics,

• worse UX,

• and the uncomfortable feeling that compliance was still fragile.

Users clicked “Reject”.

Data disappeared.

Funnels broke.

And yet, the responsibility still stayed with me as the product owner.

That’s when I realized: consent is not a technical solution — it’s a legal workaround.

GDPR changed the cost structure of analytics

Under GDPR, you don’t just collect data — you defend it.

You need to justify:

• why you collect it,

• how long you keep it,

• who processes it,

• and how it’s protected.

For a small SaaS, that’s not just overhead.

It’s cognitive load that steals focus from the product.

I didn’t want analytics to be the most legally complex part of my stack.

Privacy-first stopped being an ideal. It became a constraint.

At some point, I stopped asking “How can we track more?”

And started asking:

“What do we actually need to know to run this business?”

The answer was surprisingly small:

• how many people visit,

• what pages and features matter,

• what converts,

• what doesn’t.

None of that required tracking individuals.

That’s why CheckAnalytic is intentionally minimal

CheckAnalytic is built around a simple idea:

If you don’t collect personal data, you don’t need to manage consent or defend collection.

So it:

• doesn’t use cookies,

• doesn’t identify users,

• doesn’t build profiles,

• doesn’t require consent banners.

It focuses on aggregate behavior, not people.

That’s a conscious trade-off — and I’m okay with it.

What I gave up — and why it was worth it

Yes, I lost:

• user-level journeys,

• long-term identifiers,

• hyper-granular attribution.

But I gained:

• predictable data,

• cleaner UX,

• lower legal stress,

• faster setup,

• and more trust in the numbers I see.

For my use cases, that was a net win.

I don’t think CheckAnalytic.com is for everyone

If you need:

• individual user tracking,

• advanced attribution models,

• deep personalization, you’ll need heavier tools.

But if you’re a SaaS founder who wants:

• clarity without surveillance,

• analytics without consent fatigue,

• insight without legal anxiety, then this approach might resonate.

The question I keep coming back to

If a SaaS product can:

• grow,

• iterate,

• and make solid decisions without tracking individuals…

Why do we still assume that invasive tracking is the default?

I don’t have a universal answer.

I just built the tool I needed — and it turns out others needed it too.

posted to Icon for Check Analytic
Check Analytic
  1. 2

    I'm going to give this a try now for my site.

    1. 2

      If you have any questions, I will be happy to answer them!

      1. 1

        Thank you very much! I'll sure have some.

  2. 2

    This resonates deeply. The shift from "analytics as insight" to "analytics as liability" is something a lot of EU-based founders feel but struggle to articulate.

    What strikes me is your framing around the question "What do we actually need to know?" Most teams I've worked with never ask this - they default to maximum tracking because that's what the tools encourage. Then they drown in data they never use while creating compliance headaches.

    The aggregate-first approach makes sense for 90% of product decisions. You don't need to know *who* dropped off at step 3 - you need to know *that* step 3 has a problem.

    Curious about one thing: have you seen patterns in which types of teams adopt this mindset fastest? My guess would be dev-heavy teams who've dealt with GDPR audits firsthand.

    1. 1

      Yes. There is a pattern, and it is quite clear.

      This approach is most quickly adopted not by size, but by the maturity of the team's thinking.

      These are usually small teams where decisions are made quickly and analytics are viewed not for the sake of reports, but for the sake of action.

      They have already understood that:

      • privacy is an operational constraint, not a feature,

      • stable aggregated data is more important than hyper-detail,

      • less tracking = fewer risks and disputes over numbers.

      Enterprise and data-driven corporations come to this later — more often as an addition than as a basis.

      1. 2

        "Maturity of thinking" over team size - that's the framing I was missing. It explains why some 3-person startups get this immediately while some 50-person companies never do.

        The "analytics for action vs analytics for reports" distinction is sharp. I've seen teams generate beautiful dashboards nobody uses because the data isn't connected to any decision. Meanwhile, a founder with one metric they actually check daily makes faster progress.

        Your point about enterprise adopting this as "addition rather than basis" is interesting - it suggests the aggregate-first approach works best when baked in from day one, not retrofitted. The teams who start minimal stay minimal. The ones who inherit bloated tracking rarely simplify.

  3. 1

    Very cool analytic tool for start ups want a clean easy-to-use analytic tool, is this tool only offer web analytics?

    1. 1

      Not just on websites.

      1. SaaS products and web applications

      Not just landing pages, but also:

      • dashboards,

      • onboarding,

      • key product screens,

      • internal tools for customers.

      2. Marketing websites and content projects

      • blogs,

      • documentation,

      • changelog,

      • help centres.

      3. Internal panels and closed systems

      • admin panels,

      • partner portals,

      • client dashboards.

      When analytics is needed by the team, not marketing.

      CheckAnalytic is suitable wherever:

      • Decisions are important, not user profiles,

      • Privacy is a restriction, not a formality,

      • Signals are needed, not noise.

      This is not "web analytics for the sake of web analytics."

      It is a tool for observing the product in the real world.