Adriaan

For Simple Analytics right now, it doesn't.

That might change when we're at scale and have a dedicated content team. But with a small team moving fast, focus beats coverage.

But for us? The opportunity cost was too high. Every hour spent wrangling translations was an hour not spent shipping features or writing content.

The question isn't whether multilingual content can work. It's whether the return justifies the complexity.

If you're Stripe processing payments in 40 countries, multilingual might be needed. If you're targeting SMBs in specific regions, local language is important.

So we made a call. English only. Hardcoded. No more translation overhead.

Here's the thing though: this might be completely wrong for your business.

Every blog post meant 5 translations. The translation libraries made development slower. SEO kept breaking.

And the data told the story: in Google Search Console, the first meaningful international search result appeared at query 228. That's 227 English queries before that.

We just killed multilingual support at Simple Analytics. All of it.

For the past few years, we've maintained content in 5 European languages. The theory made sense: we're a European privacy tool, GDPR is strongest here, why not speak to founders in their language?

But the reality was brutal.

I found ffmpeg!

#knowyourmeme

I just started bouncing every cold email from Gmail, Outlook, and similar addresses.

To prevent me from going through email as a painful task, I just moved the effort to the sender. Just send from your own domain, and I will receive your email.

Otherwise, you can instructions to do so.

The Real Bottleneck: Why Faster Coding Doesn't Speed Up Projects The bottleneck in software development has never been programming, but understanding the problem. On the underestimated ROI of understanding.

www.heise.de/en/blog/The...

The bottleneck in software development has never been programming, but understanding the problem. On the underestimated ROI of understanding.

Show me the workflows that are still running and delivering value six months from now. Not the ones someone set up yesterday and tweeted about because it felt cool.

That's when we'll know if this is here to stay or just another shiny object.

The question is whether the output quality holds up long enough to matter. Whether the time saved is worth the supervision cost. Whether you're building something durable or just riding another hype wave.

I'm genuinely curious about the long-term use cases.

Quick setup, immediate win, excitement. Then three months later you realize it's producing the same repetitive output and you stop checking.

The question isn't whether it can automate tasks. It can.

I'm watching the OpenClaw hype cycle with the same feeling I had during the GPT wrapper boom.

Everyone's setting up overnight workflows and celebrating because something automated happened. That dopamine hit is real.

But I've seen this pattern before.

You are missing 20% of your website data with GA4 Using Google Analytics 4 in combination with a mandatory cookiebanner leads to data loss of 20% on average. Use Simple Analytics to get the full picture.

When we tested a cookieless setup alongside GA4, that missing traffic reappeared. Not because users changed behavior, but because the analytics method changed.

The gap isn't a bug.
It's baked into cookie-dependent analytics.

Full case study: www.simpleanalytics.com/blog/you-ar...

But 20-30% of visitors clicked "reject" and vanished from the dashboard.

GA4 wasn't broken. It was working as designed.

The model requires consent to track.
No consent, no data.
Simple as that.

For years I thought the data gaps in Google Analytics were my fault.

Maybe I misconfigured something.
Maybe the tracking code broke.
Maybe I was missing a filter.

Then cookie banners became mandatory, and the picture cleared up fast.

Traffic kept flowing to the site.

But for most teams shipping products and iterating quickly, clarity beats precision.

The goal isn't perfect attribution.

It's faster, better decisions.

In the video we just shared, you can see how we approach this differently at Simple Analytics.

We show what happened without layering assumptions on top.

Does this work for everyone? No. If you're running complex B2B campaigns with 6-month sales cycles, you probably need more.

Here's what I've noticed: most teams don't lack attribution data. They lack the confidence to act on imperfect information.

GA4's complexity gives them an excuse to keep analyzing instead of deciding.

Attribution should help teams make decisions faster.

In GA4, it often does the opposite.

More models. More rules. More meetings debating which number is right.

Meanwhile, the actual work waits.

But if you're spending more time configuring your analytics than using them, you've adopted the wrong tool for your actual workflow.

In Simple Analytics, you open the dashboard and see your pages. That's it.

Not because we dumbed anything down, but because we only collect what actually drives decisions for most businesses.

GA4 can do a lot. And some companies genuinely need that.

Here's the thing though: GA4 isn't broken. It's built for enterprises running multi-channel attribution across apps, web, and offline conversions.

But most marketing teams aren't enterprises.

They're making decisions based on three metrics, yet maintaining a tool built for 300.

Ever notice how something as simple as top pages became hard to answer in GA4?

What used to be a default view now needs events, configurations, and a dashboard you have to explain before you can trust it.

Sometimes the boring answer is the right one.

The result? For bigger teams managing consent across tools, we've become the one analytics platform that just works. No exceptions in the consent flow. No data loss from rejected cookies. No friction.

We're not bypassing privacy law. We're finally aligned with it.

At Simple Analytics, we took a different approach. We don't collect personal data at all. No cookies, no localStorage, no sneaky workarounds.

Which means no consent banner needed.

Ever notice how cookieless analytics platforms still show cookie banners?

Turns out, most aren't actually cookieless. They've just moved tracking to localStorage, fingerprinting, or server-side methods. Still tracking individuals. Still need consent.

Elasticsearch's scalability heavily relies on shard strategy, refresh settings, segment merges, and heap pressure, which can be cumbersome. Its primary strength is searching, a feature we use much less in our product.