bibb Analytics: Designing Measurement Like a BI Problem

Start with goals, not tools

I want to thank Pascal. Through our conversations, he helped me clarify the business goals behind marketing analytics measurement. His deep understanding of how site analytics, attribution, and business value connect helped bibb get these goals right.

The first move in any BI engagement is not to open Power BI. It is to understand what questions the business needs to answer and what decisions depend on the data.

Same principle applies here.

At a high level, bibb has two main goals: user experience and commercial outcomes. You cannot achieve the latter without getting the former right. The commercial priorities are clear: sell templates, grow the newsletter, deliver on sponsor commitments. Everything else is either a leading indicator or a diagnostic signal. We organized this into three layers.

This is a balance game. Early on, we decided that UX comes first and that no commercial decision should compromise it. For example, bibb reached 48K users in the year to August 2026. We decided against ads anyway. Sponsor content is clearly labeled and only accepted when it brings real value to our readers.

The same applies to the free tools. They should be fully usable with no purchase required. Users should come back because the content and tools are genuinely useful. That is the bar. Our 28.3% return rate tells us we are on the right track.

With our high-level goals defined, we can now move forward to our layer measurement strategy: conversions, engagement, and diagnostics.

The three layers of measurement

The layers are not a hierarchy of importance. They are a way to group and organize the measurement model.

Conversions Layer. These are the outcomes the business exists to produce. A reader who buys a template is telling us the content and tools delivered enough value to pay for. A visitor who subscribes to the newsletter is making a longer bet, choosing to keep hearing from bibb without an algorithm deciding whether they do. A reader who clicks through a sponsor placement is completing a deliverable we promised. All three are things we can point to and say: this happened.

Engagement Layer. Not everything worth measuring converts immediately. Theme downloads, tool usage, blog visits, carousel and lightbox interactions do not generate revenue directly. Users who engage at this level accomplish our foundational User Experience goal and convert at higher rates. That makes them useful signals for diagnosing the funnel. Not proof that something worked, but evidence that something is moving in the right direction.

Diagnostics Layer. Consent acceptance rate, client-side errors, AI magic failure rate. These are not KPIs. They are checks. But they also serve the User Experience goal directly. A spike in client-side errors means something in the product is broken. That is a UX problem before it is an analytics problem. The kind of signals you look at when something feels off, not when you are building a report.

This separation matters for the same reason it matters in any data model. You do not want to aggregate across layers. A lightbox impression and a template purchase are different facts. Keep them in different buckets, measured with different expectations.

What is next?

Strategy without implementation is just a document. The next post will cover Google Analytics: how sessions work, how UTMs flow into attribution, and how we keep session context intact when a user crosses from bibb.pro to Gumroad to complete a purchase. That last part is harder than it sounds.