From Vague Signal to Debug Target: How Structured Funnel Analysis Unblocked an EU Fintech Product Engineering Team
Every business has a funnel but most teams don't fix their biggest leaks because they don't know where to start. Here, 40% of activations were being lost until it was pinpointed and turned into a clear fix.
The engineering team at a Nordic BNPL platform already knew users were abandoning their credit activation flow. Monitoring dashboards showed it. Product meetings discussed it.
But knowing a problem exists and knowing how to fix it are two very different things — and for months, the team had the first without the second.
The platform issued credit to approved users — but a meaningful portion never completed a purchase.
Without step-level instrumentation, the dev team was flying blind — and three questions had no answers:
Which step is bleeding users?
Without step-level data, every drop-off looked the same. There was no way to rank the problem or prioritize a fix.
Is this a UX issue, a latency issue, or a bug?
The team was guessing. Fixes were being shipped based on intuition, not evidence.
Where should we spend the next sprint?
Business stakeholders flagged declining activation rates — but the gap between "metric is down" and "here's what to change in the codebase" remained unbridged.
We mapped every state transition from credit approval through to first purchase completion — and quantified exactly where users were falling off.
Build the funnel model.
Mapped each stage of the credit issuance and purchase activation flow. For every step: user volume entering, drop-off count and rate, and absolute revenue impact.
Rank by absolute loss, not percentage.
A stage losing 15% of 10,000 users matters far more than one losing 40% of 200. We reordered the priority list entirely based on this.
Translate findings into debug targets.
The output wasn't a dashboard — it was a structured data model engineers could act on directly: drop-off rates mapped to API transitions, timestamps showing where delays concentrated, and flags where timing anomalies pointed to bugs rather than user behavior.
40% of lost activations traced to a single, previously unquantified stage
Drop-off pattern flagged as a bug — delays too uniform and fast to be voluntary abandonment
Engineering backlog reprioritized — highest-impact drop-off moved from unknown to top of queue
Repeatable diagnostic framework established — no new analysis required each cycle
Case Studies
This is just one example.
I have many more case studies with full technical walkthroughs, data, and methodology.
Work with me