Events
Identify the actions that actually matter: CTA clicks, form starts, submissions, purchases, sign-ups, and meaningful interactions.
I make the behavior between arrival and conversion visible, so you can see where people move forward, where they stop, and which parts of the journey deserve a closer investigation.
Illustrative data only. The point is not the numbers. It is knowing where to look.
You know how many people arrived.
You know how many converted.
But between those two numbers, people clicked, hesitated, explored, abandoned forms, returned, ignored calls-to-action, moved between pages, or disappeared completely.
That behavior is where many useful clues live.
If those actions are not measured properly, the final conversion rate tells you that something happened without showing you where to investigate next.
Not every click deserves a dashboard. We focus on behavior that helps explain movement toward or away from conversion.
Identify the actions that actually matter: CTA clicks, form starts, submissions, purchases, sign-ups, and meaningful interactions.
Find the stages where a disproportionate number of people stop progressing through the journey.
Connect individual actions into a sequence so the journey can be measured as a path, not a pile of unrelated metrics.
Compare useful segments, devices, acquisition sources, or journey stages when those differences can reveal a meaningful pattern.
Are important customer actions missing, duplicated, incorrectly configured, or buried beneath meaningless events?
At which stage does progression weaken, and how large is the loss compared with the stages around it?
Do important patterns change across devices, traffic sources, landing experiences, or other useful segments?
Which findings are strong enough to justify deeper investigation, experimentation, or a change in priority?
Define the meaningful stages between discovery and the outcome the business cares about.
Make the important events and transitions visible with a cleaner measurement structure.
Follow progression, abandonment, and useful behavioral differences through the journey.
Turn the strongest evidence into a clearer list of what deserves attention next.
The exact work depends on the measurement problem, but the goal stays the same: turn behavior into something useful for decision-making.
A clearer definition of the actions and milestones worth measuring.
A measurable view of how customers progress through important stages.
Identification of journey stages showing meaningful loss or unusual behavior.
Useful patterns and differences that help explain where deeper investigation may be warranted.
A practical hierarchy of what the evidence suggests deserves attention first.
A drop-off tells us where something happened, not automatically why a person made the decision they made.
Good conversion intelligence narrows the search. It gives us evidence, reveals patterns, challenges assumptions, and shows where deeper investigation or testing makes sense.
The goal is not pretending data knows everything. The goal is making fewer decisions in the dark.Make the journey visible, find where momentum disappears, and use the evidence to decide what deserves attention next.
Bigin Investigation