The Map vs the Clickstream

The Map vs the Clickstream

Draw the user journey your team assumes — a retention curve across the touchpoints of a flow — then watch the real clickstream drop onto the exact same axis. Where you thought friction lived, and where people actually fall out, become one picture: the gap is scored at every step, the biggest real cliff is marked, and the touchpoint where your map is most wrong lights up. Runs entirely in your browser (0 uploads, works offline).

How to use it

1. Read the seeded flow's five touchpoints (Landing → Signup → Setup → First value → Activated).
2. Drag each assumed retention slider to draw the curve your team believes in — how many people you think survive to each step.
3. The real clickstream curve is already on the same axis. Compare the two lines, read the per-step gap score, and see the marked cliff (biggest real drop) versus your map's biggest miss.

What this clears up (the fundamentals)

• Assumption is not data — a journey map is a hypothesis. Overlaying the real drop-off curve on the same axis is the cheapest way to test it, and the gaps are almost never where the team expected.
• Retention curve vs step-over-step drop — retention (% of the original entrants still here) and the drop between two steps are different lenses. The biggest cliff is a step-to-step conversion problem; the map's biggest miss is a retention problem. This tells them apart.
• The biggest drop-off is a conversion, not a count — 900→300 loses more of the flow than 1000→900 even though both lose users. Ranking by step-over-step % is how you find where to actually spend design effort.
• One axis beats two decks — the reason teams argue about friction is that the assumed curve and the analytics live in different documents nobody lays on top of each other. Share one axis and the disagreement resolves itself.

Where it's used

A journey-mapping intuition builder — a fast way to feel how far a team's stated map drifts from the clickstream, and to practise predicting the real cliff before the reveal. It's a miatz build-lab concept playable: play it here, then learn to build the overlaid dual-curve chart yourself.

FAQ

What's the difference between the assumed curve and the clickstream?

The assumed curve is the retention your team predicts at each touchpoint — intuition drawn as a line. The clickstream is the real, anonymized per-step user counts turned into the same kind of curve. Putting both on one axis is the whole point: the distance between them is the assumption gap.

How is the "biggest drop-off" chosen?

By step-over-step conversion, not raw counts. Each step's drop is the share of the previous step's users who didn't make it ((prev − here) / prev). The step with the largest such drop is the real cliff — where people actually fall out, regardless of how many entered the flow.

What is the gap score at each touchpoint?

actual% − assumed% at that step, in percentage points. A negative gap means reality is worse than your map (you were optimistic there); a positive gap means it's better than you feared. The touchpoint with the largest absolute gap is your map's biggest miss.

Is anything uploaded?

No. The seeded clickstream, your assumed curve, the overlay, and every gap score are computed in your browser — nothing is transmitted or stored. Turn off your Wi-Fi and it still works.

Limits

A teaching model on one seeded flow with synthetic, anonymized clickstream and a single retention lens — it omits segment splits, time-to-convert, re-entry loops, and the emotional why behind a drop (a survey or session replay tells you that). Use it to build the map-vs-data instinct, then run the same overlay on your own funnel export.

Related

Part of the miatz playgrounds. Explore the rest from the playground catalog.

Bookmark this page (Ctrl+D, or ⌘D on Mac) — it works offline the next time you need it.