Onboarding Metrics

Surfer — Standard Plan

v1.1 — May 2026

Krystian Prorok

One metric per step — the signal that tells us if it's working. We measure first, then optimize. The targets below are what good looks like. The last section describes what those numbers unlock next.

Primary metrics

> 75%

Completion rate

Are users finishing the full flow?

< 4 min

Time to complete

Is the flow fast enough to not lose people?

Idea

Quality / Confidence

Valuable but hard to measure without post-onboarding correlation data. Candidate for next iteration.

Per-step metrics

Brand Setup

Primary metric: GSC connect rate

Metric	What it tells you	Target
GSC connect rate	Are users comfortable connecting Google accounts?	> 60%
Edit rate per AI-generated field	Which fields does the AI get wrong? High rate on key topics degrades prompt quality downstream.	20–40% healthy
Time on step	Is reviewing AI suggestions causing friction?	< 90s
Drop-off rate	Did users abandon the flow at this step?	< 15%

Pages / Posts

Primary metric: Average changes to AI pre-selection

Metric	What it tells you	Target
Average changes (adds + removes)	How much effort does selection take? Under 5 changes means the AI pre-selection is accurate.	< 5 changes
Time on step	Is the selection task efficient?	< 60s
Drop-off rate	Did users abandon the flow at this step?	< 15%

Prompts

Primary metric: Which categories are deselected most

Metric	What it tells you	Target
Deselection rate per category	Which AI-generated categories are irrelevant for most users?	Baseline only
% who changed default selection	Are users engaging with prompt review at all?	Baseline only
Time on step	Is category review fast enough?	< 45s
Drop-off rate	Did users abandon the flow at this step?	< 15%

3.5

Team Invite optional · skippable

Primary metric: Team invite rate

Metric	What it tells you	Target
Team invite rate	Teams that onboard together retain better.	Baseline only
Skip rate	Is this step causing drop-off?	If > 80% skip → move post-onboarding

Completion

Primary metric: Return visit within 24 hours

Metric	What it tells you	Target
Return visit < 24h	Did the user come back to see first ChatGPT data?	> 50%
Time to first data	How fast does the product deliver value?	< 24h

Time-to-return — drop-off recovery

Segment	Definition	Signal	Action
Same-session	Returns < 30 min	Interrupted, still motivated	None needed
Same-day	Returns < 24h	Strong intent	None needed
Prompted	Returns after nudge	Nudge is working	Optimise nudge timing
Organic (2–7 days)	Returns without nudge	Recoverable	Send nudge before day 7
Churned	No return after 7 days	Lost — note the step	Diagnose, redesign

Key lens: Split all metrics by connection method (GSC vs URL). URL users are expected to show lower pre-selection accuracy, longer time on step 2, and higher drop-off. That split is the primary diagnostic for what to improve first.

What's next

Once baseline data is in, these are the levers.

1
Segment by company type — Compare completion rates and deviation across segments once brand classification is reliable. Same UI, different benchmarks.
2
Validate the page impact score — Correlate Step 2 scores with actual ChatGPT citation data. If high-scored pages appear in citations more often, the ranking model is working.
3
Instrument field edits — Track which AI-generated values get replaced, and with what. This is the training signal for improving brand extraction.
4
Correlate onboarding with 30-day retention — Do GSC users retain better? Do users who edit more prompts churn faster? These become leading product health indicators.
5
A/B test Concept A vs Concept B — Use the completion rate baseline from this version to measure which concept performs better.