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Product Analytics

Funnel Optimisation

Mapped the booking funnel, identified drop-off points, and ran cohort experiments that nearly doubled conversion.

Key Result
45% 82%
booking conversion
Intellect | Senior Data Analyst

The Booking Funnel

Where Users Were Dropping Off

Funnel Entry 100%
PHQ4 Questionnaire -30% drop-off
Provider Match 70%
Booking Confirmed only 45%
Session Completed 57.6%

Funnel before optimisation — multiple friction points causing compounding drop-off

01

The Problem

The clinical booking funnel at Intellect had multiple friction points. There was a 30% drop-off at the PHQ4 questionnaire — users were abandoning because the clinical screening felt heavy for someone just wanting to talk.

Additionally, the auto-matching system was producing poor commitment — match-to-booking conversion sat at just 45%, and session completion was only 57.6%. Users who were auto-matched didn't feel ownership over their choice.

30%
PHQ4 drop-off rate
45%
match-to-booking rate
57.6%
session completion
02

The Hypothesis

H1: Decoupling the clinical screening from the general booking flow and simplifying the questionnaire would reduce drop-off.

H2: Giving users the ability to pick their own provider (instead of auto-matching) would create stronger psychological commitment, leading to higher booking and completion rates.

03

The Approach

Mapped the complete clinical booking funnel in Mixpanel, tagging every step from entry to session completion.

Identified the PHQ4 questionnaire as the single biggest attrition point (30% drop-off).

Recommended decoupling clinical and SOS entry points — so non-clinical users bypass the heavy screening.

For the provider selection hypothesis: designed a cohort experiment replacing auto-match with a user-selected provider listing.

Tracked match-to-booking rate, session completion, and repeat-matching as success metrics.

Experimented with combining match and booking into a single committed flow.

04

The Result

Booking Conversion
2% 4%

Top-of-funnel conversion doubled after flow optimisations

Match-to-Booking
45% 82%

User-selected providers created stronger commitment

Session Completion
57.6% 70%

Users followed through with chosen providers

Repeat-Matching
19.6% 13.4%

Users were happier with their first match

Combined Match + Booking Flow
+5%

Higher booking rate from single committed flow

05

Business Impact

Nearly doubled top-of-funnel conversion, directly increasing platform revenue.

User-selected matching created stronger therapeutic relationships (higher completion, lower churn).

Reduced wasted provider time from repeated re-matching.

The funnel mapping methodology became the template for all future product analytics at Intellect.

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