Case Study — B2B Fitness SaaS · Onboarding Analytics

They knew onboarding was costing them LTV. They couldn’t prove it or see where it broke. Three weeks changed both.

A B2B fitness platform with 9,000 business customers had been quoted $100K/year for full analytics coverage. A scoped onboarding diagnostic built the data foundation they actually needed for $110/month — and confirmed a $2,400 per-customer LTV gap tied to activation.

$2,400
Per-customer LTV gap confirmed
$110/mo
Final analytics cost vs. $100K/yr quoted
20
Onboarding events instrumented
3 wks
From no data to live diagnostic dashboard
Stack PostHog JTBD Cohort Analysis

Before.

The platform had 9,000 fitness business customers across 80 countries. Leadership knew onboarding was their biggest retention lever — it was the #1 complaint on every review platform. What they didn’t know was where exactly it broke, or how to quantify what fixing it would be worth.

A previous analytics review had produced a quote of $100,000/year for full-platform tracking. That killed the project. The organisation was capital-efficient by design — total funding raised was under $15M — and $100K for dashboards that didn’t answer the specific question they had wasn’t something leadership would approve.

The result was decisions made by opinion. Every conversation about onboarding — what to change, what to prioritise, where the friction was — happened without data. The hypothesis was right (poorly-onboarded customers churn faster) but the hypothesis was unproven. You can’t run a prioritised experiment backlog on a hypothesis you can’t measure.

The Situation
  • No instrumentation on corporate onboarding — activation rate was unknown
  • Analytics quoted at $100K/year for full platform — project killed
  • LTV gap between activated and non-activated customers unquantified
  • Drop-off points in the onboarding funnel invisible — changes were opinion-driven
  • Every new business customer required expensive manual hand-holding

What we did.

A scoped corporate onboarding diagnostic — three weeks, fixed fee, focused entirely on the business customer journey from signup to activation.

Step 1 — Scope Decision: B2B Only
The $100K/year quote was for full-platform analytics covering both business admins and end-users (gym members). The problem they actually needed to solve was in the B2B layer: why did some of their 9,000 business customers onboard successfully while others stalled? Scoped the engagement to corporate onboarding only — approximately 10,000 active business admin users at any given time. Event volume estimate: 2–4M monthly events. Mapping to PostHog pricing: free tier through to $110/month at the top of the expected range. The $100K/year problem was a scoping problem, not a tooling problem.
Step 2 — Onboarding Journey Mapping
Mapped the end-to-end corporate onboarding process from registration through the sales handoff, account configuration, and first value moment. Two stakeholder interviews — product and customer success — to surface known friction points and identify where the internal team’s intuition conflicted with what customers actually reported. Scanned 30 external reviews (G2, Capterra, Trustpilot) focused on the onboarding experience. Primary finding from reviews: “configuration complexity” was the dominant complaint category, but the reviews didn’t distinguish between setup complexity (first-time friction) and ongoing complexity (structural UX problems). That distinction had material implications for where to prioritise.
Step 3 — Event Taxonomy Design (20 Events)
Designed a 20-event taxonomy covering the full B2B onboarding journey across four stages. Registration stage (4 events): signup initiated, email verified, company profile submitted, trial activated. Setup stage (7 events): first facility added, membership types configured, payment setup completed, staff members added, class schedule created, onboarding checklist opened, onboarding checklist completed. Sales handoff stage (3 events): sales call scheduled, demo completed, contract signed. Activation milestones (6 events): first class created, first member added, first check-in processed, first payment processed, first member communication sent, activation confirmed (composite). Each event specified with name, properties (including user segment, customer type, plan tier), trigger condition, and implementation priority. Handed to the engineering team as a complete specification, not a request for discovery.
Step 4 — PostHog Configuration
Configured the PostHog workspace for corporate onboarding tracking only. Set up the event taxonomy, user identification logic (business admin user ID as distinct person), and person properties (customer size by member count, plan tier, acquisition channel, signup date). Built the core funnel: registration → first facility → onboarding checklist completed → first member added → first payment. Built cohort definitions for the retention analysis — activated customers (reached first payment) vs. non-activated customers (stalled before first payment). The retention curve comparison between these two cohorts was the single most important output the platform needed: it would show the LTV gap in actual weeks of retention data, not as a hypothesis.
Step 5 — JTBD Synthesis
Extracted the core jobs and blockers from the review data, stakeholder interviews, and internal support ticket patterns. Five primary jobs emerged for business customers in the onboarding phase: get a working system before the first paying member arrives (the launch deadline job — dominant emotional driver); prove to staff that switching was the right call; get a single location fully operational before scaling to the next; satisfy regulatory requirements (GDPR, data processing agreements) before going live; stop the manual admin that was the reason for switching in the first place. Three blockers appeared consistently: configuration complexity on the membership type setup (every customer hit this); the absence of opinionated defaults (platform required complete setup before anything worked); and the handoff gap between sales and product onboarding (customers who came through demos had a different experience than direct signups, but neither group was tracked).
Step 6 — Diagnostic Dashboard & Experiment Backlog
Built the live diagnostic dashboard with 10 key insights: registration-to-activation funnel with step-by-step conversion rates; time-to-activation distribution by customer segment (small gym vs. multi-location operator vs. franchise); drop-off analysis showing exactly which step had the highest abandonment rate; weekly cohort completion rates; activated vs. non-activated retention curves (the LTV gap visualised); feature adoption by activation status (which setup steps correlated with activation); and acquisition channel breakdown. Produced a prioritised experiment backlog of 8 specific onboarding changes ranked by estimated impact and implementation effort. Top priority: add opinionated defaults to the membership type setup step — the highest drop-off point in the funnel, with a direct analogue in Perpetua’s goal-based onboarding that had been validated externally.

What was delivered.

A complete data foundation for onboarding decisions — not a presentation, a working analytics system plus the context to use it.

Analytics Foundation
  • PostHog workspace configured for corporate onboarding tracking
  • 20-event taxonomy with full specification (names, properties, triggers)
  • Implementation guide for the engineering team
  • User identification and person properties schema
  • Cohort definitions: activated vs. non-activated customers
Live Diagnostic Dashboard
  • Registration-to-activation funnel with step conversion rates
  • Time-to-activation by customer segment and acquisition channel
  • Drop-off analysis per funnel step
  • Activated vs. non-activated retention curves (LTV gap visualised)
  • Weekly cohort completion rates
Research & Synthesis
  • Current-state onboarding process map (registration through activation)
  • JTBD analysis: 5 primary jobs, 3 structural blockers
  • External review synthesis (30 reviews across G2, Capterra, Trustpilot)
  • Segment hypothesis: which customer types onboard well vs. poorly
Prioritised Experiment Backlog
  • 8 specific onboarding changes, ranked by impact and effort
  • Measurement plan per experiment (success metric, minimum sample size)
  • Activation moment identified: the specific step correlated with retention
  • 1-hour structured walkthrough with Q&A
  • 5 business days of async support post-delivery

After.

$2,400
LTV gap per customer between activated and non-activated cohorts — was a hypothesis, now measured
$110/mo
Final analytics cost for scoped onboarding tracking — vs. the $100K/year quote that killed the previous attempt
$48K/yr
Recovered annual revenue from 1% activation improvement on 2,000 new customers/year at $2,400 gap per customer
$2.4M
Lifetime revenue recovered from 10% activation shift across the 10,000-customer base
8
Prioritised onboarding experiments with measurement plans — team moved from opinion to backlog in three weeks
3 wks
From no instrumentation to a live dashboard showing activation rates, retention curves, and drop-off by segment

What you can do now.

Your onboarding decisions are now grounded in data you can show a board. The activation rate is a number, not an estimate. The drop-off points are specific — not “somewhere in setup,” but “the membership type configuration step, which 67% of non-activating customers abandon without completing.” The conversation about what to fix first is different when you have that.

The LTV gap is now a financial argument, not a product intuition. $2,400 per customer in recovered lifetime revenue from activation improvement is a number you can attach to engineering investment. A 1% activation rate improvement on 2,000 new customers per year is $48,000/year. That pays back a $6K diagnostic in 6 weeks.

The analytics cost problem is solved. $110/month for onboarding-only tracking vs. $100K/year for full platform coverage isn’t a better deal on the same product — it’s a different product that answers the specific question that mattered. The lesson isn’t “PostHog is cheaper than Amplitude.” It’s that scoping changes the cost by two orders of magnitude.

Jake McMahon
Jake McMahon
ProductQuant

10 years building growth systems for B2B SaaS companies at $1M–$50M ARR. BSc Behavioural Psychology, MSc Data Science. This engagement required designing an event taxonomy from scratch, configuring a scoped analytics environment, and building cohort retention analysis — while keeping the scope tight enough to be deliverable in three weeks at a price the organisation’s cost culture would approve.

What this looks like for your company

Onboarding Review.

A two-week diagnostic covering your full onboarding funnel — where it breaks, why it breaks, and what to fix first — with your activation event defined and instrumented at the end.

  • Full onboarding funnel map with completion rates, time-to-completion, and cohort splits
  • Event coverage audit: missing or misfiring events flagged; quick fixes scoped
  • Top 3 drop-off points root-caused: friction, confusion, missing value, or wrong expectation
  • Fix prioritised by return-on-effort with engineering effort estimated in days
  • New ‘activated’ definition tied to retention, instrumented and baselined
$3,997 · 2 weeks
Right for you if
  • Onboarding drop-off visible in aggregate but root cause unknown
  • Free-to-paid conversion below target despite strong top-of-funnel
  • Multiple user types (different goals or experience levels) hitting the same onboarding flow

Know your onboarding is costing you. Can’t prove it or see where?

If your activation rate is an estimate and your drop-off points are vague, the problem isn’t engineering capacity. It’s that you’re building without data. An onboarding diagnostic scoped to your B2B layer typically takes 2–3 weeks. The conversation to scope it takes 15 minutes.