ACTIVATION DEEP DIVE — $4,997 · 2-WEEK SPRINT

Jake McMahon — ProductQuant

8+ years B2B SaaS · Behavioural Psychology + Big Data (Masters)

Find the exact step where new users stop becoming customers.

Your team has been debating where the activation funnel breaks. This sprint ends that debate in 14 days — the drop-off points confirmed with your data, not assumptions. Three fixes ranked by impact — so more signups become paying customers this quarter.

Start the Deep Dive → See what’s included ↓

3 prioritised fixes with data behind them — or full refund · 2-week delivery

WHAT YOU HAVE AT THE END

Activation funnel mapped End-to-end from signup to core action, every step confirmed with data

Drop-off confirmed With event data and session replays — not assumptions

Top 3 fixes ranked By impact — effort and expected return per fix

Experiment designs Ready to run — one per fix, hypothesis and metric defined

90-min readout Walk-through with your team, questions answered

$4,997 · fixed price · 2-week sprint

YOU ALREADY KNOW SOMETHING IS WRONG

Activation rate declining — root cause unclear

“We’re getting signups but they’re not reaching the moment where they actually get the product. Every sprint we talk about fixing activation and every sprint we can’t agree on what’s actually broken.”

VP Product — B2B SaaS, $5M ARR

Team debating onboarding vs UX vs product — no resolution

“We had a standup where three people said it was the onboarding, two said it was the UX, and one said the product just isn’t ready. Nobody had data. We decided to revisit it next sprint.”

Head of Growth — Series A

Drop-off visible in aggregate, invisible by step

“We know users are dropping off somewhere in the first 14 days. We can see it in aggregate. But we can’t see which step is the problem — the funnel just shows a number getting smaller.”

Product Manager — B2B SaaS

“Activated” undefined — no metric to optimise

“Every time I bring up activation in planning someone asks ‘but what does activated mean for us?’ We’ve been having that conversation for six months. There’s no agreed definition so there’s no agreed metric.”

CEO — Seed stage

WHAT THIS TYPICALLY UNCOVERS

The biggest drop-off is almost never where your team thinks it is.

The biggest activation drop is rarely where your team thinks it is.

In our experience, the step with the lowest completion rate typically isn’t the one teams debate in standups. The data tends to point somewhere upstream — a step nobody flagged because it looked fine in aggregate.

Instrumentation gaps often hide the real drop-off step.

Many funnels have missing or misfiring events between steps. You can’t optimise a step you can’t measure — and the gap is typically right where the funnel breaks.

Users who activate in the first 48 hours typically retain far longer.

Time-to-activation is usually a stronger predictor of retention than which features users try. The sprint identifies that window and the steps that slow users down inside it.

Your definition of “activated” may not match what predicts retention.

Teams often define activation around a feature milestone — “completed onboarding” or “created first project.” But when you check against retention data, a different action typically predicts who stays.

WHY THIS IS DIFFERENT

Most teams start with a theory about where users drop off. We start with the events that already prove it.

“Find the aha moment” is advice that assumes you already know where users leave. You don’t — that’s the problem. This sprint measures completion rates at every step in your actual funnel, from the data your analytics tool already has. The drop-off points are confirmed, not theorised.

Your PM gets a funnel map for roadmap decisions. Your engineer gets an activation event spec to instrument. Your team gets three fixes ranked by the revenue they recover — not by gut feel. No translation required.

TIMELINE

From raw event data to knowing which fix recovers the most revenue — in 14 days.

WEEK 1

Map + Diagnose

Read-only access to your analytics tool. Every step in your actual product journey mapped from event data. Instrumentation gaps identified. Drop-off rates measured. Session replays reviewed at the top exit points.

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WEEK 2

Rank + Scope

Top 3 fixes ranked by impact-to-effort. Each scoped by type — copy, UI, or engineering — with dependencies documented. Experiment designs drafted for each fix.

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DAY 14

Readout + Handover

90-minute session with your product and growth leads. Funnel walked through step by step. Fixes ranked and scoped. Everything handed over — nothing withheld.

Day 15: your team ships the fix that recovers the most lost activations.

WHAT YOU GET

Your team stops debating activation and starts shipping fixes.

Week 1 · Mapping

Activation Funnel Map, End-to-End

Every step from signup to the core action, mapped as it actually works in your product — not as designed, as experienced by real users. Steps that exist only in assumptions are identified and separated from steps with real drop-off data behind them.

Which steps lose the most signups — and how many
Completion rate at each step, sourced from event data
Instrumentation gaps and misfiring events flagged
Distinction between activation blockers and accelerators

Week 1 · Diagnosis

Drop-Off Analysis with Significance Data

The 2–3 steps where the funnel breaks, confirmed with event data and session replay review. You stop arguing about where the problem is because the data makes it visible — by step, by cohort, and by user behaviour at exit.

Which step is costing you the most revenue
Cohort breakdowns: which user types drop where — plan, channel, device
Time-to-drop analysis — fast droppers vs. users who linger and leave
Session replay review at each confirmed drop-off step

Week 2 · Prioritisation

Ranked Fix List — Top 3 by Impact

Not a list of everything that could be improved. The three changes that move the activation rate most for the least implementation effort — scoped and ready to hand to your product team.

How much revenue each fix recovers, so you build the right one first
Implementation effort classified: copy change, UI change, or engineering work
Dependencies identified — fixes that need to run in sequence vs. in parallel
Quick wins clearly separated from structural changes

Week 2 · Experiments

Experiment Designs for Each Fix

One experiment design per fix, so your team can run a controlled test rather than shipping blind. Hypothesis stated, success metric defined, minimum detectable effect calculated from your baseline activation rate.

Hypothesis: what change is being made and what outcome is expected
Primary metric: the activation event being moved
Guardrail metrics: what not to break in the process
Sample size estimate based on your current traffic

Week 2 · Readout

90-Minute Readout Session

A live session with your product and growth leads. The funnel map walked through step by step. Drop-off points explained with the data that confirmed them. Fixes ranked and scoped. Questions answered. Your team leaves knowing exactly what to build first and why.

Full funnel walkthrough with data at each step
Drop-off explanations with context from session replays
Fix ranking discussion — challenge the prioritisation
Activation definition agreed before the session ends

On cost of delay: every signup that doesn’t activate is revenue your product already earned the right to collect. If 100 signups come in per month and 30% activate, that’s 70 users who wanted to pay but never reached the value moment. The deep dive finds the step that lost them — and turns existing signups into paying customers without touching acquisition spend.

FIT CHECK

Teams with event data and a real activation gap get the most from this.

GOOD FIT

B2B SaaS at $2M–$20M ARR with measurable activation drop-off

Event data available · activation gap confirmed

The situation

You’re getting signups but a meaningful share never reaches the core value moment in the first 14 days. You have event data in an analytics tool — PostHog, Mixpanel, Amplitude, or similar — but the data hasn’t been structured into a funnel that reveals where the drop-off happens. Activation rate is a metric you track; it’s just not moving.

What you leave with

The exact steps where your funnel is breaking — confirmed, not estimated
The top 3 fixes ranked by impact, scoped for your team to ship
An activation definition validated against retention data — the argument is over

Signups you’ve already acquired start converting at a higher rate — new revenue from traffic you already have.

NOT A FIT

Pre-product, no analytics, or activation isn’t the constraint

Wrong stage or wrong problem

When this sprint doesn’t apply

If you haven’t shipped a product yet, there’s no funnel to map. If your analytics tool has fewer than a few weeks of event data, the analysis won’t be reliable enough to rank fixes with confidence. And if activation isn’t the bottleneck — if users are activating fine but churning at 90 days — then this sprint is pointed at the wrong problem.

Better starting points

Instrumentation Setup

Get your event tracking in place before running any analysis sprint.

Focused Sprint

Answer “where does churn concentrate?” when retention is the bottleneck.

The Foundation

Covers activation alongside retention, competitive, and go-to-market.

What this sprint doesn’t cover

The Activation Deep Dive delivers the analysis and ranked recommendations. Your team does the building. If you need the full picture — including implementation — that’s a different engagement.

Implementing the fixes — your engineering team ships the changes
Redesigning the onboarding UX — the sprint identifies where, not how to redesign
Ongoing experimentation — the sprint delivers experiment designs, your team runs them

For full implementation → Growth LAB

Jake McMahon — ProductQuant

Jake McMahon

8+ years building retention, activation, and growth programs inside B2B SaaS · Behavioural Psychology + Big Data (Masters)

I run this sprint myself. The funnel mapping, the cohort analysis, the session replay review, the fix prioritisation — all of it. Your activation problem is not generic. It’s specific to your user journey, your product, and the gap between what users expect when they sign up and what they actually encounter. Generic activation frameworks tell you to “reduce friction” without telling you where friction lives in your funnel.

The sprint produces assets your team acts on directly. The funnel map tells your designer where to change the UX. The activation event spec tells your engineer what to instrument. The fix rankings tell your PM what to build first. No interpretation required — everything is formatted for the person who needs to use it.

I won’t do this:

Recommend a definition of “activated” without validating it against your retention data
Identify drop-off points from surveys when event data is available
Deliver a list of improvements without ranking them by expected impact
Suggest instrumentation changes that require engineering without a clear spec

What if your instrumentation is poor?

We start with what exists. If event data is sparse, we use session recordings, Stripe data, and support ticket patterns to fill the gaps. The funnel map is built from everything available — not only ideal instrumentation. The instrumentation gaps become part of the deliverable: what to add, in what order, to make the next analysis sharper. You don’t need perfect data to start — you need enough to find the drop-off.

Teams Jake has worked with

PRICING

One price. Everything your team needs to act.

$4,997

one-time · fixed price

2-week sprint

Full activation funnel map with completion rates at every step
Drop-off analysis with cohort breakdowns and session replay review
Ranked fix list — top 3 by impact, scoped by effort
Experiment design for each fix
Activation event validated against retention data
90-minute readout session with your team
All assets formatted for your PM, designer, and engineer
Everything stays with your team permanently

3 prioritised fixes with data behind them — or full refund. No conditions.

Book a 30-minute call →

3 fixes that recover the most lost activations — backed by your data — or full refund. If the data can’t support a ranked fix list, we tell you in week 1 and scope what’s possible. The deliverable either exists or it doesn’t.

Questions.

Or book a call →

What if we don’t have good instrumentation? +

We start with what exists. If event data is sparse, we use session recordings, Stripe data, and support patterns to build the funnel map. The instrumentation gaps become part of the deliverable — you leave with a spec for what to add, in order, so each future sprint is better instrumented than the last. You don’t need perfect data to run the sprint. You need enough data to find the drop-off.

How is this different from a UX audit? +

A UX audit identifies design problems from visual and usability review — what looks broken. This sprint identifies activation problems from behaviour data — what’s actually causing users to not reach the value moment. The drop-off points are confirmed with completion rates, not design intuition. The fixes are ranked by quantified impact, not severity of the UX problem. Both can be true at once — but this sprint tells you where to invest your engineering capacity, not your design capacity.

What do we own at the end? +

Everything. The funnel map, the drop-off analysis, the ranked fix list, the experiment designs, the activation definition, and the instrumentation spec. All formatted for your team to use directly — the funnel map for your PM, the instrumentation spec for your engineer, the fix rankings for your roadmap. There’s no dependency on ProductQuant after the sprint ends.

Do you run the fixes or just recommend them? +

The Deep Dive delivers the analysis and the fix recommendations. Your team implements. If you want full implementation — the funnel redesigned, the instrumentation built, and the experiment run to confirm the improvement — that’s a Growth LAB engagement. The Deep Dive is built for teams that have engineering capacity and need the analysis to know where to point it.

What’s the guarantee? +

If the sprint doesn’t produce 3 prioritised fixes with data behind them, you get a full refund. The guarantee is simple: if the data genuinely can’t support a ranked fix list — which is rare — we tell you that in week 1 and scope what’s possible. We don’t reach day 14 and deliver something that doesn’t meet the brief.

How do you get access to our data? +

Read-only access to your analytics tool — PostHog, Mixpanel, Amplitude, or whichever platform you use. For session replays, view access to Hotjar, FullStory, or your session recording tool. No write access is needed, and access can be revoked at any time. Most teams share access via a guest login or read-only API key. The data stays in your systems throughout.