ACTIVATION DEEP DIVE

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 — 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.

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

Fixed price · 2-week sprint

We build a clear picture of where signups get stuck.

You get a report showing the exact step new users drop off, with three fixes to get more paying customers. No guesswork.

PRODUCT MANAGER

"Why do 70% of users who start a trial never finish the setup?"

We trace their clicks to find the confusing step. You see the exact screen where they give up. Now you can fix that one thing instead of guessing.

MARKETING DIRECTOR

"Which ad channel brings users who actually pay?"

We connect signup sources to who becomes a customer. You see which campaigns drive real revenue. Now you can stop wasting budget on clicks that don't convert.

CUSTOMER SUPPORT

"Users keep asking how to do the same thing."

We find the feature everyone tries but can't figure out. You get a screenshot of the confusing button or menu. Now you can make a tutorial or redesign that spot.

WEEKLY REPORTING

"Is our new onboarding tutorial working?"

We show if users who see the tutorial complete more steps. You get a before-and-after comparison. Now you know if the change helped or if you need to try something else.

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 often hidden by incomplete data.

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.

Time-to-activation is a critical predictor of long-term retention.

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

Start with the events that prove where users drop off, not with theories.

You need to know where users leave before you can find the 'aha moment'. 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.

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

21 deliverables that turn activation drop-off into engineering-ready fixes.

Week 1 · Mapping

End-to-End Activation Funnel Mapping

Your full journey from signup to first core action is mapped as a measurable funnel with completion percentages at every step. For the first time, product, engineering, and growth can see exactly where users fall off.

Step-by-step completion rates from actual event data
Typically 3–7 highest-leverage drop-off points identified
Shareable activation funnel map with real percentages
Written activation definition agreement for product, growth, and leadership

Week 1 · Diagnosis

Quantitative + Qualitative Drop-Off Diagnosis

Event data shows where the funnel breaks; session replays and heatmaps explain why. You get the specific moments where users become confused, stuck, or disengaged instead of a dashboard screenshot with no diagnosis.

50+ drop-off session replays reviewed and annotated
Heatmap analysis of key activation screens
Cohort breakdowns by plan, channel, and device
Root cause documentation for each major drop-off step

Week 2 · Prioritisation

Revenue-Sized Prioritisation System

Every meaningful drop-off is sized in dollar terms and classified by effort versus impact. The roadmap is ranked by revenue unlocked, not by whoever has the strongest opinion in the planning meeting.

Revenue impact calculation per drop-off point
Instrumentation gap analysis with 5–10 specific gaps
Implementation effort classification matrix
Quick wins separated cleanly from structural changes

Week 2 · Experiments

Top 3 Fixes + Experiment Designs

The three highest-impact changes are documented with enough detail that an engineer can scope and build them without a follow-up conversation. Each fix also has a ready-to-run experiment design so your team can validate before committing to a full build.

Engineering-ready specs with acceptance criteria
3 experiments with hypothesis, success metric, and sample size
Drop-off analysis dashboard built in your analytics tool
Session replay highlights with timestamped annotations

Week 2 · Readout

20+-Page Report, Readout, and Implementation Guidance

The full activation funnel report documents the data, session replay findings, cohort breakdowns, gaps, and prioritised recommendations. A recorded 90-minute readout gets the team aligned, then 30 days of guidance keeps implementation and experiment setup on track.

Complete written analysis your next VP can understand in an hour
Follow-up session to pressure-test prioritisation against roadmap capacity
Email support for experiment setup questions
Everything above for $4,997, with no hourly billing or scope creep

On cost of delay: every signup that doesn’t activate is revenue your product already earned the right to collect. The deep dive finds the step that loses 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 with measurable activation drop-off and available event data

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.