For Activation Teams

User Activation Sprint

Increase your revenue by fixing your funnel.

In one quarter we find where your funnel leaks, why, and which fix turns more sign-ups into paying customers, with a new experiment live every week. The same loop took Deviniti's trial-to-paid conversion from 26% to 59% in three months, while monthly recurring revenue grew 33%.

Book a free intro call →How the 3W Loop works
For product teams that own an activation, onboarding or trial-to-paid metric, with enough new users each week to run an experiment.
The 3W Loop: where it hurts in product analytics, why it happens from story-based interviews, whether the fix works in a scoped experiment

Activation work with product teams at

  • Taxfix
  • elyps
  • UX Pilot
  • Deviniti

The Cheapest Revenue You Will Find

Most revenue plans start with more sign-ups. The cheaper number to move is the share of sign-ups who ever pay.

You already paid for them.Every sign-up cost something to win: ads, content, sales time. Each one who leaves before activating is spend with no return.
Activation multiplies everything after it.Conversion, retention and expansion only ever work on the users who activate. Lift activation, and every later number starts from a bigger base.
The fix keeps paying.Money spent on acquisition stops working the day you stop spending it. A fix to the funnel keeps working on every future sign-up.

Four Funnels, One Loop

Different products, different funnels. The same three questions asked in the same order, and read in revenue wherever the numbers are public.

+33% MRRDeviniti, in three months: trial-to-paid 26% → 59%Tableau data showed retention was healthy and activation was not. One week-three behavior predicted every paying customer, interviews with trial users in their third week showed what stopped them, and experiments on the discoverability of one setting moved it. The behavior hit its target, and the revenue it predicted overshot. Read the case →
80% → 60%Onboarding drop-off, elypsAnalytics showed the churn was not spread across the funnel: roughly eight in ten new sign-ups left at one screen, right before identity verification. Simplifying a regulated KYC check was off the table, so the team looked for why people left there instead. A “Do it later” option, with a reminder at home, cut drop-off by a quarter without changing a single KYC requirement. Read the case →
$450k+ MRRUX Pilot, up from $300k across the engagementOne experiment from that engagement: product analytics turned up a correlation too large to ignore, between a higher-fidelity generation mode and paying. The mode existed, but it was off by default for new users. One experiment on 120,000 users over 11 days settled it, lifting free-to-paid conversion 44.7%. Read the case →
EmbeddedOn the Activation team, TaxfixA fractional Senior PM on Taxfix's Activation team, merging two onboarding experiences into one for an app used by more than a million people. Eight concepts, prioritized hard, A/B tested through the tax-season peak with guardrails on activation, booking conversion and product regret. The results stay with Taxfix. Read the case →

Common Problems We Solve

If you own an activation metric, some of these will sound familiar:

Sign-ups are up. Revenue is not.Acquisition is doing its job, and the number the business actually cares about has not moved with it.
You know where people drop off, but not why.The funnel chart shows the leak. It does not show what the people who leave were trying to do.
Nobody agrees what “activated” means.Every team has its own definition, so nobody can say whether an onboarding change worked.
The obvious fix is off the table.The step people abandon is there for a reason: a legal check, a payment detail, a setup the product cannot work without.
Onboarding keeps getting redesigned, and the number does not move.Each redesign ships as a whole, so nobody learns which part helped and which part hurt.
Experiments take too long to ship.By the time a test is live, the team has already moved on to the next idea.

How It Works

Step 1 · Weeks 1 to 2

Where: find the leak

We start in your product analytics, not in the backlog. First, the question that decides everything after it: is this an activation problem or a retention problem? Then the step in the funnel that loses the most people, and the early behavior that separates the users who become customers from the ones who leave. You end week two with one activation metric everyone agrees on, and a simple model of what each point of it is worth in revenue.

Step 2 · Weeks 2 to 4

Why: find the reason

Analytics shows where people leave. It cannot show why. Story-based interviews with users who dropped off at that step, and with the ones who got through, turn the leak into a short list of explanations you can test. At elyps, the reason turned out not to be the step itself.

Step 3 · Weeks 4 to 12

Whether: test the fix

Each explanation becomes a test card: what we believe, what we will change, which number has to move and by how much, and what must not get worse. Scoped experiments go live through your normal release path with guardrail metrics: the first in week four, then a new one every week.

Step 4 · End of the quarter

Learn: keep the loop

A learning card with the number, in conversion and in revenue: what worked, what did not, and why. The next loop, already scoped. And a team that runs the loop without us.

What's Included

User Activation Sprint (1 quarter) - one metric, a new experiment every week
  • One agreed activation metric: the early behavior that predicts a paying customer, tied explicitly to the revenue it predicts
  • A revenue model of your funnel: what each point of activation is worth in monthly revenue, so every experiment is read in money, not only in percentages
  • A leak map of your funnel from sign-up to that behavior, with the step that loses the most people
  • Story-based interviews with users who dropped off and users who made it through, and a synthesis your team can use
  • Test cards written before anything ships, each naming the number it has to move, with success criteria and guardrail metrics
  • A new experiment live every week from week four, run with your team in your release path
  • Weekly working sessions in the flow of work, not in a separate workshop
  • A readout every two weeks for the person who owns the number, in revenue terms
  • End of the quarter: a learning card with the number, and the next loop already scoped
Format
One quarter · A new experiment every week · Remote or on-site
Recommended involvement
The product manager who owns activation, a designer, an engineer or tech lead, whoever owns product analytics, and the leader who owns the number

What We Need From You

A sprint only proves something if the setup lets it. Before we start, we agree on five things:

  • Access to your product analytics, and someone who can add an event when we need one
  • A team that owns the metric: product, design and engineering, with time to act on what we find
  • Room in the release path for a new experiment every week, behind a flag or to a share of new users
  • Revenue or billing data we can tie the activation metric to, even if only as an export
  • Enough new sign-ups each week to test with. If you do not have them yet, we will tell you on the first call

FAQs

How soon will we see a number move?
It depends on your traffic and your release path, so we will not promise a date. In the plan, the first experiment goes live in week four and a new one every week after that. At Deviniti, trial-to-paid conversion went from 26% to 59% inside three months. At UX Pilot, the experiment that settled it ran for 11 days.
How do we know it made money, and did not just move a percentage?
Because the metric is chosen for exactly that. In week two we pick the early behavior that predicts a paying customer and tie it to your revenue, so every test card names the money it is meant to move. At Deviniti the behavior hit its target and the business number it predicted overshot: trial-to-paid went from 26% to 59%, and monthly recurring revenue rose 33% in the same quarter.
Can we really run an experiment every week?
Yes, if the release path allows it, and that is agreed before we start. Weekly is how often a new experiment goes live, not how long each one runs: an experiment runs until enough users have seen it to read the result, which at UX Pilot took 11 days on 120,000 users. So experiments run side by side on different steps of the funnel, and the loop never waits for one result before starting the next.
Our analytics are a mess. Can we still do this?
Yes. Checking that the events you rely on mean what you think they mean is part of the first two weeks, and so is adding the one or two you are missing. A loop run on numbers nobody trusts proves nothing.
Who builds the experiments, you or our team?
Your team. We design them with you, write the test cards, set the guardrails and read the results together, but the changes ship through your codebase and your release path. That is also why the loop keeps running after we leave.
Can we run experiments in a regulated product?
Yes, with guardrail metrics and sign-off written into the test card. We have done it in a neobank's KYC flow and in a tax-filing app through its peak season.
How is this different from a conversion-rate optimization agency?
A CRO agency tests changes to pages. This works on the product: it finds the behavior that predicts a paying customer, explains why people do not reach it, and changes the product so more of them do. And it leaves your team running the loop, not holding a report.
What happens after the quarter?
You keep the learning card, the test cards and a team that can run the loop on its own. If the number moved and you want to take the same approach to the next metric or the next team, that is where Product Coaching & Advisory or the Product Operating Model Transformation picks up.

Reading for Activation Teams

Six field notes on the loop behind this sprint, written from engagements like it:

Metrics

How to improve user activation: find the behavior that predicts a paying customer

Rule out retention, find the early behavior your paying customers share, then test the smallest fix with guardrails.

Product Discovery

What is the 3W Loop? Where, Why, Whether

Three questions, asked in order, each with its own instrument. One loop run end to end on a real onboarding problem.

Experimentation

Stop overthinking the idea. Design the experiment.

Six steps that turn an argument into a test, and how to pick a method that could actually change your mind.

User Research

Interview your churned users, not just the ones who stayed

They already decided, so they have no reason to be polite. Three questions to ask about the day they left.

Metrics

What good retention actually looks like for B2B products

Before fixing activation, check it is not a retention problem. The benchmarks, and what to do under the line.

Product Discovery

We ran the whole 3W Loop in one morning with 28 people

A 150-minute masterclass run on the participants' own metrics rather than a tidy case study.

Service Area

Designed for product teams in B2B and consumer products with enough new users each week to run an experiment. Available remotely (global) and on-site across Europe. Aleksander is based in Wrocław, Poland.

More paying customers from the sign-ups you already have.

Book a free intro call →