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PLG activation engine

  • plg
  • activation
  • onboarding
  • saas

Instrument the signup-to-aha funnel, run in-app onboarding experiments, score PQLs, and loop the learnings back, weekly, not quarterly.

Get access See how it runs
6 Steps in the flow
6 Tools orchestrated
3 Human checkpoints
Weekly Run cadence
measured weekly Activation rate

The objective

What this system is built to do

The aha moment either happens in the first session or mostly doesn't. Strip every step between signup and first value, instrument the funnel with Amplitude or PostHog, and run weekly experiments, not quarterly redesigns.

Activation rate is the only number that matters before you scale spend. Instrument every step from signup to the aha moment, remove friction first and add onboarding second, and run one experiment per week until activation clears 40%. Not doing: product tours, multi-step setup forms before first value, or PQL scoring before activation is instrumented.

How it runs

The steps your team follows

The workflow, exactly as it runs inside . It plays through step by step, or pick any node on the map.

1Instrument the activation funnel
2Identify friction & run drop-off analysis
3Build & run in-app onboarding
4Score PQLs & route to paid
5Measure activation & paid conversion
6Run experiment retrospective & sharpen
Start
1

Instrument the activation funnel

Weekly
  1. Agree the activation event: the single action that predicts retention at 3–5× vs. users who don't take it.
  2. Wire or events: signup → first session → key actions → activation event.
  3. Build a funnel report: conversion at each step, segmented by signup source and plan.
  4. Set up session recordings in for sessions that dropped off before the activation event.
  5. Human approval, review the funnel; identify the top drop-off step before moving to experiments.
2

Identify friction & run drop-off analysis

Weekly
  1. Watch 10–15 session recordings of users who dropped off at the top drop-off step.
  2. Note: required fields, blank empty states, confusing UI copy, missing demo data.
  3. Categorise each friction point: setup tax (defer it), navigation confusion (simplify it), or missing value signal (add it).
  4. Rank by estimated impact × implementation ease.
  5. Human approval, pick the single highest-impact friction to fix this week.
3

Build & run in-app onboarding

Weekly
  1. Build or update the onboarding checklist in or , max 4 items, each tied to a step toward the activation event.
  2. Use contextual tooltips (appear when the user reaches that screen) not a forced tour.
  3. Pre-fill as much as possible: import demo data, default workspace name, skip optional fields.
  4. Run A/B test if >200 new signups/week; otherwise sequential rollout.
  5. Instrument every checklist item completion back to , kill any item <40% complete rate.
4

Score PQLs & route to paid

Daily
  1. Define the PQL threshold in or : e.g. used core feature 3+ times in 7 days + team size >1.
  2. For users crossing the PQL threshold: trigger a targeted in-app upgrade prompt (not a generic banner).
  3. If running a reverse trial: surface the prompt on day 11 of 14, when users know exactly what they'd lose.
  4. Route high-intent PQLs (daily active + PQL) to a sales or success motion via alert.
  5. Human approval, review PQL → paid conversion weekly; adjust the threshold if <10% are converting.
5

Measure activation & paid conversion

Weekly
  1. Pull the weekly activation rate: % of signups hitting the activation event within 7 days, by cohort.
  2. Measure median time-to-value (TTV): hours from signup to activation event.
  3. Track trial-to-paid conversion: activated users → paid, by 14 and 30 days.
  4. Flag any cohort where activation rate dropped >3pp week-on-week, investigate before next experiment.
  5. Write one-paragraph summary: what moved, what didn't, what we'll test next.
6

Run experiment retrospective & sharpen

Weekly
  1. Review the experiment from this week: what was the hypothesis, what moved, what didn't.
  2. Update the friction log: resolved items off, new observations from recordings added.
  3. Decide next experiment: from the ranked friction backlog, pick the next highest-impact item.
  4. If activation rate >40% and TTV <12h: shift focus to PQL scoring and paid conversion experiments.
  5. Feed the updated ICP signal (who activates + converts fastest) back to acquisition targeting.
1 / 6

Setup

The stack

The tools this system drives, and the context you bring once. Connect your own accounts, or swap in your team's equivalent.

Tools to connect

AmplitudeFree – $49 / mo Powers Instrument the activation funnel Or Mixpanel, PostHog
PostHogFree – usage Powers Instrument the activation funnel Or Amplitude, Mixpanel
Userflowfrom $240 / mo Powers Build & run in-app onboarding Or Appcues, Chameleon
Appcuesfrom $249 / mo Powers Build & run in-app onboarding Or Userflow, Pendo
JuneFree – $149 / mo Powers Score PQLs & route to paid Or Amplitude, Mixpanel
SlackFree – $7 / mo Powers Score PQLs & route to paid Or Discord, MS Teams

Context to bring

Your documents 4 files
  • Activation definition The “aha” moment and the events that prove a user got there.
  • Onboarding map The steps a new user should hit, in order.
  • Product analytics access Amplitude / PostHog plus event access.
  • Messaging & tone The nudges, in-app copy and lifecycle voice.

Proven in market

Run by 84 teams, verified on real accounts

PLG activation engine is built and verified by Growth Division, the GTM team behind 150+ companies. Here is what the same play has produced for their clients.

+400% trial activations in 6 months

NOAN · SaaS

$100k → $1.2M ARR in 12 months

Musiversal · Music / SaaS

Built & verified by Growth Division

What we've learned running it hundreds of times

The small, hard-won details that decide whether it works, from every run.

+400% trial activations (NOAN)
3× more completions vs tours
30–50% lift from reverse trial
activation rate high confidence
Before 22%
After 38%

An onboarding checklist lifted activation more than a product tour

A checklist anchored to the aha moment outperforms a tour because the user controls the pace and skips what they already know. Instrument each step; drop any that less than 40% complete.

A/B test (tour vs checklist)n≈1,800 signups6 weeks
h median TTV high confidence
Before 47h
After 19

Removing a required setup step cut time-to-value in half

Upfront setup forms are a conversion tax. Collect only what the product needs to deliver first value; defer everything else until after the aha moment. Users who've already seen value will tell you their industry, they won't before.

Funnel analysis + sequential rolloutn≈2,400 signups4 weeks
trial-to-paid conversion medium confidence
Before 8%
After 15%

A reverse trial lifted paid conversion by nearly 2×

A reverse trial works because users experience the paid tier first, the downgrade feels like loss, not the upgrade like gain. Pair it with a PQL filter to time the in-app upgrade prompt precisely; a prompt on day 1 is background noise, on day 11 it lands when users know exactly what they'd lose.

Cohort comparison (free-first vs reverse trial)n≈900 signups8 weeks
The full lab notebook Every experiment charted, with the raw before/after and the decision rules behind each call. Unlock free

Get started

How to run this system

Live in an afternoon. Clone it, connect your tools, set your inputs, and turn it on.

  1. 1

    Add to

    Clone the PLG activation engine into your workspace, wired end to end.

  2. 2

    Connect your tools

    Amplitude, PostHog, Userflow +3 more.

  3. 3

    Set your inputs

    Your ICP, targets and brand rules, once.

  4. 4

    Turn it on

    It runs weekly on autopilot, checking in with you only where it matters.

~30 min / week your time on autopilot · vs 8–12 hrs / week by hand
measured weekly Activation rate, produced for you

Early access

Run the PLG activation engine in your workspace.

is in stealth with a first cohort of scaleups. Join the waitlist and we’ll open this system, and the full library, to you first.

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