FEATURE LAUNCH SPRINT

Jake McMahon — ProductQuant

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

Ship a feature and know within 30 days whether it worked.

Define what adoption means for your next feature, instrument it correctly, and build the dashboard you check on day 30.

Start the Sprint → See what’s included ↓

Adoption defined, instrumented, and measurable before launch.

WHAT YOU HAVE AT THE END

Adoption defined What “adopted” means for this feature, agreed before launch

Instrumentation spec Every event engineering needs — developer-ready, tiered by priority

PostHog dashboard Adoption tracking built and showing live data before launch day

30-day monitoring plan Post-launch measurement with decision points and red-flag triggers

Success criteria Team-agreed targets at 30, 60, 90 days — no post-mortem arguments

Fixed price · 2-week sprint

We build the dashboard that tells you if your feature worked.

You get a simple, clear dashboard that tracks exactly how people use your new feature. No guesswork, just the numbers you need to decide.

PRODUCT MANAGER

"Did anyone actually use the new button we added?"

We track every click on that button from day one. You'll see exactly how many users found it and tried it, so you know if it's in the right place.

CUSTOMER SUPPORT

"Are users getting stuck on the new checkout step?"

We show where people pause or drop off during the new process. You can see the exact point of confusion and fix it fast.

WEEKLY REPORT

"What's the adoption rate for the feature we launched last month?"

Your dashboard updates daily with the percentage of active users trying the feature. You have the final answer ready for your leadership meeting.

ENGINEERING TEAM

"Do we need to build more advanced settings for this feature?"

We measure how deeply users explore the feature's options. You'll see if they use the basics or need more powerful tools to be successful.

YOU ALREADY KNOW THIS PATTERN

Feature ships to silence — nobody defined what success looks like

“We shipped the feature. A month later someone asked if it was successful. Nobody had a clean answer. We ended up looking at usage counts and disagreeing about what the numbers meant.”

VP Product — B2B SaaS, $8M ARR

Six weeks of post-launch debate with no shared answer

“Six weeks after launch we were still arguing about whether the feature was successful. Engineering thought yes. Product thought no. CS had never been asked. It only ended when someone got enough political capital to call it.”

Head of Growth — Series B

Release notes views used as a proxy for actual adoption

“We used release notes views as our adoption metric for a full quarter. When we finally looked at actual feature usage, the numbers were completely different. We wasted weeks trying to understand the discrepancy.”

Product Manager — B2B SaaS

Instrumentation gaps that can’t be recovered post-launch

“The analytics were supposed to be set up before launch. They weren’t. We had no clean data for the first 30 days — the window that matters most. There’s no way to recover that.”

Engineering Lead — Series A

WHAT THIS TYPICALLY REVEALS

Define adoption wrong, and you won't find out until the retrospective.

“Users who tried it once” is not adoption.

Using “users who clicked the button” as an adoption metric doesn’t distinguish between a user who tried it once and one who integrated it into their workflow. The adoption definition needs to predict retention, not just measure curiosity.

The first 30 days of data are the ones you can’t get back.

Instrumentation gaps in the launch window are permanent. The cohort that adopted (or didn’t) in the first month is the signal you need most — and it’s the one that disappears when tracking isn’t in place from day one.

Without pre-agreed criteria, every stakeholder reads the data differently.

Engineering sees usage counts and thinks the feature worked. Product sees retention and thinks it didn’t. CS was never asked. The argument repeats at every retrospective until someone gives up. Pre-agreed criteria replace the argument with a dashboard.

Features that miss their window rarely get a second chance.

If adoption misses the target in the first 30 days and nobody has a monitoring plan, the team moves on to the next sprint. The feature quietly dies. A monitoring plan with decision points catches the miss early enough to course-correct.

WHY THIS IS DIFFERENT

Define success before a line of code ships, not after the fact.

Without a pre-launch plan, you ship the feature, wait, look at whatever data is available, and argue about whether the numbers are good. The measurement framework wasn’t in place when the critical window opened.

This sprint runs before the feature ships. Week one produces the adoption definition — what “adopted” means as a specific user behaviour, not a usage count — the instrumentation specification with every event tiered and developer-ready, and the success and failure criteria the whole team agrees on. Week two produces the PostHog dashboard and the 30-day monitoring plan with specific decision points.

The result: when the feature ships, the team has a shared definition of success, the data is clean from day one, and the 30-day readout is a dashboard walkthrough instead of an argument. If adoption misses the target, the monitoring plan tells you exactly where to look first.

TIMELINE

From kickoff to a measurement-ready launch.

WEEK 1

Define + Specify

Kickoff call to understand the feature, existing analytics, and team assumptions. Adoption definition produced — the specific behavioural action that constitutes adoption. Instrumentation spec written and handed to your developer. Success and failure criteria drafted for team review.

→

WEEK 2

Build + Plan

PostHog dashboard built once Tier 1 events are confirmed live. Success criteria finalised and signed off. 30-day monitoring plan produced with decision trees. Stakeholder readout template delivered.

→

DAY 14

Handover + Launch

Full handover session with your product and engineering teams. Dashboard walked through. Monitoring plan reviewed. Everything owned by you permanently. The feature ships with measurement in place from day one.

Day 15: the feature ships and the dashboard starts counting.

WHAT YOU GET

16 deliverables that make launch success measurable from day one.

Deliverable 01

Feature Adoption Definition Workshop

A structured session to define exactly what "adoption" means for this feature — not a vague goal of "engagement up," but specific behaviours, measured at specific points in time. Without this, every post-launch review becomes a debate about whether the numbers are good.

Deliverable 02

User Journey Mapping for New Feature

The full path a user takes to discover, try, and get value from the feature is mapped as a measurable sequence of steps. This is the foundation for every instrumentation and measurement decision that follows.

Deliverable 03

Baseline Usage Analysis of Comparable Features

How similar features in your product have performed at launch is analysed to produce credible 30/60/90-day adoption targets. You enter launch with targets that reflect what's actually achievable, not optimistic projections.

Deliverable 04

Instrumentation Gap Analysis

The events you'd need to measure adoption properly that you're not currently tracking are identified and documented. You know exactly what to ask engineering to add before launch — not after.

Deliverable 05

Success Threshold Research for Your Product Stage

What healthy adoption looks like for a feature at your product's current stage is researched and documented. You know from day one what you're benchmarking against.

Deliverable 06

Feature Adoption Definition Document

A single written document defining what adoption means for this feature, agreed by product and engineering before launch. Every post-launch review starts from this definition rather than re-litigating what the goal was.

Deliverable 07

Instrumentation Specification (Tiered by Priority)

Every event the feature needs to track is documented with an implementation priority — what must ship on launch day, what can follow in week two, and what can wait. Engineers work from a clear spec; nothing gets missed under launch pressure.

Deliverable 08

PostHog Adoption Dashboard Built and Connected

A live dashboard showing feature adoption, daily active users, and progress toward 30/60/90-day targets — built and connected to real data before launch day. You open a working dashboard on day one, not a blank screen.

Deliverable 09

Success and Failure Criteria with 30/60/90-Day Targets

Specific, written criteria that define what good looks like at each milestone — and what bad looks like. Your team knows by day 30 whether to accelerate, iterate, or escalate, not three months later.

Deliverable 10

30-Day Post-Launch Monitoring Plan

A structured plan covering what to watch in the first 30 days and what actions each metric movement should trigger. Your team responds to signals rather than waiting for a scheduled review.

Deliverable 11

Stakeholder Readout Template

A reusable template for reporting feature performance to leadership and investors, with your benchmarks already embedded. A monthly performance review goes from a two-hour preparation exercise to a 20-minute fill-in.

Deliverable 12

Developer Implementation Guide

Everything an engineer needs to implement the instrumentation spec correctly: event names, properties, expected values, and priority tiers. Zero back-and-forth between product and engineering during implementation.

Deliverable 13

Dashboard Configuration Documentation

Step-by-step documentation of how the PostHog dashboard was built, so your team can extend it, maintain it, or replicate it for future feature launches.

Deliverable 14

Monitoring Plan with Red Flag Triggers

Specific signals that indicate a problem requiring immediate action — defined before launch so your team is making decisions rather than improvising when the data looks unexpected.

Deliverable 15

Team Alignment Session (Recorded)

A live session with your product and engineering team to walk through every output and confirm shared understanding before launch. Recorded so anyone who couldn't attend gets full context.

Deliverable 16

Launch Day Monitoring Support + 30-Day Performance Review + Day-15 Optimisation Session

Direct access on launch day to interpret early signals in real time. A day-15 working session to review what the data is showing while there's still time to adjust before the 30-day mark. A structured 30-day performance review to close the loop.

Everything above for $3,997. No hourly billing. No scope creep. Everything stays with your team.

FIT CHECK

Your next big feature ships soon. Will you know if it worked — or debate it for a quarter?

GOOD FIT

B2B SaaS shipping a major feature with no measurement plan in place

Feature shipping soon · adoption undefined

The situation

A significant feature is shipping in the next 30–60 days. Your team doesn’t have a clear, agreed-upon definition of what adoption means for it. The instrumentation isn’t set up yet. Your last feature launch led to weeks of debate about whether it worked — and nobody had a clean answer.

What you leave with

Adoption defined as a specific behaviour, agreed before launch
Instrumentation spec your developer implements in 1–2 days
PostHog dashboard showing live adoption data from launch day

At 30 days, the team has a shared, data-backed answer to “did it work?” — and a plan for what to do next.

NOT A FIT

Bug fixes, minor changes, or teams that need full product analytics

Wrong scope or wrong starting point

When this sprint doesn’t apply

If you’re shipping a bug fix or minor UI change, the scope doesn’t warrant a measurement sprint. If you already have a feature measurement process and consistently follow it, you don’t need this. And if you need a full product analytics foundation built — not just the feature layer — that’s a different engagement.

Better starting points

App Launch Sprint

Full measurement foundation for an entire product launch — KPI framework, dashboards, targets.

Activation Deep Dive

Map your activation funnel, confirm drop-off points, rank the top fixes by impact.

The Foundation

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

What this sprint doesn’t cover

The Feature Launch Sprint delivers the adoption definition, instrumentation spec, dashboard, and monitoring plan. Your team does the implementation. If you need the events implemented or the feature redesigned based on adoption data, that’s a different engagement.

Implementing the events — your engineering team instruments from the spec
Redesigning the feature based on adoption results — the sprint identifies what to measure, not what to change
Ongoing monitoring — the sprint delivers the monitoring plan, your team runs it

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 adoption definition workshop, the instrumentation spec, the PostHog dashboard build, the monitoring plan — all of it. Your feature is not generic. The adoption definition needs to match the specific behaviour that predicts retention for this feature in your product, not a template that says “users who clicked the button.”

The sprint produces assets your team acts on directly. The instrumentation spec tells your developer exactly what to implement. The dashboard tells your PM whether adoption is tracking before the retrospective. The monitoring plan tells the team what to do when the numbers deviate. No interpretation required — everything is formatted for the person who needs to use it.

I won’t do this:

Define adoption as “users who clicked the button” without understanding retention correlation
Set success criteria that make the feature look good regardless of outcome
Build a dashboard nobody opens because it answers the wrong questions
Leave the team without a plan for what to do when adoption misses the target

Could our PM define the adoption metric themselves?

Possibly the instrumentation list — but the adoption definition is harder. Most PMs default to “users who used the feature at least once” as their adoption metric. That definition doesn’t distinguish between a user who tried it once and a user who integrated it into their workflow. Getting that definition right — specific enough to measure, behavioural enough to predict retention — is the core of the sprint. The instrumentation spec, dashboard, and monitoring plan all follow from it.

Teams Jake has worked with

PRICING

One price. Everything your team needs to launch with measurement in place.

$3,997

one-time · fixed price

2-week sprint

Feature adoption definition — specific, behavioural, agreed before launch
Instrumentation specification — tiered, developer-ready
PostHog adoption dashboard — built before launch day
Success & failure criteria — agreed by the team before ship
30-day post-launch monitoring plan with decision trees
Stakeholder readout template — reusable for future launches
Full handover session with your product and engineering teams
Everything stays with your team permanently

Adoption defined, instrumented, and measurable before launch — or full refund.

Book a 30-minute call →

Adoption defined, instrumentation spec confirmed, dashboard built, success criteria agreed before launch. If we don't deliver these agreed-upon outcomes, you pay nothing.

Questions.

Or book a call →

What if the feature has already shipped? +

The sprint can run post-launch — the adoption definition becomes retrospective, the instrumentation spec gets retrofitted, and the focus shifts to the 30-day monitoring plan and stakeholder readout. The most valuable deliverable post-launch is often the success/failure criteria, because it replaces the ongoing argument about whether the feature worked with a shared, documented answer.

What does the instrumentation specification include? +

Event names, properties, data types, naming conventions, and implementation priority tiers. A document your developer can implement from directly — no additional guidance required. Tier 1 events are written to be implementable in a day or two for a competent developer.

How is this different from the App Launch Sprint? +

The App Launch Sprint ($5,997, 3 weeks) builds the full measurement foundation for an entire product launch — KPI framework, core dashboards, 30/60/90-day targets, team training. This sprint is narrower and faster: define adoption for one specific feature, instrument it, build the 30-day monitoring plan. Two weeks, $3,997. Use this when the product already has a measurement foundation and you need the feature layer specifically.

How much of our team’s time does this require? +

1–2 hours per week from the PM or product lead — one kickoff call, one review at the end of week one, and the handover session in week two. The developer needs one session to walk through the instrumentation spec and confirm it’s implementable in your stack.

How quickly can we start? +

Kickoff within 1 week of signing. The sprint runs 2 weeks from kickoff. If your launch date is tight, the instrumentation spec can be prioritised and delivered in the first two days so engineering can start implementing immediately while the rest of the sprint runs in parallel.

What does the guarantee mean exactly? +

Adoption definition documented and agreed, instrumentation spec confirmed implementable, PostHog dashboard built and showing live data, success/failure criteria signed off, 30-day monitoring plan delivered. All specific and verifiable. If any of those aren’t delivered before launch, you pay nothing. The guarantee is specific because the scope is specific.