70% feature adoption. 5% monthly churn. You're measuring the wrong thing.

Your dashboards track clicks, sessions, and feature adoption. None of them answer the question that matters: are users actually getting their job done? We map your 3-5 core user jobs, measure completion rates, and show you where users fail — and what it costs you in retention.

2 weeks · $5,997 · Money-back guarantee

Map My Jobs → See how it works ↓

For B2B SaaS companies at $10M-$50M ARR

THE 3-MINUTE BREAKDOWN

Jake McMahon explains why feature-based analytics miss the point — and how job-based measurement changes product decisions.

80% of software features are rarely or never used. But the features aren't the problem — the job paths are.

Users don't open your app to 'use the reporting feature.' They open it to answer a question for their boss by 3pm. If the path to that job is broken, no amount of feature polish helps.

A feature driving the highest retention correlation was found by only 13% of users.

The feature existed. The value existed. The discovery path didn't. Feature analytics said 'low adoption.' Job analytics said 'the job isn't completable.' Different diagnosis. Different fix.

When you measure what users actually care about, you optimize what they care about. Retention follows.

One client found that users completing their core job retained at 2x the rate of those who didn't. The feature existed. The completion path was broken. Feature analytics called it 'low adoption.' Job analytics called it 'a fixable path problem.'

THIS IS YOU

Four signs your analytics are measuring the wrong thing.

Your feature adoption dashboard shows 70% adoption. Churn is still 5% monthly.

Users click the feature. That doesn't mean they completed the job. 'Used reporting' and 'got the answer they needed' are different metrics. You're measuring the input, not the outcome.

Your PM says 'users love the new feature.' Retention didn't move.

Usage went up. Retention didn't. Because the feature improved a step in the middle of a job — but the bottleneck is at the beginning. Nobody reaches the improved step.

Three different user types go through identical onboarding.

A CFO doing financial reporting and an analyst building dashboards hire your product for completely different jobs. Same onboarding, same feature tour, same empty state. One succeeds, the other churns. Your analytics can't tell you which or why.

You shipped 12 features last quarter. You can't tell which ones moved retention.

Feature-level analytics tell you adoption. Job-level analytics tell you impact. You need to know: did the feature help users complete a job they couldn't complete before? Without that, every feature is a $42K bet on a hunch.

What feature-based analytics actually miss.

$42K

average cost per feature shipped without measuring job completion

Every feature that doesn't connect to a user job is engineering time that doesn't compound. 80% of features are rarely or never used — not because they're bad, but because they're disconnected from jobs.

13%

of users found the feature with the highest retention correlation

The feature was there. The job it served was critical. The path to discovering it was invisible. Feature analytics said 'low adoption.' Job analytics said 'the discovery path is broken.' One leads to deprecation. The other leads to a 3x increase in adoption.

87%

of users never found the highest-value feature at one SaaS company

Not because they weren't interested. Because the product organized around features, not around the jobs users came to do. The path from 'I need to do X' to 'here's the feature that does X' didn't exist.

THE SHIFT

From tracking features to measuring job completion.

	BEFORE	AFTER 2 WEEKS
What you measure	Page views, clicks, sessions, feature adoption %	Job completion rates for your 3-5 core user jobs
Activation metric	'Logged in' — 80% of churners activated	Completed first core job — correlated with retention
Product decisions	Which features are used most?	Which jobs have the lowest completion rate?
Retention insight	Churn is 5% — we don't know why	Users who complete Job X retain at 2x+ — only 24% complete it
Roadmap priority	What features do competitors have?	Which job completion gaps cost the most retention?
User segments	Plan tier, company size	Job-based: which jobs each segment hires you for

THE PROCESS

Three steps. 2 weeks. Your analytics organized around what users actually care about.

DAY 1-2 · JOB DEFINITION WORKSHOP

60-minute workshop with your product team. We define the 3-5 core jobs users hire your product to do. Not features — jobs. 'Generate a report for my board by Thursday.' 'Onboard a new team member without a support ticket.' 'Find out why activation dropped this week.' Each job has an entry point, key steps, and a measurable completion state.

Job Definition Map (3-5 Jobs)Completion Criteria per Job

DAYS 3-9 · MAP + BUILD

Map each job to the event path in your analytics. Identify where events exist and where there are gaps. Build the Job Completion Dashboard: for each job, the entry rate, step-by-step progression, completion rate, and drop-off points. Segmented by plan, cohort, and user type. Correlate job completion with 30/60/90-day retention.

Job Completion DashboardJob-Retention Correlation Analysis

DAYS 10-14 · INSIGHTS + WALKTHROUGH

The Under-Discovered Jobs Report: jobs with high retention correlation but low completion rate — your biggest opportunities. For each: where users drop off, what's blocking completion, estimated retention impact of fixing it. 60-minute walkthrough: we walk through every job, every completion rate, and the 2-3 highest-impact fixes.

Under-Discovered Jobs ReportPrioritized Fix List60-min Walkthrough Call + Recording

YOUR GUIDE

87% of users never found the highest-retention feature. We fixed the job path.

ProductQuant installs growth operating systems for B2B SaaS companies. The JTBD dashboard is the layer most teams skip — they measure features because that's what analytics tools default to. But users don't think in features. They think in outcomes.

The shift from feature analytics to job analytics changes every product conversation. 'Should we build Feature Y?' becomes 'Which job does Feature Y serve, and what's the completion rate for that job?' If the job is already completing at 80%, building another feature for it is waste. If the job is at 20%, the feature might be the unlock.

At one healthcare SaaS, reorganizing analytics around patient intake jobs revealed that practices completing the 'automated intake' job retained at 2x+ the rate of those on manual workflows. The data was there. The job framing wasn't.

3-5

core jobs mapped per engagement

1.8x

retention multiplier from job path fix

13% → 40%+

adoption after discovery path built

THE WORK

What happened when analytics measured jobs instead of features.

E-COMMERCE SAAS

1.8x

retention multiplier

13% → 40%+

feature discovery

The highest-value feature had zero discovery path. Job analytics revealed that the 'compare products across suppliers' job completed for power users but failed for 87% of casual users. The feature existed — the job path didn't. Discovery path built. Adoption: 13% → 40%+. $2.5M in annual opportunity mapped across 3 job completion gaps.

HEALTHCARE SAAS

$272K-$505K

annual impact

30-60 days

early churn signal

Reorganized analytics around patient intake jobs instead of feature usage. Revealed that practices completing the 'automated intake' job retained at 2x+ the rate of those stuck on manual workflows. Churn prediction model built on job-completion signals predicted at-risk accounts 30-60 days early.

If the dashboard doesn't at least one job where completion rate is below 50% with measurable retention impact, full refund.

Every SaaS product has jobs that users try to complete and fail. The dashboard makes those failures visible — with completion rates, drop-off points, and retention correlation. If your product is the rare exception where every job completes perfectly, you pay nothing.

Map My Jobs — $5,997 →

Questions.

Or book a call →

What's a 'job' in this context?+

A job-to-be-done is the outcome a user hires your product to achieve. Not a feature, not a page — a real-world outcome. 'Generate a board report by Thursday.' 'Onboard a new hire without filing a support ticket.' 'Figure out why activation dropped.' We define 3-5 of these in a workshop with your team.

We already track feature adoption.+

Feature adoption tells you what's clicked. Job completion tells you what's accomplished. A user can 'adopt' the reporting feature (clicked it, opened it) without completing the job (got the answer they needed). The gap between adoption and completion is where retention lives.

What if our event coverage is limited?+

If your instrumentation has gaps, we'll map what's measurable now and spec what to add. The dashboard will show completion rates for jobs where events exist and flag the specific events needed for jobs where coverage is incomplete — so you know exactly what to instrument next. Most companies have enough coverage for 2-3 core jobs on day one.

What's the investment?+

$5,997. 2 weeks. Full refund if we can't find at least one job with below 50% completion and measurable retention impact.

How is this different from an activation funnel?+

An activation funnel measures one path — signup to 'activated.' Job completion measures 3-5 different paths — each representing a different reason users hired your product. A user might activate (complete the onboarding job) but churn because the 'ongoing reporting' job never completes. Job analytics catches both.

What happens after the 2 weeks?+

You have a running dashboard your team uses for product decisions — jobs mapped, completion rates measured, retention correlated. Your team can act on the prioritized fix list independently. If you want hands-on support implementing the highest-impact fixes, we can discuss that on the walkthrough call.