Case Study — PPC Automation Platform

How an Amazon PPC Platform Turned 45% Stalled Signups Into a $2.5M+ Revenue Opportunity.

Post-seed Amazon PPC automation platform scaling fast. High signup volume, but nearly half never launched a campaign. The VP of Product knew activation was broken. The hidden story: a 1.9× retention advantage that only 38% of users ever experienced.

Stack

JTBD

45%

Signups stalled before activation

Missing analytics events discovered

Distinct user segments identified

1.9×

Retention lift for early activators

$2.5M+

Annual revenue impact projected

10.1×

Feature discovery multiplier

Context.

Company Profile

Amazon PPC automation platform helping sellers manage and optimize Amazon ads
~20–40 employees, post-seed stage, scaling
Primary users: Amazon sellers (DIY sellers, agency owners, brand managers, power users)
Stack: Amplitude, Python, JTBD synthesis
High signup volume with significant activation drop-off

Team & Data

VP of Product driving growth and activation initiatives
3,500+ data points analysed from platform usage
Existing analytics with critical instrumentation gaps
Multiple feature surfaces with no systematic discovery measurement
Growing user base but retention showing structural ceiling

Before ProductQuant.

The VP of Product saw healthy signup volume. The platform was growing. But only 55% of signups ever completed the onboarding flow and launched their first campaign. The other 45% signed up, poked around, and disappeared. The team knew activation was a problem. They didn't know how bad it actually was.

What they didn't know: users who activated early retained at 1.9× the rate of users who didn't. This was the strongest retention signal in the entire product — and only 38% of signups were experiencing it. The other 62% never got far enough to feel what made the product sticky.

Even worse: the 1.9× retention lever was invisible to the team. They couldn't segment activated vs non-activated users because the analytics events that defined activation milestones simply didn't exist. 28 critical events were never instrumented. The team was optimising a funnel they could only see the first two steps of.

The Problem

45% of signups stalled before launching a campaign
1.9x retention advantage confirmed but only reaching 38% of users
28 critical activation milestone events entirely missing
No visibility into which user segments dropped off where
Feature discovery rates varied wildly with no systematic optimization

What they tried before us.

Attempt 1 — Email onboarding sequences

The team built drip email campaigns pushing new signups through setup steps. Emails linked to help docs and feature guides.

Outcome: Email open rates were healthy, but activation didn't budge. Users read the emails and still didn't launch campaigns.

Attempt 2 — Knowledge base articles

Comprehensive documentation covering every feature, written for the average user.

Outcome: Self-serve support tickets decreased slightly. Activation remained flat. Generic content didn't address the specific barriers each user type faced.

Attempt 3 — Webinar training sessions

Live and recorded product walkthroughs demonstrating how to set up and optimise campaigns.

Outcome: Attended users had slightly better outcomes, but reach was limited to ~5% of signups. Not scalable.

Why it didn't work: All three attempts addressed the surface symptom (low activation) without understanding the root cause. The team didn't know there were 4 distinct user segments, each with a different definition of activation and a different path to value. One-size-fits-all onboarding can't work when one-size-fits-all doesn't exist.

The diagnosis.

The VP of Product assumed the onboarding flow itself was broken. A JTBD analysis of 3,500+ data points told a fundamentally different story. The product had 4 distinct user populations, 28 blind spots in the analytics, and one feature acting as a hidden rocket fuel for everything else.

Finding 1 — The analytics blind spot

28 critical events that should track activation milestones were entirely missing from Amplitude. The team couldn't see where each user segment dropped off because the events that would tell them were never instrumented. They were trying to diagnose a patient with a stethoscope they hadn't plugged in.

Finding 2 — Four different users, four different activations

The JTBD synthesis revealed 4 distinct user segments — DIY Sellers, Agency Owners, Brand Managers, and Power Users — each with completely different activation paths. A DIY Seller activated when they saw their first automated bid change. An Agency Owner activated when they onboarded their first client. These paths shared almost no steps in common, but the product had one onboarding flow for everyone.

Finding 3 — The Strategic Objectives multiplier

Users who set “strategic objectives” during onboarding discovered features at 10.1× the rate of users who didn't. This was the single highest-leverage action in the entire product. But only 8% of users ever touched the strategic objectives feature. The most important activation gate was buried in a sidebar menu.

Feature	Discovery Rate	Status
Campaign Manager	76%	Strong — primary landing surface
Budget Management	62%	Moderate — needs visibility improvement
Keyword Research	45%	Below target — hidden behind secondary nav
Bid Optimization	34%	Critical gap — core value feature under-discovered
Reporting Dashboard	29%	Major gap — retention driver, low exposure
Strategic Objectives	8%	Critical — 10.1x multiplier, 92% of users miss it

The four user segments

Segment 1

DIY Sellers

Individual Amazon sellers managing their own PPC campaigns. Want to save time on bid management and see immediate ROI. Activation event: first automated bid change showing a performance improvement.

Activation path: Setup wizard → connect account → launch first campaign → see bid change impact

Segment 2

Agency Owners

Digital marketing agencies managing PPC for multiple clients. Need bulk operations, client reporting, and multi-account dashboards. Activation event: first client account onboarded with a campaign live.

Activation path: Create agency profile → onboard first client → launch client campaign → generate report

Segment 3

Brand Managers

In-house marketing leads at brands selling on Amazon. Focused on brand positioning, advertising strategy, and ROAS targets. Activation event: first strategic objective set and linked to a campaign.

Activation path: Set brand profile → define objectives → link to campaigns → measure against targets

Segment 4

Power Users

Advanced sellers and aggregators managing high-volume portfolios. Need custom rules engine, API access, and bulk editing. Activation event: first custom automation rule applied across portfolio.

Activation path: Import portfolio → configure rules engine → apply automation → monitor at scale

The fix.

Four interventions, each targeting a specific layer of the activation problem — from analytics infrastructure to product experience design.

Fix 1 — Segment-Specific Onboarding Flows

Four distinct onboarding flows built, each mapped to a specific user segment. DIY Sellers start with account connection and campaign setup. Agency Owners start with client onboarding. Brand Managers start with strategic objectives. Power Users start with portfolio import and rules configuration. Each path optimised for that segment's activation event.

Fix 2 — Instrument Missing Analytics Events

All 28 missing events identified, defined, and instrumented in Amplitude. Event taxonomy rebuilt with consistent naming and properties. Activation milestones now fully visible for each segment. Drop-off analysis per segment becomes possible for the first time.

Fix 3 — Feature Discovery Optimization

Strategic Objectives promoted from sidebar menu to activation gate. Users are now prompted to define objectives during onboarding. Bid Optimization and Reporting Dashboard given guided discovery paths via contextual tooltips and empty-state prompts. Feature discovery treated as a designed experience, not an accident.

Fix 4 — Retention Playbook Per Segment

Each segment now has a documented retention playbook: which features to push, when to push them, and what signals indicate disengagement. The 1.9× retention lift that only 38% experienced can now be extended to all users through targeted re-engagement sequences and feature prompts timed to each segment's usage patterns.

Feature	Current Discovery	Target Discovery	Primary Lever
Campaign Manager	76%	85%+	Empty-state onboarding prompts
Budget Management	62%	75%+	In-app contextual tooltips
Keyword Research	45%	60%+	Navigation restructure + guided prompt
Bid Optimization	34%	55%+	Day 5 email + post-campaign prompt
Reporting Dashboard	29%	50%+	Campaign completion trigger + dashboard shortcut
Strategic Objectives	8%	60%+	Activation gate — required step in onboarding

The result.

Before vs After metrics with quantified revenue impact.

1.9×

Retention advantage now extendable to all users — segment-specific paths remove the structural ceiling that limited it to 38% of signups

$2.5M+

Annual revenue impact projected from activation improvement, feature discovery uplift, and retention compound across all four segments

Missing analytics events identified and instrumented — activation funnel now fully visible per segment

Distinct segment-specific onboarding flows with documented activation events and retention playbooks

10.1×

Feature discovery multiplier confirmed on Strategic Objectives — now promoted from sidebar to activation gate

8% → 60%+

Projected Strategic Objectives adoption after moving it into the onboarding flow as a required step

Key Lesson

One-size-fits-all onboarding creates structural activation ceilings. This platform had a 1.9× retention lift sitting in the data, but only 38% of users ever reached it because the onboarding path was designed for an “average user” that didn't exist. The 4 segments needed fundamentally different first-action goals. Segment-specific paths aren't a personalisation feature — they're the core of an activation strategy. When 8% of users discover the feature with the 10.1× multiplier, the problem isn't user behaviour. It's product architecture.

What you can do now.

Know which of your users activate and which don't

Segment-specific activation funnels showing drop-off at every step, for every user type. Every bottleneck identified, every missing event documented, every fix revenue-sized.

Turn feature discovery from accident into design

Every feature has a discovery rate, a target rate, and a specific intervention to get there. The features that drive retention no longer depend on users stumbling into them.

Address a $2.5M+ revenue opportunity

Activation improvement, feature discovery uplift, and retention compound modelled as a prioritised experiment backlog. Every intervention has a dollar value attached. Every segment has a playbook.

See What This Costs → Book a 15-Minute Call

What this looks like for your company

Activation Deep Dive.

A structured analysis of your activation funnel — finding the missing events, segmenting your users by actual behaviour, and building the roadmap to get every cohort to the 1.9× retention experience.

Complete activation funnel audit: every step, every event, every gap
User segmentation analysis: find your DIY Sellers, Agency Owners, Brand Managers, and Power Users
Missing event discovery: find the 28 (or more) events hiding your activation data
Feature discovery audit: identify the “Strategic Objectives” in your product — the hidden multiplier features nobody finds
Segment-specific onboarding playbook: exactly what to build for each user type, with revenue impact prioritised

$4,997 · 2 weeks

Right for you if

Signup volume is healthy but activation rate is stuck below where you need it to be
You have retention data that suggests some users love the product, but you can't get everyone there
Feature discovery feels random — users find different things and you can't explain why

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