Map your expansion triggers in 2 weeks.

You track MRR in Stripe. You have no idea what drives upgrades. 30%+ of revenue should come from expansion — most companies are at 10%. Not because the product can't support it, but because nobody has mapped what makes customers grow.

You get: Expansion Trigger Map · Tier Cannibalization Analysis · NRR Decomposition · 3-5 Experiment Specs · 60-min Walkthrough

2 weeks · $4,997 · Money-back guarantee

Map Your Expansion Triggers → See how it works ↓

For B2B SaaS companies at $3M-$80M ARR

THE 3-MINUTE BREAKDOWN

Jake McMahon explains why expansion revenue is the most neglected lever in SaaS — and the 2-week diagnostic that maps what triggers upgrades.

A 1% improvement in pricing yields 11% increase in profits. Monetization is 4x more efficient than acquisition.

Yet most companies spend 80% of growth budget on acquisition and 5% on monetization. The highest-leverage work is the work nobody's doing.

NRR 95% → 110% at $10M ARR = $40M enterprise value swing at typical SaaS multiples.

Net revenue retention is the single most correlated metric to valuation. A 15-point NRR improvement changes your fundraise, your multiple, and your exit.

Most companies split NRR across pricing (one team), retention (CS), and expansion (sales). Nobody owns the full number.

When a metric is split across three teams, no one is accountable. Contraction looks like churn. Expansion looks like upsell. The full picture lives nowhere.

THIS IS YOU

Four signs your expansion revenue is leaving money on the table.

Your mid-tier is too close to your top tier — everyone buys cheapest.

When the gap between plans is too small, there's no natural reason to upgrade. Customers get 80% of the value at 50% of the price. The packaging is killing expansion.

You don't know which feature usage predicts expansion.

Your analytics track logins and page views. They don't track which product behaviors correlate with upgrades. The signal is in your data — nobody's looked for it.

NRR responsibility is split across 3 teams. Nobody owns the number.

Pricing sits with product. Retention sits with CS. Expansion sits with sales. When a customer downgrades, who's accountable? When a customer could upgrade, who triggers it? The answer is usually nobody.

'Land and expand' worked at $3M. It stopped working at $15M and nobody adjusted the playbook.

Early customers expanded because they were enthusiastic adopters. At scale, expansion requires mechanism design — triggers, packaging, timing. The playbook that worked at $3M doesn't work at $15M.

What flat NRR actually costs.

$40M

enterprise value difference between 95% and 110% NRR at $10M ARR, at typical SaaS multiples

NRR is the single most correlated metric to valuation. A 15-point swing changes your fundraise, your exit, and your options.

monetization is 4x more efficient than acquisition — yet most neglected lever

A 1% improvement in pricing yields 11% increase in profits. Yet most companies spend 80% of growth budget on acquisition and 5% on monetization.

50%+

of reported churn is actually contraction — customers downsizing, not leaving

You see churn in your dashboard. But when you dig in, half of it is contraction — departments quietly reducing access to essential-only users because they can't see the value of the higher tier. It's a packaging problem, not a product problem.

THE SHIFT

From flat NRR to expansion triggers mapped.

	BEFORE	AFTER 2 WEEKS
Upgrade triggers	Unknown — sales-dependent	Mapped to product behaviors with data
Pricing tiers	Feature-based, no expansion path	Value-per-segment with expansion designed in
NRR	Quarterly report metric	Weekly operating number with leading indicators
Expansion experiments	Zero	3-5 specs ready to run
Revenue risk	Invisible	Concentration mapped by segment
Contraction	Discovered at renewal	Predicted and intervened

THE PROCESS

Two phases. 2 weeks. Expansion triggers mapped.

WEEK 1 · MAP EXPANSION SIGNALS

Connect to analytics + billing. Map feature adoption by plan, cohort behavior by tier, expansion triggers from existing data. Identify which product behaviors correlate with upgrades — and which plan boundaries are killing natural expansion.

Feature Adoption by PlanExpansion Signal Map

WEEK 2 · QUANTIFY + DESIGN EXPERIMENTS

Tier cannibalization analysis — where mid-tier is too close to top tier. Revenue concentration by segment — where you're over-indexed and under-leveraged. NRR decomposed into gross retention, expansion, and contraction by cohort. 3-5 expansion experiments designed with hypothesis, metric, and success criteria. Day 14: 60-minute walkthrough — you leave with an Expansion Trigger Map your team can execute Monday.

Tier Cannibalization AnalysisRevenue Concentration ReportNRR DecompositionExpansion Experiments (3-5)60-min Walkthrough Call

YOUR GUIDE

One client found they won 78% of deals when integration depth was discussed first. Lost 67% when pricing came up first.

The narrative order was costing them deals. Most companies assume pricing is a pricing problem. It's usually a positioning problem, a packaging problem, or a discovery problem. ProductQuant finds which one.

At one e-commerce SaaS, 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. We built it. Adoption went to 40%+.

ProductQuant installs growth operating systems for B2B SaaS companies. The expansion work — trigger mapping, pricing architecture, NRR decomposition — is the layer most companies skip because it sits between product, sales, and CS. Nobody owns it. We do.

78%

win rate with right narrative order

1.8x

retention multiplier from feature discovery

13% → 40%+

feature discovery after path built

THE WORK

What happened when expansion triggers became visible.

E-COMMERCE SAAS

$2.5M+

annual opportunity found

1.8x

retention multiplier identified

Highest-retention feature found by only 13% of users. Discovery path built. Adoption: 40%+. $2.5M in annual revenue invisible because 3 measurement gaps in the activation-to-expansion funnel. Once mapped, experiments targeting expansion produced results within 8 weeks.

HEALTHCARE SAAS

$272K-$505K

annual impact

Segment-level

LTV analysis

Revenue concentration risk identified — 70%+ of revenue from one segment. Segment-level LTV analysis revealed which customer types had highest expansion potential. Churn prediction model gave 30-60 day early warning, preventing contraction before renewal conversations.

3 data-backed expansion opportunities — or full refund.

If we can't identify at least 3 expansion opportunities with experiment specs ready to run, you pay nothing. We map what triggers upgrades from your actual data — product behavior, pricing architecture, cohort patterns. If the data doesn't opportunities, you don't pay.

Map Your Expansion Triggers — $4,997 →

Questions.

Or book a call →

We already have pricing consultants.+

Pricing consultants optimize your pricing page. We map what product behavior drives upgrades and build expansion into the product itself. The pricing page is the last step, not the first.

We need to fix churn first.+

Expansion trigger mapping often reveals why customers aren't upgrading — tier gaps that are too small, feature gating that blocks natural growth, pricing that punishes usage. Fixing those is expansion work and retention work at the same time.

What data do you need?+

Analytics platform (Amplitude, Mixpanel, PostHog, or Segment) + billing system (Stripe, Chargebee, or Recurly). Read-only. NDA before we start. No codebase access required.

What's the investment?+

$4,997. 2 weeks. If we can't find 3 expansion opportunities, full refund.

What if we don't have great analytics?+

We'll tell you on the discovery call if your data is ready. We need an analytics platform and billing system with enough event history to identify patterns. If instrumentation needs work first, we'll be upfront about that before you commit.

What happens after the 2 weeks?+

You have an Expansion Trigger Map and 3-5 experiment specs ready to run. Your team can execute them independently. If you want hands-on support running the experiments, we can discuss that on the walkthrough call.