Worked examples

Series A SaaS

Illustrative B2B SMB SaaS at ~$3M ARR. ARPU $185, 78% gross margin, 3.2% monthly churn.

Consumer subscription app

Illustrative mobile fitness/wellness app on a monthly plan. ARPU $9.40, 68% gross margin, 8.5% monthly churn.

Struggling early-stage tool

Illustrative pre-PMF tool for freelancers. ARPU $52, 72% gross margin, 7.5% monthly churn.

PLG SaaS

Illustrative self-serve B2B tool, mostly content + community + product virality. Quarterly: $185K spend, 820 new customers.

Sales-led enterprise SaaS

Illustrative six-figure ACV product with AEs, SDRs, SEs. Quarterly: $2.4M spend, 18 new customers.

DTC consumer brand

Illustrative Shopify-based skincare/apparel, mostly Meta + TikTok ads. Monthly: $84K spend, 1,650 new customers.

Early-stage B2B SaaS

Illustrative: 1,000 customers at period start, 65 cancel during the month — 6.5% monthly churn.

Constant 6.5% monthly churn projects about 55% annual cohort churn. New-customer acquisition also determines net growth.

Consumer subscription app

Illustrative: 50,000 subscribers at period start, 4,500 cancel during the month — 9% monthly churn.

Constant 9% monthly churn gives a modeled lifetime of about 11 months. Compare this estimate with observed cohorts and CAC payback.

Enterprise SaaS

Illustrative: 200 enterprise accounts at period start, 2 churn during the month — 1% monthly churn.

Constant 1% monthly churn gives a modeled lifetime of 100 months. A short observation period does not establish that customers will stay that long.

Fast-growing SaaS

Illustrative: $500K starting MRR. +$80K new, +$30K expansion, −$10K contraction, −$25K churn → +15% net new MRR.

MRR grows 15% this month, while NRR is 99%. New customers offset the net revenue loss from the starting customer base.

Mature plateau

Illustrative: $2M starting MRR. +$100K new, +$50K expansion, −$30K contraction, −$120K churn → flat net new MRR.

A Quick Ratio of 1.0 means new and expansion MRR exactly replace churn and contraction this month.

Net negative

Illustrative: $300K starting MRR. +$20K new, +$5K expansion, −$15K contraction, −$45K churn → contracting.

A Quick Ratio of 0.42 means about $2.40 is lost for every $1 added this month. Review the losses alongside acquisition and expansion costs.

Quick-win feature

Illustrative: $40K engineering cost, $15K/month gain — 2.7-month payback, 350% ROI in year 1.

The model gives 350% first-year ROI with no implementation delay. Validate the $15K net monthly benefit and compare alternatives before prioritizing.

Platform investment

Illustrative: $400K cost, $20K/month gain — 20-month payback, 80% ROI over 3 years.

The assumed benefits recover the cost in 20 months. Explain any additional platform value separately and compare payback with your cash needs.

Marketing experiment

Illustrative: $25K cost, $8K/month gain for 6 months only — break-even in ~3 months, 92% ROI net.

Time-boxed benefits change the math: a campaign that stops paying after 6 months still clears 92% ROI, but only because the payback landed inside the benefit window.

B2C good/better/best

Illustrative: a $9/$19/$39 consumer SaaS ladder with a 50/35/15 mix across 20,000 customers — blended ARPU comes out at $17.00.

This mix produces $17 blended ARPU, below the $19 middle price. Use the weighted blend when projecting revenue.

Bottom-heavy mix

Illustrative: a $5/$15/$30 ladder where 80% of 5,000 customers sit on the entry tier — blended ARPU is dragged down to $7.75.

The 80% entry-tier share triggers this model’s concentration flag. Investigate customer needs before changing packaging or prices.

B2B two-tier with elasticity check

Illustrative: a $49/$99 team-tool ladder (third tier off) with 800 customers and a -0.6 elasticity assumption — the +10% sweep point wins on revenue.

At the assumed -0.6 elasticity, the +10% price scenario raises modeled revenue despite a 6% customer decline. Test that demand assumption before acting.

Series A SaaS roadmap

Illustrative example: Six features for a B2B SaaS at ~3,000 customers.

Consumer mobile roadmap

Illustrative example: Six features for a mobile app at ~600K MAU.

Marketplace roadmap

Illustrative example: Six features for an early-stage two-sided marketplace.

Growth team backlog

Illustrative example: Seven ideas mixing high-confidence quick wins with speculative big bets.

In this example, the AI-pages idea scores 108 and the checkout fix scores 512. Confidence of 3 produces a score 70% lower than Confidence of 10 with the other inputs unchanged.

Onboarding ideas

Illustrative example: Six ideas that all target the same new-user audience.

These example ideas target the same audience. Their ICE scores range from 162 to 336; RICE could still rank them differently because it uses a different effort scale.

Marketing channel tests

Illustrative example: Six channel bets where confidence varies wildly.

In this example, Google Ads scores 432 and email scores 405. The untested channels have lower Confidence estimates; use small tests to gather evidence before revising those scores.

Onboarding optimization

Illustrative example: Ten onboarding ideas spread across all four quadrants.

In this example, three small changes fall in Quick Wins and the full redesign is a Major Project. The custom onboarding estimate places it in Time Sinks; that does not apply to all custom work.

Growth experiments

Illustrative example: Ten growth ideas, from a referral CTA to a new pricing tier.

The example estimates place the referral CTA in Quick Wins, the pricing tier in Major Projects, and the webinar series in Time Sinks. Review the evidence before funding any of them.

Tech debt vs features

Illustrative example: Ten items mixing debt paydown with feature work.

The example compares technical debt and feature work on the same scales. Caching and CI changes have higher estimated impact relative to effort than the proposed rewrites.

B2B SaaS feature audit

Illustrative example: Six features across all five Kano categories for a B2B SaaS roadmap review.

SSO and audit logs receive high model priority scores in this example. The score does not predict their effect on churn or growth.

Consumer mobile app

Illustrative example: Six features for a consumer app, including a fully-built Reverse feature.

The forced login is 90% implemented and still classifies as Reverse — implementation level measures completeness, not whether users want the feature at all.

Marketplace trust audit

Illustrative example: Six features for a two-sided marketplace where trust is the product.

In this marketplace example, verification and secure checkout are classified as basics. Validate those categories for your own users.

Q3 SaaS roadmap

Five roadmap candidates scored on impact, value, feasibility, and risk — the billing work beats the AI feature on the math.

In this example, feasibility and risk scores lower the AI feature’s rank.

Vendor selection

Four options (three vendors + build in-house) scored on cost, features, support, and security.

Change the weights to see whether cost, support, or security changes the ranking in this example.

Next hire

Four headcount options scored on revenue impact, team leverage, urgency, and ramp cost.

The same criteria make the assumptions behind each hiring option easier to compare.

B2B SaaS: expansion offsets losses

$100K starting MRR, +$6K expansion, −$2K contraction, −$3K churn, 200 → 196 customers.

In this single-period example, GRR is 95% and NRR is 101%: expansion offsets revenue losses. One period cannot show whether a retention curve has flattened.

Consumer mobile: monthly losses

$50K starting MRR, +$1K expansion, −$1.5K contraction, −$4.5K churn, 10,000 → 9,100 customers.

This example has 91% monthly customer retention and 90% NRR. Track later periods to see whether losses continue.

Revenue contraction example

$80K starting MRR, +$0.5K expansion, −$4K contraction, −$12K churn, 400 → 330 customers.

This example has 80.6% NRR. Repeating that monthly rate would substantially reduce starting-cohort revenue, but one period alone does not diagnose product-market fit.

Consumer messaging app

8M MAU with 4.8M DAU — a 60% stickiness ratio.

In this example, 4.8M of 8M monthly users are active on the measured day. If DAU is a monthly daily average, 60% corresponds to about 18 active days per monthly user in a 30-day month.

Productivity SaaS

250k MAU with 75k DAU — a 30% stickiness ratio.

The illustrative ratio is 30%. If using monthly average DAU, users active on all 22 workdays of a 30-day month would yield about 73%; match the window and expected usage frequency.

Weekly-cadence B2B tool

40k MAU with 6k DAU — a 15% stickiness ratio.

The illustrative ratio is 15%. With average daily users, once-a-week use is about 14% (1 in 7 days), so low daily frequency may fit a weekly product.

E-commerce checkout

50,000 monthly visitors, 1,200 purchases — a 2.4% conversion rate.

This illustrative funnel converts 2.4% of visitors. Examine where the remaining visitors leave before selecting a change.

SaaS trial to paid

10,000 trial signups, 1,800 convert to paid — an 18% trial conversion rate.

This illustrative trial cohort converts at 18%. Compare only with trials using the same signup requirements, qualification rules, and conversion window.

B2B demo to opportunity

800 demo requests, 96 become qualified opportunities — a 12% conversion rate.

Small funnels swing hard: at 800 demos, ten deals either way moves the rate by 1.25 points — choose a reporting window that reflects volume and sales-cycle length.

B2B SaaS at 50

240 responses: 60% promoters, 30% passives, 10% detractors across enterprise and SMB segments.

Illustrative survey: 60% promoters minus 10% detractors gives NPS 50. Enterprise scores 65 and SMB rounds to 43; the overall score uses response counts, not an average of segment scores.

E-commerce at 25

Illustrative survey: 400 responses, with 45% promoters, 35% passives, and 20% detractors.

NPS is 25. Passives remain in the denominator even though they add neither a positive nor a negative contribution. Review all groups’ feedback.

Telecom at -10

Illustrative survey: 300 responses, with 30% promoters, 30% passives, and 40% detractors.

NPS is -10 because detractors outnumber promoters by 10 percentage points. Use comparable survey benchmarks and qualitative feedback to interpret it.

PLG dev tool

Developer tool with a crisp milestone: 1,080 of 2,400 signups create a first project — a 45% activation rate.

Illustrative example: 1,080 of 2,400 signups is 45%. Assess whether creating a project is linked to later value and retention.

B2B SaaS trial

Team-collaboration trial: 980 of 3,500 signups invite a teammate — a 28% activation rate against a two-person milestone.

The 28% rate measures teammate invitations. It is not directly comparable with a 50% login rate because the events differ.

Consumer app with onboarding friction

Fitness app burying its value moment behind a 12-step setup: 2,600 of 20,000 installs finish a first workout — 13% activation.

This illustrative 13% rate warrants checking setup friction, audience fit, and whether the workout milestone suits the measurement window.

Customer preference survey

Estimate a single preference percentage in a 50,000-customer base at 95% confidence, ±5 percentage points and an assumed 20% response rate.

With a 50% expected proportion and simple random sampling, this example needs 382 completed responses. An assumed 20% response rate gives 1,910 invitations. This does not plan NPS precision.

High-precision launch survey

Large audience at 95% confidence with a tight ±2% margin and a 15% in-app response rate.

For a large population and a 50% expected proportion, tightening ±5 to ±2 percentage points multiplies the unrounded sample by (5/2)² = 6.25, giving 2,401 completed responses. At an assumed 15% response rate, plan 16,007 invitations.

Pricing research

2,500 eligible customers at 90% confidence with a ±7% margin, analyzed across 5 pricing segments.

With a 50% expected proportion, 131 responses meets the overall planning target. Split evenly across five segments, that is about 26 each, with wider margins. Calculate each segment separately for the precision you need.

E-commerce checkout test

Checkout flow converting at 3%, testing for a 10% relative lift with 8,000 visitors a day.

At the default 95% confidence and 80% power, this illustrative example needs roughly two weeks of traffic. Reaching the sample target does not guarantee significance.

Low-traffic B2B signup test

Signup page converting at 5%, testing for a 10% relative lift with 400 visitors a day.

At the default settings and 400 eligible users per day, the planned sample takes roughly five months. Check whether that duration fits the decision before launching.

High-traffic onboarding test

Onboarding step converting at 40%, testing for a 5% relative lift with 50,000 users a day.

At the default settings, this illustrative example reaches the sample target within a day. Plan for relevant usage cycles as well; sample completion does not guarantee significance.

B2B SaaS Series A pitch

Bottom-up sizing for a horizontal SaaS tool: 250K addressable companies at a $1,200 entry-tier ACV, targeting 3% share in 4 years.

This example multiplies addressable customers by annual revenue per customer. Its capture share is an assumption to test, not a benchmark.

Consumer subscription app

80M potential users at about $84/year ($6.99/mo), narrowed hard by geography and segment, chasing 1% of a very competitive market.

In this example, geography, segment fit, and payment coverage reduce the addressable market before capture share is applied.

Enterprise vertical SaaS

Top-down sizing from a $4.5B analyst market figure: a mature vertical where a well-funded specialist can defend a 5% share over 5 years.

Top-down sizing is only as good as the analyst number it starts from — use it to cross-check a bottom-up estimate, not to replace one.

Steady scale-up team

Six developers and six illustrative sprints averaging about 45 points with little variation.

Low variation makes this history easier to use for planning, provided upcoming conditions are similar.

New team finding its pace

A five-dev team in its first quarter: velocity climbs from 18 to 34 points as estimates calibrate and carry-over shrinks.

When velocity changes, review recent history and upcoming capacity before choosing a baseline.

Interrupt-driven team

Plans 40 points every sprint, lands anywhere from 19 to 41 — unplanned work of up to 16 points keeps blowing up commitments.

The example includes substantial unplanned work. Review intake and planned scope together before changing estimates.

Short cycle times

Sixteen illustrative small work items with a 1-day median and about a 2-day 85th percentile.

These sample items have short cycle times. They do not establish a DORA performance tier.

Typical sprint team

Fourteen illustrative items with a 4-day median and a 7-day 85th percentile.

The median describes the middle item; P85 describes an upper historical percentile. Neither guarantees future delivery.

Review bottleneck

A 5-day median hides four items that sat 13-20 days in code review — the 85th percentile blows out to ~16 days.

A wider upper tail warrants investigation. In this example, the item descriptions identify review waits; the percentile alone cannot.