Some tools (A)I built

RevOps Calculator

Five models I use to think about revenue: pipeline forecast coverage, retention & NRR, a revenue bridge, free-to-paid conversion attribution, and expansion/churn signals. Enter your numbers and get instant analysis. Everything runs in your browser; nothing is stored or sent anywhere.

Pipeline forecast

Targets & assumptions

Annual ARR target ($) Your full-year new ARR goal.

Months elapsed (YTD) How many months into the fiscal year you are (e.g. 7 = end of July).

ARR closed to date ($) New ARR actually closed and booked so far this year.

Required pipeline coverage ratio How many $ of pipeline you need per $ of remaining target. Industry standard is 3–4x.

Pipeline by stage

Enter the total value and estimated close rate for each active pipeline stage.

Stage	Pipeline value ($)	Close rate (%)	Weighted value
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Output

Weighted pipeline

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Coverage ratio

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ARR gap to target

–

Pro-rata target (YTD)

–

Pacing vs target

–

Monthly run rate needed

–

Fill in your numbers above to see analysis.

Retention & NRR

Starting ARR & retention inputs

Starting ARR ($) Total ARR from existing customers at the start of the period.

Churn ARR ($) ARR lost from customers who cancelled entirely.

Contraction ARR ($) ARR lost from customers who downgraded but stayed.

Expansion ARR ($) ARR gained from existing customers via upsells, cross-sells, or seat growth.

Planning inputs

Annual ARR target ($) Your full-year ARR goal including both new and retained revenue.

New logo ARR (forecast) ($) Expected ARR from brand new customers this period (not expansions).

Output

Gross Revenue Retention

–

excl. expansion

Net Revenue Retention

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incl. expansion

Ending ARR (retained base)

–

Projected ARR (w/ new logo)

–

Gap to target

–

Additional new ARR needed

–

Fill in your numbers above to see analysis.

Revenue bridge

Shows how you get from opening ARR to ending ARR. The standard executive view in SaaS RevOps.

Inputs

Opening ARR ($) Total ARR at the start of the period, before any changes.

Annual target ($) The ending ARR you're trying to reach by year end.

New logo ARR ($) ARR from brand new customers acquired this period.

Expansion ARR ($) Additional ARR from existing customers (upsells, seat growth, add-ons).

Churn ARR ($) ARR lost from customers who cancelled entirely. Enter as a positive number.

Contraction ARR ($) ARR lost from customers who downgraded but didn't cancel. Enter as a positive number.

Revenue bridge

Opening ARR	–
+ New logo ARR	–
+ Expansion ARR	–
– Churn ARR	–
– Contraction ARR	–
Ending ARR	–
Annual target	–
Gap to target	–

Fill in your numbers above to see analysis.

Free → paid conversion by feature batch

Group your product into discrete feature batches, the way a nav sidebar does, and see which one converts free accounts best. "Last touch" here means the batch a customer used most recently before they upgraded.

Conversion totals

New MRR from these conversions ($) Total new monthly recurring revenue generated by the conversions in the table below. Allocated across batches proportional to their share of conversions.

Feature batches

For each batch, enter how many free/trial accounts used it, how many of those accounts converted with this batch as the last thing they used before upgrading, and the average days between that last use and the upgrade.

Feature batch	Free/trial accounts using it	Conversions (last touch)	Avg days to upgrade	Conversion rate
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Output

Best converting batch

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Fastest last-touch signal

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Highest conversion volume

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Free/trial accounts tracked

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Attributed MRR (best rate batch)

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Total conversions modeled

–

Fill in your numbers above to see analysis.

Expansion & churn signals

Score accounts on usage-based signals to flag who's about to expand and who's about to churn before either one emails support. Built on the same event data usage-based billing already tracks: plan utilization, seats, activity trend, and recency.

Dollar impact assumptions

Avg incremental MRR per expansion account ($) What an expansion-ready account is worth on average if they upgrade seats or plan.

Avg MRR at risk per at-risk account ($) What you stand to lose on average if an at-risk account churns.

Accounts

Usage vs plan over 100% and rising usage/seats point to expansion. Falling usage, falling logins, and days of silence point to churn risk. Edit any account to see its signal update live.

Account	Usage vs plan %	Seat utilization %	Usage growth 30d %	Login trend 30d %	Days since active	Signal
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Output

Top expansion signal

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Top churn risk

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Expansion-ready accounts

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At-risk accounts

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Estimated expansion pipeline

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MRR at risk

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Fill in your numbers above to see analysis.

Comp plan stress-tester

Configure a comp plan for a hypothetical usage-based SaaS company. The tool models how a rational rep would behave across three scenarios (durable growth, contract-reshaping, and low-quality growth) and flags where rep incentives diverge from company value, with the structural fix attached. Illustrative economics; designed to expose incentive risk, not prescribe a universal comp plan.

Plan mechanics

The sliders below define the plan. Everything is live: change a slider and the scenarios re-score.

OTE (annual)

Variable / base split

50% var

Annual quota

Commission paid on

Accelerator after quota

1.5x

Payout timing

Kickers & ownership

Annual-conversion kicker rate

Kicker paid on

Renewal bonus rate

NB → post-sale handoff

Gross margin (company)

75%

Clawback on early churn

Scenarios: same plan, three rep behaviors

Each scenario is the same rep facing the same book. The only difference is which behavior the plan rewards more. Edit any cell to test your own case.

A. Durable growth

Booked ARR

Realized usage

Annual conversion

Net expansion

12-mo retention

Rep payout–

Company value–

Payout / value–

B. Contract reshaping

Booked ARR

Realized usage

Annual conversion

Net expansion

12-mo retention

Rep payout–

Company value–

Payout / value–

C. Low-quality growth

Booked ARR

Realized usage

Annual conversion

Net expansion

12-mo retention

Rep payout–

Company value–

Payout / value–

Alignment score

Directional alignment

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Configure a plan to score it.

Incentive risk findings

Built by Karl Krecke. Inputs stay in your browser and are never stored.

The thinking behind them

My approach to revenue operations

Make data simple

Build reliable, end-to-end reporting that cuts through unnecessary complexity, maintain a single source of truth, and surface the patterns that matter in customer behavior.

Build for self-service

Equip teams to answer their own questions without waiting on RevOps.

Automate relentlessly

Eliminate manual processes that waste time and don't scale.

Standardize key metrics

Keep clear, consistent definitions for the metrics that matter most.

Connect the dots

Help teams see how their work impacts everyone else's.