Digests

• Marketing attribution models: first touch, last touch, linear, W-shaped and data-driven, compared on one B2B deal: First touch, last touch, linear, U-shaped, W-shaped and data-driven attribution models compared, with the credit math worked on one $100,000 deal.
• CAC payback benchmarks for B2B: by ACV band, motion and growth rate: The median B2B SaaS CAC payback was 16 months in 2025, 10 months for the fastest quarter. Benchmarks by ACV band, growth rate and the gross-margin formula.
• MQL to SQL conversion rate benchmarks and B2B funnel conversion rates by stage: B2B SaaS MQL to SQL conversion runs about 38%, or 26% to 51% by channel. Stage benchmarks, definitions and worked math for each funnel step.
• LinkedIn ads benchmarks: CPC, CPL and CTR by objective, from published data: LinkedIn ads benchmarks: the median B2B advertiser paid $376 per lead in 2025, against a $202 spend-weighted figure, with a $9.39 CPC and 0.67% CTR.
• Average B2B Sales Cycle Length by Deal Size and Segment: B2B sales cycles run from within 14 days under $2,000 ACV to 6 to 18+ months over $500,000. Gong data puts a $97k average deal at 69 days.

• Guides (4): Explanations of the ideas demand teams argue about, with the definitions and the numbers on the table.
• Calculators (5): Each calculator shows its formula and a worked example before you touch an input, so you can check the arithmetic by hand.
• Funnel benchmarks (5): Stage-by-stage conversion, cost and cycle numbers from published sources, with the median, the middle half and where each number came from.
• Playbooks (6): Programs written as steps, each ending in the number it should move and how to read it.
• Templates (3): Working documents to copy: service levels, scoring sheets, dashboards. Each one explains every field.
• Methods (3): How to measure: attribution, scoring, service levels between marketing and sales, and the definitions behind them.
• Glossary (12): The words of pipeline work, defined once, the way this site uses them.
• Tools (11): every calculator and template, with its formula and a worked example.
• Agencies: demand and pipeline agencies, listed alphabetically and never ranked, with facts from their own pages.
• Voices: what practitioners published that a reader can learn from, with our take.

Pages are written by the Take the Market Desk, which uses AI models to find sources, read them and draft, and are edited before they are published. Every factual sentence is checked against a passage on a source page that was read for that page, numbers are computed in code from those sources, and each page lists its sources. Every page shows the day it was first published and the day a fact last changed. How we work