Pre-Series A B2B SaaS | £400K ARR | Founder-led sales
Planning to hire two AEs within 8 weeks to accelerate growth.
ICP undefined. Pricing set reactively. No qualification framework. Conversion metrics unreliable. Hiring plan based on hope.
4-week Revenue Architecture Sprint. Defined ICP with firmographic and behavioral criteria. Rebuilt pricing logic. Designed qualification framework.
Funnel math model. ICP scoring matrix. Qualification playbook. Pricing decision tree.
Hiring delayed 6 months. Founder closed £180K in next quarter. Pipeline quality improved—disqualification rate increased from 12% to 41%. When AE hired 6 months later, onboarding took 3 weeks instead of projected 12 weeks.
Series B B2B SaaS | £3.2M ARR | 4-person sales team
Persistent forecast miss—averaging 68% accuracy.
CRM stages misaligned with buyer journey. No exit criteria between stages. Weighted pipeline calculated using arbitrary percentages. No historical conversion data. Forecast built from gut feel.
10-week Sales Engine Design. Mapped actual buyer journey. Redesigned CRM stages with objective exit criteria. Installed historical conversion tracking.
Buyer journey map. Stage exit criteria. Conversion rate database. Weighted forecast model using real conversion rates.
Forecast accuracy improved from 68% to 91% within 3 months. Pipeline visibility increased—leadership could identify stalls 4 weeks earlier. Sales cycle predictability improved—variance reduced from ±9 weeks to ±3 weeks.
Post-Series A B2B SaaS | £1.8M ARR | 60+ customers
Win rate declining quarter-over-quarter (47% → 31% → 23%).
ICP defined 18 months ago, never validated. Targeting based on "companies like our best customers" (vague). No analysis of which customer segments actually retained. Sales team chasing volume, not quality.
6-week engagement combining Revenue Architecture Sprint and data analysis. Reverse-engineered ICP from existing customer base. Analyzed retention, expansion, and support load by segment.
Validated ICP criteria. Segment profitability model. Targeting playbook. Disqualification framework. Identified 3 high-value segments representing 71% of revenue but only 34% of customer count.
Win rate stabilized at 38% within 2 quarters (up from 23%). Pipeline volume decreased 29% but pipeline value increased 41%. Sales cycle shortened by 18 days. Churn reduced from 14% to 9% annualized. Expansion revenue increased 34% year-over-year.