Growth Hacking Course
Your ICP, built from your win data - delivered as a ranked account list your sales team works from day one.
Request a sample reportWho you win, who you lose, what drives deal value. Tested statistically against your own deal history.
Fit becomes a 0-100 number, weighted by revenue contribution. Including the disqualifiers that predict a loss before the first call.
The formula, applied to the market. A ranked, tiered list of lookalike accounts - CRM-ready, sized to your sales capacity.
Recent findings from delivered work, anonymised.
The client's core targeting belief was reversed by the data. Their "best" segment spent 25% less than average. Their strongest segment wasn't on the target list.
ICP analysis, European B2B SaaS scaleup, 2026Top-tier conversion in the 201-500 employee band vs baseline. Below 50 employees: near zero. Above 1,000: below baseline.
ICP analysis, European B2B SaaS scaleup, 2026Accounts scored into priority tiers from a model built on ~3,100 customers. Delivered with import fields and priority tags.
Target list build, European B2B SaaS scaleup, 2026Of the revenue difference between customers was explained by the scoring model. Company age predicted nothing. A handful of factors carried nearly all the weight.
ICP analysis, European B2B SaaS scaleup, 2026Top-quartile deals came from companies ~25× larger - but size predicted deal value, not win rate. Optimise for conversion alone and you chase small, easy deals.
ICP analysis, PE-backed B2B services company, 2026Of lost deals were lost on price. Half were "client decided not to progress". Most wasted pipeline came from accounts with no reason to act.
Loss analysis, PE-backed B2B services company, 2026Two projects, end to end. Client details removed.
The belief: client-facing businesses are our best customers. The data: they spent 25% less. The scoring model explained 73% of customer value, and 4 factors carried nearly all the weight.
The market split by country - customer profiles differed enough that each market got its own scoring formula.
Delivered: 29,183 accounts scored into priority tiers, imported into the CRM, with a prospecting playbook: filters, tiers, who to call first.
The task: shift revenue from legacy services to higher-margin lines. 5 years of CRM history: 5,000+ deals across ~940 companies.
One blended ICP became four - each service line had a different buyer, sometimes an opposite one. The strongest signal was a disqualifier: an interim CTO halved the odds of winning.
Delivered: four validated profiles, loss and deal-value analysis, and a scored whitespace target list sized to sales capacity.
You provide one CRM export. Everything else is enriched externally - around 20 external signals per company, taking the data from around 65% complete to over 97%. Statistical testing separates signal from noise. The formula is scored against the market. The list lands in your CRM.
The filters, the tiers, who to work first, how to re-score as results come in.
Where markets differ, each gets its own weights.
Plain-English findings - including the assumptions your data disproved.
Request a sample report - the methodology and output structure from a delivered project, client details removed.
Request a sample report3 connected things: an ideal customer profile built from your closed-won data, a scoring formula that turns "good fit" into a 0-100 number, and a ranked CRM-ready target account list - every company in the market that looks like your best customers, tiered by priority.
Most companies do. This tests it. The analysis starts from your closed-won and closed-lost deals, enriches every company with external data, and measures which factors predict wins and deal value. Where your existing ICP holds up, you get the evidence behind it. Where it doesn't, you find out before the next quarter's budget follows it.
One CRM export: closed-won and closed-lost deals with company names, values and dates. Everything else is enriched externally - recent engagements took the data from around 65% complete to over 97%. Messy CRM data is normal. Gaps that limit confidence are flagged.
The audit module diagnoses: it tells you whether your current ICP holds up against your data. This service delivers all 3 pieces - the validated ICP, the scoring formula, and the ranked lookalike target list, ready to import into your CRM.
A ranked list of net-new accounts, each scored against your ICP formula and sorted into priority tiers, delivered CRM-ready with import fields and tags. It comes with a prospecting playbook: the exact filters, what each tier means, who to work first, and how to re-score as conversion data comes in.