Your outbound is only as good as who it reaches. This work builds a scored, enriched prospect list grounded in your actual ICP - not a bulk export of every company that roughly fits.
Request a sampleConverting your ICP criteria into a buildable filter set - firmographic, technographic and behavioural - that can be applied to any data source consistently.
Filling in the company-level data your CRM is missing: industry classification, headcount, revenue band, location, and ownership structure - standardised and verified.
Which tools each target company uses - the tech stack indicators that predict fit, and the ones that predict a loss before the conversation starts.
Which accounts are actively researching your category right now - surfaced from intent data and mapped against your ICP to find the overlap worth prioritising.
Every account scored against the ICP criteria and sorted into tiers: high-confidence targets, secondary fit, and watch-list - so sales knows where to start.
Finding the right buyers within each target account - matched to your typical buying committee by title, seniority and function, with verified contact data.
Recent findings from delivered work, anonymised.
A market that looked like one ICP split cleanly into four distinct buying segments when analysed at company level - each with different win conditions, deal profiles and what to say to them. The same message had been going to all four.
Segmentation analysis, B2B professional services, 2026Of accounts by company size drove the majority of high-value wins. Concentrating outbound on the top two size tiers, and filtering out accounts below the threshold, reduced list volume by 60% while improving average deal size.
Segmentation analysis, B2B professional services, 2026A single technographic disqualifier - the presence of an interim CTO at the target company - predicted loss with more reliability than any of the positive ICP signals. It had never been tracked. Adding it to the qualification layer removed a meaningful proportion of the pipeline that had never been likely to close.
Segmentation analysis, B2B professional services, 2026Request a sample and see how a delivered segmentation and list-building project is structured - actual methodology, output format, and scoring framework, client details removed.
Request a sampleBuilding a prospecting list of companies and contacts that match your ideal customer profile - enriched with firmographic data (company size, industry, location), technographic data (which tools they use), and intent signals (which topics they are actively researching). A well-built list means outbound effort reaches the right accounts; a poorly built one means pipeline waste from the first touch.
Bought lists are built on availability, not fit. They include every company that matches a broad firmographic filter - not the ones that match your specific win conditions. Without enrichment and scoring, sales can't prioritise, and marketing can't personalise. The result is high-volume outreach to the wrong accounts and low conversion from sequences that aren't relevant.
Segments are built from your ICP analysis - the evidence of which company profiles actually convert and at what deal value. We then apply those filters to target market data, enrich each company with technographic and intent signals, and score every account against the ICP criteria. The output is a tiered list: prioritised accounts that match the full ICP, secondary accounts that match most of it, and a watch list of companies showing early intent signals.
It's strongly recommended but not required. If you have an existing ICP you trust, we can work from it. If the ICP is vague or contested internally, list-building without one produces a list of companies that look approximately right rather than ones you have evidence of winning. Both can be done together.