ICP Model from a Fragmented Client Record
Full ICP framework, partner network segmented by activity, and decision-maker fields in CRM — a repeatable acquisition and qualification system.
The challenge
Revenue was real but the client model was invisible — who was generating it, why they bought, and who actually controlled the money.
The business had 13 active Capital division clients and 6 Homes clients on record at the time of analysis. One client alone accounted for approximately 57% of total Capital revenue — a concentration risk that was not visible until the data was properly structured. Of 25 Homes referral partners, fewer than half were active, and 68% had no formal agreement in place. The CRM captured transaction data but not the reasoning behind purchases, the real decision-maker hierarchy, or lifetime value across cross-sell pathways. Geography compounded the problem: the second-largest concentration of the target diaspora population in the UK had zero clients on record — not because the market wasn't there, but because acquisition had followed relationships rather than strategy. The result was a firm making individual good decisions while operating without a systematic understanding of its own client base.
The system
Decision system built
We conducted a structured data extraction and segmentation exercise across all client and partner records, then layered in qualitative analysis from adviser interviews and transaction narratives. The output was a two-division ICP framework covering demographic profile, decision motivation, hidden stakeholder mapping, realistic lifetime value, and acquisition channel. Partner records were re-categorised by activity, deal volume, and agreement status. A hidden decision-maker tracking schema — covering parent name, city, capital source, and decision authority level — was defined and mapped to CRM field requirements. Revenue per ICP type was modelled conservatively against observed transaction sizes and realistic cross-sell pathways.
System components
Two-division ICP segmentation (Capital and Homes) with distinct risk profiles, motivations, and acquisition economics
Partner network audit: active/dormant classification, deal attribution, and formal agreement gap analysis
Hidden decision-maker tracking schema with mandatory CRM fields (parent name, city, capital source, authority level)
Revenue concentration analysis with single-client dependency flags and lifetime value modelling
Geographic gap mapping against diaspora population data to identify untapped acquisition markets
How we worked
Engagement scope
Data audit and segmentation across both divisions, adviser interviews, partner network review, CRM field redesign, ICP documentation, and revenue concentration modelling.
Timeline
Four-week structured analysis with a further two weeks for CRM schema redesign and adviser handover.
Operating model
Joint working with the founding team and senior advisers. Analysis was led by us; field definitions and ICP validation were stress-tested in working sessions with the people actually running client conversations before anything was finalised.
Outcomes
Business impact & measurable results
Full ICP framework, partner network segmented by activity, and decision-maker fields in CRM — a repeatable acquisition and qualification system.
Identified that one client represented 57% of Capital revenue — a risk that had not been visible in any existing reporting, prompting immediate pipeline diversification effort
Re-classified 25 Homes partners: fewer than half active, 68% without formal agreements — enabling a structured partner reactivation and formalisation programme
Defined a repeatable ICP qualification checklist used by advisers to score and prioritise inbound leads, reducing time spent on non-qualifying enquiries by an estimated 30%
Hidden decision-maker tracking embedded into CRM: no deal now progresses without a recorded parent or capital controller — directly reducing late-stage deal dropout caused by undisclosed veto stakeholders
Geographic whitespace identified in the second-largest diaspora market with zero current client presence — informing the next-phase acquisition strategy
Governance
Trust, collaboration & governance
All revenue figures and client records treated as strictly confidential — analysis outputs were shared only with decision-makers, not distributed across the team
ICP model validated against real transaction narratives before being treated as definitive — we did not impose a framework that didn't survive contact with actual deals
CRM field changes agreed with the advisers who would use them daily, not imposed top-down — adoption depends on the people doing the work believing the system makes their job easier
Reframe
A client database is the primary input for every future acquisition decision. Segmentation turns instinct into a model.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.