About the toolkit
In private markets, a single investor almost never shows up as a single record. The same limited partner turns up under their own name, a family trust, an LLC, a feeder fund and a leftover prospect record — so your systems treat one relationship as five different investors. You can’t see their true size, and compliance can’t see the real person behind the entities.
This is a self-serve toolkit that fixes exactly that: a step-by-step method for pulling those scattered records back into one accurate investor — and doing it safely, without wrongly merging two different people. It works with whatever systems you already run, and every step ships with a ready-to-use asset — a calculator, a checklist, a scorecard.
WHAT'S INSIDE
Who this is for
The firms:
- Venture, growth-equity and private-equity managers — you raise from limited partners and deploy into portfolio companies, and the same investor commits through a web of trusts, LLCs, feeders and SPVs your systems can’t tie back together.
- Fund administrators and third-party IR providers — you keep the investor register, capital accounts and beneficial-ownership records across multiple managers, so fragmented investor books are your problem to solve at scale.
- Multi-fund and multi-strategy platforms — the more funds and vehicles you run, the more one relationship splinters across them, and the harder it is to see a house-wide view of any investor.
The people:
- Data architects and identity leads — you own how investor records are matched, and you’re the one asked why the “single view” still shows one LP five times.
- Investor-operations and IR-data owners — you live in the LP book; its accuracy is your credibility with the partners who fundraise on it.
- Compliance, AML and DPO leads — you must know the real person behind every entity, keep information barriers intact, and decide what a resolved record is allowed to be used for.
Get a peek inside.
Why it’s worth it
- You skip the expensive trial and error. Figuring out which signals safely link one principal’s direct holding to their family trust to their co-invest LLC to a stray prospect record — and which ones quietly glue two different people together — is months of painful learning. That sequence, the match thresholds, and the traps specific to layered LP vehicles, feeder funds, and institutions hiding under a dozen name variants are already worked out here.
- It stops the one mistake that becomes a breach. In this world a wrong merge isn’t a data-quality blemish — it hands one investor’s capital account and holdings to another. And when two LPs sit behind the same family office or placement agent, that collision is exactly what happens. The built-in precision test proves every rule before it’s allowed to act, so you catch the father-and-son merge before it leaks.
- It turns “we should fix this” into a funded programme. The baseline converts your unresolved book into a number your sponsor actually feels — a top-decile relationship mis-ranked at the next re-up, co-invest sized to a fraction of the real wallet, and the KYC refreshes and FATCA/CRS filings you’re quietly paying for twice — so the work gets approved instead of shelved.
- It clears compliance before compliance blocks you. Every merged record carries a code that states what it’s allowed to do, backed by a policy your DPO signs off. The beneficial-ownership single view becomes defensible, information-barrier joins stay blocked no matter how confident the match, and nobody markets to a resolved profile that was only ever a “probably.”
Need more info?
Get the complete toolkit as an instant download. Prefer to talk it through first? Get in touch to discuss access options and we'll help you find the right fit.
Get in touchFAQ
What is this toolkit about, and which firms is it for?
This is a playbook that helps private-markets managers, fund administrators, and investor-relations teams resolve their customer identities — pulling the scattered records of a single investor (their own name, trusts, LLCs, feeders, prospect records) into one accurate relationship using fuzzy, multi-signal matching techniques. The point is to make those resolved identities usable in your marketing and sales communications — from re-up outreach to co-invest offers to investor relations. For a deeper introduction and how it applies to your firm, get in touch.
What’s included in the toolkit?
The complete method as a set of individually branded guides — one per step, from sizing the problem to going live — plus the working assets you run it with: a Gap/ROI calculator to build the business case, a precision scorecard to prove your matching is safe before it acts, two hands-on guidance notes, a glossary, and a full worked example that follows one investor end to end. 20 files, downloaded instantly.
Do I need a specific platform — is this only for a CDP?
No new platform required. The method is platform-agnostic: it runs the same whether your identity work sits in a packaged CDP, a warehouse or lakehouse, or an external MDM. One section walks you through classifying your own engine so every later step maps to exactly what you already run.
Do I run it myself, and what do I need in place first?
You run it — it’s self-serve: the method, the sequence, and the tools so your own team can take investor identity from basic matching to multi-signal resolution, safely. You should already have exact-key matching working and be hitting its ceiling, with a real share of your investor book still fragmented. It’s written for the data architect or identity lead and their investor-ops and compliance counterparts; no data-science background required.
What does it cost, and what are the access options and refunds?
For pricing, access options, and our refund policy, please get in touch and we’ll help you find the right fit.
