How do you score a portfolio company's go-to-market?
Score twelve checks from 0 to 2 and add them up out of 24. The checks cover the CRM foundation, inbound, targeting, and signals and outreach, and all read data the company already holds. Rule of thumb: 0 to 8, fix the data first; 9 to 16, fix inbound and targeting; 17 to 24, ready to scale outreach.
You bought a company, or you are about to. The data room shows revenue. It does not show whether the go-to-market machine scales or needs rebuilding. These twelve checks answer that in a week, from the CRM, the inbox and the website.
What are the twelve checks?
Four areas, three checks each, every one scored 0, 1 or 2. Zero is the red flag. Two is what good looks like. One means it exists but runs by hand or covers part of the business.
| # | Area | Check | Scores 0 when | Scores 2 when |
|---|---|---|---|---|
| 1 | CRM foundation | Duplicates | Several records per account, cleaned by hand if at all | One record per account, and dedupe runs on its own |
| 2 | CRM foundation | Data rules | Fields filled when a rep remembers, with no source or date | Required fields, each stamped with its source and date |
| 3 | CRM foundation | Account hierarchy | Parents and subsidiaries sit as unrelated records | Parent and child companies linked, so a rep sees the whole group |
| 4 | Inbound | Lead enrichment | A form fill arrives as a name and an email | Enriched and scored the moment it lands |
| 5 | Inbound | Routing | Leads sit in a queue until someone looks | The right rep gets it in minutes, with an alert |
| 6 | Inbound | Attribution | Nobody can say where closed deals came from | Every closed deal traces back to its source |
| 7 | Targeting | Lookalikes | No list beyond the accounts reps already know | A list of companies that match the best current customers |
| 8 | Targeting | Market map | Target accounts live in spreadsheets and heads | Every target account sits in the CRM with a tier |
| 9 | Targeting | Buying committee | One contact per account | 3 to 5 roles mapped per target account |
| 10 | Signals and outreach | Signals | Reps hear about funding and hires on LinkedIn, if at all | Funding, hiring and competitor moves feed a weekly call list |
| 11 | Signals and outreach | Investor network | Warm paths exist only in the board's heads | Warm paths from the fund and the board to target accounts, written down |
| 12 | Signals and outreach | AI outreach | Cold domains, pasted copy, replies in one inbox | Warmed domains, AI drafts, a person approves, replies routed |
How do you read the total?
Add it up out of 24 and read the band as a rule of thumb.
0 to 8: fix the data first. Nothing else works on a broken CRM. Routing a lead to the wrong duplicate, or scoring a lookalike list against customers with no industry field, wastes the build.
9 to 16: fix inbound and targeting. The leaks cost deals the company already earned, and targeting decides whether outbound has a list worth sending to.
17 to 24: ready to scale outreach. The foundation holds, so signals and outbound have data to run on.
Two checks carry extra weight. If duplicates or data rules score 0, treat the total as the lower band whatever the rest says. Clay's portfolio score matrix has the same rule: if the data foundation row scores 1, start there whatever else scores.
How does this compare with Clay's portfolio score matrix?
Clay publishes a seven-row matrix for the same job, scored 1 to 3 from manual to agentic, across data foundation, outbound, inbound, expansion, retention, closed-lost and reporting.
| This scorecard | Clay's matrix | |
|---|---|---|
| Rows | 12 | 7 |
| Scale per row | 0 to 2 | 1 to 3 |
| Bands | 0 to 8, 9 to 16, 17 to 24 | 7 to 10, 11 to 16, 17 to 21 |
| Scores on its own | Account hierarchy, buying committee, investor network, signals feed | Expansion, retention, reporting |
| Built for | A company whose pipeline is the problem | A company with a customer success motion to score as well |
Use theirs for a mature software company with a customer success team, and this one when the question is whether the company can find and win new customers.
Who scores it, and where does the data come from?
One person with CRM admin access, in week one. Duplicates and data rules come from a CRM export, hierarchy from the account list, the inbound checks from the form tool and the timestamps on the lead queue, attribution from closed-won opportunities, and targeting from the account list set against the customer list. Signals, the investor network and outreach come from asking the sales lead and the board, and from the sending tool if one exists.
Hg's blueprint starts the same way, with a readiness check on data quality before any AI workflow, because early failures "come from poor data foundations rather than AI tool limitations".
An interactive version is coming as a tool on this site, with the band and the first fix worked out for you. Until then, the table above and a spreadsheet do the job.