The RevSyt account scoring framework
Our own six-dimension model, published in full. This is how a RevSyt score is produced — not an industry standard, and not a black box.
Last updated · Written by the RevSyt team at Meta-Insyt LLC
How does RevSyt score an account?
RevSyt scores six dimensions that sum to 100: industry fit (0–20), buying trigger signals (0–20), company size fit (0–15), growth signals (0–15), technology fit (0–15) and urgency indicators (0–15). Each dimension is scored from public evidence about the account, assessed against the technology profile you built.
The six dimensions and their weights
| Dimension | Max | What raises the score |
|---|---|---|
| Industry fit | 20 | The company operates in a sector where the technology profile's problem is real and recurring |
| Buying trigger signals | 20 | Dated, attributable events that create a reason to act — leadership change, migration, M&A, expansion, compliance deadline |
| Company size fit | 15 | Headcount, revenue or footprint falls inside the band the product is built and priced for |
| Growth signals | 15 | Sustained hiring in the relevant function, new sites or entities, announced expansion |
| Technology fit | 15 | Evidence that the current stack makes the product an addition, an adjacency or a credible replacement |
| Urgency indicators | 15 | A time-bound window — a deadline, a launch date, a stated timeline |
Fit dimensions total 50 and event-driven dimensions total 50. That balance is deliberate: a score that only measured fit would rank the same accounts first every quarter.
Step 1 — the technology profile
Before any account is scored, RevSyt builds an intelligence profile for the exact vendor and product you sell: what it does, who buys it, which personas are involved, what triggers a purchase, who competes with it, and which observable signals indicate a need. Scoring is always relative to this profile, which is why the same account can score very differently for two different products.
Step 2 — evidence gathering
Each account is researched independently against a fixed source hierarchy: annual reports and filings first, then the official website and newsroom, then the official LinkedIn presence, then reputable news, industry publications, job postings and reliable company databases. Evidence is dated, and newer reliable evidence overrides older evidence rather than sitting alongside it. Details are in the research freshness framework.
Step 3 — scoring and output
Each analysis returns, alongside the six dimension scores:
- An analytical summary specific to the company.
- Buying signals found — and an empty list when none are evidenced.
- Missing signals — what was looked for and not found, so a low score can be interpreted.
- Target personas drawn from roles that actually exist at the company.
- Regional market presence where public evidence supports it.
- Why now, an outreach angle and a recommended next step.
- Data quality — a freshness status and the source period behind the research.
Reading the score
| Band | Interpretation | Suggested action |
|---|---|---|
| 80–100 | Strong fit with dated, current triggers | Work this week with a signal-led message |
| 60–79 | Good fit; triggers present but weaker or older | Sequence on a normal cadence |
| 40–59 | Partial fit or thin evidence | Monitor; re-score when new evidence appears |
| Below 40 | Mismatch, or very little public evidence exists | Check the missing-signals list before discarding |
A score is an ordering aid, not a verdict. RevSyt does not claim to predict revenue, close rates or deal size, and we do not publish benchmark figures we cannot substantiate with real data.
Determinism and consistency
Analyses run at low temperature and recent research is cached for a short window, so repeated runs on the same account are stable. A consistency pass reconciles contradictory company facts — for example, an acquisition that was announced and later terminated is not described as a completed ownership change.
Known limits
- Only public evidence is available; contracts, renewal dates and budgets are not.
- Companies with little public footprint score low for lack of evidence, not necessarily lack of fit.
- Weights are RevSyt's judgement, not a statistically derived industry constant.
Frequently asked questions
How does RevSyt score accounts?
RevSyt scores six dimensions out of 100: industry fit (20), buying trigger signals (20), company size fit (15), growth signals (15), technology fit (15) and urgency indicators (15). The total is the sum of the six.
Is the RevSyt scoring model an industry standard?
No. It is RevSyt's own model, published so customers can see exactly how a score is produced. It draws on common account-scoring practice but the dimensions and weights are ours.
Why are buying triggers weighted as highly as industry fit?
Fit decides whether an account is worth selling to at all; triggers decide whether it is worth selling to now. Weighting them equally keeps the score useful for sequencing rather than only for segmentation.
Can the same account score differently on two runs?
RevSyt caches an account's research for a short window and runs at low temperature so repeated analysis is stable. Scores change when new evidence appears, not at random.
Does a low score mean the account is bad?
It can mean either a genuine mismatch or a lack of public evidence. RevSyt reports missing signals so you can tell the two apart.