AI use case · Is your data ready for lead scoring?Lead scoring learns who you won. Make sure you know.
A lead score is a model of your past wins. If outcomes are missing, if one person lives as three records across marketing and the CRM, or if “won” deals never paid, the score learns to find leads that look busy rather than leads that become customers.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What lead scoring needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostCompletenessAccuracyConnectivityThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where lead scoring breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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Marketing ↔ CRM
The same person as a lead in marketing and a contact in the CRM
What it does to the AIEngagement is credited to one record and the outcome to another; the model never sees them together.
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CRM ↔ Billing
Deals marked won that never became paying customers
What it does to the AIThe score learns to find leads that resemble wins, not customers.
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Web ↔ Marketing
Form fills full of test, junk and competitor entries
What it does to the AINoise in the training set that the score then rewards.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
What does the model count as a conversion: a meeting, an opportunity, or a paying customer?
Anything short of paying teaches it to find leads that create activity, not revenue.
- Q2
Which of our fields does the score rely on, and how full are they?
A score built on industry and company size is guessing wherever those fields are blank.
- Q3
How do you treat people who exist in both marketing and the CRM?
If the tool scores each record separately, one prospect can be hot and cold at the same time.
- Q4
Can a rep see why a lead scored high?
If the reason isn’t visible, reps won’t trust the score, and they’ll be right not to.
How a Truth Score Read tests it
Lead scoring, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Join marketing and CRM
Leads and contacts from both exports, matched by email, name and domain, so every person is seen once.
- 02
Check the outcome
Every closed deal’s result, and whether won deals appear in billing as paying customers.
- 03
Test the fields the score needs
Fill rates and validity on the won and lost records specifically, plus junk and test entries.
- 04
Give the verdict
Deploy now, Needs work or Blocked, with the duplicates and gaps listed by name.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Lead scoring, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWhat data does AI lead scoring need?
Closed deals with a clear outcome, each linked back to the lead it came from, with the fields the score uses filled in on those records. Then one record per person across marketing and the CRM, so engagement and outcome sit together.
Our lead scores feel random. What is usually wrong?
Three causes are common: the model is learning from a weak target (meetings rather than revenue), the fields it depends on are mostly blank, or the same person is split across systems so their engagement never meets their outcome. Each can be checked from exports.
Do we need marketing automation data as well as the CRM?
For behavioural scoring, yes. Firmographic scoring can run on the CRM alone, but any score that uses opens, visits or form fills needs the marketing export, matched person by person to the CRM.
Is lead scoring a good first AI project?
It can be, because it sits on data the business already keeps and mistakes are cheap: a rep ignores a bad score. That is exactly why it is worth testing first. If the data can’t support lead scoring, it won’t support the more ambitious plans either.
Is your data ready for lead scoring? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.