AI use case · Is your data ready for customer churn prediction?Your churn model needs one customer. Your systems hold three versions.
Churn prediction promises to flag at-risk accounts before renewal. It can only do that if it can see each customer whole: the contract in the CRM, the invoices in billing, the complaints in support. When those don’t share an ID, the model scores fragments.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What customer churn prediction 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 mostConnectivityCompletenessThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where customer churn prediction breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
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CRM ↔ Billing
Active customers in the CRM with no billing account
What it does to the AIThe model scores accounts that aren’t paying, and misses some that are.
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Support ↔ CRM
Tickets tied to no account
What it does to the AIComplaints, an early warning, never reach the model.
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Product ↔ Billing
Usage logged against workspace IDs that map to no invoice
What it does to the AIEngagement can’t be tied to the revenue at risk.
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
How do you join our CRM, billing and support records?
If the answer is “by company name”, ask what happens when the names are spelt differently. They will be.
- Q2
What is your definition of churn, and can we change it?
Cancellation, downgrade and non-renewal are different events. A model trained on a blurred target gives blurred warnings.
- Q3
How many churned customers do you need to learn from?
A model trained on a few dozen leavers is guessing. Ask for their minimum, then count yours.
- Q4
What happens to customers the tool can’t match?
Unmatched customers can be dropped silently, and they may be the ones most at risk.
How a Truth Score Read tests it
Customer churn prediction, 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
Find the customer three ways
Customers from the CRM, billing and support exports, matched by ID, name and domain, including the ones spelt differently in each.
- 02
Test the renewal calendar
Every active contract checked for an end date that exists, is in the future, and agrees between CRM and billing.
- 03
Check the history and the signal
Billing history depth, migrations, and whether usage or engagement data can be tied to a paying account.
- 04
Give the verdict
Deploy now, Needs work or Blocked, and which segment, if any, could start first.
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
Customer churn prediction, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWhat data do I need to predict customer churn?
At minimum: one customer ID shared by the CRM and billing, renewal dates on contracts, and enough billing history to include customers who left. Support tickets and product usage make the warnings earlier and sharper, but only if they join to the same customer.
We have lots of customer data. Why are our churn models weak?
Volume is rarely the problem; joins are. Each system holds a slice of the customer, and if the slices don’t share a key the model sees three partial customers instead of one whole one. That is a cross-system check, and it can be done from exports.
Do we need product usage data?
Not to start. A churn model on contracts, billing and support can be useful. Usage makes it better, but only when usage is logged against something that maps to the paying account.
How long does it take you to check?
A Truth Score Read on churn takes your CRM, billing and support exports and returns a signed memo within 5 working days of complete exports.
Is your data ready for customer churn prediction? 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.