Close probability
For each lead, from your CRM history of won and lost leads.
SOLUTION
Score every lead, and optimize on the ones that close.
Optimizing on cost per lead fills the CRM with leads sales never calls back. Datahash scores each lead on its chance to close, weights each stage by what it is worth, and sends that back to Meta, Google, and the other platforms, so campaigns buy the leads your business wants.
IN ONE SENTENCE
Lead quality scoring is a solution that estimates each lead's probability of closing from CRM history, weights lead stages by their value to the business, and sends those scores to ad platforms so campaigns optimize on qualified leads instead of form fills.
WHEN THIS IS YOU
The signs teams bring to us.
- Lead volume is up and sales says quality has collapsed.
- Every lead counts the same to the ad platforms, while your CRM knows which ones became customers.
- Duplicate and junk leads inflate your results.
- Leads wait too long for a first call, and the good ones go cold.
What we compute.
Stage weights
What a registration, a qualified lead, a site visit, or a booking is worth relative to a sale.
Junk and duplicate flags
So bad leads stop teaching the platforms the wrong thing.
Speed-to-lead priority
So your sales team calls the highest-scored leads first.
Scored leads and weighted stages, back to the platforms.
Illustrative. No client numbers.
How an engagement runs.
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Discover
CRM stages, stage history, lead sources, and identifiers reviewed with sales and marketing.
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Scope
Stage definitions and weights, platforms, and the test agreed, with a go or no-go on fit.
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Execute
Scores and stage values delivered to each platform, verified before bidding uses them.
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Measure
Cost per qualified lead and pipeline read against your current campaigns, then scaled.
Questions your team will ask.
How is this different from sending CRM stages to the platforms?
Sending stages tells the platforms which leads progressed. Scoring adds what each lead is likely to be worth before it progresses, and weights stages by their real value. Platforms learn faster, because they get a useful signal on day one instead of waiting weeks for a close.
What if we do not have much closed-lead history?
We start with rules: stage weights agreed with your sales team, plus junk and duplicate suppression. Those improve quality without any prediction. A close-probability model comes in once there are enough won and lost leads for it to beat the rules, and discovery tells you where you stand.
Will lead volume drop?
Often, yes, and that is the point. Optimizing on qualified leads usually means fewer, better leads and a lower cost per qualified lead or per sale. We agree upfront which metric the test is judged on, so a drop in raw volume is read in context.
Which CRMs do you work with?
Salesforce, HubSpot, Zoho, Dynamics, Pipedrive, and custom CRMs through files or APIs. What matters is that stage changes are recorded with timestamps, because the score learns from how leads moved through your funnel, and which ones your sales team won or lost.
Adjacent moves on the same stack.
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