The Setup
A real estate coaching company running a high-volume book-a-call funnel, with every lead landing in Close CRM. On paper, the pipeline looked healthy — sixty thousand leads.
The Problem
The marketing team was celebrating a strong lead month. The setter team was exhausted — they were dialling the same prospects within days of each other. The CEO was looking at both reports and couldn't tell who was right.
The ad agency's attribution report said lead volume was up. The sales team's close-rate dashboard said conversion was down. Both were technically accurate. Nobody could square the two.
When the CEO asked the setter lead what was happening on the calls, the answer was that the team kept reaching the same prospects — multiple times within days.
What We Found
We pulled the full lead dataset from Close CRM and started looking for duplicates. Not exact duplicates — those the CRM already caught. We looked for near-matches: the same email written a different way, the same phone number in a different format, and name variations that resolve to the same person.
Of the 60,000 leads in Close CRM, 20,000 were duplicates. Thirty-three percent of the pipeline was phantom.
Close rate, cost per lead and conversion were all wrong for the same reason: one in three records was a duplicate. The close rate had never been measured on a clean list.
What We Built
The fix was a three-layer deduplication — email, phone, and name-variation matching. Ingestion was changed to lookup-first: every new submission is checked against the existing base before a record is created, which means cost per lead is now calculated on a deduplicated base.
The After
Effective close rate — measured on unique leads instead of the raw list — was not the number the sales team had been reporting. It never had been.
Most importantly, the CEO got three numbers he could defend: close rate, cost per lead and conversion, each computed on a deduplicated list.
Build time: six weeks. Total client time: three hours.
Closing
One in three records on that list was a duplicate. Take your monthly ad spend and multiply it by 0.33. That's what one-in-three duplicates would mean if your list looks like theirs. We don't know if it does. You can find out in ten minutes.
Want this for your business?
Want this for your own numbers?
No pitch and no deck. Thirty minutes that ends with a clear picture of what we would build for you and what it involves.