Services / Revenue Operations
Deal stages that mean something, a database without duplicates, and a pipeline number your leadership can act on.
Almost every reporting problem is a data problem wearing a disguise. Before anything can be forecast, the pipeline has to describe reality: stages defined by what the buyer has done rather than how the rep feels, records that exist exactly once, and fields that everyone fills in the same way.
We start with an audit of the database. Duplicate rate, field completeness, stage distribution, and how many opportunities have sat untouched past the point of plausibility. For a SaaS fintech, 38% of reported leads were duplicates, eliminated through CRM deduplication to restore reporting accuracy. Every number downstream had been wrong by that margin.
Deal-stage architecture is the other half. A stage should have an exit criterion a third party could verify, not a feeling. Defined that way, the conversion rate between two stages becomes a real measurement, and the forecast stops being a negotiation.
This connects directly to money. $33.7K in revenue leakage was identified within 60 days of unifying a SaaS fintech's CRM, operations, and finance systems. The leakage had been there all along. It became visible once the data agreed with itself.
Hygiene is not a one-off project. We leave governance rules, required-field policy and monitoring behind, so the database does not quietly decay back to where it started.
What is covered
of one client's reported leads were duplicates, eliminated through deduplication to restore reporting accuracy. Every number downstream had been wrong by that margin.
How It Works
We measure duplicate rate, field completeness, stage distribution and record age. The output is a picture of how far the data sits from reality, quantified rather than described.
We rebuild deal stages around verifiable exit criteria and agree them with the people who have to use them. A stage definition nobody follows is worse than no definition at all.
Deduplication, merge rules, field standardisation and enrichment, run against a backup with a reversible path at every step.
Required-field policy, validation rules, a hygiene reporting cadence and decay alerting, so the database stays clean after we leave.
Tell us what your CRM says your pipeline is worth and how confident you are in it. We will audit the data and show you the gap.
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