A growth-stage SaaS team was drowning in 10,000 leads a month with no way to prioritize them. In six weeks we built a custom, explainable AI scoring model from their own historical deals and wired it into Salesforce. Win rates rose 28% and the sales cycle shortened by 25 days.
Context
The company was a growth-stage B2B SaaS business at roughly $25M ARR. Marketing was doing its job, generating more than 10,000 inbound leads a month, but volume without prioritization is just noise.
The Problem
The sales team was spending about 60% of its time chasing leads that would never close, because there was no systematic way to know which leads deserved attention first.
- More than 10,000 inbound leads per month, with no way to rank them.
- Roughly 40% of deals slipped to the next quarter or never closed.
- The average sales cycle sat at 90 days.
- Leadership wanted to use AI but had no in-house expertise and did not know where to start.
"We knew AI could help, but we didn't have the expertise in-house. We needed someone who understood both AI and our sales process."
Chief Revenue Officer
What We Did
We built the model on their own data, made it explainable, and put it where reps already work. Three phases, six weeks.
Weeks 1 to 2: AI readiness assessment
- Analyzed two years of historical deal data, more than 5,000 closed deals.
- Identified 47 data points correlated with winning or losing.
- Mapped current data quality and found the gaps in tracking.
- Built the business case with the projected ROI, so the work was justified before we built it.
Weeks 3 to 4: Model development
- Built the initial machine-learning model on the historical data.
- Tested three algorithms; a random forest performed best.
- Reached 82% prediction accuracy in testing.
- Created an explainable scoring methodology, so reps could see why a lead scored the way it did.
Weeks 5 to 6: Implementation and training
- Integrated the model directly into Salesforce.
- Built a dashboard surfacing the high-priority leads.
- Trained the sales team to interpret and trust the scores.
- Set up monitoring to track model performance over time.
The Result
The team stopped guessing which leads to work. With scores they understood and trusted sitting right inside Salesforce, reps spent their time on the leads most likely to close. Win rates rose 28%, the sales cycle dropped by 25 days, and leadership got the AI capability they wanted, built on their own data and explainable enough to actually use. This is AI-native RevOps applied to the top of the funnel.
Frequently Asked Questions
How long does an AI lead-scoring project usually take?
A first working model on clean data is typically weeks, not months. The longer pole is trust, getting reps to act on the scores, which comes from transparency and a few visible wins.
Do we need a data scientist for this?
No. Modern CRM and AI tooling handle the modeling; the hard part is the operational design, what to score, how to route, and how to keep it honest. That is the work we do.
Will the model just reinforce our existing biases?
It can if you let it, which is why we validate against outcomes, keep humans in the loop, and revisit the model as the ICP shifts.
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