The client had extensive CRM data on bookings, pipeline, product use, and engagement, but account prioritization still depended on experience and relationship history. PivotX built deal and prospect scoring models that rank active opportunities, identify strong prospects, and give teams a consistent way to set priorities.
The company had CRM data on booking history, pipeline trends, product use, and engagement. It could report past performance but could not rank active deals by likelihood to close or identify prospects that resembled its fastest-converting customers.
Prioritization was based on relationship history, company size, and individual judgment. Consistent and systematic it was not.
Before working with PivotX, the company had years of data on bookings, pipeline activity, product use, and customer engagement, but no systematic way to use it to rank opportunities.
PivotX built two models using the company's CRM data and third-party company information. One scores active deals by likelihood to close. The other identifies prospects that resemble the company's highest-converting customers.
The company can now rank active opportunities and identify new prospects based on patterns in its own performance data.
Before PivotX's involvement, representatives prioritized accounts based largely on relationship history, company size, and individual judgment.
PivotX added the model results to Power BI, where sales and revenue operations can see priority accounts, risk signals, and updated scores as pipeline data changes.
Sales can focus on accounts with stronger signals, while revenue operations gains a clearer view of pipeline likelihood and timing.
Your sales reps rely on relationships and instinct because the data does not provide a stronger signal.
You have a large prospect database with no systematic way to identify which accounts to prioritize.
Your revenue forecast reflects pipeline stage but not the probability that each deal will close.
PivotX has taken companies from initial assessment to production-grade AI in weeks, not quarters. The cases below aren’t proofs of concept. They’re actual production.
We can review the manual steps, handoffs, and systems involved and identify a practical place to begin.