Revenue operations
Lead intelligence engine
Scored, prioritized lead lists built from CRM, service-management, and market data.
Client not named
Case result
A lead intelligence engine that scores and prioritizes accounts from CRM, IT service-management, and external market data.
What changed
Lists that used to take an analyst days now generate on demand.
The situation
Enterprise sales teams rarely suffer from a shortage of data. They suffer from a shortage of ranked, defensible decisions. The account records sat in the CRM. The support and infrastructure history sat in the IT service-management system. The market context sat outside both. Each of those three views told part of the story about which accounts were ready to buy, and none of them told it alone.
The practical effect was that prioritization ran on instinct and on whoever had the most recent conversation. Building a genuinely prioritized list meant an analyst pulling exports from several systems, reconciling them by hand, and producing something that was already stale by the time it reached a seller. That work was expensive enough that it only happened for the largest campaigns, which meant most of the year ran on guesswork.
What is portable is the pattern: the signal was already paid for and already sitting in systems the business ran every day. It was simply never being read together.
What we did
The first task was not modeling. It was working out which signals actually preceded a closed deal, rather than which signals were easy to query. That meant looking backward across won and lost accounts and identifying the combinations that separated them: activity patterns in the service-management system, record changes in the CRM, and external market events indicating a shift in the customer's own situation.
From there the engine has three parts. A collection layer pulls the relevant slices of CRM and service-management data on a schedule. A scoring layer weighs those signals together and produces a ranked list rather than a flat export. A delivery layer hands sellers a finished, prioritized list with the reasoning attached.
Two design constraints mattered more than the model itself. The first was traceability. Every score decomposes into the signals that produced it, because a seller who cannot see the reasoning will not work the list. The second was unit cost. The cost of producing one list was tracked from the first prototype, which kept the design honest about what the system was worth relative to what it consumed.
The steps, in order
- Mapped the account signals that actually preceded a closed deal, across CRM records, IT service-management activity, and external market data.
- Built a scoring model that weighs those signals together rather than ranking on a single field such as company size or last-touch date.
- Automated list generation, so a scored, prioritized list is produced on demand instead of assembled by an analyst over several days.
- Made every score decompose into the signals that produced it, so a seller can see the reasoning rather than being asked to trust a number.
- Tracked the cost to produce one list from the first prototype, so the economics were part of the design rather than a later justification.
Lead intelligence
Systems involved
- Salesforce
- ServiceNow
- External market and firmographic signals
- Signal scoring and ranking model
- Automated list generation and delivery
What changed
Producing a scored, prioritized list stopped being a project and became a routine operation. What previously took an analyst several days of pulling and reconciling now runs on demand.
That change in cost changes behavior, which is the part that matters. When a prioritized list costs little to produce, it gets produced continuously instead of once a quarter for the largest campaign. Sellers work a ranked list rather than a flat account export. Territory planning and campaign planning both draw on the same scored view, so the organization stops arguing about whose spreadsheet is correct.
What it proves
Measuring output against running cost is the entire argument. Most AI work never gets measured against its own running cost, which is why so much of it stalls after the pilot and quietly disappears at the next budget review. This approach is instrumented for that comparison from the start, which is what carries a system past the demonstration stage and into daily use.
The same discipline carries into client work. Define the decision the system is meant to improve, instrument the cost of running it, and measure the output against what actually closed.
What this case supports
The cost of running the system is instrumented from the first prototype, so it can be evaluated against what it produces rather than argued about.
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