
The marketing and sales teams needed a more intelligent way to prioritize outreach by identifying which leads were most likely to convert, even before they explicitly showed buying intent for the flagship product. The existing rule-based approach lacked nuance, often overlooking valuable signals of readiness.
As a result, high-potential Marketing Captured Leads (MCLs) were either deprioritized or contacted late in the cycle, leading to missed revenue opportunities. An ML-based lead scoring model was deployed to improve prioritization accuracy and provide deeper visibility into lead behavior across multiple data domains.
An ML-based lead scoring model was deployed to transform lead prioritization for the sales development team.
Analyzed leads across firmographic data (company size, industry, location) and engagement signals (website visits, content downloads, email interactions) to identify conversion likelihood.
Calculated the probability that a given lead would result in a successful closed-won deal. This score surfaced promising opportunities early, helping sales reps focus on high-potential prospects.
Enabled sales development representatives to systematically work the most valuable Marketing Captured Leads (MCLs) first. The MarketNext platform eliminated guesswork.
The MarketNext offering eliminated the limitations of rule-based scoring. Sales development representatives gained intelligent prioritization based on actual conversion probability, not arbitrary rules. High-potential Marketing Captured Leads were identified early and engaged at the right time.
The company now operates with data-driven lead management. Sales teams can focus on prospects most likely to close while marketing teams can refine targeting based on what actually drives conversions.
ML-based lead scoring
Firmographic + engagement analysis
Total Lead Score metric