Overview
Industry
Hi-tech
Region
North America | EMEA | APAC
Company Size
1.5K - 2K
Featured Solution
AI-Powered Lead Scoring Model
About the Client
The client is a publicly listed enterprise technology company headquartered in North America, with operations spanning EMEA and APAC. With a product portfolio built around cloud infrastructure, SaaS analytics, and developer tooling, they serve mid-market and enterprise customers across financial services, manufacturing, and technology verticals.
When Activity Became a Poor Measure of Intent
The client had a static lead scoring model, as most organizations do. Points were added for email opens, deducted for inactivity, and adjusted when a campaign underperformed.
Over time, the model accumulated logic rather than intelligence. The scoring framework had drifted far from what it was meant to measure. Leads reached MQL status based on activity frequency, not buying intent. For example, a contact who attended three webinars in a quarter could score higher than a VP of Engineering evaluating a product for a procurement decision.
As a result, Sales started deprioritizing marketing-qualified leads and sourcing pipeline independently. Marketing, in turn, was unable to demonstrate revenue contribution because the handoff itself had broken down.
The Challenges of Rules-Based Lead Scoring
As customer journeys evolved, the organization’s rules-based scoring framework struggled to keep pace. This created several challenges:
High MQL Volume, Low Pipeline Impact
Large volumes of marketing-qualified leads entered the funnel, but many failed to translate into meaningful sales opportunities. Sales teams gradually lost trust in lead qualification and increasingly sourced pipeline through their own efforts.
Limited Account-Level Visibility
Contacts were scored individually, without considering buying committee dynamics. This made it difficult to identify high-value opportunities across complex B2B buying groups.
Lack of Actionable Qualification Insights
The scoring model assigned numerical values but offered little context on product interest, buying stage, or recommended next steps. Sales received leads without the intelligence needed to prioritize outreach effectively.
Limited Visibility Into Marketing's Revenue Impact
As sales bypassed marketing-qualified leads, it became increasingly difficult to demonstrate marketing’s contribution to pipeline and revenue. The disconnect reduced confidence in both the qualification process and campaign performance.
Engineering an AI-Powered Lead Qualification Engine
Grazitti partnered with the client to replace their static, rules-based model with an AI-powered predictive lead-scoring framework. This included four sequential phases:
- Phase 1 — Created a Unified Buyer Intelligence Layer
The first step focused on consolidating customer intelligence from multiple systems into a unified buyer profile. This included:
- Adobe Marketo Engage: Lead activity, event-related activity, email activity, website activity, firmographic enrichment
- Salesforce CRM: Product subscription data, customer success indicators
- ETL Platform: Analyzing buyer profile by combining behavioral, demographic, firmographic, temporal, and intent signals
- Data Governance Layer: Role-based data access controls, PII anonymization and sensitive data handling, AI response monitoring across evaluation, drift, and relevancy
- Phase 2 — Build the Predictive Lead Scoring Model
Developed a supervised machine learning model trained on historical business outcomes, including Closed Won opportunities, Sales Accepted Leads, disqualified leads, product adoption, and engagement history.
The model evaluated each buyer profile across eight dimensions:
- Propensity to Buy
- Conversion Probability
- Buying Intent
- Product Interest
- Account Readiness
- Churn Risk
- Expansion Potential
- Next-Best Action/Recommendation for upselling
Unlike traditional scoring, the AI model continuously refined its predictions as new data became available.
- Phase 3 — AI-Powered Journey Orchestration
We embedded the model’s outputs into Adobe Marketo Engage, transforming qualification intelligence into automated engagement.
- High-intent accounts triggered dynamic nurture streams calibrated to their stage and product interest.
- AI-driven segmentation updated in real time as score components shifted.
- Account-based marketing journeys were structured around buying committee composition.
- Sales received real-time alerts when accounts crossed qualification thresholds, with routing logic that matched leads to the most contextually relevant rep.
The model’s recommendations covered not just who to engage with, but when to engage, through which channel, and with what content. This turned static nurture sequences into responsive, outcome-oriented journeys.
- Phase 4 — Intelligent Sales Handoff
Instead of passing a lead with a numeric score, we redesigned the handoff to deliver contextual buying intelligence to Sales. It included:
- Why the lead was sales-ready
- Products of interest
- Buying stage
- Stakeholders involved
- Recommended conversation starters
- Suggested next-best action
- This increased trust in marketing-qualified leads and improved sales productivity.
From Lead Volume to Revenue Impact
Within six months of deployment, the client had measurable improvements across both marketing and sales performance. SAL volume increased, but more significantly, the quality of those leads held up through the funnel in a way that prior cohorts had not. Sales teams that had quietly deprioritized marketing-qualified leads began routing through them again.
Marketing, sales, and operations were now working from a single qualification framework. The model also improved with every closed deal, every disqualified lead, and every rep interaction logged in Salesforce.
Highlights
41%
Higher Sales-Accepted Lead Rate
28%
Increase in Opportunity Conversion
25%
Boost in Sales Productivity
30%
Improvement in Revenue Attribution Accuracy
Conclusion
The impact of replacing a rules-based scoring model with a continuously learning one showed up in the quality of leads sales teams picked. SAL rates climbed, low-quality leads stopped consuming sales capacity, and marketing's contribution to pipeline became measurable. The handoff that had quietly broken down was rebuilt on a foundation that both teams trusted.
Our Resources
Testimonials
Our Partners
Looking to rebuild lead qualification around signals that actually predict revenue?
