Carson Group Achieves 96% Lead Conversion Accuracy with AWS-Powered ML Scoring

Background

Investment advisor firms like Carson Group operate in a market where lead conversion rates average 1–2% on purchased leads, forcing advisors to evaluate 50–100 leads to acquire a single client. Carson had rich lead data in Salesforce — conversion histories, campaign metrics — but relied on manual review and static rule-based scoring. As client base and lead volume grew, the manual approach became unsustainable. Carson engaged Provectus to build a machine learning scoring system that would allow advisors to focus time on the highest-probability prospects.

What Was Implemented

• Discovery phase: Provectus analyzed labeled lead records, conversion metrics, campaign spend, and existing rule-based scoring to identify predictive signals
• Model build: ML classification model trained on historical conversion data; inputs include engagement history, demographics, campaign source, and behavioral signals
• Automated retraining pipeline to keep model current as lead data accumulates
• Direct Salesforce integration: scores appear alongside lead records in existing workflows, requiring no manual export or lookup
• Output formats designed for both sales teams (individual lead prioritization) and marketing teams (campaign performance analysis)

Results

The ML model reached 96% accuracy on the test dataset for lead conversion prediction, with 88% recall on actual convertible leads and 67% precision. Deployed in five weeks from discovery to production, the system transformed lead evaluation from sequential manual review to AI-scored prioritization — making the process approximately 20× faster , according to Provectus. Better lead filtering reduces acquisition costs directly (industry cost: $2,000–$5,000 per acquired client). Marketing teams gained visibility into which campaign channels produce leads with highest predicted conversion value.

Lessons

• A five-week delivery timeline from discovery to production is achievable when the client has clean, labeled historical data in an existing CRM
• Automated retraining pipelines are essential to maintain model relevance as market conditions and lead patterns change
• Integrating scores directly into existing sales workflows (Salesforce) drives adoption by minimizing behavior change
• Precision and recall trade-offs matter: 67% precision means one-third of flagged "high-potential" leads don't convert — human relationship skills remain critical for closing