What Actually Predicts Lead Conversion?
An analysis of three years of sales data uncovered unexpected patterns.
Typically, sales teams rely on intuition and superficial signals to rank leads. However, when we trained a model using real conversion results, it challenged many beliefs about what qualifies as a "good" lead.
The Common Belief
The common belief is that sales teams often think wealthier prospects are better leads, as higher property values, larger budgets, and increased engagement activity seem like clear indicators of success.
What the Data Showed
The analysis of three years of sales data showed that property value was a negative predictor. The most important factors were project urgency, the ability to pay, and the lead's previous success in their neighborhood.
The Implication
The implication is that intent signals, such as urgency and capacity, are more effective than engagement signals like email opens and page views. When scoring was redesigned based on these insights, success rates increased by 31% over traditional CRM-based scoring.
vs. standard Salesforce lead scoring, by prioritizing intent signals over engagement metrics
Engagement Metrics vs. Intent Signals
Most lead scoring tools focus on engagement metrics like email opens, page views, and form fills. While these are simple to track, they reflect activity rather than true intent. A casual browser might seem interested, whereas a genuine buyer could remain silent until they are prepared to act.
Engagement Signals
What most CRMs measure
These show activity, but not commitment. A tire-kicker can score high on all of these.
Intent Signals
What actually predicts conversion
These indicate readiness to purchase. A subtle lead with clear intent signals often results in quicker closures.
Why Does This Matter?
Most CRM scoring tools rely on engagement metrics because they are simple to track automatically. However, engagement indicates interest rather than actual readiness. The difficulty lies in capturing intent signals, which often requires integrating CRM data with external sources like property records, census data, and market trends—sources that typical tools don't access. When we shifted our scoring approach to focus on intent signals rather than engagement, we saw a 31% improvement in conversion rates compared to standard Salesforce lead scoring.
Test your intuition
Two leads. One will convert, one won't. Can you tell which one based on property value alone? The answer might surprise you.
Which Lead Will Convert?
Test your intuition against the data
Why Intent Signals Outperform Engagement Metrics
When we trained models on three years of real conversion data, the results challenged common assumptions. Here's what the data showed about what truly predicts a sale.
What the Data Revealed
Signals ranked by how well they predict actual conversion. Click any row to understand why.
The Key Insight
The three most important predictors all connect to willingness to act: urgency (timeline commitment), capacity (ability to pay), and local success (lower perceived risk). Demographic factors such as income and property value, which are more obvious signals, ranked lowest.
Combining Multiple Data Sources
The main difference from standard CRM scoring is incorporating external data that shows intent signals your CRM doesn't detect on its own.
Where the Data Comes From
Combining internal CRM data with external sources that reveal intent
Learns from your actual sales outcomes
Merge CRM data with external sources to create a complete profile of each lead
Use patterns from past wins and losses to forecast who's most likely to convert
Push the prioritized rankings back to your CRM so sales can see them immediately
Why external data matters: Your CRM captures what leads communicate directly. External sources supplement this by revealing what they don't, such as financial capacity, local market conditions, and demographic patterns linked to buying behavior.
Scoring That Learns From Your Business
The difference between static rules and adaptive scoring: one approach applies the same rules to every business, while the other learns what truly works for yours.
Deep Historical Analysis
Before building anything, we analyze your historical sales data. Our custom research tools help identify what has worked: which leads converted, which stalled, and why. This analysis reveals patterns that generic tools might miss, as they lack knowledge of your specific business.
Model Tailored to Your Reality
Every business has its own key drivers of conversion. A roofing company's main predictors differ from those of a SaaS firm. We develop customized models tailored to your specific context, trained on your data, and aligned with your market dynamics. We do not use generic templates.
Continuous Feedback Loop
Scoring continues beyond deployment. We monitor which predictions are correct and which are not. As new conversion data arrives, we retrain the models to reflect changing patterns. Markets evolve, buyer behavior shifts, and our scoring adapts accordingly.
What Any Sales Team Can Apply
Regardless of whether you develop custom scoring, these insights can help you better prioritize leads today.
Focus on Intent Rather Than Activity
A lead stating "I need this done next month" holds more value than one who reads all emails but never sets a clear deadline.
Question Your Assumptions
The data indicated property value as a negative predictor. What "obvious" signals could be misleading your team?
Look for Ability to Act
Urgency without capacity causes stalls, while capacity without urgency causes delays. Seek both: ready to move and able to pay.
Leverage Social Proof
Neighborhood penetration proved more important than expected. Past successes in an area build trust that speeds up new deals.
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