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AI Models

Leveraging Machine Learning for Effective Lead Scoring

Mark Zalzal

6 min read

Feb 9, 2024


As a marketing manager, you find yourself in a complex situation. Your team is under constant pressure to generate leads, but for some reason, the sales team isn't converting as many as they should. You're scratching your head, wondering where the problem lies.

The Common and Traditional Approach

Blame the sales team?! 

You decide to send more leads through the pipeline. The more opportunities you send the more they are likely to close, it seems logical right? 

Weeks pass. The quantity of interactions goes up, but the quality and conversion of leads remain stagnant. Sales teams are still unhappy, and now your marketing team is overextended and frustrated. Despite putting in extra effort, the additional leads didn't quite deliver the results you had hoped for.

The Solution: Lead Scoring with Machine Learning

This is where lead scoring comes into play. Lead scoring is a systematic approach to evaluating and prioritizing leads based on their likelihood to convert into customers. It's like having a crystal ball that helps you identify the most promising leads, allowing you to focus your efforts and resources where they matter most.

Now, there are two flavors of lead scoring - traditional lead scoring and lead scoring with machine learning (ML).

Traditional Lead Scoring

Traditional lead scoring relies on a predefined set of criteria and rules to assign scores to leads. These criteria typically include factors like demographics, industry, company size, and engagement with your marketing materials. While this method is a step in the right direction, it has its limitations. These factors are essentially scored based on simple analytics and gut feelings. On a chart you can analyse three, maybe four dimensions if you’re data savvy. Imagine trying to spot patterns hidden within 50 dimensions!

[ Picture displaying the difference between a traditional Lead scoring model vs. an ml-powered LS model]

ML Lead Scoring

Enter ML lead scoring. It is about using algorithms to predict lead quality based on past data. This new approach will automate checking the lead quality using smart analytics and predictive models, making the whole process more data-driven and less about guesswork.

Essentially, you’ll be predicting:

  • Lead intent

  • The chances your sales team will qualify the lead

But how can I predict both the customer’s and our rep’s actions? Do I even have the data?

What if I told you that your potential customers and sales representatives may have left behind some clues – like footprints in the sand – indicating their interest in buying your product. These footprints are often subtle and not easily spotted, even with well-organized data dashboards. That's precisely why you might benefit from using a more sophisticated model to uncover these hidden patterns.

The Mechanics of the ML Model

Identifying the Crucial Criteria

The ML model analyzes historical data to identify patterns and characteristics of leads that successfully converted. It looks beyond superficial metrics like email domain or website time. The model considers a multitude of factors, assigning different weights to each based on their conversion impact.

For example, it might find that leads from certain industries interacting with specific content on your website are more likely to convert. These insights are invaluable!

Applying the Insights

Armed with this knowledge, you can refine your marketing strategies. You focus on leads that the model identifies as high quality, thereby improving your sales funnel's efficiency. The best part is that the model continuously learns and updates its criteria, keeping your strategies aligned with evolving market trends.

Measuring Lead Quality

One of the biggest advantages of an ML-based lead scoring system is its ability to provide a real-time, accurate metric of lead quality. No more waiting weeks to see if a lead converts. You have immediate feedback, allowing for quick adjustments in strategy.

Here’s an example of a simplified lead scoring model, try it out by plugging in attributes of a customer and see how this impacts a lead’s score.

The Impact on Costs and Efficiency

Let's talk numbers. Imagine you have two salespeople managing the inbound funnel leads, each getting paid $50 per hour. You receive 500 leads per month, which you initially spend equal time qualifying all of them. On average, it takes 30 minutes to evaluate and process a lead and only 20 of them get qualified. Suppose you tune your model to make sure that 95% of the time, you won’t miss out on any potential leads.

Now, let's calculate the savings with an improved lead scoring ML model:

Traditional Lead-Scoring

ML-powered Lead-Scoring

Total Leads Generated



Leads Chased by Sales



Hours Spent on Leads

500 * 30 min = 250 hr 

200 * 30 min = 100 hr 

Hourly Wage per Employee



Monthly Cost (Hours x Wage)



While the number of closed deals is almost equal (5% lower), your savings grew by $7,500. Which is one sales rep that could be re-assigned to your outbound efforts to generate more value to your bottom line. 


Integrating a machine learning model for lead scoring into your marketing strategy is a smart, data-driven approach to understand and enhance the quality of your leads. It's important to note that these ML models are not here to replace humans but to elevate their work. This approach can ultimately lead to improved sales conversions and higher team morale.

So if you had an automated lead scoring system, how much time and money would you have saved?

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