Supercharge Lead Scoring with AI

By Brenna Lofquist, Client Services Operations Manager at Heinz Marketing

AI can significantly enhance lead scoring within marketing automation platforms (MAPs) by analyzing vast amounts of data, identifying patterns, and predicting which leads are most likely to convert.

Pre-AI, MAPs were limited to only the information being collected and couldn’t provide deeper insights. Now a days, more MAPs have AI built in or you can integrate an AI tool to use alongside your MAP.

Before we dive into how AI can enhance lead scoring, let’s take it back to basics really quick.

Lead Scoring

Lead scoring, as you probably know, is the process of ranking or scoring leads with the goal of identifying their likelihood to convert. It assigns a numerical value (or score), and in some cases a letter rank, to each lead based on various attributes and behaviors that indicate their level of interest and fit for a product or service.

Examples of lead scoring attributes are:

• Demographics and firmographics: Information like job title, company size, or industry
• Behavioral data: Actions a lead takes such as visiting a certain web page, opening an email, or attending a webinar
• Engagement level: Frequent interactions like multiple visits or clicks on a high-value web page
• Fit and intent: Fit refers to how closely a lead matches your ideal customer profile (ICP), while intent could be visiting product or pricing pages
• Negative signals: Some behaviors, like visiting a Careers page or unsubscribing from emails, decrease a lead’s score because they are likely not a good fit

Using AI with Lead Scoring

There are a handful of ways that MAPs are incorporating and using AI to enhance lead scoring, so let’s get right into it. I’ll provide the benefit for each as well as platform examples.

Predictive Lead Scoring

AI-driven predictive models can evaluate historical data from past customers and prospects to identify which behaviors, firmographics, and demographics signal higher conversion potential.

• Benefit: Predictive scoring is dynamic, meaning it updates as new data comes in. The more data, the better insights it can provide
• Platform examples:
  • HubSpot: Their predictive lead scoring uses machine learning to analyze historical data and calculate scores based on how similar new leads are to previous customers
  • Marketo: Offers predictive lead scoring that learns from past marketing outcomes and applies those learnings to score new leads

Behavioral Analysis

AI can analyze behavior on websites, emails, social media, and more to determine which interactions (e.g., visiting certain pages, downloading content) correlate with a higher likelihood of purchasing.

• Benefit: Allows for a more accurate and real-time adjustment of lead scores
• Platform examples:
  • Pardot: Tracks website interactions and email engagement to help score leads based on actions like form submissions or page visits
  • Act-On: Uses behavioral scoring by analyzing interactions across email, web pages, and social media, providing real-time lead scoring updates

Account-based Scoring

AI can analyze multiple stakeholders or buying committee members within a target account. It can aggregate signals from various individuals in an organization to create an overall score for the entire account.

• Benefit: Provides an account score, not just on an individual lead level
• Platform examples:
  • 6sense: Uses AI to score accounts by aggregating signals from all stakeholders within a company. It analyzes buyer intent, fit, and engagement to prioritize the account as a whole
  • Terminus: Terminus’ AI-powered lead scoring looks at engagement across entire buying teams in target accounts and helps marketing teams prioritize outreach

Intent Data Integration

AI can process external data (e.g., research trends, content consumption patterns from 3^rd party sites) to score leads based on their buying intent.

• Benefit: Marketing and sales teams can prioritize leads showing signs of being in an active buying cycle
• Platform examples:
  • Bombora: Integrates intent data into CRM and marketing platforms to score leads based on their search and content consumption behavior outside your website
  • Demandbase: Their AI tools analyze buying signals, external web behavior, and engagement across various channels to provide insights on which leads are actively in a buying cycle

Predictive Demographics

AI algorithms can use demographic data such as industry, company size, location, and job title to predict which leads align with your ICP

• Benefit: You can identify ICP leads in an automated way and assign higher scores
• Platforms examples:
  • ZoomInfo: Uses AI to score leads based on job function, department, company size, and other demographic data, helping sales teams prioritize
  • Infer: Infer’s AI models use demographic, firmographic, and technographic data to score and rank leads based on ICP fit and readiness to buy

Lead Prioritization and Segmentation

By analyzing trends, AI can help segment leads into different categories, prioritizing the ones who are most likely to convert. It can automatically re-score leads as new data (e.g., interactions with campaigns) comes in.

• Benefit: Prioritization and segmentation are more automated and strengthened with AI insights
• Platform examples:
  • Leadspace: Uses AI to segment leads into different tiers based on fit, intent, and behavior, making it easier to prioritize the highest value prospects
  • Lattice Engines (now part of Dun & Bradstreet): AI-based segmentation and prioritization of leads based on historical data, fit, and behavior

Continuous Learning

Machine learning models can continuously refine the lead scoring process based on new outcomes. For example, if leads with certain attributes start converting at higher rates, AI can adjust the scoring model in real-time.

• Benefit: Improves accuracy over time
• Platform examples:
  • Salesforce Einstein: Their AI lead scoring continuously refines and adjusts scores based on real-time inputs from marketing campaigns and lead behaviors
  • Zinfi: Uses AI algorithms that adjust and optimize lead scoring over time based on campaign performance and engagement patterns

Enhancing Nurture Programs

AI can help identify where each lead is in the buyer’s journey, scoring based on their progression.

• Benefit: Ensures the most relevant content and actions are delivered to leads at the right moment, pushing them down the funnel faster
• Platform examples:
  • Marketo Engage: Its AI tools help score leads based on their progression through the funnel and deliver content that matches their stage in the buyer’s journey
  • ActiveCampaign: Combines AI-powered lead scoring with marketing automation to trigger personalized nurture sequences based on a lead’s score

Data Enrichment

AI can pull in additional data points from external sources, enriching the profile of a lead.

• Benefit: Increases the accuracy of scoring based on more holistic information
• Platform examples:
  • Clearbit: Enriches lead profiles by pulling in firmographic and demographic data, improving lead scoring accuracy with more complete data sets
  • InsideView: AI enriches CRM records with real-time company and contact information, enhancing the quality of data for more precise lead scoring

In Summary

AI plays a pivotal role in transforming lead scoring by automating the analysis of large data sets and provides more accurate predictions of conversion potential. With the capabilities mentioned above, AI enhances the ability to identify and prioritize high-quality leads efficiently.

This not only improves sales and marketing alignment but also ensures resources are focused on the most promising prospects, leading to better conversion rates and higher ROI. Ultimately, AI-driven lead scoring is crucial for modern marketing strategies, enabling smarter, data-driven decision-making.

Interested in discussing AI in lead scoring? Reach out for a free consultation with one of our experts. 

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