What is Predictive Lead Scoring? AI Sets You Up for More Human Marketing

Predictive lead scoring brings the power of AI and machine learning to marketing. Learn more about this groundbreaking technology.
Article Outline

Time was, marketers needed a crystal ball to tell the future. Now, with the advent of predictive lead scoring models such as Act-On AI Predictive Lead Score, it’s getting a little easier to predict the outcome of a lead’s progress through your funnel.

Traditional lead scoring still has its place, but amplifying and refining your lead scoring with AI and machine learning promises to make it easier than ever to identify high-potential prospects. Let’s dive deep into the world of predictive lead scoring, including Act-On’s own AI Predictive Lead Score. We’ll cover the definition , the differences between predictive and manual scoring, and lay out some potential use cases.  

Screenshots and a user image highlight the concept of Act-On AI Predictive lead score, a new predictive lead scoring product
Use Act-On AI Predictive Lead Score to predict the probability of leads to convert through machine learning.

What is predictive lead scoring?

This cutting-edge methodology leverages advanced machine learning and data analysis techniques to assign scores to leads based on their likelihood of converting into customers. It goes beyond the constraints of manual lead scoring, which relies heavily on predetermined criteria such as job title, industry, and company size. Instead, AI-based lead scoring delves into a multitude of data points, including historical lead behavior, engagement patterns, and demographic information, and constantly updates based on positive outcomes. This approach empowers marketers to make informed decisions, focusing their efforts on leads that exhibit a higher probability of conversion.

For example, the new Act-On AI Predictive Lead Score feature uses a machine learning model to predict how likely a contact is to convert to a sale. The score, updated daily, changes over time as new contact behaviors come into the system.

What’s the difference between manual lead scoring and AI predictive lead scoring?

Traditional lead scoring relies heavily on predefined criteria and manual evaluation. While it can provide a basic level of lead prioritization, it often overlooks complex patterns and behavioral nuances that AI predictive lead scoring can capture through the power of machine learning. In short, it’s more subjective.

By analyzing vast amounts of data, AI predictive lead scoring models can identify hidden patterns in customer behavior. With Act-On AI Predictive Lead Score, we use trained machine learning models to correlate specific behaviors to positive outcomes, generating a new score daily for each contact. We’ve engineered our ML propensity models to identify the patterns that make leads likely to end up closed-won or closed-lost.

What’s more, the method adapts and improves over time, continually refining its predictions based on new data, whereas current lead scoring models must be updated manually. AI lead scoring provides a more objective view of a lead’s likeliness to convert.

But let’s be honest: we don’t want to be entirely objective or subjective. Marketing is an art, not a science. Often, combining and balancing both manual and predictive lead scoring is the best path forward for sophisticated marketing organizations (as we’ll discuss further on in the blog).

AI predictive lead scoring use cases

It’s important to keep in mind that, for now, predictive lead scoring models are a supplement for your current manual lead scoring systems, not a full replacement. Every business has unique scoring criteria that should be taken into consideration when scoring leads.

So, read through our list of predictive lead score use cases. They can help you get the most out of AI predictive scoring generally, and Act-On AI Predictive Lead Score specifically.

  1. Prioritize leads for sales and marketing investment: Predictive lead scoring revolutionizes lead prioritization by allowing you to focus your efforts on leads with the highest conversion potential. This ensures that your sales and marketing teams invest their time and resources where they are most likely to yield results, resulting in more efficient lead management and increased conversion rates.
  2. Build segments for each funnel stage to divide and conquer: Use predictive lead scoring to help segment your prospects based on their likelihood to convert. Sort them into top, middle, and bottom of funnel based on predictive lead score values. Where you draw the line will vary based on your specific business, but for instance, scores of 0-30 for top of funnel, 31-65 for mid-funnel, and over 65 for bottom of funnel. Then, nurture each segment with different content, and encourage your partners on the sales team to prioritize those BOFU prospects.
  3. Plan to allocate resources more efficiently: Effectively allocating marketing resources is crucial for success, and predictive scores help you do just that. By directing your efforts toward leads with higher predictive scores, you can get the most out of your budget and resources. 
  4. Use predictive learnings to align with sales: Effective collaboration between marketing and sales teams can mean the difference between humming along at top speed and stalling out on the shoulder. Predictive lead scoring facilitates cross-functional alignment and builds trust. Hand off high-scoring leads to sales teams from your predictive model. What comes next is the important part: listen to their feedback about how these leads performed compared to hot leads from your traditional lead score model. That feedback can help you finetune your manual scoring and determine the best application for your AI-scored leads, and improve the acceptance of MQLs among sales partners. 
  5. Deliver content for the lead’s position in the sales funnel: Predictive lead scoring software empowers you to deliver content that matches an individual lead’s needs and pain points. For example, high-scoring leads can receive content that speaks directly to your product’s advantages over competitors, while lower-scoring leads can be nurtured with educational resources that cover more broad topics. Bonus: Use generative AI content generation, such as Act-On AI Create in conjunction with predictive score to refine content even further. 
  6. Devise new re-engagement strategies: Not every lead converts immediately. That’s where re-engagement comes in. AI lead scoring helps identify leads that are worth re-engaging. By analyzing historical data and behavioral patterns, you can pinpoint leads that may have shown interest in the past but require additional nurturing to move closer to conversion.
  7. Take your segmentation game to the next level: Effective segmentation is pivotal for targeted communication, and predictive lead scoring offers a whole new dimension to your data. Categorize your leads according to how well they score in your predictive model, then try testing campaigns against your traditional segments like “hot leads” or “warm leads.” Does the predictive version perform better? How can you adjust your manual lead scoring accordingly?
  8. Leverage data to update and improve ideal customer profiles: Analyzing the attributes of high-scoring leads can help you refine ideal customer profiles for your sales team. Look for any trends that start to show up in leads that score high in your automated lead score that might not have shown up previously in your manually scored leads. Act-On Predictive Lead Score is then fed back into your CRM platform to enrich your understanding. Use those insights to adjust your approach to your ICP.
  9. Add dimension to marketing ROI tracking: Predictive lead scoring offers a tangible way to track the success of your marketing initiatives. By monitoring the conversion rates of high-scoring leads, you can assess the return on investment (ROI) for different campaigns and strategies. Try comparing the ROI for leads between your traditional manual lead score and predictive score.
  10. Build long-term relationships with your prospects: Predictive lead score software isn’t just about immediate conversions; it’s about building lasting relationships. Maybe you identify a group of leads that scores well in your predictive score, but not so well according to your manual score. Nurture these leads and watch their behavior carefully. If they start to convert, you know you’re on the right track. If they don’t, you can nurture them more slowly over time to build engagement and interest.

All of the above use cases add dimension and intelligence to your marketing functions. As you implement them, take a step back: apply what you’ve learned to your overall approach to marketing strategy. High-scoring leads offer valuable insights into the attributes and behaviors that contribute to successful conversions. By analyzing these insights, you can refine your overall marketing strategy to better align with the needs and preferences of your target audience.

Manual or predictive score? The answer is both

Keep in mind that manual lead scoring is a highly subjective process. It involves a lot of best guesses and assignments of points tied to different criteria. Depending on how those points are assigned, and why, the correlation between action and results can be hard to track, and somewhat arbitrary. Solutions like Act-On AI Predictive Lead Score are a more objective measure of user behavior.

The truly sophisticated marketer will combine the best elements of both manual and predictive lead scoring. If the manual scoring is set up properly, both types of scoring give a good directional indication of buyer behavior. Combining both can give the most complete representation of a given business’ complex and unique sales funnel, content, and prospect buying behaviors. 

If you have set up a manual lead score and used a “set-it-and-forget-it” approach, you may be expending time and energy on low value leads. Adding predictive score to the mix can improve the discipline and rigor of your scoring system.

The future of marketing, now available in the present

Solutions like Act-On AI Predictive Lead Score represent a paradigm shift in the way we approach identifying, prioritizing, and marketing to leads. By combining AI and machine learning with the art of marketing, we can craft personalized and effective campaigns that resonate with prospects on a deeper level.

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