Lead Scoring: Using AI to Align Sales and Marketing
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Lead scoring is a methodology used to rank and prioritize marketing leads based on data indicating their sales-readiness. By scoring leads, organizations can ensure sales teams followup with the right leads at the right time. However, developing accurate lead scoring models has historically required extensive manual analysis and guesswork. Now, with advancements in AI, lead scoring can be automated and continuously optimized based on real outcomes.

The Importance of Lead Scoring for Revenue Operations

For revenue operations managers charged with maximizing revenue through sales and marketing alignment, lead scoring is essential. Without lead scoring, sales teams waste time following up with low potential leads rather than focusing on those most likely to convert.Meanwhile, marketing teams struggle to identify the most promising segments to nurture.

Lead scoring provides a common language for marketing and sales to discuss leads through hard data rather than intuition. Revenue operations leaders can leverage lead scoring methodologies powered by AI to pinpoint the highest converting leads based on historical data. As models improve over time, sales and marketing alignment and efficiency increases.

Creating a Lead Scoring Model

At its core, lead scoring works by awarding points to leads based on certain attributes associated with sales-readiness. The points accumulate into a lead score. To build a model,revenue operations analysts can follow this framework:

First, determine which lead attribute smatter most for sales-readiness in the context of your business. Common factors include:

  • Demographic data
  • Behavioral activity
  • Marketing engagement
  • Product interest
  • Firmographic data

Secondly, define a scale for scoring different attribute values with points. For example, a CTO might receive 50points for their senior job title, while an entry level software developer would receive only 5 points.

Finally, set score thresholds that map to sales follow-up priorities. Leads with scores above 800 might be marked “hot” for immediate sales contact, while leads under 300 need further marketing nurturing.

By following this approach, revenue teams can create a customized lead scoring model tailored to their business needs. But doing so has historically required extensive manual analysis of past deals to determine which factors matter most.

Harnessing AI for Lead Scoring

In the past, lead scoring methodologies have relied on human intuition and guesswork. But now with artificial intelligence, lead scoring can be automated and continuously optimized by algorithms. Here’s how it works:

AI lead scoring software ingests historical sales and marketing data encompassing every past lead along with their ultimate outcome — won or lost deal. Algorithms analyze this dataset to determine which lead attributes were most predictive of sales success. The software then builds scoring models optimized to rank leads based on likelihood to convert.

Unlike rules-based models, the AI lead scoring engine updates scoring models on its own by incorporating new sales outcomes. So over time, scoring accuracy improves more and more. When a supposed “hot lead” set high by the model ends up not converting, the AI adjusts scoring weights to prevent similar misclassifications going forward.

Revenue operations can tap into predictive insights from AI lead scoring tools through sales guidance on high-potential leads as well as marketing analytics illuminating the best segments to nurture. Let’s explore some specific use cases:

AI Lead Scoring Use Cases

  1. Sales Alerts on Hot Leads

AI lead scoring directs sales reps towards inbound leads that match historical patterns of success. When a new marketing qualified lead comes in, the lead scoring AI can analyze attributes and immediately classify the lead as “hot” or “cold” for follow up.

These insights help sales teams respond quickly to inbound leads tagged as “hot” by the algorithms. Reps no longer waste time vetting every single lead through manual processes. The AI effectively tells sales reps where to focus for the highest conversion rates.

  1. Predictive Lead Lists

For outbound sales initiatives, AI lead scoring helps teams identify the companies and contacts with the highest potential in their market. The lead scoring AI generates targeted lead lists recommending ideal next accounts to pursue.

These AI-generated lead lists tap into the predictive power of lead scoring models. Outbound teams see higher conversion rates and pipeline velocity by focusing on the leads with attributes scoring highly based on historical deal data.

  1. Dynamic Lead Nurturing     Prioritization

By integrating lead scoring insights, marketing automation platforms can automatically determine the best nurturing track for each lead. Leads with high scores get sent content to accelerate sales conversations. Meanwhile, leads with lower scores receive moreeducational nurturing focused on product value.

As lead scoring models improve overtime, nurturing priorities stay dynamic.

Leveraging AI for Continuous Optimization

The biggest advantage of AI-powered lead scoring is how models continuously optimize without any manual intervention. Revenue operations analysts no longer need to manually analyze won/loss data to update scoring methodologies. Instead, algorithms handle model refinement automatically by ingesting new sales outcomes.

Let’s walk through an example:

Initially, the lead scoring AI might determine that company size is a top indicator of sales readiness. So leads from large enterprises receive high scores. However, over time deals start closing with mid-market companies rather than big corporations.

The algorithm picks up on this trend and adjusts scoring to weigh company size less and factors like buyer intent more. So going forward, mid-market companies with high research activity get higher lead scores.

No manual analysis is required for the model optimization to occur. The AI lead scoring engine handles constant learning and improvement as new data comes in. As a result, lead scoring stays dynamic and predictive.

Analytics for Sales and Marketing Insights

Sophisticated AI lead scoring platforms provide rich analytics into model factors and trends. Revenue teams gain visibility into the attributes that drive scores calculated by the algorithms. They see which factors hold the most predictive power based on ongoing sales outcomes.

These insights help sales leaders double down on the right signals for opportunity management. Meanwhile, marketing gains perspective on their best performing segments for continued nurturing.

For example, analytics might show that buyers with Director and VP titles engage differently with content. From there, marketing leaders can tailor nurturing tracks based on seniority. The analytics layer transforms lead scoring from just a methodology for routing leads into a strategic tool for revenue optimization.

Getting Started with AI Lead Scoring

For revenue teams looking to revamp lead scoring with artificial intelligence, what are the steps to get started?

  1. Upload historical sales and marketing data. Lead scoring algorithms require a training dataset encompassing past leads and their ultimate outcome — won or lost deal. CRM tools and marketing automation platforms offer straightforward exporting  to feed data to AI engines.
  2. Configure a lead scoring model. While algorithms do the heavy lifting, revenue teams still tailor models  to their business priorities and definitions of a “hot” lead. This step  involves setting parameters for scoring factors, weights, and lead  definitions.
  3. Integrate lead scoring insights     across systems. With a model built, integration enables sales and  marketing workflows to leverage scored leads. Platforms like Salesforce, Marketo, and Hubspot integrate directly with lead scoring AIs through  APIs.

The Right Data Foundation

Of course, the accuracy of AI lead scoring depends entirely on the quality of data fed into the algorithms.Revenue teams should ensure their underlying CRM and MAP data provides sufficient depth into past leads before deployment. Records should capture granular engagement attributes and activities over time rather than just static firmographic fields.

With clean historical data encompassing both won and lost deals, lead scoring AI gains maximum insight into the attributes indicating sales readiness. The richer the data foundation, the better algorithms can score leads on likelihood to convert. Maintaining reliable pipelines and opportunity records remains crucial — even as lead scoring models handle the analysis automatically under the hood.

The Future of Intelligent Revenue Operations

Looking ahead, lead scoring is just one area of revenue operations getting revolutionized by artificial intelligence. Algorithms help teams optimize everything from email sequences to pricing models using the same automated, self-learning approach.

As an integrated AI layer, predictive lead scoring combined with other revenue intelligence tools provides a force multiplier effect on sales and marketing performance. Win rates go up. Cycle times drop down. Ultimately, revenue teams spend less time processing leads manually and more time focused on high value activities — powered by intelligence learned from hard won experience.

Lead Scoring using Relevance Ai

Watch this tutorial on how to simply score leads using Relevance AI. Try out the simple lead scoring tool.

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