Predictive lead scoring uses artificial intelligence to evaluate your leads on their likelihood of becoming customers. Predictive lead scoring analyzes data from your marketing campaigns to help your sales agents focus on the best candidates.

This article will outline predictive lead scoring, including its benefits, key components, best practices, and steps for setup.


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What is Predictive Lead Scoring?

Predictive lead scoring is a method used by sales and marketing teams to analyze and score a company’s leads based on their potential to become a customer or make a purchase.

Usually an add-on that integrates with your CRM system, predictive lead scoring uses a machine-learning algorithm to identify patterns in lead attributes, determining which properties most reliably translate into sales. These AI algorithms track not only behaviors, but firmographic data and negative qualities to profile the most optimal leads, in order to help sales teams concentrate their efforts on the leads who are most likely to become customers.

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Predictive lead scoring automatically analyzes thousands of lead attributes:

  • Firmographics: A lead’s industry, geographical location, role in the company, the company’s employee size, the technologies the company uses, company value, and more
  • Behavioral data: Which of your web pages the lead visits and how often, how many ads and marketing emails they’ve clicked, how frequently they’ve contacted your company, communication channels utilized, language used, and more
  • Negative data: The qualities and attributes that indicate non-customers. For example, certain geographical locations, language used in transcripts, and webpages visited–like your company’s “Careers” page–may correlate with leads that don’t translate into customers.

Analyzing thousands of these data points for all your leads, predictive lead scoring evaluates each lead on their likelihood of becoming a customer. Typically, the resulting score ranges somewhere between 1-10,  1-100, or an A-F grade.

This score, called the “Lead Score”, helps your sales team know who to target with outbound communications. You can also automate call center workflows that trigger based on certain lead scores, sending messages through particular communication channels once a lead achieves a certain score.


Predictive Lead Scoring vs Traditional Lead Scoring

Traditional lead scoring requires administrators to manually enter lead attributes into the CRM to create lead-scoring algorithms, while predictive lead scoring automatically determines which attributes are most important in sorting leads into customers and non-customers.

Since there are thousands of potential attributes that determine which leads become ideal customers, traditional lead scoring often involves human error and missed opportunities to filter leads. Further, manually inputting hundreds of contact attributes–and negative attributes–into the CRM is a painstaking task, in addition to the careful analysis of selecting the most important attributes.


Benefits of Predictive Lead Scoring

Here are the most significant benefits of implementing a lead scoring system:

  • Increased sales
  • Saved time for sales teams and administrators
  • Scalable
  • Data-based decisions and growth
  • Improved communication between marketing and sales teams


Increased Sales

Predictive lead scoring boosts sales because it filters the leads that are most likely to become customers or make a purchase. This pre-qualifies leads and enables sales teams to focus their efforts and time on leads with a far higher return. You can also integrate lead scores with other CRM and call center workflows–involving other communication channels, auto dialers, and outbound IVR–which leads to more sales.

When set up correctly, predictive modeling facilitates higher lead conversion, higher purchase rates, and more profit overall.


Saves Time for Sales Teams and Administrators

Compared to traditional lead scoring, predictive lead scoring saves administrators and sales teams hundreds of cumulative hours over time. Traditional lead scoring is a tedious process because it requires administrators to manually enter every attribute and negative trait that qualifies or disqualifies leads. Even before manually entering attributes, your team must evaluate lead conversion data to determine which attributes most reliably predict customers. As your company expands or alters its customer base over time, administrators must update these attributes, requiring an ongoing effort.

Predictive lead scoring automatically handles every step of the lead-scoring process: 

  • Tracks lead conversion to determine the most important attributes
  • Evaluates and scores each lead
  • Continually updates lead-scoring attributes to predict conversion likelihood

In doing so, it saves your sales and administrative teams a tremendous amount of time.


Supports Scaling

Your CRM platform’s predictive lead scoring algorithm continually tracks which of your leads’ qualities most reliably indicate conversion into sales and customers. As your company scales to reach new audiences, demographics, and industries, the predictive analytics algorithm prioritizes new attributes and drops outdated ones. This supports more seamless business scaling.

Customer attributes that commonly shift while your company scales:

  • Geographical data
  • Industry
  • Language used
  • Communication channels
  • Marketing materials

Predictive lead scoring saves your team from having to manually factor in these changes when customizing the CRM lead-scoring system.


Data-Based Decisions and Growth

Machine-learning algorithms track thousands of behavioral and firmographic customer data points–most of which the human mind is likely to overlook. In a traditional lead-scoring system, it’s likely that your team misses some critical attributes that distinguish customers from non-customers.

Predictive lead scoring provides data-driven growth by identifying the most conversion-correlated lead attributes and directing your marketing efforts toward them. Administrators can use predictive lead algorithms to identify customer patterns that they otherwise may not have considered.


Improves Communication Between Marketing and Sales Teams

Lead conversion requires coordination between marketing efforts and sales outreach, and this communication can be challenging without automated workflows and systems for filtering leads. Predictive lead scoring facilitates this communication by determining which marketing materials are most important, analyzing how each lead interacts with these materials, and filtering the most qualified leads based on this analysis.


Key Components of Predictive Lead Scoring

Here are the technological components your company should have in place in order to effectively implement predictive lead scoring:

  • CRM system
  • Predictive scoring software or algorithm
  • Call center software
  • Data analytics
  • Contact list


CRM System

A customer relationship management (CRM) software application is the most important part of predictive lead scoring, because it houses your contacts, records their firmographics, and tracks their behavior. Popular CRM systems, like HubSpot and Salesforce, often include traditional lead scoring in their higher-tier subscription plans but offer predictive lead scoring software as a separate CRM add-on.

A CRM system includes several of the components on this list–data analytics, marketing campaigns, and contacts. CRM systems integrate with your phone system or call center, combining multiple communication channels into a workflow and tracking the whole process–including how your customers navigate the system. When the lead-scoring algorithm determines a score, it populates the CRM system’s contact list.


Predictive Scoring Software or Algorithm

Predictive lead scoring is an AI-based algorithm that combines several machine-learning processes: tracking lead conversion journeys, calculating the most significant data points that precede customership, and scoring each lead based on these data points. As your marketing campaigns evolve and convert high-quality leads over time, the algorithm continually refines its model to score potential customers using the most relevant attributes.

Many CRM systems offer a predictive lead scoring algorithm as a software add-on.


Call Center Software

Cloud-based call center software is important for lead scoring because it provides multiple communication channels that strengthen and customize your marketing campaigns.

Contact center as a service (CCaaS) platforms provide key communication channels:

  • VoIP phone service
  • SMS texting
  • Email
  • Web chat and self-service chatbots
  • Social media

Omnichannel contact centers unify these channels into one agent interface. Since CCaaS platforms integrate with CRM systems, they populate the agent dashboard with relevant customer data, like sentiment and lead score, providing sales reps with context. Further, higher-tier CCaaS pricing plans enable you to automate workflows–like sending an automated email or initiating an auto dialer sequence–once a customer achieves a certain lead score. This enables you to make the most of your predictive lead-scoring AI.


Data Analytics

Most CCaaS platforms and VoIP phone systems track advanced data analytics: call center metrics and KPIs that track your call center volume, agent activity and performance, channel usage, and more. While these metrics may not directly inform your predictive lead scoring model, they do provide important information about which channels to use in your workflows and which agents provide the best service.

CRM systems, on the other hand, track a wealth of customer behavior and demographic data that directly informs your predictive lead-scoring algorithm. While traditional lead scoring systems require marketing and salespeople to determine which attributes filter quality leads, predictive lead scoring systems calculate this automatically.


Contact List

A list of contacts, or leads, is important because the predictive scoring algorithm analyzes and scores this list. A critical part of any marketing or sales campaign, a contact list organizes all your company’s potential leads and customers. A CRM system houses the contact list–recording contact information when someone responds to a marketing campaign, gathering historical data, categorizing contacts, and determining how to reach out to them.

Predictive scoring software categorizes and filters your contact list according to their potential to become a customer, before proceeding to the corresponding workflow.


How to Implement Predictive Lead Scoring

Because predictive lead scoring utilizes AI to determine attributes and score leads, it’s easier to implement than traditional lead scoring.

Traditional lead scoring requires tracking lead conversion rates, isolating the metrics that most significantly correlated with lead conversion, then manually inputting those metrics into your CRM system’s contact properties.

Predictive lead scoring, however, requires just a few steps to implement:

1. Purchase and integrate lead-scoring software

2. Turn on Predictive Lead Scoring in your CRM contact properties

3. Gather lead data

4. Create workflows

5. Evaluate and compare lead-scoring models


Step 1: Purchase and Integrate Lead Scoring Software

Most CRM platforms–like HubSpot and Salesforce–include traditional lead scoring in their higher-tier plans. However, most CRM providers sell predictive lead scoring software as a separate add-on. Once you purchase predictive lead scoring software, follow the software-provided guidelines to integrate it with your CRM system.


Step 2: Turn on Predictive Lead Scoring in your CRM Contact Properties

Once you’ve integrated predictive lead scoring software, navigate to your CRM system’s Settings menu, then Properties, then Contact Information. From here, you can enable predictive lead scoring. It might be called one of the following: Likelihood to Close, Contact Priority, or Predictive Lead Scoring.


Step 3: Gather Lead Data

Once you’ve enabled predictive lead scoring, you must gather enough leads and customers for the algorithm to detect patterns in customer attributes. Typically, the algorithm requires between 40 to 100 leads to analyze these patterns. Depending on how many leads your marketing campaigns generate, this process can take anywhere from one day to several weeks. Make sure to gather lead data through all available channels, running your marketing and sales campaigns as normal. The CRM system and predictive lead scoring algorithm will gather a dataset automatically in the background.


Step 4: Create Workflows

Once the algorithm has gathered enough big data to identify attributes for lead conversion, it will begin scoring your pre-existing and new leads. In order to take advantage of these lead scores, create workflows that automate actions using lead scores as a trigger. You can automate these workflows in your CRM system–or in your call center if it’s integrated with your CRM system.

Example workflows include:

  • Sending an email to all leads who score above 50
  • Adding all leads who score about 70 to an auto dialer campaign list
  • Assigning all leads who score above 90 to sales agents, for personalized follow-up


Step 5: Evaluate and Compare Lead Scoring Models

Although your predictive lead-scoring software should outperform your traditional lead-scoring model, it’s helpful to confirm this by tracking call center metrics like your lead conversion rate. With the implementation of lead predictive lead scoring, your lead conversion rate should be higher and more efficient than it was before you implemented the algorithm.

The most important metrics to track are:

  • Lead conversion rate: Should be higher than it was before you implement predictive lead scoring
  • Customer purchase rate: Similar to the lead conversion rate, your company’s overall purchase rate should increase when you implement predictive lead scoring
  • Agent call volume: Ideally, agents are able to convert more leads with the same number or fewer calls than they were making before
  • Communication channel activity: Track which channels your leads, agents, and workflows most commonly utilize. Over time, you might find that certain channels are becoming more popular with your leads, which may inspire you to shift your workflows to emphasize these channels


Best Practices for Predictive Lead Scoring

Here are the most important practices to keep in mind when implementing and tracking predictive lead scoring:

  • Cater your CRM system to your marketing needs
  • Integrate predictive lead scoring with call center automations and workflows
  • Set norms for how agents respond to predictive lead scores
  • Routinely compare and reevaluate your predictive lead scoring model


Cater Your CRM System to Your Marketing Needs

When selecting your CRM system, consider the multiple things: the number of contacts you expect, your budget, the channels your company uses, the workflows you plan to implement, and the amount you’re spending on predictive lead scoring software.

Predictive lead scoring software can be pricey, and it’s often an add-on to your already costly CRM system. Further, higher-tier CRM systems cost significantly more than low-tier systems. If your company only expects to have a few thousand contacts at once, and the low-tier CRM system includes the marketing automations you want, you might preserve your budget better by opting for a lower-tier CRM subscription and investing in a more robust predictive lead scoring software.


Integrate Predictive Lead Scoring with Call Center Automations and Workflows

Predictive lead scoring software is most useful when integrated with call center automations–particularly multichannel workflows–that utilize contact scores. You can trigger marketing emails, follow-up SMS texts, auto dialer lists, and sales-agent contact assignments for certain lead scores. These workflows make agent efforts more efficient and take advantage of your CRM’s built-in functionality.

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How Agents Respond to Predictive Lead Scores

To help agents hit the ground running with your system’s lead scores, organize protocols for how agents should respond to various score levels. One effective way to organize lead-score response is to segment lead scores into tiers, delineating a particular response to each tier.

Sample Lead Score Response Protocol (1-100):

  • 80 or above: Move to the top of sales-agent priority for immediate contact
  • 60-80: Agents call after the higher-priority leads
  • 30-60: Send a marketing email
  • 15-30: Send a promotional email
  • 0-15: Send a nurturing email


Routinely Reevaluate Your Predictive Lead Scoring Model

To make sure that your predictive lead scoring model is benefitting your marketing strategy, track important metrics like lead-conversion rate and purchase rate to make sure they improve from before you implemented predictive lead scoring.

To monitor your model even more closely, track the conversion rate of each lead-score subsection. For example, the top 10% of your lead scores should have a conversion rate above 80%. The top 25% of your lead scores should have a conversion rate surpassing 60%. The top 50% of your lead scores should convert at a rate above 30%. The bottom 50% of your lead scores should see a steep drop off in lead conversion rate.


Predictive Lead Scoring Maximizes Marketing Efforts

Predictive lead scoring software extracts thousands of data points from your multichannel marketing efforts–facilitating the flow from marketing to sales. This extracts greater value from your marketing team’s efforts while helping sales agents use their time more effectively. The software’s machine-learning algorithm also continually refines itself, supporting business growth and saving time in the long run.