Predictive Lead Scoring: 6 Comprehensive Aspects

• January 3rd, 2022

Predictive Lead Scoring FI

In this era of Automation and Advanced Analytics, every organization wants to transform the process of identifying Leads from a plethora of people that have some interest in the product/service you offer. To address this issue, Lead Scoring comes into the picture. Lead Scoring is simply a methodology where you assign scores to your Leads based on Demographic Data (like Job Title, Name, Address, etc.) and their online behaviors. Based on these scores, you can identify if the Lead is Sales-Ready or not for your organization. 

The traditional Lead Scoring methods used in the past manually assigned scores to your Leads based on their behavior i.e. previously, customers were ranked based on the criteria set by the organization and the Ideal Customer Profile (ICP) had to be identified manually. This method had many drawbacks like it was error-prone, it was unable to analyze voluminous data, it required more efforts to identify Leads, etc. To simplify this entire process, Predictive Lead Scoring came into play. This approach utilizes Artificial Intelligence (AI) and Machine Learning (ML) Algorithms to predict the Lead Score of customers thereby improving the productivity of the Marketing and Sales team.

This article will give you an in-depth overview of Predictive Lead Scoring and why most companies are acquiring this methodology to analyze their Leads. We will also look at some of the platforms that you can use to enable Predictive Lead Scoring to your business or organization.

Table of Contents


Before diving deep into the Predictive Lead Scoring methodology, you must be familiar with the below prerequisites to help you understand the technique efficiently.

  • Familiarity with Marketing and Sales Automation tools.
  • Working knowledge of Machine Learning Algorithms would be an added advantage.

Introduction to Predictive Lead Scoring

Predictive Lead Scoring
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Predictive Lead Scoring is a methodology used by organizations to identify Leads that are Sales-Ready and thus maximizing the Returns on Investment (ROI). This methodology utilizes Big Data and Machine Learning Algorithms to predict the Lead Score of the existing customers. It is a data-driven approach that is dependent on the Historical and Demographic Data of the customers that makes it more accurate for evaluating the Lead Scores.

Moreover, Predictive Lead Scoring techniques can be easily integrated with other CRM (Customer Relationship Management) tools like Salesforce, Hubspot, etc. which makes it more convenient for the organizations to make strategic decisions and improve Sales Productivity. Last but not the least, it also enhances the overall Purchase Rates and Conversions in an organization.

For more information on Predictive Lead Scoring, click here.

Benefits of Predictive Lead Scoring

Predictive Lead Scoring has gained wide popularity in the Marketing Automation Industry. Some of the benefits of using Predictive Lead Scoring include:

  • Data-Driven approach: In the case of the traditional Lead Scoring methodology, the factors that were considered for evaluating the Lead Score were completely based on the judgment of the Sales Executive. So, to perform Lead Scoring, certain assumptions and guesses needed to be taken into consideration. But, this is not the case with Predictive Lead Scoring.
  • Less Error-Prone: Predictive Lead Scoring is less error-prone because of its Data-Driven nature. Moreover, it utilizes the use of Machine Learning and Artificial Intelligence Algorithms to evaluate the Lead Score and thus reduces the errors.
  • Quicker and Comprehensive: Predictive Lead Scoring methods are usually faster compared to the traditional Lead Scoring as the methodology is completely based on the Machine Learning Algorithms to evaluate the Lead Scores. It also provides a detailed and complete profile of the Lead thus improving the accuracy of the scores.

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Hevo Activate takes care of all your data preprocessing needs and lets you focus on key business activities, and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always this-ready data in your desired destination.

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Key Attributes for Predictive Lead Scoring

The Predictive Lead Scoring model utilizes certain Key Attributes to score the Leads. Key Attributes can be defined as the characteristics possessed by the Leads like Customer Profile Data, Account Profile Data, Sales Performance, etc. You will get to know more about the Key Attributes that are considered in Predictive Lead Scoring in this section. Some of the Key Attributes include:

  • Customer Profile Data: This attribute includes Demographic Data about the customers like Job Title, Address, Date of Birth, Name, Age, etc.
  • Customer Purchase Data: This attribute includes the purchase history information about the customer like Money Spent on Buying the Products, Frequency of Purchases, Online or Offline Payments done, etc.
  • Customer Engagement Data: This attribute mostly includes the online behavior of the customer like the Number of Times the Page was Viewed, Sign-up for a Free Trial, Number of Times Downloaded, etc.
  • Account Profile Data: This attribute consists of the information about the Account Type, Company Size, etc.
  • Marketing and Sales Campaign Data: Marketing and Sales Campaign help organizations understand their customers in a better way. This helps them segment their Leads thereby concentrating on a particular section of Leads at a time. For example, a campaign can help analyze various channels from which the customers have read about the product like Social Media, Referral, Newspaper, etc.

Once these Key Attributes are collected, they can be passed on to the Machine Learning models for the training and can be used to evaluate the Lead Score of the customers.

Working of Predictive Lead Scoring

Working of Predictive Lead Scoring
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The first step in the Predictive Lead Scoring method is to identify the key and target attributes that are concerned with your organization. These Key Attributes (like Retention Rate, Conversion Rate, Quality of Service, etc.) can be easily analyzed by the Customer Relationship Management (CRM) software that your company or organization uses. 

After the identification of the Key Attributes, the Predictive Lead Scoring technique uses a series of Machine Learning Algorithms to create a model that can accurately score the customers based on Historical and Demographic Data. These models can easily handle massive amounts of data and run efficiently without any guesswork. These models are also able to analyze the shortcomings that caused Leads to reject the products.

Depending on the requirements and types of attributes, various approaches can be used to model Predictive Lead Scoring. One of the most common Machine Learning Algorithms that are used to model Predictive Lead Scoring is “Logistic Regression”. This algorithm helps to identify the percentage of Leads that can be converted into customers based on the Key Attributes taken into consideration for a particular organization.

Another model that is widely used in the Predictive Lead Scoring technique is the “Random Forest”. It is a collection of “Decision Trees” based on the Key Attributes selected. For example, this model creates a collection of decision outcomes, and based on these outcomes, it determines the Leads that have greater chances of being converted into customers. 

Limitations of Predictive Lead Scoring

Predictive Lead Scoring has achieved huge success in the field of Marketing and Sales Automation. However, it does have some limitations. Some of these include:

  • Requires a Large Amount of Data: To predict the Lead Score accurately, you need to have a huge dataset of customers to train the Machine Learning model and identify the behavioral patterns of your customers. This is one of the biggest limitations of the Predictive Lead Scoring methodology.  
  • Requires a Vast Technical Background: Implementing a Predictive Lead Scoring model from scratch requires vast technical knowledge and skill set. Implementing a Predictive Lead Score requires the knowledge of Machine Learning, Big Data, and Artificial Intelligence as well.

Top Predictive Lead Scoring Platforms

In recent years, Predictive Lead Scoring platforms have gained wide acceptance in the market. Here is a list of few Predictive Lead Scoring platforms that are popular among organizations and businesses:


Predictive Lead Scoring: HubSpot Logo
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Hubspot is one of the popular Marketing and Sales Automation tools that provides Predictive Lead Scoring solutions for all levels of the organization. This tool provides various customizations that can be performed to analyze your Leads and Prospects. It is one of the essential tools that every organization must possess to improve productivity and Sales.


Predictive Lead Scoring: Infer Logo
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Infer is a Lead Scoring tool that can be easily integrated with various CRMs (Customer Relationship Management) and Marketing Automation tools. It provides an API (Application Programming Interface) that can be used to connect with other Marketing and CRM (Customer Relationship Management) software. Moreover, this tool has a huge built-in database of companies and prospects.


Predictive Lead Scoring: PipeCandy logo
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PipeCandy is one of the popular Marketing Intelligence tools. This tool can also be easily integrated with other CRM (Customer Relationship Management) tools and can be used for Lead Scoring techniques. This tool can be a perfect choice for handling smaller datasets and acquire valuable E-Commerce insights. It also has an “Attribute Importance” feature that allows you to choose the attribute based on which you want to score the Leads.

Predictive Lead Scoring: Logo
Image Source is one of the popular Predictive Analytics tools that help organizations not only evaluate their Lead Score but also helps to generate new Leads as well. It also provides the facility to integrate with other CRM (Customer Relationship Management) tools and so anyone can customize the tool based on their needs.


This article gave a complete overview of the Predictive Lead Scoring model. It also provided the working of Predictive Lead Scoring. It also talked about various platforms that you can utilize for Predictive Lead Scoring. Overall, the Predictive Lead Scoring methodology should be adopted by every organization to succeed in Sales and productivity and also to make strategic decisions.

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