Lead scoring evaluates potential buyers using predetermined criteria to determine which ones are likely to convert into paying customers.
Traditional lead scoring models rely on manual input from marketers to determine which criteria to use.
Predictive lead scoring models use machine learning algorithms and predictive modeling techniques to create an ideal customer profile based on past customer behavior. This enables marketers to automate the entire lead scoring process and reduce the chances of human error.
In this post, we’ll explain what predictive lead scoring is, how it works, and the benefits it can bring to your business. We’ll also take a look at the lead scoring models — traditional vs. predictive — and how to unlock enhanced intelligence for better lead scoring.
Lead Scoring Models: Traditional vs. Predictive
Lead scoring models evaluate leads and assign points based on factors such as customer fit, lead behavior, and more.
In a traditional lead scoring model, marketers select a few key actions based on their own idea of what caused leads to turn into customers in the past.
- Responded to marketing emails
- Downloaded an ebook
- Often engages on social channels
- Strong ICP fit who is a repeat website visitor
These actions are then used to assess the potential of future leads. However, traditional lead scoring models are prone to human error and misinterpretation, which is why they are mostly replaced by predictive lead scoring models today.
Predictive lead scoring models fix the shortcomings of the traditional lead scoring model by automating the entire process.
What is Predictive Lead Scoring?
Predictive lead scoring uses machine learning to predict future customers based on the behaviors of past customers. Rather than tracking a small number of variables that may or may not be meaningful, artificial intelligence can spot a variety of patterns that correlate with success. This helps businesses efficiently direct their efforts towards leads with maximum potential.
How Does Predictive Lead Scoring Work?
Predictive lead scoring works by creating an ideal customer profile (ICP) based on a company’s past customers and sales cycles. This ICP is then used as a model to evaluate incoming leads. The closer to the ICP a lead is, the higher the lead score, and vice versa.
Whereas a traditional ICP is defined based on the experience of sales and marketing teams and the weight they assign to specific actions, a predictive lead score is much more dynamic.
How 6sense Provides Enhanced Data for Your Lead Scoring Needs
6sense gauges the buying intent score for the accounts you track. The buying intent of an individual or organization can be inferred by evaluating behavior such as webpage visits, media consumption, demo or meeting requests, collateral downloads, event participation, and form submissions.
6sense also spots online research that would normally remain anonymous — such as researching solutions similar to the ones you provide, or comparison shopping you and your competitors on third-party sites like G2 or TrustRadius. These activities can reveal which accounts are “hot” long before they fill out a contact form or take other direct action to speak to a sales rep.
6sense also continuously updates contact databases to keep data clean and actionable.
Lead scoring is an important process for any business looking to maximize lead quality and minimize wasted effort. While traditional lead scoring models rely on manual input from marketers to determine which criteria to use, predictive lead scoring models use machine learning to automate the entire lead scoring process.