What Is Predictive Lead Scoring?

DEFINITION
Predictive lead scoring is a data-driven approach that applies big data and machine learning algorithms to lead scoring to find the right combination of behaviors and key attributes of existing and potential customers. Then, these attributes are automatically matched and ranked to those of new leads.

đź’ˇUnderstanding Predictive Lead Scoring

Predictive lead scoring enhances the traditional lead scoring technique through the use of data science, in particular by adopting artificial intelligence and machine learning algorithms.

It uses key insights such as historical and current data, customer engagement data, customer purchase data, and customer profile data from various channels, including organic search and your sales CRM.  

Predictive lead scoring takes this data to predict future outcomes of your sales targets and give more quality leads ready for your sales team.

đź–‹ Takeaway

Predictive lead scoring is the natural evolution of traditional lead scoring. While predictive modeling algorithms based on machine learning or artificial intelligence have been around for a while, we’re at the tipping point where you will start to see most companies starting to enable predictive lead scoring.

There is no one type of data or source of data that will solve all your problems. Still, when you combine this data alongside lead scoring, a predictive lead scoring model can undoubtedly assist you along your demand generation journey.


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What Is Predictive Lead Scoring?

To understand traditional lead scoring, imagine your marketing and sales teams manually ranking your leads on a spreadsheet based on how likely they are to convert. Typically, this is done by taking your Ideal Customer Profile’s demographic attributes and activity and ranking them on a scale of 1-100.

With predictive lead scoring models, predictive modeling algorithms do the heavy lifting for you: they use key attributes to score potential customers’ behavior and return more leads that are likely to convert. 

As you can see, traditional lead scoring is time-consuming and prone to human error. In contrast, predictive lead scoring methods use the latest data on customer and account profiles to qualify leads based on historical data of your sales performance and your warmest leads’ online behavior.

What Are The Benefits Of Predictive Lead Scoring?

By this time, you’re probably wondering how you can pitch yet another marketing tool to your C-level team.

When thinking about predictive lead scoring benefits, there are a few considerations to make as you introduce it into your lead management process.

Scale Your Lead Scoring Efforts (And Increase Lead Quality)

Think about all the data at your fingertips these days. Data from your sales CRM, marketing automation platform, chatbots, and product usage data–trying to combine all these data sets to form a basis for scoring can seem impossible.

Predictive lead scoring allows you to leverage all these sources (and more!) to make data-driven decisions about which data points are indicative of buying signals without manual effort.

You may be wondering if you have enough data. One of the cool things about this exercise is that you can frequently unearth activities and/or fit attributes on your lead that you didn’t know would have a significant impact on scoring! All you need are 5-9 data points to help build an excellent predictive lead scoring model.

Change Adoption (And Predictive Score) Based On Data

Information is changing every second within your marketing database. Decisions that you made around lead scoring a month ago could potentially be out of date today. 

One of the strengths of predictive lead scoring is that as the data changes, so do the predictions. Still, those changes need to be informed by the marketing team about AI/machine learning to ensure a good output to use within your predictive lead scoring model.

How Does Predictive Lead Scoring Work? 

The predictive lead scoring method can sound like it is magic, but the truth is that it is not–and it’s still very much in the early stages of use by marketing and sales teams.

Predictive lead scoring systems often have a “one size fits all” solution and predictive modeling that doesn’t consider the changes in lead scores over a specific time period (i.e., changes in business priorities/direction.)

Suppose your lead score system doesn’t adjust to changes in your business niches or verticals. In that case, you may end up with a misaligned lead scoring model, which in turn will result in your leads not being scored accurately and chaos in your marketing and sales teams.

In addition to that, pushing data into a system and hoping for the perfect outcome is fraught with potential issues. Ideally, you should want to prepare your data and ensure it aligns with your objectives (i.e., more enterprise leads, higher conversion rate, enhanced sales performance.)

At Breadcrumbs, we recommend (and offer) a machine learning-assisted predictive lead scoring system–a blend of the two approaches.

The synchronicity created by your sales and marketing team aligned with your ideal customer attributes and the efficiency of added artificial intelligence behavioral predictions is one of the tenants of our Revenue Acceleration Manifesto.

We think everyone deserves a sophisticated flywheel engine for lead scoring.

We think lead scoring shouldn’t be a black box–it’s your company and your leads. Your models should change as your strategy does, and black boxes just don’t cut it.

We think it shouldn’t cost you months and up to several hundreds of thousands of dollars just to create your first scoring model–you should be able to implement it and then iterate on it in minutes.

Our mission? In two words: revenue acceleration. We want to enable 2,000 companies to take it to the next level and be the first early adopters of this new modern take of lead scoring.

But we don’t want to stop there. This is why we refer to what we do as Contact Scoring. What is Contact Scoring, you ask?

It’s a supercharged version of lead scoring – it now applies to more than just acquisition, and we made it dynamic. It allows you to prioritize your leads and engage with current customers when they are most likely to buy, upsell or churn – all in one platform. 

Identifying contacts that are ready to buy and retaining those at risk of churning just got way easier.

Welcome to the age of Revenue Acceleration. 

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