Traditional vs Predictive Lead Scoring: Which is Right for You?

Over the last few years, artificial intelligence (AI) has started changing the marketing technology landscape. Everything from ads to reporting seems to have elements of AI tied to it. Lead scoring is no different.

The idea of spending hours upon hours combing through data to score leads manually seems redundant. This is where predictive lead scoring can come in and help scale your efforts.

In this article, we’ll cover what predictive lead scoring is, the difference between manual and predictive lead scoring, and considerations to make before you dive in.

Let’s get started!

What is predictive lead scoring?

At its foundation, traditional lead scoring is a way to rank your leads based on how likely they are to convert. Typically, this is done by taking the demographic attributes and activity of your ideal customer profile and ranking them on a scale of 1-100.

While there are several key criteria that can make or break the success of lead scoring, the fact of the matter is that choosing these criteria is an entirely manual (and tedious) process.

This usually involves scouring your database of thousands of contacts for the right combination of attributes that equal the perfect lead–which is by no means an easy feat.

Predictive lead scoring enhances the traditional lead scoring technique through AI. This type of lead scoring applies big data and machine learning algorithms to find the right combination of behaviors and attributes of existing customers and prospects. Then, these systems can match these attributes to those of new leads and automatically rank them against each other.

If you’re ready to abandon manual lead scoring altogether, it’s essential to keep in mind there are a few considerations and pros and cons of each system.

Traditional lead scoring vs. predictive lead scoring

Predictive lead scoring can sound like magic, but the truth is it is not (and still very much in the early stages of use by marketers).

Frequently predictive lead scoring systems have a “one size fits all” solution and models that don’t consider the temporal scoring changes (i.e., changes in business priorities/directions).

Suppose your system has a set model of what it interprets as your ideal customer, and your business changes niches or verticals. In that case, this can misalign your entire scoring system and cause chaos in the sales and marketing department.

In addition, pushing data into a system and hoping for the perfect outcome is fraught with potential issues. Ideally, you should want to prepare your data, the time frame of the data you are using, and ensure it is aligned with your objectives (i.e., more enterprise leads and purchases).

At Breadcrumbs, we recommend (and offer) an AI-Assisted system–a blend of the two approaches.

The synchronicity that is created by your sales and marketing team aligned on your ideal customer attributes, as well as the efficiency of added AI behavioral predictions, is one of the tenants of our Revenue Acceleration Manifesto.

This is why we put together five key ingredients and mixed them up with a robust algorithm to cook up the perfect recipe for a lead scoring model that is perfect for you and your business.

Predictive Lead Scoring: Breadcrumbs Ai-Assisted System
  1. Recency–Recency is the first among our lead scoring criteria. This is because it is crucial to know how recently your lead has taken action with your company assets. In this sense, recency allows you to evaluate and prioritize activities that are currently happening instead of wasting time chasing up leads that are 2+ years old.
  2. Frequency–Another important indicator that helps prioritize your leads is frequency. It means you can count how often they raise their hand at what you propose to them. Those who have recent activity with your organization and frequently interact with your company should be the top leads your sales team goes for.
  3. Online + offline activities–Actions your contacts take on and offline will help to identify what someone is interested in and how they like to consume their information. This is a treasure trove for marketers–both in terms of reaching back to them with the right type of content and what content to create in the first place.
  4. Job title–As a salesperson will tell you, the job title provides great guidance around the level of decision-making and budget authority the person has. Scoring job titles correctly will help your sales team shape that conversation, push the deal forward, and overcome any customer objections with the right person.
  5. Industry–Much like job titles, the industry field helps align your product and/or service fit to companies that have been successful with you.

While these are the standard categories we believe every company should use to score their leads, this is just the tip of the iceberg in terms of what categories you can build your lead scoring system off of.

Breadcrumbs can help with that. Our contact scoring tool will help you analyze each lead and existing contact for firmographic data (such as Job title and Industry) and engagement data (such as Online and Offline activities) to see which users are prime for a sales opportunity and route back this information to your sales team so they can act fast.

This includes high-value leads that are ready to convert into paying customers, but it also includes existing customers who may be a great fit for upselling or cross-selling. 


Sign up for free with Breadcrumbs to start your lead scoring model today or use our intuitive model builder to create an entirely custom one for your business.

Considerations for introducing predictive lead scoring to your team

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

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

  1. Scale–Think about all the data at your fingertips these days. Data from the CRM, marketing automation platform, chatbots, and usage data–trying to combine all these data sets to form a basis for scoring can be tough. Predictive lead scoring allows you to leverage all these sources (and more!) to make data-driven decisions around what data point(s) are indicative of buying signals without the manual lift. One of the cool things about this exercise is that you can often unearth activities and/or fit attributes on your lead that you didn’t know would significantly impact scoring! All you need is 5-9 data points to help build a good predictive model.
  1. Change Adoption–Data is literally changing every second within your marketing database. Decisions you made around lead scoring a month ago could potentially be outdated. One of the strengths of predictive lead scoring is that as the data changes, so do the predictions. But those changes need to be informed by the marketer to the AI/machine learning to ensure a sound output to use within your scoring model.

This is the natural evolution of lead scoring, and while predictive/AI-based scoring has been around for a while, we’re at the tipping point where you will start to see more companies embrace AI-based scoring. 

There is no one type of data or source of data that is going to solve all your problems. Still, when you combine this data together alongside the objectives of what you are looking to get out of lead scoring, Predictive lead scoring can undoubtedly assist you along your demand generation journey.

A contact-scoring software like Breadcrumbs leverages your data to crunch the numbers using machine learning to determine when you need to take action.

We’ll combine data from all of your touchpoints (your CRMmarketing automation tools, and existing product usage tools) to give you a thorough view of what activities are driving behavior.

What’s the next step? Let’s talk about how to define your objectives and develop models that produce actionable scores you can use to drive processes and activities (i.e., in-app messages, email campaigns, sales outreaches).

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