Let me pitch you a scene. You’re getting consistent leads into your database through your lead nurturing system. Forms are being submitted, brochures are being downloaded, and you are attempting to identify what leads are “hot” and which ones are “cold.” There are two significant ways to go about this, traditional lead scoring and predictive lead scoring.
It’s all about the data
Before we get into the significant differences between traditional and predictive lead scoring, let’s look at the data we are taking into our lead scoring models. Lead scoring utilizes both implicit and explicit data when scoring leads.
Explicit data is the data that we receive from leads directly, like first name, last name, phone number, email address, etc.
On the other hand, implicit data is defined by how a lead interacts with your digital marketing efforts. Implicit data ranges from clicks on your website to email engagement in lead behavior. Both traditional and predictive lead scoring utilize implicit and explicit data to help define leads. I know what you’re thinking, what’s the difference? Why should I care?
If they use the same data, what’s the difference?
Traditional lead scoring is where marketers determine the quality of a lead based on implicit and explicit data, then assign them a score. The lead score is used to determine how likely a lead is to convert and make a purchase. Weighting for these scores continually changes with content on your website and how you define “qualifying” data.
The main difference between predictive and traditional lead scoring is humans. It is difficult for us as marketers to develop lead data accurately and consistently. It does not always take into consideration the changing market. The weighting used in lead scoring metrics will vary and require consistent upkeep. Predictive lead scoring does away with the human error side of things and brings in historical and predictive analysis of your lead database.
How does predictive lead scoring work?
First off, predictive lead scoring analyzes all the final closed leads (both won and lost) and the existing leads. It takes in both implicit and explicit data, analyzing patterns and behavior to allow for proper weighting of your leads.
Next, it uses machine learning across the internet to add more information to the current data gleaned from your database. It goes through thousands of company websites, third-party websites, social media, and more to define your ideal customer.
Finally, it takes the data from your database as well as the findings throughout the internet, and discovers the similarities and identifies how closely they match. From there, the predictive lead scoring algorithm then creates a formula that will qualify your leads for you so you can quickly determine the most qualified ones.
What are the benefits of predictive lead scoring?
Predictive lead scoring replaces human error with continual analysis of your leads, adjusting the weighting when necessary. Without human error, there is less wasted time on unqualified leads for your sales team and proper attribution for your “hot” leads.
In traditional lead scoring, you have to update your scoring system manually to properly weight user interactions. With predictive lead scoring, this manual process is eliminated. The formula that is created by the algorithm will quickly adjust your lead scoring model and removes time spent on continual upkeep.
Predictive lead scoring uses a more significant dataset to develop a better picture of the ideal customer. Improved understanding of your audience provides us with better ways to create content that allows for higher conversion rates.
This type of lead scoring also allows for more qualified leads to enter your lead nurturing system. Instead of getting scrap leads that are less likely to convert, you’re allowing the data collected to create a roadmap for you and your marketing team to reach your ideal audience.
Lead scoring in action
Newmar, a leader in Class A motor coach manufacturing, needed a way to help local dealers identify quality leads, to deliver sales-qualified leads to those dealerships to drive revenue, and to ensure product availability depending on the consumer’s wants.
To fix the issue, Element Three built a system that integrates within HubSpot and passes along a customer lead score based on their online activity, precisely what they download, pages visited, and how frequently they visit the site. Additionally, our HubSpot scoring model keeps consumers engaged and moving down the funnel through automated email campaigns.
This model resulted in an estimated increase of $4.5 million in retail sales while increasing the website’s SQL-to-deals-closed rate by 71.9%. To learn more about what Element Three did in partnership with Newmar, explore our full case study.
Which one works for you?
The honest answer is, both. On one hand, predictive lead scoring provides ease of use, proper lead qualification, and automation. Traditional lead scoring allows for the collection of data with new forms and offers when there is no historical data or machine learning. Traditional lead scoring can go a long way here until predictive lead scoring can catch up. It's wise to use both of these models, monitor both, and find out what's a good mix for your situation.
There are a few details you need before you can decide whether or not you will dive headfirst into predictive lead scoring. First off, you need a lot of data about leads that did and did not close. More data will help you figure out what properties will serve as a good indicator that someone is qualified (or unqualified).
If you are a new business or small business and you're not generating many leads, staying away from predictive lead scoring might be more beneficial. You’re still in the learning phase of your leads and how to properly qualify them.
HubSpot has criteria for organizations to better understand whether or not they should use their predictive lead scoring model:
• You have at least 500 contacts that are customers.
• You have at least twice as many contacts that are marked as non-customers.
These criteria provide organizations with clear guideposts to determine whether they have a large enough database to invest in predictive lead scoring, or they should simply stick to a traditional lead scoring model.
Whatever it takes, get the right prospects.
Whether you’re going with traditional or predictive lead scoring, it’s one of the most important things you can do as a marketer to make sure you’re investing time and money into the right group of prospects—and not wasting your resources on people who aren’t going to buy. Want to see more about how we score leads ourselves? Check out this blog, and learn just why we think lead scoring is so important. And to dive even deeper, watch our webinar on how to use lead scoring and qualification to move the right prospects through your marketing funnel faster.
In another life, Ian might have studied finance, because he loves the stories that data can tell. But he didn’t—he picked marketing, and he didn’t look back. Through experience from his studies and internships, he developed an interest in digital marketing, which led him all the way here to Element Three. That interest stems from his passion for taking granular data to the big picture scope to see how changes can affect clients. When he’s not doing that, Ian is usually hunting for his next great cup of coffee, one of his other great passions.
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