In this article, you’ll learn what lead scoring is, how it can benefit your organization, and how to set up a lead scoring model that works for you.
Lead scoring has become a critical component to any successful inbound marketing strategy because it enables marketers to monitor the health of their marketing qualified leads (MQLs) while automating the conversion of those MQLs to Sales Qualified Leads (SQLs). If you haven’t yet introduced a lead scoring mechanism into your marketing automation system or are considering building your first model, this post is for you. Also, it’s important to note that in order to develop a lead scoring model, you must have already implemented a marketing automation system.
What is Lead Scoring?
Before we begin thinking about developing a lead scoring model, we first must understand what lead scoring is. The best definition I could find is from Tech Target. Their definition of lead scoring states “a methodology to rank your known prospects against a scale that represents the perceived value each lead represents to the organization.” And when you add in information from Wikipedia’s definition, you understand that scale is based on both explicit and implicit metrics. Explicit metrics include core information such as job title, company, industry, etc., while implicit metrics include individual behaviors such as interaction with emails, website pageviews, content downloads, and form submissions such as a contact us form or subscribe form.
Why Bother with Lead Scoring?
Next, think about the 100s or 1,000s of new leads your inbound, sales, and outbound marketing efforts are bringing into your business every day, month and year. The reality with those leads is most are far from being sales ready. If we force them into a sales team looking to meet sales objectives, it’s easy to push these leads away and lose them forever. In fact, many of these leads may simply be a waste of energy for a sales team who should be spending their time nurturing sales ready conversations versus pursuing those who aren’t yet interested, but could be in the future. Insert marketing, whose role is now to nurture these early stage leads and monitor their progress via a process called lead scoring. This process has become marketing’s mechanism to reconcile the divide between MQLs and SQLs within the marketing funnel. It’s used to more accurately surface leads who best fit the company’s ideal client profile defined by a score that represents a series of criteria which together, can indicate whether an individual might be ready to buy.
No Tool Visualizes a Lead Scoring Model, but They Should
At the surface, a lead scoring model sounds complex and daunting. But when you visualize it, you realize it’s a pretty simple concept and is a sliding scale. In the chart below, you can see the concept in visual form. On one axis, we’re scoring by demographic/firmographic metrics, and on the other axis, we’re scoring individual behavior. It will be up to you to determine how these metrics combine to meet a threshold that represents your best sales qualified leads.
As you can see in this model, we’ve defined a balanced approach. Where 50 points must be met from a demographic standpoint, followed by 50 points from a behavioral standpoint for a marketing qualified lead to become sales qualified. However, we could weight this scale any direction we deem necessary. For example, maybe we have a few key pieces of content that when consumed, indicate high likelihood to buy regardless of an individual’s title or role in the organization. In this case, we would work to weight behaviors a little higher than demographics and slide the scale accordingly in that direction. The point is, as you’re building your lead scoring model, visualize this simple chart, which unfortunately has yet to exist within any automation tool (that I’m aware of), to help you understand what your lead scoring model looks like.
Recommendations for Creating Your Lead Scoring Model
As you can imply from the previous chart, lead scoring is simply about assigning points and allowing those points to accumulate over time. These points will be assigned to both the explicit metrics a person provides you such as demographic/firmographic information, as well as the implicit metrics which you may infer based upon that individual’s behavior. You will then assign the point threshold for each variable to automate when a lead scores high enough to be considered an SQL. Below are examples of criteria you could consider scoring for each axis in the model.
Demographic Metrics to Score
- The basics include your standard website form fields such as job title, industry, geography, company size such as revenue size or number of employees, and lead source.
- Advanced metrics to consider if you’re using functionality like progressive profiling within your website to collect more information on your leads over time would be their experience using your type of services and whether the sales team has worked the lead before.
Behavioral Metrics to Score
- Engagement with email marketing indicated by emails opened and clicked.
- Engagement with social media such as clicking on a link in your post.
- Engagement with your webinars including registering and attending.
- Engagement with your website including pageviews which could be prioritized into case studies, blog posts, practice areas, and general marketing pages, content downloads, subscribe form submission, contact form submissions, and engagement with social sharing widgets.
In the End, Your Lead Score Model Will Be Ever Evolving
The reality is your lead score model will not be perfect out of the gate, nor should it be. As illustrated in the graphic earlier, you will always maintain the ability to slide the scale any direction you choose to weight the model to an appropriate threshold that best fits the needs of your business. For example, if you begin to find your model is passing too few sales qualified leads to the sales team, you can begin to determine how to lower your scoring threshold. Or maybe you’re faced with a scenario where the sales team is complaining that they have too many leads to keep up with, or maybe the leads coming through are too early stage. In this instance you can begin work to raise the scoring threshold. As you can imagine, the possibilities are limitless. What is most important is to keep an open line of communication between sales and marketing. It’s this relationship that will help you determine which explicit and implicit metrics matter most to your firm and when it’s time to begin adjusting the model to define a threshold that works best for you.