4 Lead Scoring and Grading Scenarios Explained

While we’ve been covering some of the recent trends in email marketing and Google search on the Pardot blog lately, I’d like to take today to return to a marketing automation 101 topic that is near and dear to our hearts: lead scoring and grading.

Whether you’re a marketing automation pro or are just getting started with an automation tool, lead scoring and grading is going to be one of the most powerful tools in your marketing arsenal (and with that, I’d like to respectfully add the phrase ‘marketing arsenal’ to our list of please-don’t-ever-use-these-again marketing buzzwords).

However, even though lead qualification is a staple functionality of a marketing automation tool, we still get a lot of questions around that topic. What’s the difference between lead scoring and lead grading, and why would you ever need both?

Let’s take a few moments to review the difference between these two features, and take a look at what the different combinations of lead scores and grades really mean for your lead qualification strategy.

Lead Scoring vs. Lead Grading

In one of our older posts, Molly Hoffmeister, fellow content marketer and blogger at Pardot, discussed how she was one of Pardot’s hottest prospects back in the day. Her lead score was off the charts — but in reality, she had no intention of buying marketing automation. She just wanted a job.

This is one of the flaws of a lead qualification system that relies solely on lead scoring, which uses a prospect’s activities to gauge their interest in your product. A lead score provides a numeric value that correlates with a prospect’s interest level — the higher the lead score, the more interested they are, and vice versa. The problem with this one-sided model is that it’s difficult to differentiate between the leads who are legitimately interested in your product and the leads who are on your site because their friend suggested they check out career opportunities, or because they’re doing industry research in your resources section.

This is why lead grading is so critical. Lead grading, which is reflected as a letter grade, looks at the flip side of lead scoring: how interested are you in your leads? By comparing each lead’s demographic data to your ideal prospect profile (including industries, job titles, company size, and more), you can determine whether or not the lead will be a good fit for your product. This prevents sales reps from wasting their time on leads who ultimately have no intention of making a purchase.

Together At Last: Grade criteria are compared side-by-side with Score criteria.

So what do all of the different combinations of lead scores and grades mean? How should you handle follow-up with leads who have high grades, but low scores? Let’s take a look at a few rules of thumb that Ali Gooch, Senior Sales Manager at Pardot, and Isaac Payne, Marketing Operations Specialist, recommend for those implementing lead qualification models of their own:

Right Conversations at the Right Time: Example: Lead Assignment 1-4 Lists (Segmentation). Option 1: Great Fit, Great Intent. (Action: Assign right to sales?). Option: Get Fit, Minimal Intent. (Action: Assign right to sales? Nurture first, then assign?). Option 3: Not Great Fit, Great Itent. (Action: Other products?). Option 4: Bad Fit, Low Interest. (Action: Delete from database?)

As you can see in the image above, there are really four scenarios that you’ll need to have a strategy in place to handle.

  1. High score/High grade

These are your money prospects! Since they have a high score (meaning they’re definitely interested in your product) and a high grade (meaning they’re a good fit for your product), it’s best to prioritize these leads when assigning leads to sales.

  1. High score/Low grade

These are leads that are showing a lot of intent and/or interest, but may not be a good fit for your product. It’s possible that these leads may be a better fit for another product of yours (or they may be job seekers), but it’s likely not worth assigning them to sales for further follow-up.

  1. Low score/High grade

Here we have leads who are a great fit for your product, but who have been showing minimal interest. Either they’re not in a position to make a purchasing decision yet, or they’re just not aware of your product. These leads may take some additional work from your sales team, or you can place them on a targeted lead nurturing campaign that will drip them resources over time to help increase awareness. This will keep your company top of mind in the event that these leads do decide to further investigate your product.

  1. Low score/Low grade

As you can probably guess, these are your lowest-value leads. Often, if you have leads with low scores and low grades in your database, you may be hanging on to old leads or be overdue for some spring cleaning (it’s that time of year, anyway!). Consider deleting these leads from your database or using a database cleaning service to make sure they’re not impacting the quality of your lists.

If you’d like more information on putting together a lead qualification strategy that works for both your sales and marketing teams, take a look at Ali and Isaac’s full 30-minute webinar, the 3 Critical Components of an Optimized Lead Flow Plan.

The Complete Guide to Automated Lead Scoring & Grading. Get the Guide.

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3 thoughts on “4 Lead Scoring and Grading Scenarios Explained

    • Hi Jean Michel,

      Glad you liked the article! Yes, you can sort your prospects by their lead score and grade by viewing the prospect list and clicking on “score.” This will display all of your prospects in order from highest score to lowest. You can also view a “Pardot rating” in the Salesforce lead or contact record, which is displayed as a series of stars, similar to the image that you linked in your comment.

      Hope this helps!

  • As an addition, it might be worthwhile to spend one of your automations on aging logic to lower a prospect’s score after a certain period of inactivity. This will prevent cumulative scores resulting in exactly what Molly described. Typically, I lower scores over 20 to 20 after 15 days of inactivity, which is in the “warm” range of my 5-point scoring model.

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