Guide to Grading (Part 2): Building Your Grading Criteria Matrix


Prospect Grading is a Pardot feature that represents how closely a prospect fits your ideal customer profile. Each prospect is assigned a letter (A-F) that is calculated by matching their data with your defined criteria. Essential for smarter lead qualification, grading can have a big impact across the organisation when setup and leveraged properly. This four-part series will walk you through how to set up Pardot grading gradually, so you can navigate the different moving parts involved in grading.

At the end of Part 1, I left you with a task to begin thinking about your model buyer. What pieces of information are used to qualify a sales prospect? Now it’s time to start mapping your grading framework.

Choosing Your Criteria

I recommend having a conversation with the sales team to garner insight on the criteria they use to qualify leads in each stage of the sales cycle. You may think you know who you’re targeting, but cross-reference with the sales team’s reality.

Be warned, some organisations have more ‘black-and-white’ criteria, and therefore easier to fit into a grading framework. If some qualification criteria are more ‘gut feel’, then it’s unlikely you can use data captured in prospect fields to determine someone’s chance of progressing further in the sales cycle – especially in high-touch, high-value products or services sales cycles. Focus on the criteria at the top of the funnel if you become overwhelmed, choosing fields that are dropdown, checkbox or number fields.
Some popular examples in B2B Marketing are:

  • Annual revenue
  • Location: country or city
  • Industry
  • Job title, department or seniority
  • Is part of a target account

Create the Criteria Matrix – Simple vs. Complex

Next, we will place our values into a matrix – a table that organises our criteria, the possible values, and the extent those values match our ideal buyer. I will provide you with two matrixes; a simpler yes/no match and the other with multiple levels of ‘match’.

 

Sense Checking

Once you have your values placed into the matrix columns, you need to check the weightings of each value make sense, that is, will produce sensible results when put into action. The main purpose is to check what Pardot would consider an A Grade prospect lines up with your perception.

Take a blank spreadsheet, and paste one value for each criteria on to separate rows. Leave the first row blank.

On the first row, in the next column, put ‘D’. D is the default grade that all prospects begin on before any value matching happens.

In the next row down, put the grade for the relevant match strength:

  • Strong match = C
  • Good match = C-
  • Weak match = D+
  • No match = D

This is perhaps the easiest way to demonstrate how grade increases work. Grades are not restricted to only A, B, C, D, F, but have + and – versions too, eg. A+, A, A-. As you can see, a grade can jump one-third of a letter (1/3), two-thirds (2/3), or by a whole letter (1).

 

Over to you for the next criteria, so get your brain into action!
What is the match strength of your next value, and by how much should the grade increase? Some examples below for guidance:

  • 1st criteria value was a strong match (—> C); 2nd criteria value is a weak match (C—> C+)
  • 1st criteria value was a weak match (—> D+); 2nd criteria value is a good match (D+—> C)
  • 1st criteria value was a good match (—> C-); 2nd criteria value is a no match (C-—> C- / no change)

Keep increasing the grade for the other criteria. Your sheet would look something like this:

That’s only 1 example individual. Repeat for other combinations of criteria, even using people from your Salesforce database as real examples (even better if they have converted into valuable customers or have been disqualified).

What’s Up Next?

Now you have a solid idea about who your model buyer is, and even have the framework mapped out and ready to implement into Pardot. In Part 3, we will start constructing the grading framework by creating grading profiles – to be announced soon.