This is Why Your Lead Scoring Sucks

No, seriously, it does.

Over the past two years I’ve had the privilege of helping more than twenty companies build or transform their marketing operations. Yet even at the best of them, I see the same recurring mistake with their lead scoring.

I’m not going to cover the basics of lead scoring in this short blog post. There are excellent resources out there if you want to learn the ABCs of lead scoring — I would start by checking out the great resources at Eloqua, Marketo, HubSpot, and Pardot.

What I do want to focus on in this post is the actual table you use to represent the blueprint of your scoring system, which will later be implemented in your marketing automation system.

Here’s part of a typical scoring table I’ve seen in endless variations:

Typical Scoring Table

Can you identify the problem in this table?

It’s a mess! But to fully understand why, we need to go back to basics just for a couple minutes and understand the difference between prospect fit and interest in our product.

Fit — is this prospect a good fit for your product? To determine fit, we’ll usually use demographic data like this:

• Job Title
• Country
• Company Size
• Industry
• Technologies in Use

What this means is that a prospect who doesn’t meet the minimum required qualification criteria for fit is someone we have no reason to be talking to.

Interest — has this prospect been showing interest in your product? To determine interest, we’ll usually use behavioral data like this:

• Web Page Views
• Forms Completed
• Visited Pricing Web Page
• eBooks Downloaded
• Time Spent on Site

Measuring interest in our product is important to help Sales prioritize their prospects, starting with those who showed more interest than others in our product.

Now that we’ve established the basic concepts of fit and interest, here’s what I don’t get about the table above (feel free to scroll back up for reference):
Why does having the correct job title (+15) earn the prospect the same score as viewing three random web pages (+5 x 3)?

In other words, if you’re looking to sell to an IT Director, would you settle for an Administrative Assistant who’s been an avid reader of your web site?

And here’s another huge miss in the typical scoring table:
If someone actually completed your Contact Us form (+10), why on earth would you assign her the same score as someone who visited two random pages on your web site (+5 x 2)?

Can you see where this is going?

To accurately prioritize your leads for Sales and be able to justify that prioritization, you must separate your qualification criteria and scoring for fit from those you use to designate interest. In addition, a prospect who meets your criteria for fit and completes a Contact Us form should get pushed to the top of your Sales call list, regardless of how many web pages or eBooks she’s read, even if this is her first visit to your site.

Here’s What Your Lead Scoring Should Look Like

To quickly recap what the flowchart above — hopefully — clearly depicts:

1. After the lead is created, start by determining its fit for your product.
2. If it’s not a good fit, archive it for future use in black-lists or suppress-lists.
3. If it’s a good fit, wait until the lead takes action.
4. If that action is completing a Contact Us form, send the lead to Sales.
5. If that action is anything else, score the lead on behavior.
6. Once the behavior score meets the Sales threshold, indicating enough interest in your product, send the lead to Sales.
7. Until that threshold is met, keep scoring and waiting for the lead to take action.

Is This How You’ve Been Scoring Your Leads?

If not, I suggest you at least consider this approach, which clearly differentiates between the lead’s fit to your product and her interest in it.

You cannot substitute one for the other and the sooner you understand this, the sooner you can start supplying Sales with higher-quality leads, who are not only a great fit for your product, but are also showing active interest in it.

I realize there are other great approaches to lead scoring. Let me know in the comments below if you have an alternative approach we can all learn from.

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