When paired with a Marketing Automation platform and CRM, a healthy lead scoring model is a thing of true beauty. Marketing empowers Sales to contact Leads who actually want to hear from them. The teams are united, working hand in hand toward common goals!
But what happens when lead scoring is broken? In short, frustration for everyone. Sales doesn’t receive quality leads. Prospects who are either not the right fit or not interested are contacted. Marketing is inundated with complaints that the leads are “bad.” Goals across several metrics remain unmet and tension forms between the two teams.
At DemandGen, we’ve worked with dozens of Marketing and Sales teams to optimize their lead scoring process. Over time, we’ve identified four common challenges that can derail many a lead scoring model:
- The model is designed to leverage data that does not exist
You’ve likely included demographics such as industry, company size, and job role as part of your Ideal Customer Profile in your scoring model. If your data set doesn’t have these fields consistently populated, however, the performance of your lead scoring model will be greatly diminished.
You’d be surprised how many thoughtfully designed scoring models lack the data strategy needed to drive success. I recently worked with a client whose lead scoring model relied on fields that were less than 25 percent populated. As a result, insufficient data is one of the first things we look for in a lead scoring optimization project.
- The model is heavily weighted toward one dimension
An ideal lead scoring model uses both demographic data (customer fit criteria) and engagement activity (interest criteria) to determine when a lead is ready to contact. These dimensions don’t have to be a 50/50 balance, but they also shouldn’t be 10/90. If your model design is heavily lopsided, you’re likely generating Marketing Qualified Leads that either aren’t the right fit or aren’t truly engaged.
I recently worked with a client whose design leaned toward the demographic profile. That in and of itself isn’t necessarily a red flag. The issue was that their model was built to score up based on whether the field was populated instead of the actual content of that data. This meant that no matter what was entered into the demographic fields, the same scores would be applied. So, job title entries of Intern and Revenue of 2 Million were treated the same as VP of Procurement and 200 Million. As a result, records that may in no way be a good customer fit were being passed to Sales. The demographic dimension of your model should always align with your Ideal Customer Profile.
- Too much value given to too few attributes
Higher-value activities and attributes deserve more weight in the lead scoring model. The challenge is when just a few of these combined can push a record to Marketing Qualified status, yet logically the sum of these alone does not warrant Sales follow-up. That high-value website visit and webinar registration are fantastic, but on their own may not warrant MQL status. It’s important to be sure your higher-point value items function healthily in the context of the rest of the scoring model. This is something you would watch for in testing during the original design and during any optimization effort.
- The model is overly complicated
You know your lead scoring model is too complex when 1) nobody on the team understands how it works anymore, or 2) you’re unable to determine what is driving its success or failure. This normally happens when teams try to solve for every possible edge-case scenario or include obscure data points. Highly complicated models can be very difficult to measure and to maintain, and require even greater data discipline. One size does not fit all, but hedging on the side of simplicity is usually the best path.
Don’t sabotage a solid lead scoring model
Even if you have an otherwise great model, any one of these four pitfalls can derail your best efforts. These are the four most common challenges I’ve encountered when reviewing and optimizing our clients’ lead scoring models. I’d love to hear if you’ve encountered anything that prevented your otherwise spotless lead scoring model from reaching its full potential!
Greg Huckabee is a Client Engagement Manager at DemandGen, where he advocates for client solutions and resources. He loves helping marketers uncover hidden opportunities and realize the greatest possible value from their marketing automation tools.