By Lisa Heay, Marketing Planning Manager at Heinz Marketing
Lead scoring is often made out to be a behemoth endeavor, but it really doesn’t have to be. If you’ve considered implementing a lead scoring process at your organization but you’re feeling a little overwhelmed by the project, keep reading. There are ways to start out with a simple process that you can expand on later. It’s never going to be a set-and-forget type of project anyway.
First, you might be wondering why lead scoring is important. Lead scores serve as the agreed-upon guideline between the sales and marketing organizations that determine lead fit and the subsequent follow up actions that should take place.
With that in mind, a mistake some organizations make is when lead scoring is solely managed by marketing, without input from sales. It has to be a joint effort—sales should not leave all of the lead scoring work to Marketing. (And Marketing should not do this in a silo.)
This approach helps both organizations measure the overall lead flow and quality, which ultimately impacts revenue. Having a common definition of lead qualification via scores helps the sales organization manage follow up processes and enable marketing to provide resources to support follow up and nurturing programs. This helps keep the flow of qualified leads consistent and steady.
Before you begin scoring leads, you need to identify who your ideal customer is. Sales participation is crucial in this step in defining what a qualified lead is for them. Once both organizations mutually agree on how they will manage and process leads based on score thresholds, you can move on to choosing your scoring model.
You’ve probably seen different lead scoring models out there. I tend to lean towards keeping things simple with an integer-based score that is a combination of demographic and behavior scores. It’s a common model, and marketing automation platforms can be set up to assign scoring values to leads based on elements of their profile and behaviors, which are then combined into a single lead score.
This is where your ideal customer profile definition comes in to play. If your ideal prospects are marketing leaders in mid-sized technology companies located in California, then your demographic elements should include title, company size, industry, and state. Other common factors could be annual revenue, technologies used, job role, etc..
These elements are things generally gathered via a form on your website or list input from prospecting efforts, events, etc..
Behavior scoring tracks engagement with marketing assets and activities. This engagement can include actions like interaction with emails via opens or clicks, asset downloads, form fill-outs, or webpage visits.
To initially decide which interactions to score, audit a few of your closed/won deals. Are you able to look through their CRM or marketing automation records to find out what kind of activities they engaged in throughout their buying journey? Is there a common piece of content that buying committee members have interacted with? Is there an event you sponsor each year that tends to generate great prospect conversations?
Your lead scoring model should not only include positive point values, but negative ones as well. It’s important to subtract points if a lead isn’t a good fit. Maybe they are employees at a competing company, or they are in a country your solution doesn’t easily serve. Maybe their job title is student, or they’ve visited your career or unsubscribe pages. Maybe their email address is firstname.lastname@example.org. Think about the factors that could indicate a prospect is not qualified to speak with sales and deduct points accordingly.
Now that you have your scoring categories in place, you can determine the point values associated with each one. Bucket your scoring categories into high-value, mid-value, and low-value tiers. You could even do this within categories. For example, you could have high-value and low-value asset downloads and assign different point values accordingly.
You want to score so one or two actions or demographic factors alone won’t push someone over the threshold to become an MQL. For example, if your MQL threshold is 50 points, your highest score value should not be more than 25 points. Marketo and HubSpot both have a lot of great resources (regardless if you use their platforms or not).
Keep in mind the scoring threshold (and score weighting) is not a one-and-done decision. Until you have hard data behind your scoring plan, you’re going to be guessing about what factors indicate a high-quality, interested leads. You are going to get it wrong, and that’s OK. Plan on 2-3 months for your implementation to start showing measurable results, and then adjust your scoring model accordingly.
And then? Adjust it again, and again, and again. Buying cycles shift and your products and solutions will change. Your content and campaign strategies change. And your lead scoring will need to ebb and flow forever alongside it.
Marketing Qualified Leads
Leads that reach your predetermined scoring threshold can be considered Marketing Qualified Leads and processed into the sales queue for follow up.
Before you get started, marketing and sales need to analyze their current lead queues, so you have an apples-to-apples comparison before and after implementation. How many MQLs are there? How many sales accepted leads are there? What is your average time to close? These factors will all assist you in analyzing the effectiveness of your lead scoring program and determine if your new MQLs are actually qualified and ready for sales. If not, what factor is commonly pushing them over the MQL threshold?
You may need to adjust an individual scoring value or the overall threshold that moves a lead to MQL (to 75 or 100 points, for example).
The collaboration between marketing and sales isn’t just at the beginning of the process. There needs to be a constant conversation to understand what is working and what isn’t. Iterate often to ensure your sales team is getting the qualified leads they need.
When you’ve nailed the foundational elements and are rocking and rolling – then you can consider adding complexities. Check out predictive scoring models and time-based scoring, for example. How long is someone reading a piece of content on your site? How much of the webinar or video did they watch?
What are your lead scoring must-know tips and tricks? Let us know in the comments!