If I asked 10 people what lead scoring is, I’d probably get 10 different definitions. I’d define it as applying a numeric measure of how likely a prospect is to buy from you based on their past behaviors.
Some people think this is for marketing, while others think a lead score is for sales. It’s actually both. Marketing has a specific use for the number and sales has a different use for the number.
Regardless, you need to be scoring all your leads if you have any hope of determining a uniform way to evaluate the quality of the leads you’re generating and helping sales prioritize their time to focus on the best potential leads first.
To clarify the definition, I took a look at some expert definitions, and you can see I’m pretty close. These all went into some degree of detail to help explain it.
Salesforce defines lead scoring as “an effective model that helps sales and marketing departments identify which prospects are potentially most valuable to the company and its current sales funnel.”
HubSpot defines lead scoring as “the process of assigning values, often in the form of numerical 'points,' to each lead you generate for the business. You can score your leads based on multiple attributes, including the professional information they’ve submitted to you and how they’ve engaged with your website and brand across the internet. This process helps sales and marketing teams prioritize leads, respond to them appropriately and increase the rate at which those leads become customers.”
Finally, ZoomInfo defines lead scoring as “the process of ranking the sales readiness of a lead using a predetermined methodology. The process goes like this: You determine which criteria or data points indicate a sales-qualified lead and then assign point values to each of those criteria, ultimately leaving you with a final score for each lead.”
This might not be as hard as you think, although lead scoring should be different for every company. I’ll share some best practices, but every company should have its own unique lead-scoring model or system.
There are simple ways and more complex ways. Let’s start simple and we’ll add complexity as we go.
First, start with demographic information. This is information collected when prospects fill out forms or when reps first talk to prospects. Your chat feature on the website could also be used to collect this type of information.
What is the demographic profile of your best customers? For instance, do you want to do business with big firms or smaller firms? Do you have a geography you want to focus on? Are there industries that you do better with? Are there titles within the company that you want to be talking to?
Take this information and score each of the answers to the above questions in a decreasing manner. The further you get from your best prospect demographic, the lower the score.
Give positive points for people who answer these questions in a way that aligns with your best prospect profile and take points away for people who are not aligned with your best prospect profile.
For example, if you want to sell to B2B manufacturing companies in the Midwest and you want to be talking to the COO, then people who match that profile would score points for B2B, industry, location and title engaged. Someone who matches this profile would score a lot of positive points.
If you were looking at applying the same model to a B2C company in the legal industry from Vermont, and the administrative assistant is on your website, they might score negative points.
For every item in your profile, points would be added or taken away based on their information. You could stop here, but we suggest you also consider using online or onsite behavior to add to your lead-scoring model.
People visiting the site could earn points for return visits, visiting important pages like your pricing page, using the chat function, downloading educational content or watching a video.
They could earn additional points for more pages viewed or more items downloaded. Again, try to keep this simple, but onsite behavior can be a major indicator of intent to buy and the level of pain associated with their situation. Both are solid indicators of future sales.
Another suggestion is to use email engagement as another way to add points. If people are opening your email and clicking on the links, they might be more engaged and more interested than those who are ignoring your emails.
You can also add points for people who engage with you on social channels. If they are taking the time to view your LinkedIn, Instagram or Twitter pages, they might be more interested in getting to know your company and therefore a better prospect.
Finally, consider taking points away for people who use personal emails like Gmail or Yahoo accounts. All these online behaviors can be scored and collated to give reps a solid picture of how engaged a prospect is and how likely they are to move ahead.
This is a great question because it’s not enough just to have the score – you have to do something with it.
If you’re in sales, you use a lead score to prioritize who you follow up with and when. With limited time in the day, sales reps should be following up on the best leads and the best opportunities first, then working their way down to prospects less qualified or less likely to close.
You don’t want to ignore anyone, but you need a prioritization methodology, and a lead score is a great way to do it.
When you think about it, the lead score aligns the sales reps with the prospects that want and need the attention first. If someone has been on your website four times a day for the past four days, you should call them first.
The person who just visited for the first time and only spent 10 seconds on one page obviously isn’t behaving as if this is a top priority. Getting to them later in the day makes sense for everyone.
Taking this a bit further, you could design very specific follow-up tracks for sales based on lead scores. Prospects with high scores could get invited to private webinars or may warrant an executive joining the sales effort.
Alternatively, prospects with low scores might remain in an automated lead nurture and not even warrant sales rep attention until they cross a numeric threshold. This keeps your reps focused on the best prospects and keeps automation working on the weaker opportunities until they become more viable, based on their score.
Marketers should be using the lead-scoring data to make sure they’re generating high-quality leads. If they continue to create leads with low lead scores, they need to consider who they’re going after and what they’re saying to those people in their marketing campaigns.
Average lead score is one of the ways to track how good the marketing team is at delivering high-quality leads to the sales team.
This is where lead scoring can get a little tricky. You do need marketing automation and/or a CRM system to install lead scoring.
Take the mathematical calculation you came up with and use the lead-scoring tools inside your marketing automation to install your model.
Generally, this just involves identifying the behavior of data and then telling the system how many points to apply or how many points to deduct as it relates to the data.
For example, a visit to the pricing page equals five points, while a Gmail email address submitted equals -10 points.
Many of these systems will walk you through the build, but having the model makes this easier. Don’t start building until you have the criteria and scoring worked out.
Once the model is built, I’d consider doing some testing. Run your model, get some scores and share those scores with sales. Ask them to validate the opportunities. Are these your best prospects? If not, you might have to make some adjustments to the mathematical model.
Then run the model for 30 days. After you have 30 days of data, sit down with sales again and review the performance of the model.
How many of the top-scoring prospects closed? How much did they close for? How long did they take to close? How does this information compare to the general sales data?
Remember, the scoring model should be outperforming general data. If it takes you 30 days to close a new customer and you have a close rate of 30%, then the top-performing, highest scored leads should be closing faster and at a higher rate. If that’s not happening, you need to adjust the model more.
I’d consider planning on optimizing and adjusting your model every 30 days for the first six months at a minimum.
If this is the first time you’ve done lead scoring, it might be a good idea to get help. No, I don’t mean Googling lead scoring and reading a few articles. I mean adding expertise to your current team.
Leveraging people who have built lead-scoring models, talking to people who have rolled out lead-scoring models and working with people who have installed lead scoring and realized improvements in the sales and marketing teams would help you get to your goals faster.
You might want to engage with them to provide guidance or oversight. You may also want to engage with them to help build your model, install your model into your CRM or marketing automation, work with you to optimize the model and get this working for your company in a fraction of the time it might take you or your team to figure it out.
You’d get the benefit of their experience, and your company would get the value of a lead-scoring system in a much shorter time frame. Sometimes this makes the most sense.