Your marketing team has been hard at work creating open-worthy emails and like-inducing social posts. Now, your business is being inundated with inbound leads—more leads than your sales team can handle. You need a way to determine which leads to pursue and which leads should be left in the slow cooker of your sales funnel.
The answer to your problem is lead scoring. If you have no idea how lead scoring works, congratulations! You’ve come to the right place.
Lead scoring is the process of assigning a lead a numerical value based on how likely they are to convert to a customer. In essence, you’re ranking your leads so that you can put them into different buckets so your sales team knows which leads to focus on first. A well-designed lead scoring system can also help your marketing team ensure they’re not just generating a buch o’ leads, and instead, they’re spending their energy creating campaigns that generate highly qualified leads.
Traditional lead scoring is a manual scoring model. Traditional scoring is human-powered. It relies on people (usually members of the sales or marketing teams) to assess which criteria, like demographics, company information, or behavioral data, are most closely associated with a lead converting to a customer. They then determine numerical values to give each of the criteria and score each individual lead.
The pro of traditional lead scoring is that it allows room for nuance and judgment calls. The con there is that people can bring bias. (Plus, it’s labor intensive.)
Predictive lead scoring uses algorithms, machine learning, and AI to expand and enhance traditional lead scoring. Unlike traditional lead scoring, predictive scoring is a highly automated process. Lead scoring software incorporates a broader range of data points, and the scoring models are refined over time as new patterns emerge. This allows businesses to develop more accurate lead data and find patterns that might have been missed due to human bias.
It’s worth noting that while automated lead scoring can be more objective than traditional scoring, it’s not entirely without bias. Algorithms and scoring criteria are prone to the same bias as the people who create the frameworks. So it’s important to work in diverse, collaborative teams when setting scoring criteria to reduce the chances of introducing bias.
The term “lead scoring model” is just a fancy way of saying, “These are the factors and framework we’re using to rate inbound leads.” Lead scoring models include explicit and implicit scoring factors. Explicit scoring refers to anything a potential customer has directly told you, whereas implicit lead scoring refers to behavioral and inferred attributes. Let’s walk through some of the most common frameworks for scoring leads. Your final lead scoring model will work with some combination of these three categories.
This lead scoring model uses your customer data to evaluate demographic data and how closely it aligns with your ideal customer profile (ICP). You might use demographics to determine lead quality by adding points for a data point that matches your ideal customer and subtracting points (aka negative scoring) for a data point that falls outside the types of customers that tend to convert. Some data points, like location and education level, may be helpful criteria for both B2C and B2B businesses, whereas other demographic data points may vary. Age, gender, and personal income will be more indicative of lead quality for B2C brands. B2B brands may want to rely more on company information as a lead scoring model.
Company information is vital for B2B lead scoring because it’s the characteristics of a business or organization that determines lead quality rather than the individual person who may be engaging with your marketing. Firmographic data points can include industry, company size, revenue, growth rate, number of locations, tech stack, or job title (who needs to make the call in order for a sale to close).
How a lead interacts with your website, emails, and other digital marketing does a lot to tell you if that person is a high-quality lead. In order for this lead scoring model to work, you need to have quality data about how your users interact with your website, emails, social media, etc. Different activities might have different ranking values—a whitepaper download might be more meaningful than an email open. The number of times a prospect takes an action matters, too. If a lead has looked at your pricing page 80 times in the past week, that’s an indication they’re a hot lead.
Email is one of the channels where you have opportunity to nurture leads at different points in the sales funnel. When a prospect completes an email sign-up form, you know they’ve opted to receive marketing emails from you, but you may not know anything else about how high-quality the lead is. How they interact with your emails—how often they open, how often they click through, which email types they most often interact with—can give your sales team actionable info about who may be ready to convert.
Because a solid email marketing software will give you the power to segment your lists, you can also tailor your messaging depending on lead quality. Creating email content for prospects who are at the top of the sales funnel allows you to keep them in the lead warmer, so to speak, increasing the likelihood that they’ll become a hot lead that the sales team can then close.
Okay, but how do you take all that and turn it into a winning lead scoring strategy? In addition to determining your lead scoring criteria and choosing your lead scoring software, these tips will help you create a lead management approach that will lead to sales and marketing sending each other celebratory memes and emojis.
For your lead scoring model to work well, marketing and sales need to be in agreement about what makes for a qualified vs. an unqualified lead. Getting both teams on the sam page ensures that your lead scoring is selective enough that sales can focus their energy on the most qualified leads without being so narrow that you’re letting potential leads fall through the cracks. Both teams should work together to set the criteria and the scoring threshold. If one team makes adjustments to the scoring criteria, those changes need to be clearly communicated. Avoiding confusion not only saves you time, it’s likely to increase your revenue.
The lead scoring threshold is the numerical value that determines lead quality. Qualified leads, commonly referred to as marketing qualified leads (MQLs) or sales qualified leads (SQLs), are considered sales-ready and likely to convert. These leads are often passed to the sales team to close.
Your lead scoring threshold is important for two specific reasons. First, it’s the goldilocks number that ensures your designation of what makes a qualified lead is neither too broad nor too narrow. It’s just right. Second, the ideal lead scoring threshold will give you insight into the effectiveness of your marketing efforts, helping the marketing team to focus on the campaigns, channels, and initiatives that are generating the most qualified leads.
Determining the appropriate lead scoring threshold is a process of trial and error. Begin by looking at historical sales and customer data. Predictive scoring can do a lot of heavy lifting here to help you look at large swaths of data to find patterns and determine what qualities a qualified lead is most likely to have.
At the beginning of your lead scoring development process, you want to calculate your lead conversion rate, the ratio measuring how well a business converts leads into customers. Set this as your benchmark. Then, carve out time to regularly review your lead scoring criterion to evaluate how effectively it’s predicting lead qualification.
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