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How to Calculate a Lead Score (With Examples)

 

Lead scoring is a key strategy for any business that wants to efficiently handle leads at scale.

There comes a point when your focus shifts from getting enough leads to figuring out what to do with them. If you spend too much time on the wrong leads, you’ll push the wrong ones through your sales funnel and miss out on more valuable potential customers

There are plenty of services that help you calculate lead score, and many teams have taken lead scoring to a new level through AI-powered conversational lead nurturing, but no matter what specific method or service you use, understanding how to do lead scoring and how to build lead scoring models on your own empowers you to implement a proper system that will boost your sales.

What is lead scoring and how does it work?

Lead scoring is the process of assessing lead quality through a quantitative system. While the models and data you use will differ based on your business, most lead scoring involves assigning points (or a numerical value) to each lead based on their behavior or data, giving them a lead rating. This lets you decide who your sales team should spend time on, and who might need more of a push through your marketing and sales funnel through lead nurturing.

In this piece, we will provide guidelines and processes for manually building a lead scoring model, and the criteria for calculating a simpler lead score.

Collect important lead data

To create a lead scoring model, your first step is to make sure you’re collecting enough valuable data. This all goes hand-in-hand with the next step, but it’s important to start with a healthy baseline of information about your contacts. If you’re not already tracking this information, you should be.

The data you should collect fall into two sets of overlapping categories: implicit and explicit data, and this data can be behavioral or demographic.

  • Explicit data is information that is factual and confirmed about your contact.
  • Implicit data is data that you infer based on the data you have.
  • Behavior data includes the actions your prospect takes online (e.g. whitepaper download) or in real life (e.g. brokers up to $50M in loans per year)
  • Demographic data includes information like title, industry, company size.

Pick the right data and create buyer personas

Your next step is determining what constitutes your ideal customer profile (ICP) based on the data you have. Essentially, you are setting up your lead scoring criteria for your model. You can create one or more buyer personas – these are hypothetical customers who are ideal in every possible way. This is who you are looking for, and you will eventually score leads based on how closely they match this ideal customer.

To come up with the ICP, you’ll need to not only dig into your analytics, but you’ll also need to talk to both your marketing and sales team. You want to find out what qualities and actions are most common/important in your current customers, and what qualities and actions contacts took prior to becoming customers.

For example, if your sales rep is saying “Half of my recent sales had watched our last two webinars,” then your lead scoring model may weigh webinar sign-ups much more highly than expected.

Once you have these insights, rooted in the data you’ve collected, you can start to work on your model.

Determine the lead score point structure for the model

There are various ways to calculate a lead score, but we will focus on building a manual lead scoring model. While this is the most time-intensive system, the elements of doing it manually are the basis for any lead scoring model that may use more complex calculations or lead scoring analysis software.

Manual scoring means you will assign positive or negative points based on the data you have on each lead. You can break these point assignments into four buckets: demographics, behavior, deductions, and relationship.

How to determine a score for each metric in your model

You want your lead score to be rooted in the data, which means it’s time to do some math. To see how a specific metric impacts the total close rate, you must first come up with the baseline close rate by dividing the total number of leads by the total that became sales.

Once you have this rate (let’s say 2%, for example), this is the number you’re looking to beat when comparing subsections of your leads based on the data you’ve collected. In other words, if you’re assigning positive points, this reflects an improvement on that 2% close rate, and the more points you assign, the higher the increase on the close rate.

Let’s say that you find that your close rate for leads who followed you on social media is 5%, and for those who attended a webinar it’s 15%. You could assign a point value accordingly (perhaps five and 15 points). Perhaps it turns out that any lead who hasn’t opened any of the last 10 newsletters has a close rate of .05% on average. You could assign them a negative point value.

These points are the basis of your lead scoring model. You can calculate these points based on increments or multiples of improvement from your baseline, or, if you have the resources, you can work with data scientists on your team to come up with a logistical regression analysis.

Not everything will work out perfectly in one-to-one multiples, and you may not always have enough data to know with full certainty how much a specific metric might affect the close rate. Thus, feel free to adjust your points system in a way that works best for you, and trust your team when figuring out what’s important and what’s not.

In the beginning stages, it’s best to start with a simple lead scoring model and work your way toward something more comprehensive. Don’t try to build Rome in a day.

Demographic lead scoring examples and criteria

Once you have your demographic data and have determined which pieces of data matter, it’s time to sort them into a hierarchy: critical, important, and influencing.

You’ll then assign more points the higher up the data point is. Here is a sample scoring framework:

 

Notice that the same property can appear in multiple levels of importance. For example, there are hundreds of titles that leads could have, so you need to find a way of breaking them into groups that you can score accordingly.

While we did not list any negative point assignments in this section, these are also important, and we discuss them in the deductions section.

Lead scoring template: behavior data

Just like with demographics, you can take your lead behavior and rank them in the critical, important, and influencing hierarchy.

Remember not to go haywire by assuming that any online engagement means a lead is sales-qualified. Consider all of the ways in which a specific behavior could be a red herring, and remember to rely on your sales and marketing teams when deciding what behaviors are most important.

Here is a sample behavior scoring sheet:

 

There are hundreds and hundreds of behaviors that may be worth tracking, so don’t be afraid to really dig in here. Just make sure that the behaviors you’re tracking each are telling you something new about the lead.

Deductions to your lead rating

Not all of your points will be positive. An unsubscribe, for instance, is not a positive indicator, but it is still valuable information. Similarly, an entry level job title (or title in the wrong department) wouldn’t be ideal for most companies. Here are some common deductions:

  • Unsubscribe, unfollow, or add to do-not-call list
  • Wrong job title
  • Wrong country/location
  • Company size not the right fit
  • Completely inappropriate estimated budget
  • Non-product website visit (eg, careers page)
  • Inactivity
  • Event no show
  • Not replying to one-on-one communications

Remember, these aren’t always dealbreakers. Make sure to include relevant deductions, but don’t assume that one deduction means the contact should be dropped.

One area that is most important for deductions is relationship, but this can be more nuanced than a simple negative point assignment, which is why we’ve given it its own section below.

Measuring relationship through rules-based lead scoring

The relationship with the contact is extremely important, and often overlooked. Imagine someone downloads three whitepapers, attends two webinars, and interacts with over 80% of newsletter blasts. They may seem like an ideal lead based on these behaviors, but actually be a student, researcher, or even blogger, and would then certainly not be worth moving through the sales pipeline.

Depending on your business, the types of relationships that matter will differ, but are some main relationships to consider.

Unlike the point scores for the other buckets, relationships may be more complicated. Rather than assigning points, you could use relationships as a classifier to decide on your action plan. For example, previous customers might go through a different funnel, or even be paired with a more senior sales rep when they become sales qualified again.

Make sure that you aren’t just assigning a zero value and getting rid of students, bloggers, etc., just because they are unlikely to become customers. They may help spread the word about your company (for free) through research, talks/presentations, articles/blogs, or social media.

Create a sales and marketing action plan

Now that you have a robust lead scoring point framework, it’s time to put everything together and make an action plan. This is arguably the most important part of your lead scoring system, because even if you have the most accurate scores imaginable, you might be wasting leads if you aren’t taking the right actions with them.

The action plan is what you will actually do with the lead at each score (send to sales, nurturing, etc). Without this in place, your lead score is just a number with no effect on your sales or marketing efforts.

The main thing to consider is when leads should go to sales, and when they should receive more nurturing. You’ll want to use a combination of the behavior score and demographics score to determine how to move forward, as a high score in both represents both lead fit (similarity to your ICP) and lead intent (interest and ability to become a customer).

Make sure your demographics score is at or below 50% of the total score makeup—even if someone seems like a perfect lead based on demographic data, if they have no discernable interest in becoming a customer, they need more nurturing.

Additionally, it’s important to consider how you’re going to nurture your prospects into leads. If you have the capacity to determine not only how strong a lead’s score is, but also what they need to move further through your funnel, you have a winning strategy. Therefore, your funnel should try to push leads to increase their score, not stay stagnant.

This most comes into play with lead relationships. If you have this data, you may want to implement different action plans (marketing funnels) based on the relationship. Additionally, you don’t want to send irrelevant nurturing campaigns to people with incompatible roles at their respective companies. This is an advanced strategy, but worth considering as you develop your lead scoring action plan.

Iterate, expand, explore on your lead scoring model

A lead scoring strategy is not a “one and done” undertaking. Your model should be an adaptable machine that grows with your company. Run regular check-ins to make sure it’s as effective as possible, and consider ways you can improve on your lead scoring process.

One of the best ways to implement a lead scoring system is through technology, as clearly the more data you try to score, the harder this gets. That’s why many companies choose to use predictive lead scoring, which harnesses the power of machine learning to calculate these numbers for you. This lets you analyze copious amounts of data much faster than even your best data scientists could.

Many of these AI-powered lead scoring systems also help automate lead nurturing efforts. While these save heaps of time and the stress of calculating this all yourself, it’s important to understand how manual lead scoring works, particularly as you decide what data to track and feed to the predictive lead scoring service, and how to best integrate them as part of your workflow.

A winning lead scoring model: the big picture

Ultimately, successful implementation of lead scoring can improve your campaigns and marketing efforts and maximize the efficiency of your funnel. Plus, it aligns your marketing and sales teams by turning them into a cohesive machine that is more than the sum of its parts.

While the specific formula and methodology may differ based on your company and ideal customer profile, lead scoring is a key element to any scalable sales and marketing strategy.

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