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Lead Scoring: Definition, Importance, and Methods for Establishing an Effective Model

Susanne Morris

Susanne Morris

November 18, 2020

Lead scoring is a methodology used to determine how likely, from 1-100, a lead is to buy. Lead scoring is an essential business process that involves collaboration between marketing and sales teams.

Most importantly, you can implement lead scoring to assist the alignment of sales and marketing teams. This process helps both teams prioritize and organize leads. It also boosts the rate at which leads become won customers.

For effective lead scoring efforts, you can assign point values to a lead for every action that they make. Of course, the lead scoring system varies between businesses; therefore, there isn't one fit-for-all model that you can use. To one business, opening the website a couple of times might not mean much, whereas to you it could make a significant difference. Identify the most important actions, assign point values to them, and approach prospects with a higher lead score.

Keep in mind, that effective marketing campaigns lie in personalization. And personalization can be achieved by lead scoring. You can distinguish between a person who is entering your website for the first time, and a potential lead who had already expressed their interest in your product. Bothering a newcomer with sales pitches will most likely scare them off, resulting in a potentially lost client. On the other hand, having a sales rep contact a person who downloaded a sample can produce a beneficial connection for both of you.

However, there are many other benefits to establishing a well-rounded lead scoring model.

This article will explore what lead scoring is, why it’s important, and how to set up a model that works best for your company.

Additionally, this article will explain predictive lead scoring and its impact on acquisition and sales.

How does lead scoring improve sales?

Lead scoring is a point-based lead management system that analyzes sales leads based on implicit and explicit customer criteria such as job title, age, etc. Furthermore, lead scoring standardizes qualified leads within your company’s customer relationship management system (CRM), tracking leads as they go through the traditional marketing funnel and convert into a customer.

How do you score leads?

While inbound marketers understand the importance of potential customers’ data points, such as age, job title, social media, etc., many have yet to create a lead management system. A common scoring technique combines a company’s typical CRM data with its established inbound marketing cycle strategy, creating an optimized version of a traditional marketing funnel.

By scoring leads with a variety of implicit and explicit criteria, which will be explained in more detail later on, the marketing and sales teams can work together to establish which leads are sales-ready and which leads should re-enter the nurture stage of the marketing funnel.

For instance, sales teams can offer marketers input on which lead criteria (discussed in more detail later) tend to win customers. Marketing teams may also find value in consulting sales members about which marketing content yields the best sales results.

A lead score is a value, typically 0-100, consisting of a combination of pre-valued interactions and demographics. This number lets marketers and sales teams know which leads are more likely to become qualified leads and won customers.

Establish buyer personas

Buyer Personas or ICPs (Ideal Customer Profiles) are useful for many inbound marketing strategies. Concerning lead scoring, creating in-depth buyer personas will paint a clear picture of the characteristics that make up your ideal customer. These semi-fictional profiles typically consist of existing customer data coupled with general market and industry observations.

Assign values to lead criteria

Once you’ve established your ideal customer, you must decide what characteristics and/or actions will make up your lead scoring criteria.

Typically scoring is divided into two categories: implicit and explicit.

Implicit scoring refers to criteria based on actions and behaviors, while explicit scoring is based on demographics.

Some examples of implicit versus explicit scoring are listed below.


  • Email interactions (opens, subscriptions, replies)
  • Web page visits
  • Social media interactions
  • Content downloads


  • Age
  • Job title
  • Company size
  • Industry
  • Location

Once you’ve done that, you can assign points to each criterion. The value assigned to each data point you've chosen to score should vary from case to case. By consulting your sales team or speaking to current customers, you may notice that most won leads have a particular job title.

In this case, you would assign higher point values to said criterion.

It is important to note that you should subtract points for certain actions or inactions. This is known as negative scoring. Your lead scoring model must adjust for actions that inflate scores to maintain fresh and accurate scores for each lead.

For example, if an individual or business repeatedly visits your site or downloads free assets you’ve created, they may be a job seeker, student, or even competition. Comparing their interactions with their demographics and other factors will clarify the lead's legitimacy and keep you from wasting valuable time on a false lead.

Why is lead scoring important?

Lead scoring increases revenue cycles, increases return on investment (ROI), and optimizes marketing and sales alignment.

Increased revenue cycles:

A well-designed lead scoring system may decrease the time a lead stays in the nurturing stage and help identify early-stage sales qualified leads that may move faster down the funnel than others.

ROI growth:

While at first glance, lead scoring seems like a redundant process within the marketing cycle, according to Hubspot, a lack of lead nurturing contributes to the 79% of marketing leads that never convert into sales. Because lead scoring is a direct indicator of a lead’s phase in the marketing cycle, establishing a proven lead scoring model will translate to an increase in ROI.

Marketing and sales alignment:

Zoom Info reported that 61% of marketers send all leads to sales, while only 27% are qualified. This disconnection between marketing teams and sales departments will create a strain over time and confuse certain leads.

Establishing a lead scoring system will increase both departments' productivity, define lead stages, and keep leads from accidentally getting ignored.

According to Hubspot, a lack of lead nurturing contributes to the 79% of marketing leads that never convert into sales.

5 best practices for a successful lead scoring model

1. Create multiple lead scoring models

Does your company offer more than one product or service?

If so, you should consider creating multiple lead scoring models.

This is directly related to understanding buyer personas and how harnessing them can strengthen your sales cycle. For instance, a general best practice for establishing buyer personas is creating different ICPs for each product or service offered.

And because lead scoring models are structured around your buyer personas, it’s understood you will also want different lead scoring models for each product and/or service your company offers.

2. Utilize negative scoring

As mentioned previously, subtracting points for certain actions or inactions will enhance your lead scoring model.

This will help balance out your scores and account for any inflation within your model. Some actions/inactions that should result in negative scoring include: unsubscribing from emails, negative social media interactions, visiting your jobs page, job title (for example, students may be downloading resources for academic reasons and are not looking to buy).

3. Determine your sales qualified threshold value

Now that you have established a point system, you will need to determine the “magic” number that separates a nurture-stage lead from a sales-qualified lead.

Because every company’s lead scoring model is different, this threshold won’t be the same for every business.

This stage of the lead scoring process requires trial and error but can be expedited by getting feedback from your sales team and current customers.

4. Set up marketing automation tools

According to Gartner Research, companies that automate lead management see a 10% increase in revenue. Lead scoring is time-consuming, and going through your CRM lead by lead will only detract from your sales goals.

The good news is most CRMs have lead scoring automation features built into them. As a lead interacts with your company, the automation tool will update their respected score.

Please note that automation does not necessarily mean accuracy.

It is important that you regularly and randomly check leads and follow up with your sales team to ensure the automation’s accuracy.

5. Create maintenance schedules

Similarly to regularly checking in on your automation tools, it is also important to regularly update your lead scoring process. Some things that may indicate that an update is needed are market changes, product or service changes, increased landing page visits, and new sales members, to name a few.

What is predictive lead scoring?

Thanks to recent advancements in artificial intelligence (AI), predictive lead scoring has arrived as a great addition to a well-established lead scoring system.

Predictive lead scoring analyzes your customers' behaviors and predicts sales by applying AI and big data to the current lead scoring model.

Traditional lead scoring utilizes marketers and sales experts, who make experience-based decisions on which leads to focus on. On the other hand, AI analyzes customer data from a quantitative perspective and calculates which leads need further nurturing and which leads are sales qualified.

More specifically, predictive lead scoring generates metrics for existing customers' perceived value compared to prospective customers' behaviors and demographics. From this comparison, machine learning algorithms create a larger picture of who within your target audience is more likely to convert to a sale or who is more likely to require extensive nurturing.

Over time, AI finds more and more commonalities between won sales and current customers, improving as it goes. Additionally, predictive lead scoring increases ROI by optimizing the workflow between acquisition and sales.

It is important to note that because predictive lead scoring relies on current customers’ data, updating your existing customers' profile in your CRM will improve your predictive lead scoring model.

Ultimately, harnessing predictive lead scoring will improve your ROI, sales and marketing alignment, as well as the potential for increased lead generation.

Some things to remember

Lead scoring is not a stand-alone marketing process. Rather, it is a methodology to be used in addition to the marketing cycle your company has established. With the advancements of AI, predictive scoring is a great supplemental tool for your sales methodology.

Scoring is meant to give attention to all leads, hot and cold. While you may want to focus on hot leads, it is important to remember that some leads may take longer in the nurturing stage, but this doesn’t mean they won’t convert into won sales.

This is not a one-size-fits-all approach. Some models may work better for other businesses. It is important to create a trial stage to evaluate what lead scoring method will work best for your company.

Final thoughts

Not only does lead scoring have the ability to generate a significant increase in ROI for your company, but it also will help unite your sales and marketing teams. In the same way, inbound marketing is a collaborative effort, so is lead scoring.

With a lead score in place, a sales team can evaluate how likely that lead is to buy and direct their efforts towards the more promising potential prospects.

In general, lead scores allow for better marketing efforts and a more fluent sales process. Sales and marketing departments can convert more leads and identify unqualified leads by implementing a lead scoring strategy.

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