Lead scoring assists 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. In this article, you will learn everything there is to know about lead scoring.
What is lead scoring?
Lead scoring is a methodology used to determine how likely, from 1-100, a lead is to buy. It's an essential business process that involves collaboration between marketing and sales teams. It also helps to prioritize leads.
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.
Lead scoring is a methodology used to determine how likely, from 1-100, a lead is to buy.
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.
Sales and marketing teams 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.
How does lead scoring work?
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.
Below, you will learn how to build a lead scoring matrix—a table of elements that are essential in defining and explaining the lead scoring process.
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.
If you're looking for data that can help you score leads, Coresignal has an offering of company data that includes firmographic and employee data.
You can find data points such as company name, size, industry, employee name, title, location, and more. With this data, you can see when your prospect company hires a new decision-maker and find the best time to approach them with a solution to their pain points.
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.
Examples of lead scores for different attributes
Lead scoring assigns numerical values to different behaviors and characteristics, helping businesses prioritize high-value leads. Every action a lead takes—such as visiting a webpage, engaging with an email, or requesting a demo—reflects their level of interest and intent to buy. Similarly, demographic and firmographic data, such as job title and company size, can indicate whether a lead fits the ideal customer profile.
A well-defined lead scoring system ensures that sales teams focus on leads most likely to convert, rather than spending time on prospects who are unlikely to engage. Below are examples of lead scores assigned to various attributes in a typical lead scoring model.
By using a structured lead scoring system, businesses can filter out low-value leads and direct their sales efforts toward those with the highest potential. The point system allows for automation and scalability, ensuring that teams make data-driven decisions to optimize their outreach strategies.
Lead scoring for B2B
There are 5 main best practices that help implement lead scoring in B2B:
- Unified customer profiles. The data about leads and customers can be collected from multiple sources: website inquiries (newsletter registrations, downloads, trial requests, inquiry forms), sales reps (calls, chats, meetings), customer support (live chat rooms, calls), and more. Unifying all the data promotes an effective lead scoring system that helps develop customer profiles to a further extent.
- Intelligent engagement scoring. If you assign scores when a user visits a page in your website multiple times, but doesn't show any signs of intent, the user is increasing its score without being a warmer lead. In this case, you might want to implement score degradation to prevent communication with unqualified leads.
- AI lead scoring setup. AI helps establish buyer personas that fit the ideal customer profile. It accounts for criteria such as seniority, job function, technological capacity of a lead, skills, and more factors. Then, you can prioritize leads and see what products respond to different leads' pain points.
- Different score values for different content types. A visit to a product page signals more intent than a visit to a blog post. Therefore, different content pieces should have different point value.
- Share the final lead score across other departments. Sharing the lead's score with both sales and marketing team helps align the two teams better. When everyone sees the full picture, they can make better-informed decisions and minimize missed opportunities.
Types of lead scoring models
Effective lead scoring models allow businesses to identify high-value prospects by analyzing different characteristics and behaviors. By using a structured approach, sales and marketing teams can prioritize leads more efficiently and increase conversion rates. Below are the key types of lead scoring models used by modern businesses.
Demographic lead scoring
Demographic lead scoring evaluates individual attributes such as job title, company size, industry, and location to determine whether a lead aligns with the company’s ideal customer profile.
For example, a B2B software provider targeting C-level executives will assign higher scores to leads with titles such as “Chief Marketing Officer” or “VP of Sales” than to junior employees. Similarly, a business focusing on enterprise clients will prioritize leads from large corporations over those from small startups.
Another critical factor is location. If a company provides services only in specific regions, leads outside those areas will receive lower scores. By refining demographic criteria, businesses ensure they invest time and resources in the most promising prospects.
Behavioral lead scoring
Behavioral lead scoring focuses on how a prospect interacts with a company's marketing assets, measuring their level of engagement and intent to buy.
- Website Activity: A lead who frequently visits the product page, pricing section, or case studies is more likely to be sales-ready than someone who only browsed the homepage.
- Email Engagement: High open rates and link clicks in email campaigns indicate that the lead finds the content relevant.
- Content Downloads & Webinar Attendance: If a lead downloads multiple eBooks or attends webinars, it signals a strong interest in the company’s offerings.
Tracking these behaviors helps sales teams differentiate between passive interest and active buying intent, allowing them to reach out at the right time with tailored messaging.
Firmographic lead scoring
Firmographic lead scoring is particularly useful for B2B companies as it assesses business-level data instead of individual attributes.
- Company Size: Large corporations often have longer sales cycles and more decision-makers, while small businesses may be quicker to purchase.
- Revenue: A company’s annual revenue can indicate whether they have the budget for premium services or enterprise-level software.
- Industry & Market Position: Some industries have higher demand for specific solutions, making them more valuable prospects. For example, cybersecurity tools are highly relevant to financial institutions but may not be a priority for small retail businesses.
By integrating firmographic data, sales teams can filter out businesses that do not fit their ideal customer profile, allowing them to focus on high-value accounts.
Predictive lead scoring
Predictive lead scoring leverages artificial intelligence (AI) and machine learning to analyze historical data and predict which leads are most likely to convert.
Unlike traditional scoring methods, predictive models adapt over time by incorporating real-time data from customer interactions, CRM records, and external databases. Some of the key benefits of predictive lead scoring include:
- Automated Insights: AI-driven models process large volumes of data to identify trends and patterns that human analysts might miss.
- Personalization: Machine learning algorithms assess each lead individually and provide tailored recommendations for engagement.
- Efficiency & Accuracy: Since predictive scoring is data-driven, it reduces guesswork and ensures that sales teams focus on the most promising leads.
Companies using predictive lead scoring and lead enrichment often experience higher conversion rates and improved sales efficiency because their teams can prioritize leads with the highest potential value.
AI in lead scoring
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 reps, 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.
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.
ML in lead scoring
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.
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.
How to build a lead scoring model: 5 best practices
1. Create multiple lead scoring models
Does your company offer more than one product or service?
If so, you should consider creating multiple models to score leads.
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 lead scoring software has 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.
Lead scoring example based on user behavior patterns
For this section, to illustrate a lead scoring model example, we'll be using terms instead of numbers: highest, high, medium, and low.
Let's say a new lead came to visit your website. They first came to read your blog section and spent a relatively significant amount of time there, going from one blog post to the next. At this point, you could value the lead at a medium score.
After some time, the lead landed on a product page and spent a few minutes reading about your offering. They also visited the About us page and read more about your company and vision. Right after that, they filled out a form so the sales reps could contact them. At this point, you could raise the score to high.
Let's say you have AI enabled for lead scoring purposes. The AI concluded, that the lead fits all the ICP criteria and could proceed to buying the product immediately. At this point, the lead's score becomes highest and the sales reps can go on and try to close the deal.
Even if you don't have AI solutions for this purpose, you can manually evaluate the quality of the lead and come to a conlusion. It will take more time, but if your business is not very large, manual evaluation is still a viable option.
You can also use the same strategy for existing customer scoring who might be ready for an upgrade.
Some things to remember
One of the lead scoring rules is that it's 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 data have the ability to generate a significant increase in ROI for your company, but it also will help unite your sales and marketing efforts. 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.
