![\"Download](\"https:\/\/no-cache.hubspot.com\/cta\/default\/53\/ee17df5d-dff8-412f-906d-f91308678766.png\")
<\/a><\/p>\nIn this article, I\u2019ll share lead scoring models for you to consider, which data to look at, how to calculate a lead score, and what to do with a lead score once you have one.<\/p>\n
Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n Lead scoring is the process of assigning a score or value to each lead which reflects how likely they are to become a customer. This process allows companies to prioritize and focus their energy on leads who are most likely to convert.<\/p>\n You can score your leads based on multiple attributes, including the professional information they\u2018ve submitted to you and how they\u2019ve engaged with your website and brand across the internet.<\/p>\n Learn more about the concept of lead scoring in the video below:<\/p>\n Lead scoring sounds easy, right? Depending on your business model and the leads in your database, it can quickly become complicated.<\/p>\n To make this process a little easier, I will walk you through the basics of creating a lead score, including what data you should look at, how to find the most important attributes, and the process for calculating a basic score.<\/p>\n If you\u2019re a small business or startup, you may not be sure if you really need a lead scoring system. While sales reps at smaller companies can sometimes \u201cfeel out\u201d their leads based on intuition, that system will pretty quickly hit limits as you scale. Here are some signs that you have tipped the scales and need a lead scoring model:<\/p>\n You may be wondering if lead scoring is outdated or if it\u2019s still a relevant sales method. The short answer is lead scoring is as important today as it has been for years.<\/p>\n I\u2019ve seen firsthand that lead scoring leads to better ROI for your sales efforts and helps to close more sales through a personalized, targeted approach.<\/p>\n We\u2019ve seen in our data that 53% of salespeople<\/a> say selling got harder in the past year. Mark Osborne<\/a>, B2B sales expert and founder of Modern Revenue Strategies<\/a>, says that for many businesses, markets are tightening due to uncertainty and higher interest rates, which has resulted in less capital. These tighter markets and longer sales cycles make every lead more precious.<\/a><\/p>\n Without a lead scoring model in place, says Osborne, you could be losing business to the competition.<\/p>\n \u201cSavvy competitors have learned to swarm on the best opportunities as soon as they identify them,\u201d he says, \u201cgiving those prospects the highest level of personalized attention and service to win those crucial deals.\u201d<\/p>\n <\/a> <\/p>\n There are multiple lead scoring models that use different attributes and metrics to score leads. Many lead scores are based on a point range of 0 to 100, but every model you create will support a particular attribute of your core customer.<\/p>\n Here are seven different lead scoring models based on the type of data you can collect from the people who engage with your business. Choose the one that best matches your marketing strategy and the data you have available.<\/p>\n Are you only selling to people of a certain demographic, like parents of young children? Or a certain ideal customer profile (ICP), like CIOs? Asking demographic (B2C) or firmographic (B2B) questions in your lead acquisition forms<\/a> can help you see how well they fit in with your target audience.<\/p>\n For instance, you can assign point values for people who fit in your target geography, job title, or industry. You can also ask intent questions, like \u201cWhy are you interested in getting in touch with us?\u201d with a few multiple-choice options. This model is also called explicit lead scoring because it uses the information that a lead gives you explicitly.<\/p>\n On the firmographic side, try tracking external company data on company information such as new leadership, M&A activity, or new investments as this knowledge can be valuable in understanding the company’s current context and fit with your value proposition.<\/p>\n While you don\u2019t want to waste your lead\u2019s time making them answer a long form, you can use a data enrichment tool<\/a> to automatically supplement the information a lead submits.<\/p>\n If someone opts in to receive emails from your company by filling out an email popup, you still don\u2019t know much about how interested that person is in buying from you.<\/p>\n Open and click-through rates, on the other hand, will give you a much better idea of their interest level. Examining behavioral data \u2014 like how many emails a lead opens and which ones \u2014 can indicate whether a prospect is engaged.<\/p>\n Similarly, a lead\u2019s engagement with your brand on social networks can also give you an idea of how interested they are. In my experience, social media is tied to referrals for generating the highest-quality leads, so it\u2019s not a channel to overlook. How many times did they click through on your company’s tweets and LinkedIn posts? How many times did they comment or share those posts?<\/p>\n These types of inferences are called implicit lead scoring. By identifying interactions and behaviors that signify interest, you can gauge levels of interest.<\/p>\n Though it may seem simple, quantifying a lead according to its source is another way to score leads. Most sales teams already know which sources provide the best leads, or can run a simple historical analysis to find out.<\/p>\n Since referrals are one of the best lead sources for most brands, you could assign more points to leads from referrals so your sales reps can prioritize reaching out to them.<\/p>\n Source<\/em><\/a><\/p>\n If you\u2019re not already collaborating with your marketing team to get this data, start now, recommends Grant.<\/p>\n \u201cOur sales and marketing team are in constant communication, ensuring that the criteria we use for scoring are aligned with real-world results,\u201d he says. \u201cThis synergy helps refine our lead scoring models and improve accuracy.\u201d<\/p>\n In my experience, how a lead interacts with your website tells you a lot about their interest and intentions. The purchase intent model uses intent data to gauge a lead’s likelihood of conversion by analyzing their web activity, behavior, and in some cases fit and demographics.<\/p>\n Take a look at your leads who eventually become customers: Which offers did they download? How many <\/em>offers did they download? Which pages \u2014 and how many pages \u2014 did they visit on your site before becoming a customer? With a little bit of historical data, you can identify common behaviors to look for, like visiting the pricing page.<\/p>\n Both the number and types of type and pages are important. You might give higher lead scores to leads who visited certain pages (like pricing pages) or filled out high-value forms (like a demo request). Similarly, you might give higher scores to leads who had 30 page views on your site, as opposed to three.<\/p>\n Similarly, you can give negative points to someone who stopped visiting your website or opening your emails.<\/p>\n With HubSpot, for instance, you can build a custom lead-scoring model<\/a> based on fit, engagement metrics, or a combination of both. You can also identify purchase signals with the analytics in the platform.<\/p>\n Predictive lead scoring takes the purchase intent model a step further by providing predictive intelligence. Instead of manually assigning scores by behavior or demographic, predictive lead scoring models use AI and machine learning to identify patterns and predict conversion likelihood. The scoring for predictive intelligence is dynamic and adjusts over time as new data becomes available.<\/p>\n While a purchase intent model shows you who\u2019s ready to buy right now, predictive intelligence analyzes patterns to show who may be ready to buy in one, three, or six months.<\/p>\n Last but not least, you should give negative scores to leads who filled out landing page forms in ways that could indicate they’re spam or not interested in buying.<\/p>\n For example, were first name, last name, and\/or company name not capitalized? Did the lead complete any form fields by typing four or more letters in the traditional \u201cQWERTY\u201d keyboard, or a phone number like 999-999-9999?<\/p>\n You might also want to think about which types of email addresses leads are using compared with the email addresses of your customer base. If you’re selling to businesses, for example, you might take points away from leads who use a Gmail or Yahoo! email address.<\/p>\n <\/a> <\/p>\n There’s a lot of data to weed through \u2014 how do you know which data matters most? Should you find out from your sales team? Should you interview your customers? Should you dive into your analytics and run a few reports?<\/p>\n I recommend a combination of all three. Your sales team, your customers, and your analytics reports will all help you piece together what content is most valuable for converting leads into customers, which will help you attach a number of points to certain offers, emails, and so on.<\/p>\n \u201cThe biggest lift in lead scoring is not defining how many points something is worth, it’s making sure everyone internally is aligned,\u201d advises Ryan Durling<\/a>, Inbound Consultant for HubSpot, in a webinar with HubSpot Admin HUG<\/a>.<\/p>\n \u201cIt\u2019s very important that before you embark on any sort of lead scoring approach that you have buy-in from everyone who\u2018s a stakeholder, who\u2019s going to be involved. That\u2019s not just the salespeople, it\u2018s not just the content team it\u2019s not just the folks who are responsible for reporting or operations \u2014 it’s everyone.\u201d<\/p>\n Sales reps are the ones on the ground, communicating directly with both leads who turned into customers and those who didn’t. They tend to have a good idea of which pieces of marketing material help encourage conversion.<\/p>\n While your sales team might claim certain content converts customers, you might find that the people who actually went through the sales process have different opinions. That’s okay: You want to hear it from both sides.<\/p>\n In fact, according to our 2024 State of Sales Report<\/a>, building and maintaining a strong rapport with customers is a key focus area for sales professionals.<\/p>\n Conduct a few customer interviews to learn what they <\/em>think was responsible for their decision to buy from you. I suggest you interview customers who have had both short and long sales cycles so you get diverse perspectives.<\/p>\n I recommend that you also complement all this in-person research with hard data from your marketing analytics<\/a>.<\/p>\n Run an attribution report<\/a> to figure out which marketing efforts lead to conversions throughout the funnel. Don\u2018t only look at the content that converts leads to customers \u2014 what about the content people view before they become a lead?<\/p>\n You might award a certain number of points to people who download content that\u2019s historically converted people into leads and a higher number of points to people who download content that’s historically converted leads into customers.<\/p>\n Another way to help you piece together valuable pieces of content on your site is to run a contacts report. A contacts report will show you how many contacts \u2014 and how much revenue \u2014 have been generated as a result of certain, specific marketing activities.<\/p>\n Marketing activities might include certain offer downloads, email campaign click-throughs, and so on. Take note of which activities tend to be first-touch conversions, last-touch conversions, and so on, and assign points accordingly.<\/p>\n Source<\/em><\/a><\/p>\n <\/a> <\/p>\n If you have one core customer right now, a single score suffices. But as your company scales, you’ll sell to new audiences. You might expand into new product lines, new regions, or new personas.<\/p>\n You might even focus more on up-selling and cross-selling<\/a> to existing customers rather than pursuing new ones. In my experience, if your contacts aren\u2018t \u201cone size fits all,\u201d your scoring system shouldn\u2019t be either.<\/p>\n With some marketing platforms, you can create multiple lead-scoring systems, giving you the flexibility to qualify different sets of contacts in different ways. Not sure how to set up more than one score? I\u2019ve gathered a few examples to inspire you:<\/p>\n Let\u2019s say, for instance, your sales team wants to evaluate customers on both fit<\/strong> (e.g., is a contact in the right region? The right industry? The right role?) and interest level<\/strong> (e.g., how engaged have they been with your online content?).<\/p>\n Source<\/em><\/a><\/p>\n If both of these attributes are a priority, you can create both an engagement score and a fit score (as seen in the graphic above) so that you can prioritize outreach to contacts whose values are high in both categories.<\/p>\n Say you\u2019re a software company that sells two different types of software via different sales teams to different types of buyers.<\/p>\n You could create two different lead scores \u2014 one for a buyer\u2019s fit<\/strong> and the other for their interest in each tool<\/strong>. Then, you\u2019d use these respective scores to route leads to the right sales teams.<\/p>\n As you grow, you might start to focus on upselling or cross-selling as much as new business. However, keep in mind that the signals that indicate the quality of new prospects and existing customers often look completely different.<\/p>\n For prospects, you might look at demographics and website engagement, whereas for existing customers, you might look at how many customer support tickets they’ve submitted, their engagement with an onboarding consultant, and how active they currently are with your products.<\/p>\n If these buying signals look different for different types of sales, consider creating multiple lead scores.<\/p>\n <\/a> <\/p>\n There are many different ways to calculate a lead score, but I think the simplest way to do it is this:<\/p>\n Download for Free<\/a><\/p>\n Your lead-to-customer conversion rate is equal to the number of new customers you acquire, divided by the number of leads you generate. Use this conversion rate as your benchmark.<\/p>\n Customers who requested a free trial at some point, customers in the finance industry, or customers with 10-20 employees could be attributes.<\/p>\n There\u2018s a certain kind of art to choosing which attributes to include in your model. You\u2019ll choose attributes based on those conversations you had with your sales team, your analytics, and so on \u2014 but overall, it’s a judgment call.<\/p>\n You could have five different people do the same exercise, and they could come up with five different models. But that’s okay as long as your scoring is based on the data we mentioned previously.<\/p>\n Calculating the close rates of each type of action a person takes on your website \u2014 or the type of person taking that action \u2014 is important because it dictates the actions you’ll<\/em> take in response.<\/p>\n So, figure out how many people become qualified leads (and ultimately customers) based on their actions or who they are in relation to your core customer. You’ll use these close rates to actually \u201cscore\u201d them in the step below.<\/p>\n Look for the attributes with close rates that are significantly higher than your overall close rate. Then, choose which attributes you\u2019ll assign points to, and if so, how many points.<\/p>\n Base the point values of each attribute on the magnitude of their individual close rates.<\/p>\n The actual point values will be a little arbitrary but try to be as consistent as possible.<\/p>\n For example, if your overall close rate is 1% and your \u201crequested demo\u201d close rate is 20%, then the close rate of the \u201crequested demo\u201d attribute is 20X your overall close rate \u2014 so you could, for example, award 20 points to leads with those attributes.<\/p>\n The simple method above for calculating a lead score is a great start. However, the most mathematically sound methods employ a data mining technique, such as logistic regression<\/a>.<\/p>\n Data mining techniques are more complex and often more intuitive than your actual close rates. Logistic regression<\/a> involves building a formula in Excel that\u2019ll spit out the probability that a lead will close into a customer.<\/p>\n This is more accurate than the technique I outlined above since it\u2019s a holistic approach that takes into account how all of the customer attributes \u2014 like industry, company size, and whether or not someone requested a trial \u2014 interact with one another.<\/p>\n If you prefer a less complex lead-scoring method, I think the manual approach above is a great place to start.<\/p>\n Creating a lead score can do great things for your business: improve the lead-handoff process, increase lead conversion rate, improve rep productivity, and more.<\/p>\n But, as you can see from the two methods above, coming up with a scoring system can be a time-consuming task when done manually.<\/p>\n Plus, coming up with scoring criteria isn\u2018t \u201cset it and forget it.\u201d As you get feedback from your team and stress-test your scores, I\u2019ve found you\u2019ll need to tweak your lead-scoring system regularly to ensure it remains accurate.<\/p>\n Wouldn’t it be easier if technology could eliminate the manual setup and continuous tweaking, leaving your team more time to build relationships with your customers?<\/p>\n That’s where predictive scoring comes in. Predictive lead scoring<\/a> uses machine learning to parse through thousands of data points in order to identify your best leads, so you don’t have to.<\/p>\n Predictive scoring looks at what information your customers have in common, as well as what information the leads that didn’t<\/em> close have in common, and comes up with a formula that sorts your contacts by importance based on their potential to become customers.<\/p>\n This allows you and your sales team to prioritize leads so you’re not harassing those who aren’t<\/em> (yet) interested and engaging those who are<\/em>.<\/p>\n The best part about predictive scoring? As with any application of machine learning, your predictive score gets smarter over time, so your lead follow-up strategy will optimize itself.<\/p>\n Download for Free<\/a><\/p>\n <\/a> <\/p>\n We\u2019ve covered a lot so far, so I want to wrap it up with a few best practices I learned from the sales leaders I spoke to.<\/p>\n Here are some lead scoring best practices to follow if you want to improve your sales in 2024.<\/p>\n In the age of AI, sales professionals would be wise to use one of the numerous AI tools to their selling advantage, especially during the lead scoring process.<\/p>\n Not only can AI tools improve efficiency, but 66% of sales pros<\/a> say that AI helps them provide a personalized experience and better understand their customers.<\/p>\n Source<\/em><\/a><\/p>\n Grant is also taking advantage of AI\u2019s power to personalize data.<\/p>\n \u201cWe\u2019re leveraging AI to analyze patterns and predict which leads are most likely to convert,\u201d he says. \u201cMachine learning models can adapt and get more accurate over time, which is a huge step up from traditional lead scoring methods.\u201d<\/p>\n I think one of the best parts about lead scoring is that it\u2019s an objective method. When the data indicates how interested a prospect is, that\u2019s something you\u2019ll want to lean into.<\/p>\n Data is one of the most important resources sales teams and marketers have at their disposal, and lead scoring has become even more data-driven with the tools available today.<\/p>\n If you want to save time spent on unqualified leads and prioritize sales-ready ones with data-driven decisions, then your sales team should use a lead scoring model that makes it easy to organize and understand your data.<\/p>\n If you think lead scoring models are outdated, it could be because the traditional methods didn\u2019t take into account real-time industry or business changes.<\/p>\n \u201cLead scoring is no longer a set-it-and-forget-it deal,\u201d says Grant. \u201cWe\u2019re making real-time adjustments based on the latest data. This agility helps us stay ahead of the curve and respond to changing market conditions.\u201d<\/p>\n\n
What is lead scoring?<\/strong><\/h2>\n
Do you need a lead scoring model?<\/strong><\/h3>\n
\n
Why is lead scoring important?<\/strong><\/h3>\n
Lead Scoring Models<\/strong><\/h2>\n
1. Firmographic or Demographic Infor<\/strong>mation<\/strong><\/h3>\n
2. Behavioral\/Engagement Data<\/strong><\/h3>\n
3. Lead Sources<\/strong><\/h3>\n
![\"lead](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/04\/lead-scoring-1-20250410-7586535.webp\" "\\"\\"")
<\/p>\n4. Purchase Intent Model<\/strong><\/h3>\n
5. Predictive Intelligence<\/strong><\/h3>\n
6. Negative Scoring and Spam Detection<\/strong><\/h3>\n
How to Determine What Data to Look At<\/strong><\/h2>\n
Coordinate across teams.<\/strong><\/h3>\n
Talk to your customers.<\/strong><\/h3>\n
Turn to the analytics.<\/strong><\/h3>\n
![\"lead](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/04\/lead-scoring-2-20250410-4737786.webp\" "\\"\\"")
<\/p>\nIs one lead score enough?<\/strong><\/h2>\n
Fit vs. Interest<\/strong><\/h3>\n
![\"purchase](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/04\/lead-scoring-3-20250410-9556504.webp\" "\\"\\"")
<\/p>\nMultiple Personas<\/strong><\/h3>\n
New Business vs. Up-sell<\/strong><\/h3>\n
How to Calculate a Basic Lead Score<\/strong><\/h2>\n
Featured Resource:<\/strong> Free Lead Scoring Template<\/a><\/strong><\/h3>\n
![\"free](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/04\/lead-scoring-4-20250410-8603383.jpg\" "\\"\\"")
<\/a><\/p>\nManual Lead Scoring<\/strong><\/h3>\n
1. Calculate the lead-to-customer conversion rate of all of your leads.<\/strong><\/h4>\n
2. Pick and choose different attributes of customers who you believe were higher quality leads.<\/strong><\/h4>\n
3. Calculate the individual close rates of each of those attributes.<\/strong><\/h4>\n
4. Compare the close rates of each attribute with your overall close rate and assign point values accordingly.<\/strong><\/h4>\n
Logistic Regression Lead Scoring<\/strong><\/h3>\n
Predictive Lead Scoring<\/strong><\/h3>\n
Featured Resource:<\/strong> Predictive Lead Scoring Software<\/a><\/strong><\/h4>\n
![\"predictive](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/04\/lead-scoring-5-20250410-6630502.webp\" "\\"\\"")
<\/a><\/p>\nLead Scoring Best Practices<\/strong><\/h2>\n
Leverage AI and machine learning.<\/strong><\/h3>\n
![\"ai](\"http:\/\/nurseagence.com\/wp-content\/uploads\/2025\/04\/lead-scoring-6-20250410-601447.webp\" "\\"\\"")
<\/p>\nLead with data.<\/strong><\/h3>\n
Make real-time adjustments.<\/strong><\/h3>\n
![\"\"](\"https:\/\/track.hubspot.com\/__ptq.gif?a=53&k=14&r=https%3A%2F%2Fblog.hubspot.com%2Fmarketing%2Flead-scoring-instructions&bu=https%253A%252F%252Fblog.hubspot.com%252Fmarketing&bvt=rss\")
<\/p>\n","protected":false},"excerpt":{"rendered":"