Lead scoring: everything you need to know in 2025

Lead scoring is a critical process for sales teams looking to hit their numbers because it enables reps to spend most of their time focusing on leads most likely to convert. 

By assigning value to each lead based on their behavior, demographics, and level of interest, businesses can streamline sales workflows and close deals faster.

If you’re looking to become an expert on lead scoring, you’re in the right place. 

Keep reading to learn more about what lead scoring is, what you’ll need to start scoring leads, the differences between predictive lead scoring and manual lead scoring, how to get started, and some best practices you can follow to achieve the outcomes you’re hoping for.

What is lead scoring?

Lead scoring is the process of assigning numerical or categorical values to potential customers based on their likelihood to convert into paying clients. 

This scoring is typically determined by analyzing factors such as engagement, demographics, behaviors, and alignment with your company’s ideal customer profile. 

By ranking leads, businesses can prioritize their efforts, ensuring sales and marketing teams focus on the most promising opportunities.

Benefits of lead scoring include:

• Improved efficiency. Lead scoring helps sales and marketing teams focus their efforts on high-priority leads, reducing wasted time and resources on less promising prospects.
  
  
• Increased conversion rates. By identifying and targeting leads that are most likely to convert, businesses can deliver more personalized and relevant messaging, improving the chances of closing deals.
  
  
• Better alignment between teams. Lead scoring fosters collaboration between sales and marketing by creating a shared understanding of what makes a lead valuable, ensuring both teams work toward the same goals.

Lead scoring has evolved from basic manual methods — like relying on gut instinct or using simple point systems — to more sophisticated, data-driven approaches leveraging artificial intelligence, machine learning, and predictive analytics. 

These modern methods not only streamline the process but also make it more accurate and easier to implement, empowering sales teams to identify high-value leads quickly and effectively.

Lead scoring is best suited for teams with an established sales funnel and closed-customer data because the historical information helps define what makes a lead truly valuable. Without that data, relying assumptions about your ICP and expected lead behavior could lead to inaccurate models and missed opportunities.

Whether you’re refining an existing lead scoring system or building one from scratch, this guide provides the insights and strategies you need to optimize lead scoring for your sales team.

What you’ll need to get started with lead scoring

Lead scoring is all about using data to evaluate how qualified a prospect is and how likely they are to buy from you. 

Typically, the process starts after a lead interacts with your company — whether through cold outreach, filling out a form, downloading content, or requesting a demo. At this point, leads can range from marketing-qualified (engaging with your resources) to sales-qualified (actively engaging with your team), and scoring them helps you prioritize where to focus your efforts.

To get started with lead scoring, you’ll need a CRM to capture lead scores and store the data needed to evaluate each prospect. The data used in lead scoring generally falls into two categories:

1. Demographic data, which focuses on who the lead is, including their job title, decision-making authority level, and how well they align with your ICP, and
2. Behavioral data, which tracks what the lead does — like attending events, clicking emails, or engaging with your site — and helps identify buying signals.

These two types of data — sometimes called explicit and implicit lead scoring data — come together to create a scoring model tailored to your business. Once you’ve gathered the necessary data, it’s time to define the behaviors and demographics that signal a high-quality lead. This is where tools like LeadIQ can make a major difference, helping you build and refine your model to ensure you’re always focusing on the right prospects.

Sounding overly complicated? It isn’t. And don’t worry! We’ll guide you through the entire process.

Predictive lead scoring vs. manual lead scoring

We’re living in the future — right? 

Thanks to advanced CRMs and specialized lead scoring tools, you can now use predictive lead scoring and automate the entire process for your team, saving time and effort.

What’s the difference between predictive and manual lead scoring anyway? And which one is right for you? Let’s break it down.

Predictive lead scoring uses machine learning and artificial intelligence to analyze vast amounts of data, automatically identifying patterns in your existing sales data and assigning scores to your leads. These systems, including tools from HubSpot and Salesforce, are great at processing large datasets quickly — making it easier to jump into lead scoring without building a model from scratch. That’s why it is so important to have accurate data in these systems today!

On the flipside, manual lead scoring requires your team to evaluate leads by hand based on specific criteria — like demographic fit and behavior signals — often using a point system. While this approach requires more effort up front, it gives you complete control and can be a valuable exercise for aligning your sales and marketing teams.

Determining which is best for your team depends on your sales tech stack, what data you have that a lead scoring solution could work with, and whether you have enough data to make a predictive model accurate in the first place. 

While predictive lead scoring systems can accelerate the process, they also give you less control over the exact lead scoring model you’re using, forcing you to default to the vendor’s model (although some customizations are available). 

However, if you want to ensure lead scoring accuracy based on your company’s specifics, it can sometimes be best to start with a manual process first — which can be an incredibly valuable exercise for your sales team to go through in general.

Either way, both approaches can help your team prioritize leads more effectively, so pick one that fits your goals and resources best and take it from there.

Manual lead scoring in 5 easy steps

Now that you have the infrastructure necessary to start scoring your leads, it’s time to set up your lead scoring model. Don’t worry — that’s a lot easier than it sounds. Even better, we’ll show you exactly how to get started in this section.

Step 1: Calculate conversion rate of all leads

The first step in setting up an effective lead scoring model is to calculate the conversion rate of all your leads. This gives you a solid benchmark of how many leads, on average, turn into closed deals — which is a crucial starting point for measuring success.

To find this rate, simply divide the number of new customers you acquire by the total number of leads generated during the same time period. 

For example, if you generated 100 leads and closed 10 deals, your conversion rate is 10%. No matter what your number is, this metric will help you guide your scoring model and identify what success looks like.

Step 2: Evaluate attributes of best closed deals

Now that you have a benchmark, you need to understand what your best customers did prior to making a purchase decision. Larger companies with sophisticated marketing and sales tracking tools will have an easier time doing this, but it’s still possible for the smaller guys — just that the process will be a bit more manual.

HubSpot lead scoring

HubSpot tracks all lead interactions, including website visits, email opens, form submissions, and content downloads, giving you a complete activity timeline. This data is stored within the lead’s profile in the CRM, making it easy for your team to analyze engagement. When you’re ready, you can export this information as reports or individual records, allowing for detailed analysis and seamless sharing across teams.

Salesforce lead scoring

Salesforce tracks a lead’s interactions across emails, website visits, form submissions, and more, compiling this data into a detailed activity timeline within its CRM. Using tools like Salesforce Einstein, these interactions can be automatically evaluated to assign lead scores based on engagement and fit. You can also export this data as reports or analyze it in Salesforce to optimize your sales strategies.

Don’t use HubSpot or Salesforce or find that they are missing important data — like what a prospect downloaded or how they first reached out? That’s totally okay. You can make this a more manual process by downloading a list of your best customers and sitting down with your sales and marketing teams to understand what data is available and even what folks remember about the deal.

No matter which route you take, it’s important to familiarize yourself with these lead scoring attributes:

• Company size
• Company industry
• Lead source (e.g., webinar vs. event vs. cold outreach)
• Signing up for the company newsletter
• Visiting a particular page on your site (e.g., case studies, compare pages, or product pages)
• Following your company on social media

Ultimately, your goal is to find similarities between your best customers. Did a majority of them download the same resource before signing a contract? Did most of them come through LinkedIn? Did they visit your compare page and then reach out to sales? 

By answering these questions, you can figure out what all of your best deals have in common.

Step 3: Calculate lead score for each attribute

For this step, you’ll want to know things like:

• 25% of individuals who follow us on social media close
• 40% of individuals who sign up for our email newsletter close
• 60% of individuals who respond to a cold email close

To figure this out, review each attribute you identified as valuable in the previous step. Calculate the percentage of all leads who took the action and compare it to the percentage of closed deals who did the same action.

Once you have a list of relevant attributes to use in your lead scoring model, you have to look at each individual attribute and assign a score to it. To figure out how to score each attribute, you’ll want to determine how many qualified leads become customers based on their specific demographics and behavior.

Demographic data attributes

For demographic data — think company size or industry — use your ICP to easily identify high-value prospects. 

For example, if you know that a majority of your customers have more than 100 employees, this is an important attribute and should be rated as a higher-value attribute in your lead scoring model. If you stumble across a company that has 100-plus employees, they’re rated top tier in this attribute category. If they have something like 75 employees, they’re rated slightly lower — and so on.

Another important attribute in lead scoring on demographic data is the lead’s job title. An individual contributor has a less valuable attribute than someone with VP or Executive in their title. Again, look at the titles your best leads have had and try to focus on broader categories rather than super-specific titles, which tend to vary significantly from one company to the next.

For example, you might want to group Software Engineer III together with Software Engineer, both of which are less valuable attributes than VP of Engineering.

Behavioral data attributes

When you’re evaluating behavioral data attributes, you want to look at actions taken by a prospect through the entire sales funnel and when they occurred within the buying process. The further down the sales funnel the attribute happened, the likely higher value of the attribute. 

For example, someone signing up for a webinar is likely a less valuable attribute than someone viewing a comparison page or requesting a demo.

Step 4: Assign point values to attributes

Still with us? If this whole process sounds complicated, take our word that it really isn’t. Just remember that you’re looking to understand which demographics and actions are most likely to lead to a closed deal.

Now that you know what percentage of closed deals performed specific actions and the demographic data that indicates a high-quality lead, it’s time to start assigning lead scoring point values to each of these attributes. 

First, look for the attributes with close rates that are significantly higher than your overall close rate. You already have your general closed/won rate from step one; now, it’s time to figure out how much more likely they are to close if they take a specific action.

For example, if your overall lead to closed/won rate is 3% and your “requested demo” close rate is 30%, then the close rate of the requested demo attribute is 30x your overall close rate. 

This means that requesting a demo is a high-value attribute and should be scored accordingly. Or, if your overall closed/won rate is 1% and folks who visit a compare landing page close 10% of the time, you know that it’s a valuable attribute but not nearly as valuable as requesting a demo. As such, it should have a lower lead score value.

The key to this step is being as consistent as possible with your point values. To give you a better idea of what your lead scores might look like, here’s an example of values placed on actions:

• Download a certain ebook – 10 points
• Follow on social media – 20 points
• Request a demo – 30 points

Now, you will want to look at demographic attributes and place a value on these. Some teams use a point-based system while others prefer a split qualification method where attributes are categorized and assigned points separately (e.g., by number and by letter). 

If you stick with the point value system, you might have a lead that earns 90 out of 100 points. In this system, your number scores might look something like this:

• 100-plus employees – 30 points
• 50–100 employees – 10 points
• VP of Marketing, CMO – 30 points
• Marketing Manager, Director of Marketing – 10 points

If you take the split approach, you may have an A50 lead or a B75 lead. In such a system, your scores might look like this:

• A Tier – 100-plus employees
• A Tier – VP of Marketing, CMO
• B Tier – 50–100 employees
• B Tier – Marketing Manager, Director of Marketing
• C Tier – 25–50 employees
• C Tier – Content Marketer, Social Media Manager

Got it? Nice.

But which scoring system is best for you? There’s no right or wrong answer. Simply put, you’ll want to use a lead scoring system that is easiest for your team and your technology to understand. Again, many of the top CRMs on the market offer predictive lead scoring. Of course, you can also have your Salesforce admin (or the equivalent) set up custom scores as leads come into your system.

That said, if your tech stack can’t handle a point plus a numerical value, you should stick with just points. On the other hand, if you’re going super old school and pulling all of this data into a spreadsheet and using an Excel formula, you can pretty much do whatever you want.

Step 5: Implement your lead scoring model

Now that you understand the contribution of an attribute to a closed deal and have assigned value points to specific behavioral and demographic attributes, it’s time to document and implement your lead scoring model. 

Remember, you may be able to implement this directly into your CRM or you may want to use a spreadsheet to calculate the lead score (pro tip: Zapier can definitely help you here by pulling specific actions into a spreadsheet automatically). By documenting your processes, you can ensure your team understands the lead scoring system and why it’s valuable to them. 

After you’ve documented and implemented the model, it’s time to analyze its accuracy as leads start to come in and are measured against the lead scoring model. As the scored leads start making their way through the funnel and close, check the accuracy to make sure it’s on point. Since needs change and markets shift, you’ll also want to tweak your model at least every six months. After all, you never know when you might find new attributes you’ll want to assign points to!

Lead scoring best practices

To be sure, getting started with lead scoring can feel overwhelming. There are many steps to kick things off, so it can understandably seem like a daunting task.

But trust us: The payoff is absolutely worth it! 

Lead scoring helps prioritize your sales efforts, ensuring sales and marketing teams stay focused on the prospects most likely to convert. Beyond that, it also enables smoother lead routing, faster follow-ups with prospects, and smarter allocation of resources where they’ll have the biggest impact.

As you build and refine your lead scoring model, it’s important to remember that it’s okay to start small and grow over time. Focus on understanding what makes a high-quality lead by analyzing demographic and behavioral data — and keep iterating as your team learns more about what a great prospect looks like.

Whether you’re fine-tuning an existing strategy or starting fresh, keep these best practices in mind to accelerate your journey to lead scoring success.

1. Explore automated predictive lead scoring

As we said, it can be time-consuming to go through the entire lead scoring model creation process — especially for busy sales teams and scrappy startups.

The lead scoring technologies either built into your existing tools — like HubSpot lead scoring or Salesforce lead scoring — can be incredibly useful and make the process faster. But you have to make sure these systems have the data they need to be successful! 

If your CRM doesn’t know what ebooks a prospect downloaded or what webinars they attended, these predictive lead scoring technologies aren’t as useful. Even worse, they could cause you to accidentally ignore high-value prospects!

2. Review your lead scoring model regularly

Many sales teams fall into the trap of setting and forgetting their lead scoring model. Believe it or not, we know sales teams that are still relying on a lead scoring model someone random employee created five years ago! 

Using a relic from the past to guide your sales efforts can no doubt be dangerous for your team’s ultimate sales success. Avoid that fate by ensuring you review your sales lead scoring process every six months — even if you’re using an automated predictive lead scoring solution. Whatever you do, you absolutely do not want to rely on some random technology’s model for your team’s ultimate success. 

3. Don’t over-rely on the lead score

Lead scoring is about trying to understand what a majority of prospects look like and what they did prior to a closed deal. But it definitely does not paint the full picture!

While lead scoring can absolutely help your team achieve better outcomes, view it more like a bell curve than a completely accurate and reliable source of information. You never know when a really high-quality customer will end up falling outside of your ideal lead score. 

In other words, just because a prospect is a 30/100 or D3 (depending on the model you use), doesn’t mean all hope is lost. In fact, a prospect that scores low could even end up being one of your best customers! 

Each sale is different, so it’s important that your team doesn’t rely too heavily on what the lead score may tell them. Instead, lead scoring is best viewed as another tool in the toolbox.

How LeadIQ can help with lead scoring

Looking to add new lead scoring tools to your team’s tech stack? LeadIQ can help.

Our purpose-built sales prospecting tools provide demographic data you need to effectively engage someone who’s a marketing qualified lead or a prospect who requests a demo.

With LeadIQ, you can easily track sales triggers — including when contacts switch jobs. 

In the event a champion, decision-maker, or power user ends up in a new role, you can receive automatic notifications and begin engaging that individual — someone who already loves your products and is therefore more likely to convert.

To learn more about how LeadIQ can enhance your lead scoring initiatives, request a demo today.