Lead Scoring: How Do You Find Quality Leads?

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• Companies using lead scoring models can increase conversion rates by up to 20%.
• Predictive lead scoring uses AI to analyze thousands of data points for accurate conversion forecasts.
• Wasting time on low-quality leads costs teams valuable resources and decreases sales efficiency.
• Only 27% of B2B leads are sales-ready, highlighting the need for accurate lead prioritization.
• Businesses aligning marketing and sales via lead scoring see 36% higher customer retention rates.

Sales is harder today than it’s been in years. With longer decision cycles and tighter markets, every lead counts—but not all leads are created equal. That’s why lead scoring has become a must-have for teams wanting to focus on high-quality leads while filtering out those who aren’t ready to buy. In this guide, you’ll learn how to put practical lead scoring models into use. These models turn data into action and help your sales process work smarter.

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What is Lead Scoring?

Lead scoring is a way to give values, often numbers, to each lead based on what they are like and what they do. These scores show how likely each lead is to become a paying customer. The point of lead scoring is to help marketing and sales teams figure out which contacts are most likely to buy (so they should get attention right away). It also helps them see who needs more time and who probably won’t bring in money any time soon.

At its core, lead scoring helps teams answer one main question: is this lead worth my team’s time right now?

Benefits of Lead Scoring

Putting good lead scoring models in place helps your whole go-to-market plan:

1. Improved Conversion Rates

By putting attention and effort on the leads most likely to buy, your team closes more deals with less time wasted. A report from MarketingSherpa shows that companies using lead scoring get a 77% better return on investment from their lead generation efforts.

2. Better Sales and Marketing Teamwork

Lead scoring needs input from different teams. It helps marketing target better and make better content. It also helps sales know what a good lead looks like. When teams agree on what’s important, there are fewer problems when leads are passed between teams, and the way buyers purchase is clearer.

3. Managing Your Work Automatically

Lead scoring connects right to your CRM and marketing automation tools. This lets the system send leads to the right sales reps automatically. It can also assign leads to nurturing campaigns automatically or even turn off leads if their score drops too low.

4. Higher Lead Quality Over Time

Making your lead scoring better, especially with predictive tools, helps bring in better leads over time. As leads that aren’t a good fit are removed and high-quality leads are moved up, your team learns and gets better faster.

5. Lower Costs to Get Customers

Lead scoring helps your team use its resources better, so you spend less money to get each customer. This helps lower the cost of getting customers. It increases the return on your marketing spending and helps you grow faster in a way that lasts.

When You Know You Need Lead Scoring

Not every organization starts with lead scoring, but most successful ones eventually do it. If any of these problems sound familiar, it’s probably time:

• Your sales reps complain about wasting time on leads that aren’t ready.
• Marketing brings in a lot of leads, but not many turn into sales.
• You have trouble knowing which leads need follow-up right away.
• Sales cycles are taking longer, with few clear signs that buyers are ready.
• Leads disappear after they first interact with you.

Without lead scoring, you might give sales too many of the wrong leads—while your best potential customers don’t get noticed.

The Anatomy of a Lead Scoring Model

Good lead scoring uses two kinds of lead information to figure out scores:

1. Explicit Data (Demographic or Firmographic)

This is information a lead gives on purpose, like on forms or from other sources. Examples include:

• Job title
• Company size
• Industry
• Company revenue
• Where they are located

This data helps see how well a lead fits what your ideal customer looks like. Someone like a CFO at a large company might get a higher score than a student freelancer.

2. Implicit Data (Behavioral)

Implicit data comes from how the lead interacts with your brand. These actions show how interested the lead is and how much they are engaging. Examples include:

• Pages they view on your website (especially important pages like pricing)
• How long they stay on your site
• Content they download
• If they open emails and click links
• Social media shares or follows
• Requests for demos or trials

Putting together “who they are” (explicit data) and “what they do” (implicit data) gives a more complete and correct idea of if a lead is ready to buy.

Overview of Lead Scoring Models

Every company scores leads a bit differently, but these basic models are starting points you can change:

1. Demographic/Firmographic Scoring

This model scores leads based on how closely they match your ideal customer. For example, a decision-maker at a big company may get 25 points, while an intern might only get 5.

Use this model when your sales depend a lot on specific types of people or businesses.

2. Behavioral Scoring

Every time a lead engages, they show something about their interest in buying. Behavioral scoring gives points to actions that show more interest:

• Visiting a product page – 15 points
• Downloading a pricing guide – 25 points
• Attending a webinar – 20 points

This helps separate leads who are just looking around from those who are really thinking about solutions.

3. Source-Based Scoring

Not all places where leads come from are the same. Leads found through searching online might show more interest than those from paid ads. You can give higher scores to leads that come from sources that usually bring in good leads, such as:

• Customer referrals
• Signups at live events
• Direct demo bookings

By finding which channels produce good leads, you can focus more on what works.

4. Purchase Intent Scoring

This model finds actions that show high interest in buying—like visiting your product pages many times or leaving items in a shopping cart—and gives scores based on that.

Use this model to focus on leads that are closer to making a buying decision.

5. Predictive Lead Scoring

Find connections between what leads do and if they eventually buy using machine learning. Predictive scoring finds patterns you might not see easily (for example, leads who read certain blog posts might buy more often).

This helps you:

• Keep learning and improving over time
• Work with large amounts of data
• Get better accuracy as you grow

6. Negative Scoring

Not everything a lead does is a good sign. Taking away points for actions that aren’t helpful removes leads that probably won’t buy. Things that often lead to negative scores include:

• Using fake or non-work email addresses
• Unsubscribing from emails
• Forms that aren’t filled out all the way
• Not interacting for a long time

Negative scoring stops bad leads from making your pipeline hard to see clearly.

How to Identify the Right Data

To figure out what information and actions to score, you need ideas from different places. Follow these 3 steps:

1. Sales Feedback

Your sales reps know best what signals matter right now. Ask them questions like:

• What actions show a lead is ready for sales?
• What types of leads often waste time?
• Who tends to make decisions in accounts you’ve won?

2. Customer Interviews

Talking to customers after they buy or when they are getting started helps show what content, events, or interactions convinced them to buy. Map these back to your process for behavioral scoring.

3. Analytics & CRM Reports

Use your tools to find:

• Content that leads to sales
• Types of people who often don’t buy
• Which channels brought in deals that closed

This way, your scoring model is based on real information, not just guesses.

Is One Lead Score Enough?

If your company deals with different types of customers, products, or markets, one score might not be enough. Think about scoring leads in different ways:

1. Fit Score vs. Engagement Score

An intern who does a lot on your site might get a high score for behavior but can’t buy anything. On the other hand, a VP who hasn’t done much might be the perfect customer. Keeping “who they are” and “what they’ve done” separate helps you be more careful.

2. Product-Based Scores

If you sell different products or packages, each might need its own scoring rules. For example, what makes a lead a good fit for your small business software might be different from what works for your large company product.

3. Lifecycle Scoring

A new lead versus a customer looking to buy more need different scoring rules. Change scores based on where leads are in the buying process, like:

• MQL (Marketing Qualified Lead)
• SQL (Sales Qualified Lead)
• Opportunity to sell more to an existing customer

Lead Scoring Calculation Methods

Manual Lead Scoring

This method is best for new companies and small teams. It involves:

1. Finding out how often leads with certain traits become customers.
2. Giving points based on how often those traits lead to sales.
3. Changing the points sometimes based on new information.

Example:

• Demo requests = 20% sales rate → 20 points
• Webinar signups = 5% sales rate → 5 points
• C-level job title = 15% sales rate → 15 points
• Gmail address = -10 points

Simple spreadsheets or CRM fields can help you use this model.

Logistic Regression

A math method that figures out how likely a lead is to buy based on what past data shows. It needs more technical skill, but it makes scoring more exact—especially when you have a lot of data.

Use tools like Excel, R, or Python if your team has people who can work with data.

Predictive Lead Scoring

Fancy tools like MadKudu, 6sense, or Salesforce Einstein let you use AI models. These models get better all the time by using live information. This helps with:

• Finding patterns humans might miss
• Changing scores automatically as behaviors change
• Connecting easily with your CRM for real-time alerts

Integrating Lead Scores into Your Workflow

After you figure out the scores, use them. Make them do something:

• Send leads with high scores straight to sales.
• Start automated emails or messages for leads with medium scores.
• Stop sending messages to cold leads until they show more interest.
• Create tasks or reminders for sales reps based on lead scores.

When all teams can see the scores, it helps make sure leads get the right attention based on if they are ready.

Lead Scoring Best Practices

• Check your lead scoring model often. What customers do changes all the time.
• Use AI or predictive tools to grow in a better way.
• Make sure scoring matches where leads are in your sales process.
• Test and improve your scoring using past results.
• Work with marketing and sales teams so the scoring shows what’s really happening.

Common Pitfalls to Avoid

Don’t make these mistakes. They can make your lead scoring less helpful:

• Giving points without looking at any data
• Thinking actions like just visiting the blog homepage mean a lead is interested in buying
• Using one score for all your products and types of customers
• Keeping old scoring rules in use for too long
• Giving too many points to leads that are just starting out, which can make you think they are ready when they aren’t

Tools to Help With Lead Scoring and Make it Better

There are many tools that can help you set up, automate, and improve lead scoring:

• HubSpot CRM: Offers custom ways to work, scoring rules, and automated actions.
• Salesforce Pardot: Good for marketing automation for B2B companies that connects to sales data.
• Marketo: Has advanced scoring and ways to group leads.
• Leadspace or Clearbit: Give you more detailed company and behavior information so you can score better.
• 6sense or Demandbase: Tools that use AI and ABM (Account-Based Marketing) to help you manage things.

How Lead Scoring Helps With Automated Content

Making content feel personal is easier when you group leads by their score:

• High scores → Show them product demos, offer live chat, or connect them with sales
• Medium scores → Send them automated campaigns with customer stories or examples of how others use your product
• Low scores → Send them helpful content to teach them more and build their interest

Over time, your content plan changes to show how buyers actually purchase. This helps keep customers longer, gets people to stay on pages longer, and makes your sales pipeline better.

Getting More Growth By Picking the Best Leads First

Lead scoring makes everything sharper in how you deal with customers, from getting leads in marketing to sales talking to them and making a sale. When you find the best leads early and correctly, your team wastes less time. Sales cycles become shorter. You also get more sales and keep more customers.

Whether you’re starting your first scoring model or making an old one better, the main point is the same: get the right message to the right person at the right time.

Start small, see how things are working, and change it often. That’s how you get better leads and more money.