Lead scoring helps businesses focus their resources on leads that are most likely to convert. It’s a model used by marketers to assign pointsto leads based on specific criteria. This helps both sales and marketing teams identify their most important leads and decide the right time to trigger an MQL to SQL handoff.

Different lead scoring models are appropriate for different use cases, but many marketing and sales teams are unsure of what their options are. In this blog, we’ll demystify lead scoring by looking at common lead scoring models, when to use them, and the best practices for applying them.

Common Lead Scoring Models

How you choose to lead-score depends on your definition of a good fitting customer and the data points you have available. Here are some common lead scoring models to consider: 

Demographic Lead Scoring

This lead scoring model is best for businesses whose main objective is to find leads based on a particular industry, geographic location, company size, or job title. Demographic lead scoring relies on clearly defined buyer personas informed by past data.

For example, your company may see a pattern in the data that shows female chief marketing officers at Fortune 100 SaaS companies in the Northeastern US are your biggest buyer demographic.

Once the personas are pinpointed, scores can be assigned based on how well the attributes of each lead fit into that ideal persona. If you’re scoring leads on a scale of 1-100, you could break down each element like this:

  • Critical attributes – These fit your buyer persona exactly and would carry the most weight in your lead scoring. You may assign a point value of 10-15 points to leads for each critical attribute they meet.
  • Important attributes – These are demographics that may be important, but don’t fit your buyer persona exactly. For these, you’d assign a lower point total, somewhere between 5-9 points for matching attributes.
  • Influencing attributes – These attributes may carry some weight with your ideal buyer persona but are much farther away from your dream demographic. Influencing attributes may add only 1-4 points to a lead score based on their lesser importance.

Company Lead Scoring

This lead scoring model is most often used by B2B companies targeting specific industries, company sizes, or company types. First, the company must review its data to uncover the company size/type/industry it’s finding the most success with. After that, it’s relatively easy to get a hold of the data to inform the rest of the model.

Researching on the company website, via online business directories, social media pages, annual reports, press releases, or government databases are all great places to find the demographic company information to start scoring these types of leads.

Point values in this model would correspond to the attributes of the company that are the most valuable to your business. For example, let’s say you want to target banks and credit unions with an annual revenue of $5 million. Do the products or services you’re offering match banks and credit unions equally, or should one segment carry a heavier point value than the other?

Should leads with less than $5 million in revenue still receive some points or should they receive zero points because, historically, you do not turn a profit with institutions in that revenue range? These are the types of discussions you must have internally to ensure the company lead scoring model is as precise as possible.

Behavior-Based Lead Scoring

This is a common lead scoring model for eCommerce sites that want to gauge how online behaviors influence conversion. For example, how many pages did a lead visit before making a purchase? Other relevant behaviors to track would be adding items to the cart, initiating the checkout process, signing up for a newsletter, watching product videos, or reading blog posts.

Web analytics and tracking tools inform this lead model by monitoring and recording user interactions in real-time. As data is aggregated about each lead, patterns emerge about the types and frequency of behaviors they’re exhibiting. This can be used to develop behavior-based lead scoring models based on the cumulative data. The model can be automated within an ecommerce platform or CRM system.

Assign scores to each behavior based on their importance and relevance to your sales funnel. For example, actions like adding items to the cart or initiating checkout might receive higher scores than merely visiting the homepage.

Establish scoring thresholds to categorize leads into different segments, such as "Highly Engaged," "Moderately Engaged," and "Low Engagement." Insights on engagement can then be used to inform targeted marketing campaigns and automated emails to re-capture the low engagement leads.

Email Lead Scoring

Businesses that rely heavily on email marketing as a lead generation tool focus on this lead scoring model. When email communication is a key pillar in your lead generation process, it’s important to know how well it works. What are the open rates? Click-through rates? Conversion rates? Unsubscribes?

Scoring models for email should get more granular than simply rating open rates. Behavior patterns like forwarding or sharing emails or engaging with personalized recommendations can be strong indicators of buyer intent. These attributes would warrant higher lead scores. Historical data about your email communications is also crucial for building out email lead scoring.

Social Media Lead Scoring

Brands whose primary sales channel is social media rely most heavily on this lead scoring model. Post click-throughs, retweets, and shares are all intent data that should be ranked. Social lead scoring should also consider the frequency of interactions, conversion actions like clicking through to the website, consistency of brand mentions, referral sources, and how location and demographics align to your target audience.

Social media lead scoring helps you identify leads that are actively engaged with your brand on social platforms, allowing you to tailor your marketing efforts to those most likely to convert. Assigning lead scores for social media profiles requires historical data about how those profiles interact with your business and the types of content they share.

Predictive Lead Scoring

In some lead scoring models, humans are using fixed data to organize a lead scoring plan. With predictive lead scoring, a wide range of data sources reveal complex patterns that would be challenging for a human to identify on their own. Organizations with access to machine learning algorithms and predictive analytics use this model to score leads based on historical data and trends that previously led to sales. 

Predictive lead scoring is highly adaptable and scalable, as algorithms can change based on how lead behavior is evolving. To do it well, though, the data used must be clean and regularly updated. Integrated data is also useful for this model because it creates the most holistic view of lead behavior and enhances accuracy.  

demystifying lead scoring infographic

Lead Scoring Best Practices

Don’t forget about Negative Scoring

Negative scores should also be considered, especially for leads that fill out forms with fake information or non-business email addresses like Google or Yahoo. Keep negative scoring attributes in mind as you develop your lead scoring models.

Diversify your lead scoring approach

If your company offers more than one product or service, you may want to utilize multiple lead scoring models. Don’t box yourself into one type of lead scoring, especially when you’re not sure which one will yield the highest results for your business.

Pick your magic number

Lead scoring is about more than just assigning scores, you must have a establish what those scores mean. Use scoring thresholds to determine which leads require immediate attention and which should be nurtured further. The thresholds will differ based on the type of scoring model you’re using, but the point is to establish a magic number that helps your team decide if that lead should be sales qualified or simply nurtured.

Don’t set it and forget it

Lead scoring is a delicate art. You must regularly evaluate and update processes in response to market changes, product or service changes, and other evolving factors. Regularly updating your lead scoring process will lead to more successful results.

Make it a team effort

Lead scoring qualification criteria should be defined collaboratively between sales and marketing. Not only does this ensure alignment, but it keeps both teams focused on activities meant to achieve the same outcomes. Part of your lead scoring discussion should also cover the lead handoff process between marketing and sales. Which information should be passed between the departments to facilitate the most meaningful conversations?

Make it easier with workflows

Once you’ve scored high value leads, you need the ability to take immediate action. Workflows that run through your CRM or marketing automation can guide leads through the appropriate buyer journey with useful and personalized information tailored to their needs. Initially building the workflows may take some time, but it’s much faster to tweak and alter these automations as your lead scoring models evolve later.  

Keep compliance in mind

Ensure your lead scoring practices comply with data privacy regulations and requirements. Make sure you handle lead data responsibly and with care.

Document the process

Document the criteria, point values, and other information related to all lead scoring models so you can go back and evaluate how they’ve changed over time. This will help you build the highest performing lead scoring models.

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