Introduction

Lead scoring models are an essential aspect of any marketing campaign, as they help businesses identify and prioritize leads based on their likelihood to convert into paying customers. With the increasing amount of data available on prospects and customers, lead scoring models have become more sophisticated over time, allowing marketers to segment their audience based on specific attributes such as company size, job title, browsing behavior, and more. In this article, we will preview the top 10 lead scoring models currently available, their benefits, and how they can help drive your marketing efforts.

Brief Overview of Lead Scoring Models

Before diving into the top 10 lead scoring models, it's essential to understand what lead scoring is in general. Lead scoring is an automated process of assigning values or scores to leads based on their level of engagement with your brand, indicating their likelihood to be interested in your product or service. Lead scores enable you to focus your marketing and sales efforts on those leads that are most likely to convert into paying customers.

The Top 10 Lead Scoring Models

• Hubspot: Hubspot's lead scoring model is built on the behavior, demographic, and firmographic data of your prospects, helping you segment your audience and prioritize leads based on their buying signals.


• Marketo: Marketo's lead scoring model allows you to customize your scoring system based on your unique sales cycle, ensuring that your system aligns with your business goals.


• Pardot: Pardot's lead scoring model includes both explicit and implicit lead scoring, taking into account a prospect's job title, company size, website activity, and more.


• SharpSpring: SharpSpring's lead scoring model is built on a Grading system, with leads receiving a grade based on their dealings with your business, including email opens, form submissions, website visits, and more.


• Act-On: Act-On's lead scoring model scores leads based on both their behavioral and demographic attributes, in addition to scoring them based on their readiness to buy.


• Salesforce: Salesforce's lead scoring model allows you to build custom criteria to score your leads and identify those with the highest potential conversion rates.


• ExactBuyer: ExactBuyer's lead scoring model is centered around firmographic data, industry, company size, and keywords, helping you identify potential customers that match your ideal customer profile.


• Radius: Radius offers a predictive lead scoring model that uses artificial intelligence and machine learning to identify high-value accounts and prospects based on their browsing behavior, website activity, and more.


• InsideView: InsideView's lead scoring model uses a proprietary algorithm that considers firmographic data, industry, competitive position, and financial stability to generate its scores.


• Leadiro: Leadiro's lead scoring model uses a combination of behavioral and demographic data to score your leads, with points awarded based on engagement levels, industry, job title, and more.

Demographic-Based Scoring

Demographic-based scoring is a lead scoring model that uses demographic information to determine a lead's potential value. This information could include factors such as job title, company size, industry, location, and more. By analyzing this data, businesses can better understand the needs and behaviors of potential customers, and create more effective marketing and sales strategies.

How Demographic Information is Used

The demographic information of a lead can provide a lot of valuable insights to businesses looking to generate more leads and enhance their lead scoring system. Below are some ways demographic information could be used:

Building More Targeted Audiences

By analyzing demographic data, businesses can create more targeted audiences for their marketing campaigns. For example, a B2B company selling software could use demographic information to target decision-makers at companies of a certain size and industry. This would ensure that their marketing campaigns are reaching the most relevant audience, which can improve conversion rates and sales.

Creating Effective Marketing Campaigns

Demographic data can be used to create more effective marketing campaigns that speak to the needs and priorities of different customer segments. For example, a company selling fitness equipment could create different campaigns for different age ranges to better connect with potential customers based on their interests and preferences.

Improving Lead Scoring Models

Demographic data can also be used to improve lead scoring models, which help businesses prioritize their lead nurturing and sales efforts. By analyzing the characteristics of high-value customers, businesses can create more accurate scoring models that prioritize leads with similar characteristics.

Effective Models and Examples

Below are some effective models that use demographic data to score leads:

• Firmographic Scoring: This model scores leads based on company-related demographic information such as industry, company size, revenue, and location.


• Job Title and Role Scoring: This model scores leads based on their job title and role within the organization, as this can impact their decision-making power and authority.


• Geo-Targeting Scoring: This model scores leads based on their location, allowing businesses to focus on leads in specific geographic regions that may be more likely to convert.

An example of an effective demographic-based scoring model would be a B2B software company that scores leads based on firmographic data such as industry and company size, as well as job title and role within the organization. Through this model, they can prioritize leads that work at companies of a certain size and industry, and have decision-making power within their organization.

In conclusion, demographic-based scoring is an effective way to better understand potential customers, create more targeted audiences, and improve lead nurturing and sales efforts.

Behavioral-Based Scoring

Lead scoring models help businesses determine the best leads for their sales team to follow up with. One approach to lead scoring is behavioral-based scoring, which involves tracking a lead's behavior and engagement with a company's marketing efforts.

How Tracking Behavior Indicates Level of Interest and Likelihood to Convert

Behavioral-based scoring looks at how a lead interacts with a company's website, emails, social media, and other digital assets. It takes into account actions such as:

• Page views


• Downloads


• Email opens and clicks


• Social media engagement


• Form submissions

By tracking these actions, businesses can gain insights into a lead's level of interest and engagement with their brand. For example, a lead who has visited multiple pages on a website and downloaded a white paper is likely more interested than someone who has only visited once and not taken any further actions. This information can be used to prioritize leads for sales follow-up.

Examples of Successful Models

Companies can use a variety of models for behavioral-based scoring, from simple point systems to more complex algorithms that take into account multiple factors. One successful approach is the engagement scoring model, which assigns points to different actions based on the level of engagement they indicate. For example:

• 10 points for downloading a white paper


• 5 points for visiting the pricing page


• 3 points for opening an email


• 1 point for following the company on social media

Other successful models include tracking a lead's progression through the sales funnel or assigning negative points for certain actions that indicate disinterest. By constantly monitoring and tweaking their models, businesses can optimize their lead scoring process and improve their sales team's effectiveness.

Fit-Based Scoring

When it comes to lead scoring, determining a lead's fit with your business is just as important as measuring their level of engagement. Fit-based scoring takes into account how well a lead aligns with your ideal customer persona and how likely they are to benefit from your product or service. By prioritizing leads based on their fit, you can focus your resources on those who are most likely to convert and become long-term customers.

Why Fit Matters in Lead Scoring

Before you can score leads based on their fit, you need to have a clear understanding of your ideal customer persona. This includes demographic information like age, location, and job title, as well as psychographic data like their interests, values, and pain points. By creating a detailed profile of your ideal customer, you can identify leads who share those same characteristics and are therefore more likely to be a good fit for your business.

Scoring leads based on fit can help you prioritize certain leads over others. For example, if a lead matches all of your ideal customer characteristics and has shown some level of engagement with your content, they may be a high-priority lead that you want to follow up with right away. In contrast, if a lead doesn't match your ideal customer profile and hasn't engaged with your content, they may not be worth pursuing.

Notable Fit-Based Scoring Models

• HubSpot's Fit and Engagement Scoring - This scoring model takes into account a lead's fit with your ideal customer profile as well as their level of engagement with your brand.


• Salesforce's B2B Lead Scoring - Salesforce uses a fit-based scoring model that takes into account a lead's demographics, firmographics, and engagement history.


• ExactBuyer's Fit-Based Scoring - ExactBuyer offers fit-based scoring that takes into account a lead's industry, company size, location, and other factors to determine their fit with your business.

Predictive Scoring

Predictive scoring is the process of using historical data and machine learning algorithms to assign a score to leads based on their likelihood to convert into paying customers. This technique can save time and resources by allowing sales teams to focus on leads that have a higher probability of closing.

Use of Machine Learning and Predictive Analytics

Machine learning algorithms and predictive analytics are used to analyze large datasets and identify patterns that can be used to predict which leads are most likely to convert. The algorithms use historical data on lead behavior, such as website visits, email opens, and content downloads, to identify factors that are correlated with conversion.

Using these factors, the algorithms can assign a score to each lead that indicates the lead's likelihood to convert. The score can be used to prioritize leads for follow-up or to trigger automated workflows that move leads through the sales funnel based on their score.

Effective Models

Effective models for lead scoring are highly dependent on the unique characteristics of each business. However, some common models include:

• Demographic scoring: This model assigns a score based on factors such as company size, industry, and job title


• Behavioral scoring: This model assigns a score based on actions taken by the lead, such as website visits, content downloads, and email opens


• Fit scoring: This model assigns a score based on how closely the lead matches the ideal customer profile, such as matching personas or account-based marketing criteria

Effective models are constantly being refined based on new data and feedback from the sales team. By using predictive scoring, businesses can improve the efficiency and effectiveness of their sales processes, resulting in higher conversion rates and increased revenue.

Time-Based Scoring

When it comes to lead scoring, it's not just about the specific actions that a lead takes, but also the timeline in which they take those actions. It's important to take into account the timeline of a lead's engagement with your business because it can help you prioritize your sales efforts and identify when leads are most likely to be ready for a sales conversation.

Importance of Time-Based Scoring

By looking at the time-based dimension of lead behavior, you can identify patterns and trends that are not as apparent with other lead scoring models. For example, a lead who downloads an eBook and subscribes to a newsletter might seem like a highly engaged lead, but if these actions were taken several months ago and there has been no activity since, they may not be as high of a priority as a lead who clicked on a link in a recent email and visited your pricing page.

Time-based scoring models take into account the recency, frequency, and consistency of lead activities to determine their score. Leads who have engaged recently, frequently, and consistently will have a higher score than those who have not.

Examples of Time-Based Scoring Models

• Last Activity Date Scoring: This model assigns higher scores to leads who have taken action more recently, such as opening an email, visiting a webpage, or filling out a form.


• Recency, Frequency, Monetary (RFM) Scoring: This model considers how recently a lead has engaged with your business, how frequently they have engaged, and how much money they have spent (if applicable).


• Predictive Scoring: This model uses machine learning algorithms to predict a lead's likelihood of becoming a customer based on their behavior and engagement with the business over time.

Overall, time-based scoring models are an important component of a comprehensive lead scoring strategy. By taking into account the timeline of a lead's engagement with your business, you can prioritize your sales efforts and identify the most promising leads for conversion.

Multi-Dimensional Scoring

Multi-dimensional scoring is the process of using a combination of different lead scoring methods and criteria to create a more comprehensive and accurate scoring system. By combining different scoring techniques, businesses can get a deeper insight into leads and classify them more effectively.

Benefits of Multi-Dimensional Scoring

The benefits of using a multi-dimensional scoring system include:

• More accurate lead prioritization: By using multiple scoring criteria, a business can ensure that the highest scoring leads are truly the most valuable prospects.


• Improved lead segmentation: A more comprehensive scoring approach allows businesses to segment leads into a wider range of categories based on different factors.


• Greater insight into prospects: Using different scoring metrics can help businesses build a more complete picture of a prospect and their needs.


• Deciding on next steps: By using a combination of scores, businesses can determine the best way to engage with prospects and what steps to take next in the sales process.

Noteworthy Examples of Multi-Dimensional Scoring

Some of the noteworthy examples of multi-dimensional scoring include:

• HubSpot Marketing Hub - which uses criteria such as behavior, demographics, and firmographics in their lead scoring system.


• Salesforce Einstein Analytics - which leverages machine learning to analyze various lead attributes and predict their likelihood of conversion.


• Pardot - which uses a combination of implicit and explicit scoring based on email engagement, form fills, and website behavior to score leads.

Custom Scoring

Custom scoring models can be a game changer for businesses looking to improve their lead qualification process. A custom scoring model is designed specifically for a business’s unique needs, and can help prioritize leads based on the most important factors.

Advantages of Creating a Custom Scoring Model

• Increased accuracy in lead scoring


• Ability to take into account unique criteria and variables


• Improved lead prioritization and allocation of resources


• Alignment with specific business goals and objectives


• Improved collaboration between sales and marketing teams

Guidance on Developing an Effective Custom Model

Developing an effective custom scoring model requires careful consideration of a business’s unique needs and ideal buyer personas. The following are some key steps to developing an effective custom lead scoring model:

1. Identify the criteria and variables that will be used in the scoring model


2. Assign point values to each criterion or variable based on its importance


3. Test and refine the scoring model to ensure accuracy and effectiveness


4. Collaborate with sales and marketing teams to ensure alignment with business goals and objectives


5. Regularly review and update the scoring model to ensure continued effectiveness

Scoring with AI and Automation

The process of lead scoring can be time-consuming and labor-intensive, but with the help of artificial intelligence (AI) and automation, it can be streamlined and made more efficient. By using advanced technology, businesses can more effectively prioritize leads, identify potential customers, and allocate resources to maximize their sales efforts.

AI-powered Scoring Models

AI-powered scoring models can be highly effective in determining which leads are most likely to convert into customers. These models use machine learning algorithms to analyze a variety of data points, such as website behavior, social media activity, and email engagement, to identify patterns and predict which leads are most likely to become customers.

One successful example of an AI-powered scoring model is the lead scoring model developed by ExactBuyer. By using real-time contact and company data, as well as audience intelligence solutions, this model can help businesses build more targeted audiences and identify new accounts within their territory. ExactBuyer's model has helped businesses such as Brex, Gorgias, Ramp, and Northbeam see significant improvements in booked demos, qualified deals, positive replies, and list building times.

Automated Lead Scoring Processes

In addition to AI-powered scoring models, businesses can also benefit from automating certain aspects of the lead scoring process. Automating tasks such as lead data collection, data analysis, and lead assignment can help businesses save time and resources, while also ensuring consistency and accuracy in the scoring process.

One example of an automated lead scoring process is the lead scoring feature in HubSpot. HubSpot's platform automatically scores leads based on factors such as engagement level, demographic information, and behavior, and assigns them to the appropriate sales reps. This automation can help businesses prioritize their sales efforts and focus on the leads that are most likely to convert into customers.

Choosing the Right Lead Scoring Model

If you want to increase the efficiency of your sales funnel, lead scoring is the way to go. By using a lead scoring model, you can better qualify leads and discover who is most likely to convert into a customer. However, with so many different models available, it can be difficult to know which one is best for your business. In this guide, we'll provide advice on how to choose the best lead scoring model for your business, taking into account factors like industry, business goals, and available technology.

Step 1: Define Your Business Goals

The first thing you need to do when choosing a lead scoring model is to define your business goals. Are you looking to increase sales? Build brand awareness? Improve customer retention? Your answer to this question will help you choose a model that aligns with your goals.

Step 2: Determine Your Ideal Customer Profile

Once you've defined your business goals, the next step is to determine your ideal customer profile. Who is your target audience? What are their pain points, interests, and demographics? Answering these questions will help you create a persona of your ideal customer, which you can then use to create your lead scoring model.

Step 3: Evaluate Available Technology

Before choosing a lead scoring model, it's important to evaluate the available technology. Some lead scoring models require more advanced technology, such as machine learning or artificial intelligence, while others can be implemented with simple software tools. Assess the technology you currently have available and choose a model that's suitable for your resources.

Step 4: Consider Industry Best Practices

While every business is unique, there are still best practices that should be considered when choosing a lead scoring model. Research what has worked for other businesses in your industry, and see if you can adapt those models to fit your own needs.

Step 5: Test and Refine

Finally, it's important to understand that your lead scoring model is not set in stone. Testing and refining your model can help you better understand what's working and what's not. Continuously testing and refining your model will lead to better results and ultimately help you achieve your business goals.

Conclusion

In conclusion, selecting the best lead scoring model is critical for businesses that want to increase conversions and sales. By utilizing a variety of lead scoring models, businesses can prioritize their sales efforts and focus on the leads that are most likely to convert.

Key Takeaways

• Lead scoring is a crucial part of the sales process.


• Scoring models can be based on demographic data, behavioral data, or a combination of the two.


• Lead scoring models need to be refined over time to be effective.


• Using an AI-powered lead scoring solution can save time and increase accuracy.

We encourage readers to try out different lead scoring models to find what works best for their business. By experimenting with different variables and criteria, businesses can create a custom lead scoring model that maximizes their chances of success.

If you're interested in implementing a more sophisticated lead scoring system, ExactBuyer offers real-time contact and company data solutions that can help you build more targeted audiences and prioritize your sales efforts. Contact us to learn more.

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