In today’s business world, marketing and sales teams are always looking for ways to improve lead generation and qualification. Identifying high-quality leads that are more likely to convert into customers is essential for any business’s success. Converting leads into customers is a challenging process that involves multiple stages, from raw lead to MQL, SQL, Bant qualified lead, and ultimately, customer. However, when scoring leads manually, there’s a higher risk of missing out on potential customers due to the limitations of rule-based scoring.

Fortunately, machine learning can provide a significant advantage in this scenario. By leveraging machine learning algorithms, businesses can score leads more accurately and effectively, leading to a higher conversion rate and increased revenue.

In this blog post, we’ll explore how machine learning can be used to score leads for Company X using various features such as demographics and behaviors.

The Importance of Lead Scoring:

Lead scoring ranks leads based on their likelihood of becoming a customer. This process allows businesses to focus their sales efforts on leads that are most likely to convert, leading to better sales performance and revenue growth. By combining machine learning with various features, businesses can automate lead scoring, allowing them to score leads more accurately and efficiently.

Explicit Lead Scoring:

Explicit lead scoring is based on observable or directly shared information, often collected via an online form or registration process. Factors that can be considered in explicit lead scoring include

• job title/role
• company size
• industry Machine learning algorithms can analyze this information and identify patterns that indicate a higher likelihood of conversion.

Implicit Scoring:

Implicit scoring is based on the actions that prospects take while engaging with Company X. Factors that can be considered in implicit scoring include

• Clicks on links in emails
• Completion of forms
• Visits to product benefits pages
• Visits to any company web or blog pages Machine learning algorithms can analyze these actions and identify patterns that indicate a higher likelihood of conversion.

Features for Lead Scoring:

To score leads effectively using machine learning, it’s essential to consider various features that can be used to assess the likelihood of a lead converting into a customer. Here are some key features that can be used for lead scoring:

1. Company-Specific Demographics: include factors such as industry, company size, and location. These factors help identify the companies that are most likely to become customers. For Example: Industry, company size, revenue, location, years in business, number of employees, etc.
2. Filmographies: refers to the characteristics of the company’s management team, such as job titles and education levels. These factors help determine the decision-makers who are most likely to engage with your product or service. For Example: Company name, industry, company size, revenue, location, ownership type, etc.
3. Techno-graphics: refer to the technology stack used by the company, such as software and hardware. This information helps determine whether the company is a good fit for your product or service and whether you can provide the necessary technical support. For Example: Technology stack, software usage, IT budget, tech adoption rate, etc.
4. Website Behavior: includes factors such as pages visited, time spent on the site, and actions taken, such as filling out a form. This data can help identify leads who are more engaged and interested in your product or service. For Example: Pages visited, time spent on each page, clicks, downloads, search terms, etc.
5. Social Media Behavior: includes factors such as engagement on social media platforms, posts shared, and followers. This information can help identify leads who are more likely to become customers. For Example: Social media platforms used, frequency of use, engagement level, etc.
6. Email Engagement: includes factors such as open rates, click-through rates, and responses to emails. This data can help identify leads who are more interested in your product or service. For Example:Open rates, click-through rates, conversion rates, bounce rates, unsubscribe rates, etc.
7. Sales Interaction History: includes factors such as previous purchases, interactions with sales representatives, and customer feedback. This information can help identify leads who are more likely to become customers. For Example:Previous purchases, sales interactions, customer service interactions, etc.
8. External Data: refers to information collected from outside sources, such as industry reports and news articles. This information can help identify potential customers and their needs. For Example: Economic data, market trends, industry reports, news articles, public records, etc.

Behavioral Data

Behavioral data refers to the actions a lead takes on your website or other digital channels. This type of data can provide insights into the level of interest a lead has in your product or service.

Some examples of behavioral data that can be used in lead scoring include:

• Pageviews: The number of pages a lead has viewed on your website can give you an idea of their level of interest. For example, a lead who has visited your pricing page multiple times is likely more interested than someone who has only visited your blog.
• Time spent on site: The amount of time a lead spends on your website can also be a good indicator of their interest. If a lead spends a long time reading a whitepaper or watching a demo video, they are likely more interested than someone who quickly navigates away.
• Content downloads: If a lead downloads a whitepaper or ebook, it can be a strong indicator of their interest in your product or service.
• Form submissions: If a lead fills out a form to request more information or to sign up for a trial, it is a good sign that they are interested in your product.
• Email engagement: If a lead opens or clicks on your marketing emails, it can be a strong indicator of their interest.

Machine Learning for Lead Scoring

Machine learning algorithms can analyze your data and identify patterns that indicate a lead’s likelihood to convert. By looking at historical data on leads that have converted, machine learning algorithms can identify which demographic and behavioral factors are most predictive of conversion.

This is accomplished by training a model on historical data of leads that have converted to customers, and then using that model to predict the likelihood of new leads converting. Here are five examples of features that can be used in lead scoring models:

1. Demographic Information: Age, location, job title, and other demographic information can be used to determine whether a lead fits your ideal customer profile.
2. Firmographics: Information about a lead’s company, such as industry, size, revenue, and number of employees, can be used to determine the potential value of the lead.
3. Website Behavior: Data on how a lead interacts with your website, such as pages visited, time spent on site, and forms completed, can be used to determine how engaged the lead is and how likely they are to convert.
4. Email Engagement: Information on how a lead interacts with your email campaigns, such as open rates, click-through rates, and email replies, can be used to gauge their level of interest and engagement.
5. Sales Interaction History: Data on a lead’s interactions with your sales team, such as phone calls, meetings, and demos, can be used to determine the level of interest and engagement of the lead.

For example, a lead scoring model for a software company might use features such as job title, company size, website behavior, email engagement, and sales interaction history. The model might assign higher scores to leads who have a job title that indicates they are decision-makers, work for larger companies, have spent significant time on the pricing page of the website, have opened and clicked on multiple email campaigns, and have had multiple conversations with the sales team.

Another example could be a lead scoring model for a B2B marketing agency. This model might use features such as industry, company revenue, website behavior, email engagement, and social media behavior. The model might assign higher scores to leads who work in industries that the agency specializes in, work for companies with high revenue, have downloaded multiple resources from the website, have engaged with email campaigns and follow the agency on social media.

Once the algorithm has been trained, it can be used to score new leads automatically. Each lead is assigned a score based on their demographic and behavioral data, and the leads can then be prioritized based on their score. This allows your sales team to focus their efforts on the leads that are most likely to convert.

However, building your own lead scoring model requires extensive feature engineering and data collection, followed by training the model. Ultimately, using AI and ML for lead scoring can lead to better sales performance and revenue growth.

There are several popular tools available in the market that use AI and ML for lead scoring.

1. Salesforce Einstein: It is an AI-powered platform that uses machine learning to score leads based on various factors, including demographics, engagement history, and online behavior.
2. Marketo: It is a marketing automation platform that uses AI and predictive analytics to score leads based on factors such as behavior, demographic data, and firmographics.
3. HubSpot: It is a sales and marketing platform that offers an AI-powered lead scoring tool that allows users to set up custom lead scoring models based on various criteria, including website behavior, social media activity, and demographic data.

Conclusion

Lead scoring is crucial for any B2B organization looking to optimize sales and marketing efforts. By using both demographic and behavioral data, you can identify which leads are most likely to convert and prioritize your efforts accordingly. Machine learning can help automate this process, making it more efficient and effective. By using machine learning for lead scoring, you can ensure that your sales team is focusing their efforts on the leads that are most likely to become customers.

Reference

1. Lead Scoring: The Complete Guide for B2B Sales and Marketing – 2023 Update
2. Lead Scoring 101: How to Use Data to Calculate a Basic Lead Score
3. lead scoring
4. Lead Scoring and Grading in Salesforce
5. What is lead scoring + the best lead scoring models