Lead Scoring

Sales & Marketing

What is it and why is it interesting for your business?

Lead scoring is a methodology used by sales and marketing to rank and prioritize potential customers (leads) based on their likelihood of converting into paying customers.

The goal is to retain customers that already have interest and convert them to "real" customers. For instance, if someone visits your website, you want them to revisit your website and potentially buy something or use your service.

Lead scoring can be a simple classification algorithm that gives a user a score between 0 and 1 to become a potential lead or not. Based on the scores, you can split your customers up into tiers: low, medium or high potential leads. The ones you then definitely want to target are the medium potential leads as they just need a bit more of convincing. The high potential leads do not necessarily need more efforts, but do need to be followed up quickly.

In this way, your marketing efforts can be bundled and no resources are wasted. Identifying your leads means also identifying the customer behavior, tracking your website, your sales, looking at the user demographics and so on. As such, one can identify a lead early on in the process and does not have to wait for them to click on an add, a whitepaper or a form to be informed by their interest. You can then anticipate and target more leads in advance.

 

How does it work?

In most cases, identifying your leads means identifying criteria that indicate a qualified, potential lead for your business. Useful criteria can be found by looking into questions like:

  • What are the demographics of the potential leads? e.g. industry, company size and location
  • In case of b2b sales, what are the firmographics? e.g. revenue, employee count
  • What is the behavior of the users? e.g. website visits, email interactions

Each criteria is seen as a potential factor to indicate whether a user could be a potential lead. The scoring model then assigns a score based on previous behavior and the characteristics of the user. This is a predictive model that is mainly based on patterns in the past that are linked to a conversion of a lead or not.

 

What do we need to pay attention to?

In the B2B market, GDPR regulations are generally not a cause for concern. However, in a B2C use-case ensure compliance with data privacy regulations when collecting and processing lead data. Additionally, consider ethical implications of lead scoring, such as potential biases in the data or model predictions, and take steps to mitigate them.

Establish a feedback loop to continuously monitor the performance of the lead scoring model in production. Collect feedback from sales and marketing teams on the quality of scored leads and their conversion outcomes. Use this feedback to iteratively improve the model by retraining on updated data or adjusting model parameters.