Lead scoring is a system used by B2B SaaS sales/marketing teams where leads are assigned points and assessed a score based on the presence of factors that indicate a high likelihood of converting into a customer.

Lead scoring creates an objective means of determining whether a prospective customer can be qualified as a marketing-qualified lead (MQL) or sales-qualified lead (SQL). By establishing a common framework for evaluating and communicating the sales-readiness of a lead, lead scoring drives increased sales efficiency and enhances alignment between sales and marketing. 

The ultimate goal of lead scoring is to have a consistent system for evaluating the quality of a lead in terms of its likelihood to convert.

While the methodology of lead scoring is widespread, every company creates its own unique lead scoring model based on its prior experiences and knowledge of factors that make a prospect likely to convert into a customer.

Lead scoring models can range from extremely simple to highly complex. Many SaaS companies use a lead scoring model where prospects earn a score between 0 and 100 based on their firmographic characteristics, decision-maker persona, online behavior, engagement history, and other factors. The higher the score, the more likely that lead is to convert into a customer.

When developing a lead scoring system, your job is to define which specific characteristics or behaviors should result in adding or subtracting points from your prospects. 

A prospect that requests a demo on your website normally gets a big boost to their lead score, indicating a high intent to purchase and likelihood of conversion. On the other hand, a prospect that visits your “Careers” page should probably have their score reduced, since they might be looking for a job instead of trying to buy your product. It’s also normal to assign more points for prospects that fit your Ideal Customer Profile (ICP).

Lead scoring can be automated with the help of marketing software like ActiveCampaign or Salesforce Engage. Once your company has defined its lead scoring model, these tools automatically track how prospects are engaging with your website, update lead scores in your CRM, and send you alerts when a prospect earns enough points to qualify as an MQL.

Below are just a few easily identifiable benefits of lead scoring.

Alignment Between Marketing and Sales

Implementing a lead scoring system naturally creates alignment between your marketing and sales team, as it requires developing a shared understanding of:

• The characteristics and behaviors of high-quality leads,
• When a prospect should be designated as an MQL, and
• When an MQL should be qualified as an SQL.

This shared understanding streamlines the lead qualification process and ensures that customers who advance in the sales cycle are genuinely sales-ready.

Increased Sales Efficiency

Lead scoring increases the efficiency of your sales team by ensuring that your account executives are consistently dealing with the most qualified prospects in your sales pipeline. Not only does this lead to higher customer conversion rates, you’ll also have a happier sales force with higher earnings and less employee turnover.

Optimized Customer Journey

Lead scoring also plays a role in optimizing the customer journey, ensuring that your prospects only advance in the sales/marketing funnel when they’re genuinely ready and receptive to a sales conversation. 

When you can accurately measure a prospect’s purchasing intent via their engagement with your website and marketing channels, you can anticipate when they’ll be ready to buy and connect them with sales at the perfect time to convert them into a customer.

Creating a lead scoring is a process of defining the answer to this question:

Which customer characteristics and behaviors show a positive/negative correlation with the prospect’s likelihood of becoming a customer?

Here’s how to get started:

1. Benchmark Your Conversion Rate – The idea behind lead scoring is to identify prospects for which the conversion rate will be high. To measure this objectively, you’ll need to calculate your existing customer conversion rate (number of new customers divided by number of MQL/SQLs) and use it as a benchmark.
2. Identify Valued Characteristics and Behavior – Now that you’ve benchmarked your conversion rate, it’s time to choose which characteristics or behaviors should be included in your model. The goal here is to propose actions or traits that might make a prospect more likely to convert into a customer, such as:

• The prospect requested a demo on your website,
• The prospect opened your last three lead nurturing emails,
• The prospect has more than 50, but fewer than 1,000 employees, etc.

In this step, you’ll want to reference your ideal customer profile, as well as engagement data from your most important marketing touchpoints.

1. Search for Correlations – In the last step, you essentially guessed which behaviors should add or subtract from a prospect’s lead score. In this step, you will calculate the close rates for customers who demonstrated the traits/characteristics or behaviors you proposed in the previous step. In doing so, you will be able to objectively measure the aggregate impact of each of these factors on sales outcomes.
2. Assign Points for High-Impact Factors – Once you’ve calculated correlations, you should know which firmographic variables or engagement behaviors have the greatest impact on customer conversion rate. Your system should assign a large amount of points for high-intent behaviors (e.g. requesting a product demo), while deducting points for low-intent or negative intent behaviors (e.g. unsubscribing from your email list).

If you’re not keen on building your own lead scoring model from scratch, there are software programs out there that will analyze your sales/marketing data and automate the development of a predictive lead scoring system for your SaaS company.

As your company gains traction in the marketplace, lead scoring plays an important role in maximizing the efficiency of your marketing/sales process so you can scale rapidly and grow your revenue.

As part of Directive’s in-house developed Customer Generation methodology, we use 1st party data to map Total Addressable Market (TAM) and identify the most valuable customers for our B2B SaaS clients. 

By directing our marketing efforts exclusively at high-value prospects, we’re able to aggressively scale advertising spend while moving the needle on critical metrics like average contract value, trial conversion rate, and customer lifetime value (LTV).

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