Lead Conversion Starts with Signals – Use Fit Scoring or Lose Business!

Identifying the best leads is essential for any company’s success as it helps focus resources and efforts on prospects most likely to become customers. In marketing, we have limited resources (employees, money, time) – we rarely have the ability to “spray and pray” with our sales & marketing efforts. Going at the wrong person before they’re ready is time-consuming and expensive, and can ultimately hurt you in your ability to successfully reach them down the line when they actually are ready for your product. Either hit the nail right on the head the first time or you’ll set yourself backwards or miss the opportunity entirely. The process of lead identification can vary depending on the industry, company size, and target market, but here are some common strategies used by most companies:

  • Defined by the ICP (Ideal Customer Profile)
  • Weighted and/or predictive lead scoring
  • Buying behaviors / Engagement
  • Source & channel analysis
  • CRM data
  • Discovery & qualification processes
  • Feedback from sales teams
  • Customer feedback
  • Data analysis & machine learning
  • Continuous refinements

By combining these approaches, companies can optimize their lead identification process and focus their efforts on the leads most likely to become valuable customers. While all of these factors are important to consider, without one of them, the rest will provide minimal, if any, value.

No matter what, you have to start with defining your Ideal Customer Profile (ICP). Companies begin by creating detailed profiles of their ideal customers. These profiles typically include demographic information, buying behavior, pain points, and preferences. By knowing who their best customers are, companies can target similar prospects. More advanced methods to create your ICP can come from looking at all the customers or prospects who have converted and understanding their shared common attributes.  

Once you’ve determined your ICP and discovered your Total Addressable Market (TAM), it’s time to decide which leads to pursue. To determine where to focus your sales and marketing efforts, you need a way of scoring your leads across your TAM by their propensity to buy your product or service. Before you consider buying behavior, CRM data, feedback, discovery process, channel analysis and continuous refinement, you need a place to start. If you start with buyer behavior, you might spend months chasing down a lead that showed intent, only to find out their company wasn’t realistically capable of buying your product to begin with. Simply put, buyer behavior doesn’t help unless you know they are from a company likely to buy your product. Propensity to buy or predictive lead scoring is one of the best ways to take the guesswork out of lead prioritization.  

So what is lead scoring? Lead scoring is a system that assigns a numerical value to each lead based on their characteristics and interactions with the company. Positive actions, such as website visits, email engagement, and content downloads, increase the lead score. The higher the score, the more likely the lead is to be considered “hot” or “qualified.” Advanced lead scoring takes into account not only the behavior or actions but also gives “extra points” for companies with a high fit score.

A company fit score indicates whether a company is a good fit to be your customer—and worthy of sales attention. It’s calculated with fit data – details that make up a company’s firmographic profile (such as industry, country and number of employees).

A fit score, also known as a propensity to buy score, at the company level is a numerical representation of how well a lead aligns with your ideal customer profile (ICP) of a company. It helps businesses evaluate the suitability of a lead based on predefined criteria and how closely the lead matches the characteristics of their best customers.

The fit score is determined by looking at all of the positive outcomes over the last few years. This is typically about the leads that have converted into revenue. A Machine Learning model is developed in consideration of all the various factors that are common attributes across those positive outcomes. The specific criteria used to calculate the fit score may vary from one company to another, but common factors include:

  • Demographics: This includes data such as the lead’s location, company size, industry, and job title. If a lead’s attributes match the company’s target demographics, it receives a higher fit score.
  • Firmographics: For B2B companies, firmographics play a crucial role. Information about the lead’s company, such as revenue, number of employees, and business structure, helps to assess the fit.
  • Engagement (Person-level): The level of interaction that person has with your company’s marketing materials, website, and content is often a significant factor.
  • Behavioral Data – Intent (Company-level): Tracking the lead’s online behavior, such as the pages they visit, the content they download, and their time spent on the website, provides insights into their interests and alignment with the company’s offerings.
  • Technographic Data: For tech-related products or services, technographic data about the lead’s current technology stack and software usage can be relevant.
  • Referral Source: The lead’s source can indicate how well it aligns with the company’s target audience. For example, leads coming from specific marketing campaigns or referrals might be more likely to be a good fit.
  • Data Analysis and Machine Learning: In some cases, advanced data analysis and Machine Learning algorithms are used to evaluate historical data and patterns to determine the fit score.

Once these factors and buying signals are considered, the fit score is calculated using a scoring system that assigns weights to each signal based on its importance or “lift” (a numerical representation of the percentage change in odds of conversion based on that particular signal being triggered). The data is then aggregated, and the lead is assigned a fit score. The fit score helps sales and marketing teams prioritize leads, focusing their efforts on those with higher fit scores and a better chance of becoming valuable customers.

Many times, companies determine fit with firmographics alone, which is much better than skipping fit all together, but they can significantly compound the success of their fit scoring by factoring technographics (web technologies and install base technologies) into the equation. In software or technology companies, often the best Customer Data Platforms (CDPs) and fit providers will generate a fit score which takes technographics into account to provide the most accurate propensity score available.

It’s worth noting that while a fit score is a valuable tool in identifying the right companies to pursue, it is typically one part of a broader lead scoring system, which may also include intent scoring (to find the ready company), persona scoring (to find the right people), and engagement scoring (to find the ready people). Together, these scores provide a more comprehensive view of a lead’s potential and likelihood to buy your product or service.

In short, the most important step in prioritizing closeable business is the first step – determining fit scores across your Total Addressable Market (TAM)! Do not skip this step or else you’ll waste a tremendous amount of time, money and effort in pursuit of leads that were destined to be a bad bet from the start. For more information about using scoring models to optimize your lead prioritization, check out the Revenue Radar guide.

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