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If you’ve been in sales or marketing for a while, you know: not all leads are created equal. Everyone does lead generation. But some are ready to buy right now, some are only just starting their research. And some are somewhere in between these two extremes.
Scoring these leads helps businesses prioritize leads, so they can focus time and resources on those prospects that are the most likely to convert. That makes it a powerful marketing tactic for a successful marketing and sales strategy. However, choosing the right lead scoring model for your business can ba a bit tricky, as different models offer different approaches.
In this blog, we’ll break down the most common types of lead scoring models, how they work, and how you can select the right one for your business.
Before diving into different models, it’s important to understand what lead scoring is and why it matters for your business. Lead scoring assigns a value to leads based on their likelihood to convert into paying customers. These scores are calculated using various factors, like:
Lead scoring helps your sales and marketing teams work together more effectively: it ensures that high-quality leads are pursued while less-qualified ones are nurtured for later.
There are several types of lead scoring models. Each one has different methods of assigning values to leads. Let’s look at the most common models used by businesses today.
This model focuses on static information about the lead, like their age, job title, company size, or industry. These factors often determine how closely a lead matches your ideal customer profile (ICP).
For B2B companies, firmographic data like industry, number of employees, or revenue might be prioritized. For B2C companies, demographic factors like a contact’s age, location, or income level are more relevant.
Behavioral lead scoring focuses on how potential customers interact with your organization online. This can include website visits, social media engagement, email clicks and content downloads. The more engaged a lead is with your brand, the higher their score will be.
For example, someone who downloads an ebook, attends a webinar, and clicks on several email links would receive a higher score than someone who simply visits the homepage.
Predictive lead scoring uses machine learning and AI to analyze historical data from previous successful conversions. By analyzing factors like demographic, behavioral, and firmographic data, predictive models can identify patterns and predict which leads are most likely to convert.
The algorithm becomes more effective over time as it learns from past lead data and conversion success rates. Some CRM platforms have built-in predictive lead scoring models, which can be customized based on your business goals.
Implicit scoring refers to the lead’s actions, like email engagement, form submissions, or website visits. Explicit scoring on the other hand involves information that a lead has shared directly, like filling out a form with their job title or indicating budget preferences.
Some businesses use a combination of implicit and explicit scoring to ensure that both engagement and direct interest are factored into the score.
Manual lead scoring involves assigning values to different attributes and behaviors manually. This can be a good option for businesses that want full control over the scoring process. By contrast, automated scoring uses algorithms and AI to assign scores based on predefined criteria or patterns.
To determine which lead scoring model is best for your business, consider the folloowing factors.
Small to medium-sized businesses (SMBs): SMBs often start with simpler lead scoring models, like demographic or firmographic scoring, due to limited resources and smaller datasets.
The more historical data you have on lead conversion, the better predictive models will work or your business. Companies with rich datasets can maximize the potential of AI-driven predictive scoring models, whereas smaller companies with limited data might find demographic or manual scoring more effective.
If your sales cycle is long, behavioral scoring can be particularly valuable because it helps track lead engagement over time. Companies with shorter sales cycles may rely more heavily on firmographic or demographic scoring to identify qualified leads quickly.
If your products or services are complex and require more nurturing, a combination of demographic and behavioral scoring (implicit and explicit) may be necessary to capture both interest and intent. On the other hand, businesses with simpler, transactional models may prioritize demographic scoring to find quick matches to their customer profile.
Do you have the technology and people in place to manage automated or predictive models? Smaller businesses or those with fewer marketing resources might prefer manual scoring or simpler models. Companies with robust CRM platforms may be able to leverage predictive or automated lead scoring efficiently.
Choosing the right lead scoring model can signifficantly impact your business’s ability to prioritize and convert leads. While there is no one-size-fits-all approach, understanding the strengths and weaknesses of each model is the first step toward making an informed decision.
Start simple, measure your success, and adjust your approach as you gather more data. Over time, your lead scoring process will evolve to become more refined and effective in delivering high-quality leads to your sales team.
Starting with lead generation? Or are you going to start automation marketing in general? Either way, there’s a wrong way to get started and there’s a right way. Learn all about it in our marketing automation guide.
