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Lead Scoring

Lead scoring is a process sales and marketing teams use to measure the quality of leads brought into the funnel. By assigning numeric values to leads based on predictive dimensions, lead scoring helps businesses to prioritize their efforts towards the most promising prospects.

This process not only streamlines sales workflows and expedites customer acquisition, but also fosters alignment between marketing and sales teams, promoting collaborative strategies and data-driven decision making.

This guide delves into the concept of lead scoring, its numerous benefits, and how it works, providing a comprehensive understanding of this transformative technique.

What is lead scoring?

The benefits of lead

scoring

4 Common lead scoring

dimensions

Lead scoring model

examples

Lead scoring model best

practices

Find higher quality leads

with LinkedIn.

What is lead scoring?

Lead scoring is a model that predicts and grades the quality of a lead using select data points.

The scoring process assigns numeric values to leads based on dimensions like company size, job title, brand touchpoints, exposure to content, and behavioral indicators. Lead scoring means marketers can be better equipped to target specific markets, and sales teams can accurately prioritize the best leads.

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The benefits of lead scoring

The ideal outcome of a tailored lead scoring model is shorter sales cycles, increased revenue, better targeting to attract leads of a similar quality, and higher retention of customers once they’ve converted.

Identifying attributes of the businesses' most valuable customers are often used to build lead scoring models that rate the quality of prospects with similar traits. Because sales teams can prioritize these leads, they not only increase sales team’s productivity but also lead to faster sales cycles and improved customer acquisition rates.

Furthermore, it fosters alignment between marketing and sales teams to qualify what “valuable” means and promotes a culture of data-driven decision making.

Here’s a deeper look into the benefits of lead scoring:

More efficient sales processes

When a sales team has reached the tipping point between what can and can’t be processed manually, there are two options. One is to simply work leads like a numbers game, manually separating qualified and unqualified.

But a narrowly targeted, objective, numbers-based scoring system automates the process of finding the best leads, which helps sales leaders forecast deals and revenues, helping team leaders and executives to plan more exactly.

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Faster sales cycles and customer acquisition

Lead scoring helps sales teams move faster.

Instead of sifting through hundreds of leads in a CRM or spreadsheet, sales reps can hone in on high-scoring leads.

A lower score doesn’t mean the lead will never convert, but it indicates a need for more nurturing, calling for a longer sales cycle. Automated sales nurturing emails go together with lead scoring, so sales reps can focus on highly qualified leads.

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Better targeting for marketing campaigns

Lead scoring also helps marketers better target their lead generation campaigns. Instead of randomly targeting leads, lead scoring allows marketers to target lead types that are likely to convert into customers.

A well-designed lead scoring model can support marketing teams in establishing specific lead goals. Rather than having a general aim such as “marketing qualified leads,” they can channel more resources towards sales qualified leads and opportunities, resulting in more efficient use of time and resources.

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Increased alignment between marketing and sales teams

Marketing and sales teams can work together on setting lead scoring criteria, and picking lead sources, which in turn moves leads through the pipeline faster. This facilitates collaborative work like creating content to help close high propensity leads, setting up automated nurturing campaigns, and giving teams the data necessary to fuel segmentation and market expansion.

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How does lead scoring work?

Lead scoring looks different at every company.

The model uses inputs to forecast the likelihood of outputs. It evaluates discernible traits of a lead or account, such as age, job title, company size, industry, or intent signals, and subsequently assigns scores to these attributes based on their capacity to anticipate how suitable the lead is for the purpose.

For example, an executive at a large company garners more points than a self-employed lead.

If a lead has opened multiple emails from your company, or visited your website multiple times within a designated time frame, they'd receive additional points compared to a lead that hasn't interacted with your brand recently. The pages they visit matter, too; if they've only visited informational blog posts, they may not receive as many points as someone who’s visited pricing or feature pages multiple times.

After assigning points for each lead, marketing and sales leaders can agree on a "threshold" that defines a marketing-qualified lead and a sales-qualified lead. If a lead passes the threshold for a sales-qualified lead, they're sent to the sales team and then further prioritized by their score as well as manual evaluation by each sales rep.

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4 Common lead scoring dimensions

Lead scoring aims to prioritize potential customers based on their likelihood to convert. The four common elements that underpin this technique are demographic, firmographic, behavioral, and attitudinal information. These aspects offer insights into a lead's characteristics, company details, actions, and sentiments, respectively, helping businesses to identify high-quality leads and optimize their sales efforts.

Demographic information

Demographic information is a common signal used in lead scoring models. Examples of demographic lead scoring criteria include job title, age, company size, industry, and location.

In most cases, demographic information is simply cross-referenced against a company's ideal customer profile.

For example, an enterprise IT software platform would score a CIO or VP higher than an individual contributor, because the individual contributor lacks the decision-making authority that the executive has.

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Firmographic information

Firmographic is similar to demographic data but about businesses and organizations, such as industry type, company size, revenue or employee count. This data is one of the most important and commonly used in B2B marketing and sales, especially if the company uses account-based marketing.

For example, a company may discover that EMEA leads convert higher than North American leads, and therefore score those higher in their models.

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Behavioral information

Behavioral information tracks actions or events that a lead takes, such as the source of the lead, the number of website visits, which pages they visit, social media interactions, email open rates, and offer downloads.

Behavioral data can be explicit (such as when a lead fills out a consultation form or demo request) or implicit (such as visiting a pricing page). Implicit behavioral data is often referred to as "intent signals."

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Attitudinal information

Attitudinal lead scoring looks at lead sentiment towards your product, service, or industry. While behavioral data tracks what leads do, attitudinal data tracks what they say.

Using survey data and/or customer feedback to track lead sentiment over time can accomplish this.

Attitudinal data is best leveraged when combined with other dimensions such as firmographic data (are they at the right company?) and behavioral data (have they expressed enough intent?).

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Lead scoring model examples

For a concrete example, imagine a business leader at a SaaS company that sells IT software to enterprises.

They’re just beginning to build out their sales process, but they have a good understanding of their ideal customer profile. Generally, they need to sell into larger companies and convince decision makers (ideally an executive) as well as buy-in from the end user (typically a manager or individual contributor).

This business could create a model that designates higher scores for larger companies and higher job titles, as well as incorporating behavioral intent signals like pricing page visits and satisfaction scores in lead surveys.

They might build a simple model in a spreadsheet to get started, for example:

With time and data, a business leader can optimize their lead scoring model to eliminate variables, change scoring criteria, or include new dimensions.

Additionally, organizations are now leveraging machine learning to reverse engineer quality signals from existing customers. This takes the guesswork out of identifying lead scoring dimensions and uses correlations to find dimensions that are‌ impactful.

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Lead scoring model best practices

When creating a lead scoring model, remember no lead scoring model will be perfectly accurate, and overengineering could be a waste of time and resources.

Keep these best practices in mind when creating a lead scoring model:

Identify measurable dimensions

Millions of data points could predict a good fit lead. Use the ones that are actually measurable.

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Invest in tools

depending on the complexity of the model, it may be necessary to procure analytics software, intent data, and integration software to fuel the lead scoring model.

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Keep it simple

For simplicity’s sake, use only the most impactful data points.

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Establish a lead “fit score”

Pick a score threshold to qualify a lead as a good fit.

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Centralized data collection

If possible, use a CRM with great integration capabilities so all of this data is stored in the place sales reps are working.

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Prioritize lead types

Figure out which leads will be routed to which sales reps, which ones will be sent to automation sequences, and which ones will be thrown out.

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Review lead scoring model regularly

Lead scoring models should be reviewed and updated as needed on a regular basis in order to ensure they’re still relevant and effective.

Consider negative lead scoring

It’s often easier to exclude leads that are known not to be a fit than it is to predict which leads will be a great fit.

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Find higher quality leads with LinkedIn.

Lead scoring is a nearly ubiquitous technique used to quantitatively evaluate the quality of leads generated by sales and marketing teams. It allows marketers to target prospects more effectively and sales teams to waste less time on bad fit leads.

While no lead scoring model will perfectly predict lead quality, LinkedIn Sales Navigator can help sales teams proactively identify target accounts, nurture lower quality leads, and gain valuable insights on prospects that fill the sales funnel with contacts likely to close.