10 min

Assessing and classifying leads is just as important as generating new ones. Let’s look at the explicit and implicit parameters on which lead scoring is based, plus some ranking guidelines.

The main goal of a lead generation and an inbound marketing strategy is to provide your company with a number of new prospects.

It takes volumes to find lead flow regularity. This is why lead scoring is another key activity. It allows companies to understand each contact’s degree of interest in the offer. Let’s go deeper.

What’s lead scoring?

Lead scoring is the process of assigning a value, usually numerical, to each incoming lead.

The assigned score is based on parameters that are strictly dependent on the business, including the specificity and needs of each one.

What are the main benefits of lead scoring?

› Increased effectiveness and efficiency in sales
› Increased effectiveness of marketing activities due to more precise measurement and forecasting of results
› Greater alignment between the marketing team and the sales team
› Lean “sales cycles”.

Each company has a different scoring model. Nevertheless, some data and parameters are like pillars that can hardly be passed up by a company that intends to do lead scoring.

Lead scoring pillars

Lead scoring is based on the principle that each lead can be classified according to the degree of interest shown for a certain product or service.

To move forward with this classification, it’s necessary to consider two parameters: explicit and implicit. We’ll identify different lead scoring models for each, based on the type of data relating to those who interact with the company.

Explicit parameters

These are user details obtained during signing up/registration (i.e., for a service, demo, webinar, or e-book download request).

These parameters can be socio-demographic (especially for B2C) or business (for B2B). Let’s be more specific.

Demographics

Everything starts with the registration form: the company obtains a series of data to guide the score of each incoming lead based on the fields and information requested from the user. This includes age, gender, marital status, geographical origin, and other personal characteristics to identify the main features shared by those who buy the products/services, and to assign each prospect a management priority.

Rather, it’s possible to identify negative traits that don’t generate customer conversion. These leads will be given a lower or negative score that lowers management and attention priority.

Here’s an example: if your company only markets to a specific geographical area, then you’ll assign a low score to all leads whose city/state/zip code field does not fall within your target area.

Alternatively, you can assign a higher score for those with more and better details: if a lead, in addition to the mandatory fields, also fills out the optional ones (i.e., phone number), then that prospect may receive an extra score. This is because its action reveals a greater and deeper desire to connect with the company.

Company details

Likewise, in a B2B context, the form provides details like where the lead is employed, his/her corporate role, and company size.

Based on the target—that is essentially made up of start-ups, SMEs, and enterprises—a rank is given according to data provided by the lead. Here’s a lead scoring example applied to company details:

Default parameters

These collect all information needed to develop an exhaustive picture of the user, along with his/her intentions and degree of involvement. This set of data expresses the lead’s level of interest, revealing the prospect’s potential for converting into a customer.

Behavioral data

How a lead interacts with the site says a lot about their purchasing interest. This makes clarifying the actions of contacts who convert into customers key: what do they download? How much? How often? Which and how many pages do they visit before buying?

As you know, both the number and quantity of pages visited are relevant. It’s possible to assign higher scores to leads that have visited pages placed deeper into the conversion funnel (e.g. the price page) or on forms that require tighter contact (e.g. the request for a quote). Likewise, a lead that has more than 20 hits on your site will get a higher score than a lead that has less than five.

The score will also be modulated based on time spent. A lead that stops visiting your site or downloading content will see its score updated toward the bottom.

Email engagement

Email open and click percentages offer valuable information to companies on each contact’s degree of engagement. It’s possible to integrate the lead score based on the level of email interaction. This is also calibrated according to message type (lower in the case of a newsletter, greater in the case of a promotional email).

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Social engagement

The same goes for social networks, which is where the degree of interaction comes from. This makes the priority degree a lead. How many times have they clicked on your company’s tweets and Facebook posts? How many times have they retweeted or shared those posts? Obviously, the weight of social engagement on the lead score depends upon the social strategy’s centrality.

Data indicating invalidity

Some parameters are entirely negative. A classic example involves a user who incorrectly completes the form or, in extreme cases, does it without any logic (resulting in spam). Clearly, the input lead that fills out fields by typing random letters on the keyboard will receive a low, if not negative, score.

In B2B, on the other hand, the email entered into the form is relevant for lead scoring purposes. All contacts who enter personal email addresses (with any Gmail or Yahoo! domain) will receive a low or negative score. In this case, it’s not spam. Rather, it’s a way of prioritizing.

How to calculate the score

Once the contact’s quality criteria have been established, a scoring system may emerge and assign a value to each of the above parameters.

The advice is to keep the score on a scale from 0 to 100. The higher the score, the more that lead is either ready to convert or get in touch with your sales team.

For example, a contact could get close to a 100-point lead score if he/she:

  • holds a top position in a company with at least 100 employees;
  • has visited your pricing page at least five times;
  • has requested a service trial.

Conversely, a score between 1 and 50 indicates a contact that he/she:

  • doesn’t hold a managerial position in a company;
  • hasn’t visited the pricing page yet;
  • only downloads e-books and other informative material.

So, once the default values have been set for each of the above parameters, the last step is to set a score threshold for assigning the lead to the sales team.

Not only that: it’s also essential to identify brackets and clusters for the lower scores (from 1 to 20, from 20 to 40, and so on). For each cluster, it will be essential to develop different strategies for cultivating the relationship and accompany each lead to stages increasingly closer to conversion.

Marketing Automation is essential for this. It allows you to set lead nurturing strategies tailored to each recipient’s needs, interests, and behavior. What drives automation flows is the engagement level established by your lead score or the MailUp platform report.

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In summary

Lead scoring is just the starting point of an effective digital marketing strategy. The score indicates many, varied activities that can cultivate a relationship and accompany the conversion.

We suggest finding out what Email Automation can do (workflow and drip campaign) for your lead nurturing activities. All you have to do is request a free trial of the MailUp platform. You’ll have 30 days to test out all it can do.

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