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When most people first start using inbound marketing, their main concern is obtaining enough fresh leads into the funnel. However, after you have a large number of leads, you must determine who is truly interested in your product and who is simply looking around.

This is where lead scoring comes into play.

What Is Lead Scoring?

Lead scoring is the act of assigning values to each lead you produce for the business, frequently in the form of numerical “points.” You may grade your leads based on a variety of factors, such as the professional information they’ve provided you and how they’ve interacted with your website and brand across the internet. This approach assists sales and marketing teams in prioritising leads, appropriately responding to them, and increasing the rate at which those leads become customers.

Every organisation has a distinct strategy for assigning points to score their leads, but one of the most frequent is to establish the value system utilising data from previous leads.

How? To begin, examine your contacts who have become clients to determine what they have in common. Following that, you will examine the characteristics of your contacts who did not become clients. After reviewing the historical data from both sides, you may determine which factors should be significantly weighted based on their likelihood of indicating someone is a good fit for your product.

Lead scoring appears to be simple, doesn’t it? This can easily grow difficult depending on your business model and the leads in your database. To make things a bit easier for you, we’ll walk you through the basics of building a lead score, including what data to look at, how to locate the most significant features, and how to calculate a basic score.

Lead Scoring Models

Lead scoring models ensure that the values you assign to each lead accurately represent their suitability with your offering. Many lead scores are based on a 0 to 100 point scale, but each lead scoring model you develop will support a specific attribute of your core consumer.

Here are six possible lead scoring methods based on the type of data you can acquire from your customers:

  1. Demographic Information

Are you solely selling to a specific demographic, such as parents with small children or CIOs? Ask demographic questions in your landing page forms, and you can utilise the answers to determine how well your leads fit in with your target audience.

You may use this data to remove outliers from your sales team’s queue by deducting points for those who fall into a category you don’t sell to. For example, if you exclusively sell to a specific geographic area, you may provide a negative score to any lead that falls outside of the appropriate city, state, zip code, nation, and so on.

If some of your form fields are optional (such as a phone number), you may want to reward leads who provide that options information, nonetheless.

  1. Company Information

Are you more interested in selling to businesses of a given size, type, or industry if you’re a B2B company? Are you more interested in business-to-business or business-to-consumer organisations? You may also ask questions like these on your landing page forms, giving points to leads that meet your target audience and deducting points from leads who aren’t at all what you’re looking for.

  1. Online Behaviour

The way a lead interacts with your website can reveal a lot about their level of interest in purchasing from you. Examine your leads who finally become customers: Which offers did they get? How many offers did they get? Which – and how many – pages of your website did they visit before becoming a customer?

The quantity and type of forms and pages are both critical considerations. Leads who visited high-value pages (such as pricing pages) or filled out high-value forms may receive better lead scores (like a demo request). Similarly, you might award better ratings to leads who visited your site 30 times rather than three times.

What about leaders whose behaviour has evolved? If a lead stops visiting your website or downloading your offerings, they may no longer be interested. You may deduct points from leads who have stopped connecting with your website after a particular amount of time. The length of time — 10 days, 30 days, or 90 days — is determined by your average sales cycle.

  1. Email Engagement

You don’t know how interested someone is in buying from you if they have opted in to get emails from your firm. Open and clickthrough rates, on the other hand, will provide a much better indication of their degree of interest. Your sales team will want to know who read each email in your lead nurturing series or who clicked through your offer promotion emails consistently. They might then concentrate on those who appear to be the most engaged. Leads who click through on high-value mailings, such as demo offers, may also receive a higher lead score.

  1. Social Engagement

The level of engagement a lead has with your company on social media can also indicate their level of interest. How often did they interact with your company’s tweets and Facebook posts? How many times did they share or retweet those posts? If your target consumers are engaged on social media, try providing points to leads who have a certain Klout score or number of followers.

  1. Spam Detection

Last but not least, you may wish to penalise leads who filled out landing page forms in ways that could imply spam. For instance, were the first, last, and/or corporate names not capitalised? Is it possible that the lead-filled out any form fields by typing four or more letters in the traditional “QWERTY” term side by side?

You should also consider which types of email address your leads are using in comparison to your client base’s email addresses. If you’re marketing to businesses, you might deduct points from leads who use Gmail or Yahoo! email addresses.

How Do You Know What Matters Most?

That’s a lot of data to sort through; how do you judge which info is most important? Should you inquire with your sales team? Should you conduct client interviews? Should you go over your analytics and run some reports?

We recommend combining all three. Your sales team, customers, and analytics reports will all help you piece together what information is most helpful for converting leads into customers, allowing you to link certain points to specific offers, emails, and so on.

Talk to your sales team

Salespeople are the ones on the ground, interacting with both leads who became customers and those who did not. They usually have a pretty good grasp of which marketing materials increase conversion.

Which blog content and offers do your sales representatives like to send prospects to? Some of them may tell you, “Every time I send folks this specific piece of collateral, it’s simpler to close them.” This is useful information. Determine the nature of the collateral and distribute points accordingly.

Talk to your customers

While your sales team may claim that certain material converts consumers, the folks who went through the sales process may have a different opinion. That’s fine: you want to hear both viewpoints.

Conduct a few client interviews to find out what they believe is to blame for their decision to buy from you. Make sure you interview clients that had both short and extended sales cycles to gain a variety of viewpoints.

Turn to the analytics

You should also complement all this in-person research with hard data from your marketing analytics.

Run an attribution analysis to determine which marketing efforts result in conversions at each stage of the funnel. Consider not simply the material that converts leads to customers, but also the content that people view before becoming leads. You might give people who download content that has historically converted people into leads a specific number of points, and those who download content that has traditionally converted people into customers a higher number of points.

Running a contacts report might also help you piece together excellent content for your website. A contacts report will show you how many contacts were generated as a result of various marketing activities, as well as how much revenue was made. Marketing activities may include downloads of specific offers, email campaigns click through, and so on. Take note of which behaviours are more likely to result in first-touch conversions, last-touch conversions, and so on, and award points accordingly.

Is One Lead Score Enough?

If you only have one primary customer, for now, a single score will be sufficient. However, when your business grows, you will sell to new audiences. You might branch out into new product lines, areas, or personas. You may even prioritise upselling and cross-selling to existing clients over acquiring new ones. If your contacts aren’t “one size fits all,” your scoring system shouldn’t be either.

Some marketing platforms allow you to build up numerous lead-scoring systems, allowing you to qualify distinct groups of contacts in different ways. Not sure how to set up more than one score? Here are some samples to get you started:

Fit vs. Interest

Assume your sales team wishes to evaluate customers based on both fit (i.e., is a contact in the right region? Is this the proper industry? The proper role?) and level of interest (for example, how active have they been with your online content?). If both of these traits are important to you, you may establish an engagement score as well as a fit score to prioritise outreach to contacts whose values are high in both areas.

Multiple Personas

Assume you run a software company that sells two types of products to two different sorts of buyers through different sales teams. You may establish two lead scores: one for buyer fit and one for buyer interest in each instrument. Then, you’d use these scores to route leads to the appropriate sales teams.

New Business vs. Up-sell

As your company grows, you may begin to prioritise upsell and cross-sell over new business. However, keep in mind that the signs that suggest the quality of new prospects and existing clients can seem very different.

Prospects may be evaluated based on demographics and website engagement, whereas existing customers may be evaluated based on the number of customer care requests they’ve sent, their engagement with an onboarding consultant, and how engaged they are currently with your goods. Consider developing several lead scores if these buying signals differ for different sorts of sales.

How to Calculate a Basic Lead Score

There are many different ways to calculate a lead score. The simplest way to do it is this:

Manual Lead Scoring

  1. Calculate the lead-to-customer conversion rate of all of your leads.

The number of new customers you gain divided by the number of leads you produce is your lead-to-customer conversion rate. Use this conversion rate as a guideline.

  1. Pick and choose different attributes of customers who you believe were higher quality leads.

Clients who requested a free trial at some point, customers in the finance business, or customers with 10-20 workers might all be attributes.

Choosing which attributes to include in your model is an art form in and of itself. You’ll choose attributes based on interactions with your sales staff, statistics, and so on – but it’s ultimately a judgment decision. You might have five people go through the same procedure and come up with five distinct models. But that’s fine as long as your scoring is based on the previously given data.

  1. Calculate the individual close rates of each of those attributes.

Calculating the close rates of each sort of action a person performs on your website – or the type of person doing that action – is critical since it determines the measures you’ll take in response.

Determine how many people become qualified leads (and, eventually, customers) based on their activities or who they are in relation to your main client. In the following phase, you’ll utilise these close rates to “score” them.

  1. Compare the close rates of each attribute with your overall close rate and assign point values accordingly.

Look for qualities with much greater closure rates than your overall close rate. Then, decide which attributes will receive points and how many points will be awarded. The magnitude of each attribute’s closure rates should be used to determine the point values for each attribute.

The exact point values will be somewhat arbitrary, but they should be as consistent as possible. For example, if your overall closure rate is 1% and your “requested demo” closing rate is 20%, the close rate of the “requested demo” attribute is 20X your overall close rate, allowing you to award leads with those attributes 20 points.

  1. Logistic Regression Lead Scoring

The simple approach for computing a lead score described above is a fantastic place to start. However, the most mathematically sound strategy is one that incorporates a data mining methodology, such as logistic regression.

As a result, data mining approaches are more complex and frequently more intuitive to your actual closing rates. Logistic regression entails creating an Excel formula that calculates the likelihood that a lead will become a customer. It’s more accurate than the technique we’ve outlined above since it’s a holistic approach that takes into account how all of the customer’s attributes like industry, company size, and whether or not someone requested a trial interact with one another.

Predictive Lead Scoring

Creating a lead score can help your organisation in a variety of ways, including improving the lead-handoff process, increasing lead conversion rates, increasing rep productivity, and more. However, as the two ways above demonstrate, developing a scoring system can be a time-consuming effort when done manually.

Furthermore, developing scoring criteria isn’t something you can “set and forget.” You’ll need to change your lead-scoring system on a regular basis as you gather input from your team and stress-test your results to guarantee it remains accurate. Wouldn’t it be nice if technology could eliminate the need for manual setup and constant tuning, giving your staff more time to create relationships with customers?

This is where predictive scoring comes into play. Predictive scoring employs machine learning to sift through hundreds of data points on your behalf in order to select your best leads. Predictive scoring examines what information your customers provide, as well as what information the leads who did not close share and creates a formula that ranks your contacts in order of significance based on their propensity to become customers. This enables you and your sales staff to prioritise prospects, avoiding pestering those who aren’t (yet) interested while engaging those who are.

What’s the nicest thing about predictive scoring? As with any machine learning application, your predictive score improves over time, and so will your lead follow-up plan.


Lead scoring is not a complicated mathematical technique that magically sorts leads. It is a logical method that will assist you in better understanding your customer and gradually moving them along the purchasing experience.

The majority of businesses do not use lead scoring. Unfortunately, that is to their detriment. Those companies will very certainly waste time and money converting ineffective leads while ignoring the good ones unintentionally. Of course, you don’t want to be one of those companies.

Contact the marketing experts at Shergroup to help you convert leads into sales and keep you updated with your lead score.

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Last updated | 19 July 2023

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