How do you score leads?

Lead scoring is a way for sales and marketing teams to rate possible customers based on how likely they are to buy a product or service. It gives each possible customer a number score based on things like their job title, the size of their company, and the type of business they are in.

The lead score, which shows how likely a sales lead will buy and whether they meet the qualifications, is based on explicit and implicit attributes.

₷Implicit attributes: how the lead acts on specific channels, like email or website hits.

Details about the person or business include their job title, address, industry, and how much money they make.

Prospects’ scores increase when they do good things in these areas, like clicking on and saving a price page. Something as unlikely as a prospect clicking “Unsubscribe” would take money away from the total.

Like words

• Lead scoring and marketing automation
• Predictive lead scoring

Why lead scoring is essential for sales and marketing

When a business doesn’t have a lead score, it’s hard to know what to do with its marketing and sales.

How would advertisers know if their ads are reaching the right people? Also, how would buyers know if they’re talking to the right people?

They were not going to.

Because of these things, lead scoring is essential for marketing and sales.

• Knowing Where to Put Your Sales and Marketing Efforts
• One of the best things about a lead score system is that it helps the sales and marketing teams figure out where to put their efforts.
• By giving each prospect a number based on specific, known good and bad habits, they can determine which prospects are most likely to close and put those prospects at the top of the list.

When marketing teams know how many leads their efforts bring in, they can:

• They should work on leads that have higher marks.
• Do more of the things that bring in better leads.
• Remove or change the messages and strategies that bring in bad leads.
• Improve the marketing platforms that bring in the most leads.
• Get rid of the lines that aren’t useful.

The clear benefit for sellers is fewer marketing-qualified leads (MQLs) who won’t benefit from, be able to afford, or show long-term interest in what the company offers.

Besides that, it lets sales reps:

• Do not waste time selling to MQLs who are not likely to close a deal.
• Answer the leads that have the best scores first.
• ₷make outbound plans that get more leads from the “best” groups.
• Improve sales engagement based on the customer persona and the chance to win.
• If you’ve had a lead for a while, you should know when to ditch it.

So, lead scoring also helps the marketing and sales teams talk to each other better by explaining their processes and goals in a way that both groups can understand.

Increases in conversion rates

To sum up, a well-run lead scoring system significantly raises the number of deals that are ultimately finished, and with better sales efficiency, we might add.

As the deal cycle goes on, though, there are more conversions:

• MQLs are being turned into leads.
• Someone from MQL is asking for a sales call
• An MQL turns into a sales-qualified lead (SQL).
• Having a SQL turn into a chance
• And those are just a few parts of the sales process.

In short, a lead scoring system helps businesses focus on the leads they know are most likely to turn into customers and go through the sales process. They can improve the lead management method at every step of the way.

Changes to how pipeline and opportunity management works

How well sales reps move chances through the sales pipeline is a big part of how well they do their jobs.

Lead scoring helps them determine which opportunities are most likely to turn into deals so they can work on suitable leads at the right time. It also tells sales teams how to track their success and handle deals at all stages of the pipeline by:

How fast the leads move from one stage to the next.

Which leads get stuck at each stage and must be nurtured or qualified further?

Which leads bring in the most money and have the best return on investment (ROI)?

By looking at information like sales velocity for each lead, they can change how they handle opportunities to fit the leads they want. They can also provide leads with better scores and more resources immediately when they come through the pipeline.

It helps make more money.

Effective lead management and more efficient sales and marketing efforts help the business make more money.

With lead scoring, marketers are always learning new ways to get more high-quality leads, keep them interested, and finally move them into the sales funnel.

When it’s time for sales to take over, reps use the lead score to customize how they talk to people and get the most conversions possible.

More conversions mean more money coming in.

It helps you save money and time.

Without a system to handle it, knowing whether a prospect clicked on a price document or unsubscribed from an email list would be tough. It would be straightforward for mistakes to happen if these cases were written down and given to each new sales lead.

Most marketers spend about half of their budget on getting leads, but that money is wasted if they only pick leads that aren’t good. Lead scoring helps businesses focus on the leads that could bring in the most money, significantly increasing their budget.

Also, you don’t have to keep track of each lead and its characteristics by hand; sales and marketing automation software does it all for you.

Lowers the cost of getting new customers

Think about how much it costs to get a new customer. Demand creation at the start, content marketing, nurturing leads, and time spent by sales reps all add up. It can be costly to run campaigns when you have to pay for in-house sales reps and marketers and the tools they use.

Companies can reduce the number of leads they need to find and follow up with by rating them. The general cost of acquiring a lead goes down when sales reps can spend more time with good leads.

Models for Lead Scoring

Before making a lead scoring model, you must know what makes a possibility act like it does. A lot of different types of data can be used to score leads. Most businesses use a mix of the following types of data:

The goal

Intent data shows the way prospects interact with the material. To determine how likely they are to be interested in those services, it looks at what web pages they look at, how often they visit, and how long they spend on each page.

This kind of information comes in four primary forms:

• Known first-party purpose data: This is information that customers voluntarily give you, like when they sign up for an email newsletter or attend an event.
• Anonymized first-party purpose data comes from people who visit the company website and is tracked by Google Analytics and other analytics tools.
• Known third-party intent data includes filling out a voluntary web form and being watched by a partner or third-party website.
• Anonymous third-party intent data is browsing history tracked on third-party websites using the lead’s IP address. For example, when a customer views a website, reads content, doesn’t sign up for anything, or gives any personal information, this is an example of anonymous third-party intent data.
• It’s easy to get to first-party intent data stored in the company’s CRM or Google Analytics account. With these platforms, it’s simple to list goals based on what the lead has done and give each one a score.

People who buy from B2C

The traits of a business’s ideal customer profile (ICP) are called demographics. There is a person in mind when things are made. The things about a person that make them more likely to buy that product are described by demographic data.

Demographic information comes in a few different forms:

• Age
• What gender are you?
• Race
• Race or ethnicity
• Where It Is
• Level of income
• Status of marriage
• Level of education
• Employment position and job title

Fun things and hobbies

For example, a makeup brand’s marketing efforts would not be well received by young guys. If you tried to sell home office tools to construction workers, it wouldn’t do as well.

A lead scoring system would look at the above characteristics of each possible customer to give each lead a score based on personal information. After that, it would give it a score based on how many boxes that person checked. Marketers would know who to direct their ads to based on the data and the predefined ICP. This way, they would waste less money on ads that reach the wrong people.

One way for a solar company to quickly determine if a possible new customer is eligible is to look at their age, where they live, and income. The system would give a better score to customers over 40 who live in sunny areas and make more than $150,000 a year.

Profiles of B2B firms

Firmographics are like demographics, but companies use them. For a B2B company, these parts make up an ICP.

This is firmographic data:

• Size of the company
• A business
• Monthly regular income (MRR)
• People who work there
• Where it is

For example, some software companies focus on selling solutions to small businesses, while others sell to large businesses. Knowing which firmographic goals are best for a particular product helps sales and marketing teams find the best leads.

Lead scoring is the same for firmographics and demographics. Still, the stakes are higher for B2B companies because their sales and marketing teams are usually powerful and work with qualified leads differently.

Let’s say a company that sells corporate CRM software has a platform that is designed to meet the needs of manufacturing and industrial businesses. This platform might have features like native ERP integration and automated order tracking for the users’ customers.

Their lead scoring system might change scores based on firmographic details like revenue, size, industry, and company location. For example, a company with less than 250 workers might get a lower score because they don’t usually have the money or size to use all that the platform of

How People Use a Website

• As soon as a new lead enters the marketing funnel, like when they use the website, read a blog, or click on an ad, the website’s backend starts to record their actions, such as:
• How long do they stay on the site after clicking
• What kinds of things do they look at?
• No matter if they share or download something
• What items do they put in their cart?
• How often do they go back to the spot after the first time?

This information works with other business software to rate leads based on their purpose. The lead’s score would increase if they did something of high intent, like writing a paper or signing up for a webinar. It would go down if they did nothing or had low-intent behavior.

Use of Social Media or Email

Lead scoring systems can find possible customers who are very interested in the email or social media marketing that a business sends out. B2C customers may leave a message on a brand’s Instagram post, visit an online store after getting a deal, or sign up for deals. B2B customers can read excerpts from the business blog in their emails and click on the links to read more or watch a video on LinkedIn.

All of these things raise the lead’s score. Marketers can learn more about their best customers and how they interact with the business over time.

Scores for Predictive Leads

A predictive lead score results from collecting a lot of data over time. By looking at past-purpose data, demographics, and firmographics, predictive scoring algorithms can tell if a lead is like previous customers who have successfully converted (and made money).

Predictive lead scoring looks at more than just the above pre-sale factors. It also looks at the following factors from related leads:

Value over a customer’s whole life

• How often and what kinds of goods and services do people buy?
• Levels of satisfaction
• Rates of turnover and retention

All of this information is put together and sorted to find out how likely a lead is to buy. This information can come from polls, the customer’s online activity, and company software.

While a customer might not meet all the criteria based on firmographics, intent, or demographics, that same customer might get a better score if they have historically been more valuable.

Not Giving Points

Some actions are wrong for a lead-scoring algorithm. Some examples are

A customer who recently stopped getting emails or marked them as “spam.”

People who click on social media ads and then leave the website right away

A business whose demographic information is the exact opposite of the target customer’s (for example, a small business asking a big software company for a demo).

The lead scoring system gives possible customers low scores when they do any of these things. Every business should have a way to handle and react to these kinds of leads so they don’t go unnoticed.

A lead score is just a number, so it doesn’t always mean the lead isn’t worth anything. It only means they probably aren’t more important than a higher-scoring message.

How to Choose Which Lead Scoring Method to Use

It depends on the company, how much data it has access to, and how it builds its lead score model. People with more money can use a more extensive collection of customer information to make a more complex score system.

People who don’t have a lot of data can still give scores based on things like firmographics, online behaviors, or levels of engagement. But it might be based on more than just data. It could be based on assumptions and personal views about who should buy the product.

To help you choose the right type, think about the following:

What avenues of marketing and sales are we using? For example, businesses that rely on social media to get new customers should think about post-interaction over website visits, which might not be as targeted.

What kind of information is there? When scoring, start with demographics and firmographics if you don’t have a lot of customer data. Companies with better data infrastructure can switch to anonymous intent data, which can be bought from outside buyers.

In what ways does our ICP make sense? Companies in their early stages often don’t know where to look for leads. Larger companies, on the other hand, can score their leads more efficiently.

It’s essential to set SQL criteria before making a lead scoring model. This includes the things that help turn leads into customers. For instance, a company that mainly works with large companies might say, “We won’t work with any clients that have less than 250 employees.”

It would be best if you also thought about the change process. That’s where a salesperson should come in. Think about what SQLs do before they book a sales demo.

Signs That Lead Scoring Is Working

When a business gets a lead, it has something to show. Most of the time, these signs include:

Lower Rates of Unsubscribers

When leads get helpful information, they are less likely to unsubscribe from emails. When leads say that the content you send them is “valuable,” they are probably ready to buy your goods.

Better ratios of MQL to SQL

Many leads that ask to talk to a salesperson aren’t qualified. Based on statistics from Salesforce, this conversion rate could be as low as 13% on average. If more MQLs turn into SQLs, your marketing is getting the right kinds of people interested and ready to buy.

All-around high scores for engagement

Businesses use several ways to measure interest. You can get a good idea of it with the Net Promoter Score (NPS), customer happiness surveys, and Google Analytics data. A very interested audience will usually be loyal and likely to buy goods or services.

More money made per lead

High revenue per lead is the end goal of any lead-scoring method. Once predictive lead scoring is in place, companies should see a rise in the number of leads that turn into paying customers. However, tracking the ROI of predictive lead scoring properly takes time.

Less time between sales

Leads that meet the scoring standards and are likely to buy shouldn’t take long to turn into customers. How long a sales cycle lasts varies a lot depending on the type of business, how complicated the product is, and other things. But businesses that use scoring algorithms that work should have shorter sales processes compared to how their business works now.

What to Look for in a CRM’s Lead Scoring Tools

Lead score is integral to any customer-focused business, and CRM software is no different. For each market, each CRM has a different set of tools. Still, the lead score part of every CRM should be able to do the following:

Managing the pipeline and opportunities

Pipeline and opportunity tracking should be built into lead scoring tools so that sales reps can monitor how deals they’re working on are going. This helps you determine who is buying your goods or services.

The Report

The next step that makes sense for judging lead-scoring success is automated reporting. Companies can figure out what works and needs to be changed by looking at customized reports showing the sales process and results.

Automation of Email Marketing

Mailchimp, Klaviyo, or a similar tool is used by most businesses to handle their email marketing. These systems work well with CRM’s built-in features to make the lead score process run smoothly.

Making contact data consistent

Customer contact information should sync with the CRM in real time. It’s an essential part of the lead score process because it has demographic and firmographic information.

A Prediction

Bigger businesses buy this feature as an add-on, so not all CRMs have built-in prediction analytics. It could also need more IT equipment, which some businesses don’t have. But it’s essential for lead scoring to work because it tells businesses how likely a sale will happen.