5 steps to building a lead scoring model that works for you

We all know, that not all sales leads are created equal.

Some leads are genuinely looking to establish a long-term relationship with your company. Some are sales-ready, but may not commit right away, while others are just gliding through and may not buy at all.

So how do you know which leads are sales-ready? Which lead requires further nurturing? Which leads you can afford to pass?

The best way to classify your sales leads is through a lead scoring model. Rather than chasing all the leads in your CRM, the lead scoring model automatically prioritizes your leads using scores. As a result, you are focusing your time and effort on your ideal leads, building a healthy sales pipeline, and closing more deals.

Why do you need a lead scoring model?

Lead scoring is no longer a functionality that’s just ‘nice to have around’. It’s now a must-have for every business.

Whether you are a startup with a few leads, or an enterprise with a high volume of leads, you could benefit a great deal from a lead scoring model. It not only helps you identify the high-value leads but also shows you the activities and data that makes them your ideal lead. With this information in hand, you can have high-quality, meaningful conversations with your leads.

That’s not the only advantage of using a lead scoring system. When your sales-ready leads aren’t moving to the next stage in your sales process, you know you probably haven’t engaged with them enough. Similarly, if the score of a lead isn’t increasing, you know you have to make more calls or create activities for that lead.

If you are still not convinced to implement a lead scoring model for your business, ask yourself these following questions to analyze whether you could benefit from it:

1. Do you get a high volume of leads that are more than what your sales team can handle?

2. Does your sales team often complain about low-quality, unfit leads?

3. Are you losing sales opportunities to competitors because of low response time?

4. Do you have enough customer data to implement a lead scoring model?

5. Do you want to improve your sales process and build a healthy sales pipeline?

If you’ve answered yes to all or most of the above questions, you could most certainly benefit from a lead scoring model.

While choosing a lead scoring tool, it’s best to pick one that easily integrates with your current CRM software. If you haven’t implemented a CRM software yet, you can choose one like Freshsales which has a built-in lead scoring functionality.

How to build a lead scoring model 

Step 1: identify your ideal leads

Before you implement a lead scoring model for your business, you must identify the characteristics of your ideal lead so that these leads get a high score.

Your ideal leads are those whom you think will benefit the most from your solution. One of the best ways to identify these leads is to start with your current successful customers. Try to identify the characteristics that are similar to your successful customers. You can do that by answering questions like:

1. What’s the size of the company?

2. How much does the company make?

3. How many employees work for the company?

4. What are the designations of the employees?

5. Where is the company located?

6. What is the industry or vertical of the company?

7. Which department uses your solution?

8. How many employees work in that department?

There are many other questions that will help you narrow down and identify your ideal leads. You can also get in touch with your marketing team to pinpoint the target market based on their research.

Step 2: list the criteria that qualify an ideal lead

To build a good lead scoring model, you need two categories of data—the lead’s demographic information, and their interactions with your company.

Demographic data

You’ve already identified your ideal lead in Step 1. You know who is most likely to buy your solution compared to others through characteristics like business size, industry, country, annual revenue, job title, etc.

This data is usually provided by the lead when they fill a form on your website, or from conversations you have had with that lead.

You can easily identify the following five demographics for a lead:

1. Job title

2. Industry type

3. Company size

4. Location

5. Department

Behavioral data

The second type of data you need, is with regard to how a lead interacts with your company. The data is based on the lead’s actions on your website, and the emails you send to them. Behaviors such as opening your email, responding to your email, visiting your pricing page, or signing up for your solution are positive buying signals to watch out for. Whereas, visiting your careers page and unsubscribing from your mailing list are some of the signs of disinterest from the lead.

These are some of the positive buying signals you can observe from your leads:

• Top-of-the-funnel activities like requesting a demo and downloading a whitepaper.

• Middle-of-the-funnel activities like visiting your pricing page, registering for product webinars and signing up for your solution.

• Activities performed on your application such as watching the onboarding video, adding their team, adopting a new feature etc.

• Opening your company emails, or clicking a link on the email body.

Here’s a tip to keep in mind while listing the criteria of your ideal lead:

Both demographic and behavioral data are important for setting up a lead scoring model. The demographic data shows how interested you are in the lead. Whereas, the behavioral data shows how interested the lead is in your company. If you score leads with just one category, you can’t distinguish from a CEO who has no interest in your company to a manager who has a high intent to purchase.

Step 3: assign values

Figuring out what scores to assign each activity is the hardest part of lead scoring, and this is where most people go wrong.

One effective way to assign values is by equally distributing points to demographic and behavioral data. This approach makes sure your leads don’t have a high score merely because they match your ideal lead or displayed engagement with your brand.

For example, if your ideal lead is the CEO of a small business 20 points. But a student visiting your careers page will get negative points.

Lead scoring mistakes

Here are some mistakes to avoid while assigning scores:

1: Scoring by emails opened

If a prospect opens your emails, it could indicate a lot of things. They could have either genuinely opened and read your email, or opened it by mistake. Scoring by email opens does not mean that your lead has consciously opened and read your emails. It’s misleading, and inflates your lead score. Instead, score by the email clicks, which indicates that the lead has read your email and has clicked on a link from the body of the email.

2: Setting the same score for each web page

A lead who visits the pricing page is more likely to convert than a lead visiting your careers page. Setting the same score for each web page makes your lead scoring model inaccurate. Instead, set different scores for each web page. For example, add a positive score for those visiting your pricing page and negative scores for those visiting your careers page. This will help filter the leads you don’t want your team to be engaging with.

3: Avoiding negative score

One of the most common mistakes while assigning values is setting scores that go up, but never down. Don’t be afraid to add negative scores to the leads. In fact, you need to add them to avoid junk leads from falling through the cracks.

While defining the criteria to score leads, list down the criteria to score negative points as well.

1. The job title does not fit the ideal lead

2. The lead visits the career page

3. Your product or service does not cater to a particular industry type

4. You don’t ship products to a specific country

5. You don’t cater to companies of a certain size

The thumb rule of lead scoring is assigning positive scores for desired behaviors—attending webinars, visiting pricing page, etc.—and assigning negative scores for undesired behavior— visiting careers page, signing up with a personal email account, unresponsive to campaigns and so on.

4: Not setting a time limit for each event

When you score lead for behaviors like reading an email, adopting a feature, downloading a brochure and other such activities, the score shouldn’t last forever. There is no sense in retaining the score awarded, when the lead attended a webinar last month, attended an event over a year ago, or clicked on your email last week. While setting scores for behavioral data, make sure you set a timeline, after which the score is deducted.

Step 4: set a threshold for the scores

Alright, so you’ve defined the criteria that qualifies a good sales lead but not all your leads fall under the red-hot category. A few may not be ready to purchase your product as yet, and the other may not respond to your emails or calls. While setting scores, define a threshold to bucket your leads so you can instantly identify the ones who need attention, and the ones who require nurturing.

For example, you can define threshold limits by:

• ‘Hot’ or ‘sales-ready leads’ are the ones with a lead score higher than 70.

• ‘Warm leads’ are the ones with a lead score between 70 and 30.

• ‘Cold leads’ are the ones with a lead score lesser than 30.

Step 5: revisit the lead scoring model

When you start, your rules are set based on hypothesis. But six months down the line, you may notice a new trend emerging from your leads. For example, you may find active engagement levels from leads within an industry type that hasn’t been explored yet, or you may discover leads that are reaching the sales-ready score, but are still not ready to make a purchase. When this happens, you know it’s time to revisit your lead scoring model.

It is good practice to monitor your lead scoring results regularly to know when your lead scoring model requires an iteration. Be open to maintenance, tweaking, testing, and improvement. Developing a lead scoring model takes time, and it can always be improved.