5 Tips to Get the Most Out of Your Sales Lead Scoring Model

Lead Scoring

Sales lead scoring is one of the most effective ways for business owners to determine the sales lead’s potential. By evaluating sales leads and assigning a score to them, you can filter out low-potential leads before investing too much time into them.

The way you set up your lead scoring model can have a huge impact on your sales team’s performance. To learn more about how to get the most out of your sales lead scoring model, check out these five tips.

1. What is lead scoring?


Lead scoring is a great way to qualify leads and understand which leads are most likely to convert into customers. Lead scoring is a way to assign a value to each lead based on how likely they are to convert. The higher the score, the higher the likelihood a lead is to convert into a customer. Lead scoring works by first building a likelihood score. Detailed technical information on how to score leads using lead scoring can be found here. Essentially, you start with a list of likely conversion events and assign a score based on the likelihood of each event occurring given the characteristics of the contact. For lead scoring to be effective, you need to do a few things: This score is important for assessing the conversion potential of your contacts. A high score should be private, so only you know it. Lower scores are public, so you can help your contacts improve their likelihood of conversion.

Lead scoring works as follows:


The default on ConvertKit is 5, which means that contact is likely to convert into a customer given the following circumstances:

The scoring range of a contact is from 1–100. You can customize the scoring range by selecting the “Scoring range” checkbox on the contact card view. The best score for each contact is the best score they have ever received from the LeadGen classifier. If you wish to get a numeric score from your classifier, use the “Rank classifier sum” option.

Below are some typical score ranges you might encounter when scoring leads:


Your score for this person is likely below 25. This means that they are not likely to convert in the near future. It’s worth noting that your classifier has already given a score to this contact multiple times and this is what you use to calculate their likelihood of converting into a customer. Typically, you keep the score low, because if your classifier is right, this contact is a good sale (low lift) proposition but probably not a high-value lead.

2. How to create a lead scoring model for your sales team


One way to do this is by scoring incoming leads based on their level of interest in your product or service. This is called lead scoring and is a way to separate leads into different groups based on how interested they are in your product or service. Companies like Hubspot and Marketo have built tools to help you build these lead scoring models.

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3. How to determine how much weight each criteria should be given


The next step is to determine how much weight each of these criteria should be given in your overall decision. It’s certainly not an exact science, but you should give more weight to the criteria that are most important to you. At the end of the day, these are all just tools to help you use your judgment to make the best decision for YOU. We’ve established some strong characteristics of the ideal subscriber: We’re going to use these same criteria, but we’re also going to “trim” them a little bit.

Here are some simple guidelines you can use:

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4. How to make a formula for your sales team to use in their lead scoring process


For our lead generation efforts, we have a simple formula that our sales team uses in their lead scoring process to help them determine which leads to contact first and which leads to contact later. This formula is simple: Interest + Time – Budget = Priority

Let’s break this down a little. Time and interest are calculated by subtracting out a 20% contingency. You can see that time and interest are really just two different measurements of the velocity of a lead. Just as they don’t always cancel out, they can be used to help calculate a lead score.

For example, we bought a lead gen chain on a 30-day credit agreement last week and wanted to know which leads we should check out first. Instead of just looking at the number of days on the agreement, we have some margin built into our model based on our consideration of each lead’s interest, availability to pay, and position of the lead (roadie, first-time homebuyer, etc.).

This way we can ask our sales team, objectively, which leads are the most important. The Sheet can then been downloaded in the CRM where we write in the time and interest interval for each lead, which is then added to the priority list in the CRM. We’ve realized that a key advantage of using our lead scoring sheet is our sales team is effectively able to have a document that stands on its own as proof that they are actually meeting their existing leads.

This way our leads know that they have been referred to our lead scoring sheet – a source of truth that precedes the opportunity to contact, earn the sale, etc. CRM Integration

Once we’ve created an individual or team lead scoring sheet, we want this for each of our marketing automation platforms.

5. Tips on reporting your lead scoring data


One of the most important things you can do is to be able to report on how you’re doing with lead scoring. We’ve talked a lot about how to build lead scoring into your process, but you’ll want to know how you’re doing on a regular basis.

Step #2 is to create a rating out of 100 that you can use to know how strong (or weak) you are each week. You can do this either by looking at your last 6 months’ score or by looking at 5 previous weeks’ score. As long as you keep the score of the previous week, it’s easy to see your strengths over the course of the year.

Step #3 is to look at your best performing weeks over the course of the year and highlight what you did to improve. Mary Meeker gives a great example of showing this on a slide:

Step #4 is to look not at how well you did on a metric (i.e. total sends) but how well did you perform relative to your team.

Often a company will take what they’re doing in a certain facet of their business and applying it to other areas. For example, under a lead score expressed as A/B testing, businesses may split their testing efforts into two teams called A and B.

But with ARR scoring, a single metric is used across all your models. This is important because it allows for greater ownership from the business owner on what they’re doing to increase their business’s return on investment (ROI). This gives them a standard to strive toward and gives visibility as to the most important factors in place to create the best ARR possible.

Eventually, you can use metrics from each model to see trends together. Then you can use that spread of data to find solutions or ways to attack new problem areas.


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