How to Optimise your Lead Scoring Model

How to optimise your lead scoring model and lead scoring best practices

Do you know how to determine if a lead is ready to buy? Lead scoring can offer incredible insights into what your prospects are doing and how that can indicate intent to purchase. Using it to decide followup actions such as making a call or redistributing a lead into another nurture stream can really help keep your database healthy and thriving.

BUT if you do it wrong, the way you perceive your prospects and determine further actions can be skewed, resulting in fewer sales closed and fewer happy leads.

Keep reading to find out how to optimise your lead scoring model, and how you can give your sales team the best indication of who is ready to buy.

Base it on your buyer personas

You and your sales team know your customers better than anyone. You know the products that suit them best, the ideal times to call and whether they want to be called at all. Constructing a buyer persona based on what you understand of your customer base is the best way to find out whether new prospects fit the mould.

The problem

Building out a lead scoring model can rely on guesswork. Without combining data and experience from both the marketing and sales team, you will be scoring leads based on information that is irrelevant to whether they are likely to make a purchase.

The solution

Think about the key demographics of your target audience and how they are most likely to engage with your offering as well as the buying stage they are likely to be in. If your product is a high consideration purchase, think about the ways users will find more information or which pages on your website facilitate that process the best.


Consider digital body language

Behavioural data, while insightful and of high interest, can provide skewed view of user engagement.

The problem

If you’re running predominantly email campaigns and have focused your lead scoring model around the user engagement of those emails, then you’re excluding prospects who have a stronger affinity for phone calls or website visits.

The solution

Think about all of the likely behaviours of a person who is researching your product. Are they visiting your FAQ and Services pages? Are they reading blog post after blog post after blog post? Are they rereading your emails and downloading whitepapers a second or third time? Are they calling your service centre every second day with a new set of questions?

It’s important to look at users’ behaviour holistically, considering how different forms of digital body language can demonstrate interest. Remember to include information from your sales team, whether prospects have been in phone contact or if they have come in for a sales appointment.

Value quality over quantity

In aspects such as website visits of email opens, you may decide that the amount of times that the website is visited or an email is opened will indicate how interested a prospect might be. However, this tends to only have limited effectiveness.

The problem

Within your lead scoring model, you may have included website visits. This might for example, mean that in Marketo you’re receiving +5 points for each visit, or that in Eloqua you’ve created a proportional weight for a few different ranges for the amount of visits. While metrics such as website visits can indicate interest in a product, it is not necessarily a definitive measure.

The solution

For metrics such as website visits, the more reliable to way to measure interest is to quantify the score based on how much intent is associated with each page. Product pages and Contact Us can be strong indicators of interest in a product. In this case, you would create a hierarchy of your pages based on level of intent, attributing scores based on the pages that are visited.


Review it periodically

Lead scoring is a process that can built by a range of information including purchase data, market predictions and experience. Whether you’ve based the model on your target buyer persona or the best practices in your industry, it’s crucial that you take a closer look at how accurately you are predicting the

The problem

It’s just not relevant anymore. Whether it’s the old campaigns you’ve set up to lead score model to work against or simply a change in the market, an outdated lead score model can become highly inaccurate and ineffective.

The solution

Set benchmarks or triggers that will instigate a review. Whether it’s a change in management, a set number of leads being generated and synced to your automation platform, or simply each quarter, ensure that there are processes in place for optimising the performance of your lead scoring model(s).

Key takeaways

  • Have a basis. Draw from as much statistical and experiential data as possible when building your lead scoring model.
  • Consider your model holistically. Think about how each category/rule can be broken down and how you can make them as relevant and accurate as possible.
  • Test, test and test some more. Set out steps to review your lead scoring model at key turning points.
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