Let's talk about lead scoring for a few minutes. It's something that should be deceptively simple to implement, and yet many companies fail at creating a scoring system that actually works. Why does that happen? Keep reading to find out.
Simple explanation of lead scoring
Lead scoring is a method of assigning a numeric value to each lead, based on their likelihood of converting into a customer. This value is typically calculated using a combination of factors, such as the lead's behavior, demographic information, and engagement with the company's marketing efforts. By assigning a score to each lead, businesses can prioritize their sales efforts and focus on the leads that are most likely to convert.
Why use lead scoring
Lead scoring can be a valuable tool for businesses because it helps them identify which leads are most promising, and therefore most worth their time and effort. By focusing on high-quality leads, businesses can improve their sales efficiency and increase their conversion rates. Additionally, by identifying high-quality leads early on, businesses can provide a better customer experience by addressing the needs and concerns of these customers more quickly.
Why do some lead scoring models fail?
The answer to this question is very simple. Instead of creating a data-backed lead scoring model, some businesses score their leads based on some arbitrary actions taken by the leads. Don't get me wrong, these actions can and should be used (properly), but they should never be used on their own. Why? Not every lead that takes high-value actions is a good lead.
Things to consider for lead scoring
There are many different ways to implement lead scoring, and the specific approach will depend on the business's goals and the data available to them. However, in general, the process involves collecting data on past leads, and identifying the key characteristics of high-quality leads. Here are some things to consider.
Gather data on past leads
Don’t skip this step, and really have a good look at your historical data. By digging into your data on your past leads (both the great and the bad ones), you will be able to identify the common characteristics of leads that have the highest likelihood to convert. If you skip this step, your lead scoring model will almost certainly fail. You might be under the impression that your highest quality leads fit certain criteria, but the opposite might be true. Let the data tell if your ICP (Ideal Customer Profile) is actually your ICP because you might be in for a surprise!
Clean and prepare the data
Inconsistent data is one of the factors that can ruin your lead scoring model. For example, if you have different names for a country (e.g. United States, US, USA etc.), consider consolidating them into one (ISO codes work well). The same can be said for duplicate properties, or properties that store the same data but in different formats. The cleaner your data, the easier it will be to create and maintain a working lead scoring model.
Unless you ask for every single piece of information on your forms, chances are some leads will be missing key data that you will need to create a good scoring model. To overcome this problem, consider buying data enrichment software.
Identify the key characteristics and actions
Once you’ve figured out your data, you can now identify the key characteristics or behaviors that you want to use as the basis for your scoring system. These might include things like website visits, page views, time spent on the site, content downloads, form submissions, and other indicators of engagement. Once you have identified these factors, you can assign points to each one based on their relative importance and the level of engagement they represent. For example, you might assign 10 points for a website visit, 20 points for a page view, and 50 points for a form submission. My suggestion is to limit the score to a maximum of 100 points.
Split the score into different categories
You can create multiple different scores and then add them together. You can split ICP and activity data. This is not a necessary thing to do, but it helps keep things clean and more maintainable.
Evaluate and refine the model
Once the model is created, it is important to evaluate its performance to ensure it is accurate and effective. This will typically involve testing the model on new data and making adjustments as needed to improve its performance.
Overall, creating a great lead scoring model involves using data and machine learning algorithms to identify the key characteristics of high-quality leads and create a model that can predict the likelihood of a lead being high-quality.