Predictive Lead Scoring with No-Code Machine Learning

Hrvoje Smolic
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26/07/2022

Predictive Lead Scoring

In this post, we talk about implementing machine learning algorithms for successful predictive lead scoring. This method uses data from different sources and streams the results in real-time to automate lead scoring decisions.

When it comes to digital marketing, generating leads is the lifeblood of your business. Your ability to prioritize and follow up on the right ones can significantly impact your bottom line. 

Whereas traditional methods rely on manually scoring leads, machine learning algorithms can analyze lead quality so sales teams can focus on the insights they provide without becoming excessively mired in details. 

Predictive Lead Scoring Model
Image by the Author: Predictive Lead Scoring Diagram

What is Lead Scoring?

Simply put, lead scoring is a process that quantifies the engagement of a prospective customer with your company. It can be based on anything from how often they open emails to how many pages they visit. The point of lead scoring is to help salespeople prioritize which leads are worth pursuing.

What Is Predictive Lead Scoring and Why Is It Important?

"Data science and machine learning algorithms allow vendors to apply statistical techniques that identify and prioritize the sales prospects" - 

Forrester.

Predictive lead scoring helps sales reps focus on prospects most likely to become customers. It's an essential part of sales enablement and is used to prioritize leads who are more likely to convert. 

By focusing on the right leads, lead scoring helps sales teams win more opportunities. For example, the prospect is engaged with your company if its sales lead score is 9 out of 10 (or its conversion probability is 90%). Simply put, it is more likely to buy from you. 

Lead scoring can be implemented in many ways depending on your use case: tool, platform, or database-driven.

hubspot predictive lead scoring
Image by the Author: Machine Learning can help to identify main drivers for lead scoring

Predictive Lead Scoring: The Machine Learning Difference

"70% of leads are lost from poor follow-up" - 

Gartner

Lead scoring can be performed manually. However, with more and more data being generated, it's a process best left to machine learning algorithms via predictive analytics

As a technology, machine learning is experiencing exponential growth in different areas. In the case of lead scoring, it can help sales and marketing gain insights automatically based on available data. 

This concept is called "deep learning," which means that we are trying to "learn" from our data by mimicking the human brain's ability to solve problems with limited information. Since the reliability and quality of machine learning insights hinges on the information we collect, it's vital to invest in data before investing in machine learning.

Predictive Lead Scoring Data Sources

When it comes to lead scoring, there are a number of sources we can use for our data, such as:

List Segmentation/Profile Data

We can segment or profile our prospects based on their interests and other factors like demographics before grouping them in lists.

This data type is already classified, so we don't have to do it manually. The key question to ask yourself is: "Which interests are relevant to my business?"

Don't get too distracted by the fact that you have hundreds of different lists. Instead, ask yourself which information is most important to consider in your scoring algorithm.

predictive lead scoring dataset
Image by the Author: predictive lead scoring dataset example

Email Marketing

Turning leads into customers is a process with many steps since there are many opportunities for interaction before they become customers.

Email activity is one of the main sources of lead scoring data because it gives us insights into how often and when prospects engage with your brand.

By looking at the open rates and bounce rates, we can determine whether the prospect is interested in what our company has to offer. Email marketing has a lot of data that we can use to determine where a lead sits on the engagement scale.

Web Visits

Web traffic is probably the best source for lead scoring. We can use it to determine our prospects' interest level and predict who will become customers.

As with email, we can get insights into how engaged the prospects are with our brand and their key motivations.

Webinars/Lead Hooks

If you are using webinars as your lead generation tool, you can track how many leads came from the webinar.

By tracking webinar registrations, we can see how many people downloaded our webinar presentation and then submitted their information to become a lead. With this data, we can understand which leads are more engaged than others.

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Machine Learning Models Used in Predictive Lead Scoring

Machine learning algorithms can automate the lead scoring process. They learn from historical data and then make predictions for new, unseen data.

We will be going over two different machine learning models: neural networks and regressors.

Recurrent Neural Networks (RNN)

recurrent neural network is a type of neural network that processes sequential data like an email thread, or the series of web pages that a website visitor clicks on before conversion.

Neural networks can process these types of data by analyzing the current and previous data points. It's a very effective way to solve problems with sequential data.

We use RNNs to predict the future in terms of lead scoring. For example, we want to learn how likely it is that a prospect is going to become a customer based on their engagement with our brand.

The most effective way of using RNNs is by analyzing data that you already have and then using it to predict the future (as mentioned above).

Machine Learning Regression

Regression is used when we have historical data that has a predictable dependent numeric variable. For example, let's say we want to predict the sales amount based on the number of leads.

We have a clear vision of what we want to predict, and therefore, we can process historical data in an automated manner using regressors.

Limitations of Predictive Lead Scoring

Lead scoring is more effective when it comes to warm leads or those who have already shown interest in your brand. A factor which may limit you is the amount (or lack) of historical data that you have available and your ability to predict the future.

It's very difficult to accurately predict which prospects will become a customer if you don't have enough historical data. The same is true when all prospects look the same and you have no data to differentiate them from each other.

The ideal scenario is 100% of your leads will start converting in the future, assuming they are warm/hot leads.

Furthermore, you'll need vast technical knowledge to implement a predictive lead scoring model. If you aren't up for building your own custom lead scoring solution, you can opt for a no-code machine learning tool like Graphite Note, or get in touch with experts who can help you.

Conclusion

Lead scoring is a highly effective way to turn your leads into customers. It will help you determine which leads are worth engaging with and which ones are not.

If you want to get started with lead scoring, the first step is figuring out how you want to implement it based on your business model. The next step involves choosing your data source. Finally, you will have to decide which machine learning model is best for your business, or use a no-code machine learning approach.

During the process, it's crucial that you invest in your data first because it's the best way to get optimum results from machine learning and its algorithms.

Now that you are here...

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