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1 Post authored by: freejung

By now you’ve probably heard the buzz about a relatively new marketing technology called “Predictive Lead Scoring.” Marketers are excited about the prospect of using big data and machine learning to score leads more accurately than the usual rules-based techniques used in Marketing Automation.

This technology is developing fast and has enormous potential to improve efficiency and generate revenue. Right now it is in the early adoption phase, but eventually it will probably become a standard component of the marketing technology stack. Now is a good time to familiarize yourself with the basic idea of this technology, so you can assess whether or not your organization should consider adopting it.


Predictive Lead Scoring is a scientific method of predicting the probability that a particular lead will convert. It takes historical data from your CRM and behavioral data from Marketing Automation systems, and combines that with “big data” attributes gathered from multiple sources. The method uses this data to build a model of what a good lead looks like for your organization, then scores new leads against this model to determine how likely those leads are to close. That way you can correctly prioritize your sales efforts and focus on the leads that will generate the most revenue.

In other words, Predictive Lead Scoring uses data science and machine learning to uncover the hidden signals that predict the behavior of your prospects and customers: a dream come true for marketers.


Predictive modeling has been around for a while. However, using it in a business setting has generally involved hiring a team of data scientists to build a custom modeling system, assembling the required data themselves from whatever sources they can find. What’s changed is that advances in data science, computing power, and data collection have enabled vendors to offer Predictive Lead Scoring as a product you can buy “off the shelf.” Now you don’t need your own team of data scientists. You simply give the vendor access to your data. They build and test the model for you and insert lead scores directly into your CRM or Marketing Automation system with very little effort or expertise required on your part.


Traditional lead scoring can be a very effective way to increase conversion rates and make your sales operation more efficient. However, it has some drawbacks:

  • Guesswork - The rules used to score leads are determined manually by the guess-and-check method. Though we can make highly educated guesses as to what factors are likely to influence conversion and iterate to check against results, this process is slow and difficult in practice and not always satisfying. Predictive Lead Scoring automates the creation of model parameters, using statistical methods to give a rigorous, scientific result. This makes the building and testing of scoring models easier, quicker and more effective.
  • Incomplete Data - Traditional scoring only looks at the data you already have in your Marketing Automation and CRM systems, and usually only a tiny subset of that data is actually used in the model. Predictive Lead Scoring brings in thousands of data attributes to get a complete picture of the prospect: attributes such as financial data, social media presence, job postings, website technology use and sophistication, demographic and firmographic data and much more.

By replacing guesswork and incomplete data with scientific accuracy and big data, Predictive Lead Scoring can deliver better scores resulting in greater efficiency and more revenue.


There are several factors to consider when evaluating whether and when you should set up Predictive Lead Scoring...


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