February 3, 2021

Predictive Lead Scoring: Pros and Cons

It used to be that when a lead comes in, your business development representative (BDR) would manually gather all the relevant information at your disposal, ask a few questions, and make the call on how likely it is for that lead to turn into a customer. 

Over time, the lead qualification process has become much more sophisticated. It’s now a widespread practice to rely on customer relationship management (CRM) tools for lead scoring, or assigning a numerical value to leads by screening on-page data. The scores are typically based on rules derived from a small set of data and subjective opinions of what constitutes a good lead. Even so, traditional lead qualification helps businesses prioritize their sales efforts based on expected customer value, as opposed to pursuing everyone on the first-come, first-served basis. 

What is Predictive Lead Scoring?

Predictive lead scoring uses software algorithms to automatically process data about your leads - a process which used to be done manually. Essentially, the algorithm will look at everything you already know about a web visitor from an existing database (e.g. your CRM tool), determine which items of information are the most important, then complement that knowledge with other data sources including on-page behaviour (e.g. “Clicked on Pricing Page”). The algorithm can therefore predict which of your known customers are more likely to purchase by assigning weights the information about them in the database plus their on-site actions. Predictive lead scoring can even become stronger by seeing what converted customers have in common (and what unconverted customers have in common).

Predictive lead scoring is already available to any business today. Let’s sift through the pros and cons of this approach:


  • Save time by processing data automatically through an algorithm
  • Improve the quality and accuracy of scores by using rules and weights
  • Can improve by looking at common and uncommon traits in hot leads


  • Relies heavily on having an extensive, existing database to work with (e.g. a CRM)
  • Still somewhat subjective due to being based on rules and weights initially designed by humans 
  • Doesn’t account for any web visitor that isn’t already known 

What is the future of predictive lead scoring? 

Although predictive lead scoring is a fairly new and exciting leap in scoring technology, it has already been superseded by a new approach which Lift AI calls “Machine Scoring”.

Imagine being able to score leads not only based on existing CRM data but also by taking into consideration a visitor’s pre-action behavior — looking at the data you have and augmenting it with external data from vast arrays of third-party sources as well as machine-learning models that can determine which attributes actually inform buying behavior. That’s Machine Scoring. The benefits of Machine Scoring over Predictive Lead Scoring include:

  1. Utilizes much more data and real-time input analyzed through machine-learning which eliminates guesswork, improves accuracy and automates an otherwise impossibly complex process
  2. Instilling objectivity in decision-making, freeing you from human errors
  3. Greater marketing insights illuminating previously unseen patterns
  4. Evaluating leads who were completely unknown to you previously
  5. Enabling real-time actions on high-scoring leads
  6. Freeing up and then focusing your BDRs to work on high potential leads instead of time-consuming, manual lead scoring

In addition, Machine Scoring is highly cost-effective. Just take your BDR payroll and multiply it by the percentage of unqualified leads you’re getting today to calculate how much money is going to waste. Every call pursuing a bad lead is an opportunity cost for not getting the lead you want.

Since generating leads in today’s competitive online market is hard enough, making the most out of your existing lead flow is critical. Thus the benefits of testing new lead-scoring methodologies significantly outweigh the risks. 

Are there any cons to Machine Scoring? 

There’s really no reason to avoid using machine-learning tools like Machine Scoring by Lift AI.

For example, some may fear that machine-learning models could assign inaccurate scores to leads depending on the quality and accuracy of input data. That said, algorithms, unlike humans, are not prejudiced and are able to continuously get better in their judgement. If the machine-learning model is based on millions, if not billions of data points, then you can rest assured that it’s likely more accurate than any human’s calculations - even when supported by basic algorithms like predictive lead scoring.

If you’re not sure, you can always check — go over the scores manually and compare them to what you’d have assigned as a best guess, at least for the first few days. It’s not a perfect verification method, but it can’t hurt to compare.

Additionally, algorithms lack charisma, in the sense that Machine Scoring is as objective at evaluating potential prospects as possible. For example, your highly skilled BDR might occasionally upsell a low-scoring lead and turn them into a profitable client that a Machine Scoring model might have determined is not worth pursuing. But this argument doesn’t hold water if you estimate how many ideal customers the same BDR would be able to acquire instantly from high-scoring leads provided by Machine Scoring. The volume is simply incomparable.

Get started with Machine Scoring today

Lift AI is an evolved form of predictive lead scoring powered by 15 years of machine-learning data. Right out-of-the-box, Lift AI uses more than one billion profiled website visitors, 14 million sales engagements, and real-time behavioral data to score every single website visitor, even if they’re anonymous, in a process they call Machine Scoring. 

All you have to do to use it is input a small JavaScript code snippet on your website, and Lift AI will automatically start scoring your website visitors, plus integrate with your chat provider of choice, be it LivePerson, Drift, Intercom, Salesforce, or any other.

Why the chat integration? So you can take action. For example, in real-time you can trigger live chat for sales instantly on the most promising visitors, thus increasing the effectiveness of your sales team while directing low-scoring visitors to your chatbot. By leveraging millions of data points and real-time analytics, your website can get x-ray vision of visitors and take action to maximize conversion rates.

Request your free 30-day trial today and see how beneficial Machine Scoring can be for your live chat for sales — no credit card required.

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An illustration showing a website visitor being scored in real-time by Lift AI