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Traditional Lead Scoring vs Predictive Lead Scoring

Tom DiScipio

Director of Client Success, Partner, Speaker, 8+ Years Sales & Client Success Expertise

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Traditional Lead Scoring vs Predictive Lead Scoring Blog Feature

Published on June 7th, 2016

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Inbound and HubSpot Marketing together are a power combo for generating leads, but converting those leads into customers requires an entirely different set of skills.

The first step in converting leads is identifying which ones to reach out to and when to do so -- that's where lead scoring comes in.

Since inbound marketing can generate so many leads, it's easy to waste a lot of time, money, and other resources on trying to chase down every single one, but that's really not the best approach.

Inbound marketing requires giving away a lot for free, and unfortunately for salespeople everywhere, there will be a large percentage of leads who only want your free content. They have no intention, interest, or even need to make a purchase from you.

Now, that's not necessarily a bad thing. Even if people don't intend to buy from you, they may share your content with people who would, but nevertheless, that doesn't do much for your sales team right now. 

The point of lead scoring is to identify the people that stand to get the most benefit from what you offer at this time and are also the best fit for your buyer persona.

In this article, we'll compare the traditional method of lead scoring against the newer, more effective method, known as predictive lead scoring.

Traditional Lead Scoring

Traditional lead scoring is a process where sales teams determine the quality of a lead based on certain personal and professional criteria then assign them a score. This score is then used to gauge whether or not that lead is "qualified" to make a purchase and how likely they are to actually do so. 

There are two types of information that are used to score leads:

  1. Explicit data: Information that prospects provide for you, such as their job title, their company size and budget, their company email, etc.
  2. Implicit data: Information that you've collected based on their behavior on your website and in your funnel, such as page views, email open rate and click-through rate, gated content downloads, etc.

By assigning values to each of these data points, the result is a summary of those values for each lead -- the lead score.

The criteria for traditional lead scoring that most companies use is based on the BANT model that IBM originally came up with:

  • Budget - the amount of money your prospect has designated for making a purchase of the type of product or service you offer.
  • Authority - the prospect's ability to make or contribute to the final purchasing decision.
  • Need - the prospect has a problem that your business can solve or has an inferior solution to what you offer.
  • Time frame - the amount of time your prospect has before making a buying decision or before their problem needs solved.

All of the BANT criteria is explicit data collected through forms on your website or by simply asking the lead.

Two additional criteria that you should add are:

  • Demographics: Gather all of the information you can on your lead's company, along with basic demographic information about them such as age, gender, estimated salary, etc.
  • User activity: Information you can collect from your website and email marketing is incredibly valuable because actions speak louder than words. You can often learn more about your lead from their behavior than from what they tell you.

Once you've determined all of the criteria you want to collect, you will assign a point value to each. These values will vary by industry, company size, and other factors relevant to your specific business. After you've created your value system, you must decide on a threshold that lead scores have to pass to indicate that they are ready or near-ready to make a purchase.

Although this process will require some manual work to add and update information in your lead profiles, the goal is for it to be as automated as possible -- resulting in a well-oiled marketing machine that is constantly sending qualified leads to your sales team.

The Cons of Traditional Lead Scoring

The first problem with traditional lead scoring is that it's too simplified and focuses more on eliminating bad leads than it does on identifying great leads.

Eliminating bad leads is the easy part. It's not hard to tell when someone either can't make a purchase or has no interest in doing so, but identifying the great leads among those that are just average is a challenge.

Another issue is that traditional lead scoring isn't naturally adaptive, so it is far from ideal for fast-changing markets. Companies in these markets need to update their lead scoring system every quarter, but most fail to do so.

Finally, traditional lead scoring doesn't provide much opportunity for sales teams to provide feedback that can be immediately incorporated into the lead scoring process.

This results in missed opportunities and sluggish growth, not to mention, it can be demoralizing to top performers in your sales team.

Predictive Lead Scoring

Predictive lead scoring uses an algorithmic tool to predict which leads in your funnel are qualified or not qualified.

These algorithms vary by the software provider you are using, but they generally include: information collected from forms on your website, demographics, behavioral data, social information, and more.

The benefit of using predictive lead scoring software is that it takes all of the work that goes into creating your lead scoring system out of the equation.

Determining how to weigh values for each criteria is difficult and requires constant updating, but, with predictive lead scoring, the algorithm is constantly learning and improving in real-time by comparing data of your customers and leads that didn't convert, identifying the common traits of each group.

Every time a lead converts to a customer or doesn't convert, the algorithm gains new information that helps it improve and the best part is this process occurs automatically.

How Predictive Lead Scoring is Different From Traditional Lead Scoring

Predictive lead scoring takes a scientific approach that differs from traditional lead scoring in three primary ways:

  1. Predictive lead scoring uses a larger amount of data, including: new information being collected right now, historical trends, and information from several "big data" sources that help you predict lead quality based on other businesses in your industry.
  2. Predicitive lead scoring software compares your current and previous customers to create the profile of a qualified lead automatically and it adjusts with new information.
  3. Then the software automatically scores your leads against the profile of your qualified lead and identifies leads for you that are most likely to convert to customers.

With traditional lead scoring you have to gather information, update your scoring system, test it, and then wait for a period of time and analyze your results, before repeating the process.

Predictive lead scoring does all of that for you and does it quicker -- making your sales process more adaptive to changing markets.

The software that we use and recommend is the Predictive Lead Scoring app offered by HubSpot to all Enterprise customers.

Here are the following guidelines that HubSpot recommends before using predictive lead scoring:

  1. You have been storing both engaged and unengaged contacts in HubSpot.
  2. You have been marking contacts as customers for at least three months
  3. You have at least 500 contacts in HubSpot that are marked as Customers.
  4. You have at least twice as many contacts who are marked as non-customers.

If you are a HubSpot Enterprise customer and meet those requirements, you open the Predictive Lead Scoring app to start running your model.

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