Traditional vs. predictive lead scoring


With the advent of new technologies, businesses no longer have to guess which leads are worth pursuing and which leads have turned cold. In this post we discuss two kinds of lead scoring processes—traditional and predictive lead scoring—and how each can transform your business’s marketing and sales systems from the inside out.

A refresher on lead scoring

The use of lead scoring in your business cannot be underestimated. Lead scoring simply means the process of assigning scores for each of your leads. The score is determined by several factors, such as demographics, the way leads interact with your business through various marketing channels, and the probability of their conversion into a paying customer.

The lead scores can fall on a negative or positive side of the scale, in other words, how more or less likely they are to convert. Once you identify the lead scores, you can identify the current top customers.

Your top customers are your net promoters, and they are more likely to return for a purchase and help increase awareness for your brand. Using the lead scores, you can then retarget your campaigns toward leads that require more nurturing before they convert.

Because you know which leads require more attention, you can increase the productivity of your marketing and sales teams as well. Remember, generating leads doesn’t matter as much as converting them into paying customers.

Now let’s take a closer look at traditional and predictive lead scoring to help you decide which one is right for your business.

Traditional lead scoring

Traditional lead scoring attempts to measure the quality of a lead in order to determine which would turn into sales. It collects and analyzes explicit and implicit data.

Explicit data is collected through online registration forms or other demographic information (job title, company, contact details, etc.) that the prospects provide. Implicit data is collected from the prospect’s behavior on your website and other marketing channels, like the number of page views, email open rate, click rate, and others.

The marketers then manually assign a score for each of these data points. They typically use the BANT criteria—which stands for Budget, Authority, Need, and Timing—to determine the readiness of a lead for a sale.

Source: Lucid Chart

Lead scoring models can also be based on the following data:

  1. Demographic: location, age, gender, professional title, company, etc.
  2. Online behavior: how customers interact with your website
  3. Engagement: how customers specifically engage with your brand through marketing channels

Advantages of traditional lead scoring

Marketers have long used traditional lead scoring to gather information, update and test the scoring system, analyze the results, and identify better leads. With the use of customer relationship management (CRM) tools, the lead scoring process can be streamlined and integrated with the sales team.

Disadvantages of traditional lead scoring

While traditional lead scoring is tried and tested, it is also a bit too simplified and too focused on removing bad prospects rather than identifying great ones. The real challenge is to determine and to target the best leads and to better nurture all other prospects.

Traditional lead scoring is also not as adaptive for fast-changing markets because it works with a small dataset that’s manually collected and managed by the marketing team. This method is also highly subjective because a good lead is determined by the opinions or metrics made by the marketing or sales team.

Rankings also are based on a rather small dataset that focuses on the activity of prospects and their interaction—or lack of—with the website rather than their individual needs.

Repetitive tasks and lack of conversions because of the failure to optimize lead scoring methods can also discourage your teams and result in missed opportunities and slow growth for your business.

For what type of business is this ideal

Companies that rely on sales reps and their customer insights to develop lead scoring method benefit the most from traditional lead scoring.

Predictive lead scoring

Take all the benefits of traditional lead scoring and add the efficiency and effectiveness of machine learning algorithms, and you have predictive lead scoring.

Instead of relying on small datasets and the manual metrics of humans, predictive lead scoring gathers and analyzes big data to evaluate the significant behaviors of current customers and prospective leads. These data points are then ranked on a scale to distinguish between the leads who are more likely to be converted and retained, or purchase something from the business.

Predictive lead scoring allows you to automate the identification and conversion of sales for your business, allowing you to refocus campaigns and make better use of resources for a faster ROI.

Source: Towards Data Science

Advantages of predictive lead scoring

Predictive lead scoring outweighs the benefits of traditional lead scoring, because it:

  • Generates trackable metrics based on large datasets
  • Provides opportunities for the marketing teams to run better targeted campaigns and maximize ROI
  • Improves the productivity of sales teams by focusing their resources towards better customers and leads
  • Increases rates of conversion and purchases
  • Compares past and current customers to adjust information profiles of leads
  • Is less prone to error
  • Gathers information backed up by data to guide decision making
  • Identifies patterns and connections you might have missed

Disadvantages of predictive lead scoring

This is probably more of a prerequisite than a disadvantage: predictive lead scoring is only as effective and valuable as the data you have. To get accurate and useful insights about your leads, you’re going to need a lot of accurate and organized data and the necessary technology to manage it.

For what type of business is this ideal

Because predictive lead scoring needs a massive dataset to provide the best insights, it is more appropriate for businesses that follow the online behavior and engagement models.

Aside from gathering a lot of data, predictive lead scoring management requires quite a bit of expertise and investment in order to be fully effective. Before you make the switch, ask the following questions:

  • How do we want to use this score?
  • What process changes should we make to share this data and information to sales?
  • What obstacles or hindrances could prevent sales from using predictive scores?
  • What score limits should sales focus on?
  • Do we have the right tools to manage lead disposition and align sales and marketing?

Unified CRM tools like Insightly offer powerful features to streamline marketing campaigns, gather and present bigger and better data, and allow your business to provide better customer experiences.

You can’t underestimate the value of lead scoring for your business. Using the proper scoring model allows you to make data-driven decisions in lead management and to maximize ROI on your campaigns.

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