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With greater adoption of marketing automation, the built-in functionality for lead scoring in these tools are used to identify which leads have a higher propensity to buy or likeliness to convert to the next buying stage but often with inaccurate results. The idea of lead scoring isn’t broken, and implementing the mechanics of a very basic scoring system in your marketing automation tool is easy enough. In fact, many marketing automation systems do a great job at tracking a prospect’s demographic, firmographic and behavioral data that can be used to build a scoring model upon.

Demographic: Scoring on dimensions such as job title, industry, revenue and number of employees.

Behavioral: Engagement with content such as click on emails, eBook downloads, whitepaper downloads and website visits. All of these should suggest interest.

What You're Missing With Your Lead Scoring ModelIntroLike many companies today, your organization probably has a lead scoring model in place to prioritize leads for follow-up and nurture. However, this type of traditional lead scoring has a host of limitations. Before we dive into them, let us first define and establish the common understanding of lead scoring. According to the Oracle Marketing Cloud Lead Scoring Guide For Modern Marketers, lead scoring is an objective ranking of one sales lead against another. It assigns a ranking to each sales prospect based on understanding of their interests and buying intentions.

The fact of the matter is that there are still a lot more wasted calls on poor leads than on the good ones. So as a way to prioritize leads for follow-up, the concept of lead scoring was born. According to the Oracle Marketing Cloud Lead Scoring Guide For Modern Marketers, lead scoring is an objective ranking of one sales lead against another. It assigns a ranking to each sales prospect based on understanding of their interests and buying intentions.

Lead scoring typically weighs two major factors in order to determine a lead’s score:

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The first reason is that most marketers don’t have enough data in order to build a precise scoring model off of. Much of the data are simply captured upon form fills on a website or landing page, and the types of data are generally basic contact and demographic info about the lead or company such as company size, industry, and job titles. Best practices tell us that the shorter the form, the better the on-page conversion. However, the less data you collect, the less you know about your prospect. In addition, this is assuming that the prospect is not entering any incorrect info, either purposefully (e.g., “asdf@abc.com”) or unwittingly (“fat-fingering” it while typing on a mobile device).

Not Enough Data Is Available For Accurate Scoring

“The challenge that many marketers face...

is knowing exactly what data points to

base the scoring upon, how much weight to assign to these data points, what

those scores actually mean, and how to

utilize those scores effectively...”

The challenge that many marketers face, however, is knowing exactly what data points to base the scoring upon, how much weight to assign to these data points, what those scores actually mean, and how to utilize those scores effectively.

Thus, while lead scoring that occurs in marketing automation platforms today has provided tremendous benefits, it also has some serious limitations. As B2B marketers, we all know that having a lead scoring system to identify sales-ready leads or potential buyers is critical to running successful demand generation programs and to maintaining marketing-and-sales alignment. But implementing a lead scoring system that actually works is easier said than done - setting up a lead scoring system that accurately identifies the best leads takes a lot of time and effort.

Why is lead scoring so hard to do and to get it right? Here are a few different reasons.

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WHAT YOU'RE MISSING WITH YOUR

LEAD SCORING MODEL

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Lead And Company Data Is Often Not Accurate, Standardized Or Up-To-Date

Company and lead data often found in CRM and marketing automation systems get stale and are usually filled with errors or contain variations of the same information. Typically, after a lead or a contact is created in the CRM, sales reps rarely update them with new information. However, people change jobs or get promoted, companies grow or launch new products, needs change over time, etc.. Customers and prospects keeps changing but the data in internal systems that is used for targeting don’t get updated. In addition, most CRM data is not clean or normalized. When multiple people enter data over a long period of time, it becomes less and less accurate... especially without any data validation or management rules in place. For example, a data field for country can include “US”, “U.S.A.”, “United States”... which means the same thing to a person but from a data perspective these are all different.

Traditional Scoring Models Are Often Based On Guesswork

We don’t know if the data that we’re using to score are actually the right ones to use. We follow general rules of thumb, such as scoring visits to our pricing page or looking at long on-page dwell times as a qualifier. Thus, we say to ourselves, “let’s assign 50 points to any leads that engaged in these activities”. This is essentially basing our scoring on gut feeling (aka guessing). The problem is that we don’t know if these actions are truly applicable or how much importance they should carry.

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WHAT YOU'RE MISSING WITH YOUR

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In general, traditional lead scoring methods are based off of data that only gives a small glimpse of the makeup, and more important buyer-fit potential, of the company. They are also typically using a set of “rules of thumb” that are based on “common sense” rather than statistical validation off of data. These rules are hard to set up and maintain, specifically if they don’t drive superior results.

So how do we identify the right prospects who are ready to buy without having to rely on guesswork or after-the-fact behavioral data that may take a long while to gather, which extends the sales cycle and perhaps puts you at risk of losing a hot lead because you’re still gathering the data to score that very lead?

Irrelevant Data Points Used In Scoring Will Give Inaccurate Results

Lastly, (and this is related to the second reason above), our scoring may be based on false correlations because we performed “eyeballing-analysis”. What this refers to is basically the fallacy of placing emphasis on specific data points because we see them as common data points across our customers, which may not be great indicators. For example, let’s say we noticed in our data that four out of the last five customers who purchased our CRM software had red hair. So, we decide to score any new prospects with red hair really high. We even decide to get this critical info by asking what hair color they have on our landing page forms. This is obviously a very silly example, but many lead scoring models are based on this type of shoot-from-the-hip analysis.

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WHAT YOU'RE MISSING WITH YOUR

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ConclusionSo how do we identify the right prospects who are ready to buy without having to rely on guesswork or after-the-fact behavioral data that may take a long while to gather? The answer lies in predictive lead scoring, which utilizes proven scientific and statistical methods to predict who is most likely to buy. It leverages data science to identify and make sense of both the data within your customer database as well as external data signals gathered from all over the web that have a high correlation to converting prospects into customers.

Want to learn more about predictive lead scoring and how it can overcome the challenges and limitations of traditional lead scoring? Download the "Predictive Lead Scoring Guide for Modern Marketers” for a comprehensive overview.