Skip to content
Marketing Automation

Lead Scoring: B2B Marketing’s Magic Bullet (Part1)

What is lead scoring? Lead scoring is generally understood as the process of assigning a numerical value (score) to both leads and customers using a model based on various criteria, thereby categorising and qualifying them. In this series of articles, I would like to introduce you to other options for how marketing and sales can benefit from lead scoring, in addition to the classic and widely used scope.


Lead Scoring 101

Basically, the criteria needed for qualification in a lead scoring model can be divided into two main categories:  

  1. We speak of demographic data, under which information about the “type” of a customer is summarised. Classic examples of this category are company size, industry, annual revenue, or geographic location.
  2. The second category of data describes the behaviour of leads in communication and interaction with your brand or company. Examples of data from this category that should be available in virtually every marketing department are interaction data with marketing emails (opening emails, clicking a link), visits to the homepage, downloads of informational materials like gated content, or filling out a web page form.

The possibilities seem unlimited, as more than 250 data points can be considered in both categories combined.

Depending on the individual relevance, demographic and behavioural criteria are weighted differently, i.e. represented as numerical values of different sizes. Important criteria are assigned a higher relevance and thus a higher weight than those that are less important. Which of the numerous criteria are considered more important and which more trivial varies from company to company and is based, among other things, on strategic considerations and corporate goals.

If a certain minimum of data from CRM, ERP, Marketing Automation, or similar systems is already available, it is advisable to use the insights and recommendations for action gained from the analysis of this data as a basis for the numerical evaluation of the criteria. We would be happy to support your company in translating your individual goals into a lead scoring model:


Contact us to support you into a lead scoring model


Now, if the score assigned to a lead exceeds an individually defined limit by meeting various criteria, the conversion of the lead into a customer is considered more probable and can trigger a number of different reactions. Very often, the lead is handed over to the sales department for further processing. In addition to this rather common use case, there are numerous less known benefits described in other use cases.


Use Case 1: Negative Scores

An often neglected option for increasing the accuracy and meaningfulness of a lead scoring model is to make use of negative numerical values for uninteresting or undesirable expressions of certain criteria in addition to the well-known positive numerical values for criteria (example: click in an email = +5). This gives depth to the model by adding another dimension of qualification. Two options become three: 


  • A lead is qualified by assigning a positive value (Positive);
  • A lead is not qualified by not assigning a value (Neutral);
  • A lead is disqualified by assigning a negative value (Negative).


Example of how the number of employees can impact the scoring

Exhibit 1: Example of how the number of employees can impact the scoring


Demographic data in particular is wonderfully suitable because, unlike behavioural criteria, it is much easier to compare with the characteristics of an ideal customer profile (or ICP: Ideal Customer Profile). For example, if a software solution provider is mainly interested in large customers with a certain minimum number of employees, the scoring logic could be based on the number of employees and look like this:

In our example, large companies with many employees correspond to the ideal customer profile and are therefore given a positive score. Somewhat smaller companies could also be of interest under certain circumstances, so they are also assigned a positive, albeit smaller, score. Small companies with less than 2,000 employees have proven to be unprofitable in the business model of our exemplary software provider, which is why they should not be actively pursued by Sales. Companies that have between 2,000 and 2,999 employees are not assigned a score and are therefore neither qualified nor disqualified. 

Another example is job title. Influential decision makers and other promising leads are naturally rewarded with additional points. Other job titles whose responsibilities and influence predict a low likelihood of conversion may be weeded out by negative scores. This is the case, for example, for leads with the role “working student” or “intern.”

Even criteria such as company name, which at first glance seem worthless for the purpose of a scoring model, can be quite useful. A listing of competitors and the associated disqualification by point deduction keeps these unwanted leads off the long to-do lists of sales and, on top of that, can assist in maintaining a clean database (see Use Case 3: Cleaning the Database).

Example of how scoring can be used to disqualify competitors

Exhibit 2: Example of how scoring can be used to disqualify competitors

The same logic can be applied to numerous behavioural criteria in addition to demographics, because not every interaction with your website or other communication channels such as email or social media is valuable. For example, if a lead visits primarily the jobs and careers pages on your company website, it indicates that this person is more likely to become your new work colleague than a new customer and can therefore be downgraded within the scoring model. Also, unsubscribing from marketing communications via an unsubscribe link in a newsletter shows relatively clearly that a lead currently has no further interest in your company’s products or services. This action is reflected in a significant decrease in the score.

There are almost no limits to the creativity and possibilities for adjusting criteria, qualifying values, and associated numerical values. By continuously updating and readjusting the adjusting screws, even a simple scoring model can do a good job.



The use cases described in this article series show that there are more possibilities in a lead scoring model than it seems at first glance. Even a simple lead scoring model can be of great help to your marketing and sales team, as a fully automated selection and qualification of leads can save lots of manual work.

The marketing team benefits because freed-up capacities can be used elsewhere, while sales activities can be focused more efficiently on promising leads. Another argument in favour of using such a model is that the time and effort required for implementation is relatively low compared to other projects, provided that the aforementioned strategic foundations have been laid and the technical systems are in place. 

At Avaus we will gladly provide you with comprehensive advice on both the strategic and technical side of things. I look forward to your questions, suggestions, or a non-binding exchange via the contact options below.


Use case 2: Segmentation


Use case 3: Cleaning up the database


Download the entire white paper now to learn more about use cases that go beyond the familiar scope of application!


Written by Jan Lempenauer



Jan Lempenauer

Strategy Consultant



Möchtest du lieber auf Deutsch weiterlesen? Kein Problem, du kannst auch weiterhin auf der englischen Hauptseite stöbern. Wähle unten einfach deine Präferenz: