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.
Use Case 3: Cleaning up the database
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.
Read the introduction: Lead Score 101
Poor data quality in a chaotic database can not only be a real pain in the neck for the marketing team but also cost hard cash. Many technology providers (CRM, marketing automation, databases) offer flexible models based on the number of contacts stored. If there are large amounts of duplicates or contacts with bad, incorrect, or outdated data sitting in the database, cleanup can be financially rewarding. In addition, there are numerous other benefits of a clean database, such as an overall improvement in email KPIs, as well as a reliable and valuable overview of the customer base.
While a lead scoring model cannot solve every problem around data quality, it can provide important input to help weeding out and deleting contacts from the database. This can be done by assigning a high negative score to certain undesirable characteristics of criteria (example: competitors, or working students/interns). If we then analyse the data and only look at contacts with a negative score, all customer records with undesirable criteria are easily singled out. It is thus much easier and more automated to handle (i.e. delete) them in the following step.
Furthermore, not only is the “existence” of points of relevance to the marketing team, but the “absence” of points is valuable likewise. For example, if one wants to gain insight into the percentage of inactive contacts in a given database, the classic method would be to spend time combing through the data for questions like: “When was the last time a purchase was made?” and “When was the last email opened?”. When was the last email opened? Clicked? When was the last website visited? When was the last phone call?” This list can be continued for quite a bit.
However, since all of the above actions are monitored by the lead scoring model, the much more effective approach would be to simply filter the data for the last change in lead score. At a glance, it is possible to see which contacts have recently accumulated points, i.e. are active, while for other contacts no change in score has been detected for a long time, and are accordingly considered inactive. The definition of active and inactive must be determined based on individual criteria.
Use case 1: Negative scores
Use case 2: Segmentation
Summary
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.
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Written by Jan Lempenauer
