2022-10-19 | Article | Insights
User segmentation forms the basis for custom concepts of marketing strategies and targeting. This insights article describes different approaches to defining user segments, with a particular focus on Google Analytics 4 (GA4) as a data source, which provides the necessary criteria and data for segmentation into distinct segments. Examples and explanations of the possible use cases make the respective approaches more concrete.
Today, the availability of digital user data, machine learning algorithms, and easy access to analytics platforms provide the opportunity to expand targeting approaches from what used to be sketchy representations of personas based on some (assumed) user characteristics in the past to now precise, actionable, often ML-driven user segmentations allowing to find highly relevant target groups for companies and their offerings.
When approaching user segmentation, analytics systems such as GA4 can serve as an interface for providing user representations at different levels of information granularity for more task-specific user insights and to increase marketing efficiency.
Three approaches to segmentation are highlighted here, which differ in terms of the effort required and the insights generated as well as their actionability.
Rule-based segmentation offers a simple and theory-driven way of addressing customers. Logical rules are applied to define and build meaningful user segments.
A very simple example where only one criterion is used, is the segmentation of the customers depending on the remaining contract period. An example of activation could be: If the remaining term is less than 6 months, the associated customers should receive a sales call.
The well-known, customer value-oriented, RFM concept also falls into the category of rule-based segmentation. Here, customers are described in terms of their historical data with regard to the following three RFM criteria and grouped into customer segments accordingly:
With the resulting RFM segments, e.g. inactive, occasional and top customers can be identified, for each of which different marketing activities and customized offers can be defined. Experience shows that the RFM criteria are good indicators of the future purchasing behavior of customers and therefore the segments can also be used for optimizing the campaign planning.
A more sophisticated concept is cluster analysis. It is an unsupervised machine learning approach in which existing users or clients are clustered into approx. 3-7 internally homogeneous groups based on a defined set of criteria. The availability and selection of criteria is a crucial step in the analysis. In addition to the data available in GA4, CRM data could also be included, for example, in order to be able to describe customer clusters with regard to socio-demographic characteristics. The biggest advantage of the method lies exactly in this variety of possible segmentation criteria. It is particularly suitable for identifying and better understanding customer groups. For these distinct groups, individual offers and marketing approaches can then be formulated.
Last but not least, the predictive approach also offers an opportunity to segment customers. Here, again, a machine learning algorithm is used. In particular, a classification algorithm is applied to predict the probability of a certain user action based on a defined set of criteria. GA4 also provides a valid database on user-level in this approach. The result of this ML classification approach is a predicted probability for each user to perform the specified action - this is often churn or conversion, for example. Based on this probability prediction, various onsite and offsite activities can be designed and executed to target users in the most meaningful and efficient way while creating an outstanding user experience.
As outlined, user segmentation is the foundation for a personalized and targeted (marketing) communication approach, as it enables companies to act on business-critical phenomena such as churn probability, lead/ conversion probability, customer lifetime value, and the like. This can be achieved with the help of technical and data science expertise that enables the execution of projects to identify similarities and differences in characteristics such as sales, contracts, time, etc. In this way, customer knowledge can be enhanced by identifying user patterns and creating user (group) individualized messages and offers. The goal is to increase the customer base of users who return and sign up, prevent churn, create customized offers, and more. The decision which approach to use is very goal dependent. However, user segmentation is indispensable in modern marketing strategies.