Behavioral Economics: Rethinking customer segmentation
Why insights from behavioral economics and behavioral psychology are ushering in a new era in customer segmentation.
Two behavioral economics developments are ensuring that no stone will be left unturned in the segmentation of customers in the future: On the one hand, research findings from behavioral psychology are seeping[1] is slowly making its way into marketing practice. On the other hand, the legal regulations on the use of cookies are changing the previously data-driven marketing into a customer-driven one. In customer-driven marketing, gaining as comprehensive an understanding of the customer as possible is of central importance, as this is the only way to create a holistic and coordinated customer experience.
Up to now, the development of a comprehensive customer understanding, customer intelligence, has been based primarily on interaction and transaction data. Although this allows the modeling of segments, they are incomplete or not all-encompassing. The needs level is missing. This can be supplemented with insights from behavioral economics and behavioral psychology.
Paradigm shift: dynamic, pointed segmentation
If you want to do effective marketing, you have to segment - that's not new. But so far, segmentation has only been done on the basis of socio-demographic data, website data and possibly initial generic affinities. Psychographic dimensions or predictions about future user behavior are only sparsely modeled and even less used. It is time for a paradigm shift: away from broad segmentation towards dynamic and pointed segmentation. Success is achieved by merging the following analysis disciplines:
- identity resolution
The generation of data protection compliant, consolidated user identities - data science
The statistical analysis and interpretation of existing data and the derivation of patterns and trends for the development of propensity models (predictive analytics). - behavioral science
The qualitative enrichment of the collected data with psychographic characteristics (especially basic needs and relevant decision patterns)
Intelligent segmentation depends on the coupling of two data sets. Website data provides information about user behavior in terms of recency, frequency or engagement. Data from the database informs about customer history (transactions, CLV, returns or customer quality).
These data sets are linked via an identifier (usually an e-mail address). This results in the possibility of developing intelligent segments.
Behavioral economics in segmentation
The linking of the two data sets enables the enrichment of the profiles or segments by the characteristics which, according to current findings in behavioral economics and behavioral psychology, are responsible for >95% of all decisions: those of the need level.
Dependence between psychology and marketing technology
An already well-known example still demonstrates the data dilemma well: In their socio-demographic characteristics, Ozzy Osbourne and Prince Charles do not differ from each other (male, British, >70 years old, very wealthy, large residence in the country, etc.). They can thus leave comparable interaction profiles in the shop. However, on a personality and need level, they could hardly be more different. A purely data-based approach logic must therefore almost fail.
The needs dimension is therefore the decisive and, above all, hitherto hardly considered new criterion for the successful design of customer journeys. The fact that it is not yet on everyone's lips can be explained on the one hand by the fact that research findings are only gradually being transferred into practice. On the other hand, it is also due to the complexity of developing this level, which should not be underestimated.
Two ways to approach customers in a psychologically sound manner
A psychologically based approach in customer-centric marketing can be implemented in two different ways: conceptually and automatically.
- The conceptual approach is a data-based-empathic approach. Experts such as a customer journey manager, who has extensive customer knowledge and the psychological expertise, add psychographic characteristics to the existing audiences or segments.
- The automated approach is currently still at an early stage. Innovative start-ups such as Behamics in Switzerland are already demonstrating that machine learning algorithms can be used to deliver psychological triggers very effectively. However, here too, psychological knowledge is required when designing the triggers and the interaction of the systems must be clarified.
No matter which way is chosen, the chain of effect Man ® Need ® Trigger ® Conversion can unfold its effectiveness in the interaction between data and psychology.
For more information on the different aspects of customer-driven marketing, read the elaboratum white paper "Customer-Driven Marketing".
