Moving from gut feeling to data-based insights
Today, we no longer have to “invent.” We can use the customer data that is available to us to understand their behavior and needs in a data-based — i.e., real! — way.
By analyzing behavioral and interaction data, customer groups with similar characteristics emerge. You arrive at real personas rather than fictitious ones. From there, you can easily visualize those real personas using easy-to-understand graphics, enabling you to derive important conclusions.
Developing real personas requires fact-based analysis that covers the entire customer base and all customer attributes. It is, therefore, better informed and more accurate. At the same time, developing these real personas is less time-consuming than selective surveys that have to be extrapolated afterward. It picks up behavioral attributes that reflect reality and are, consequently, extremely meaningful.
We recently created data-based personas for a fitness center provider, which were then used to design tailored offers and interactions. In communication, for example, the behavioral data was used to address customers on their preferred channel.
Unlike fictional personas, data-based ones can be truly targeted and operationalized from the outset. And at the end of the day, this is crucial if you want to achieve a demonstrable impact. I still come across companies that have defined persona types — several, in many cases — but do not actually use any of them strategically or operationally.
Data-based personas are not one and done
Continually measuring interactions with the data-based customer segments leads to appropriate updates as you move forward. You add additional attributes over time and the segments get sharpened, which in turn increases effectiveness. At the same time, newly acquired customers can be automatically integrated into the right persona(s).
As a result, data-based clustering is not a one-time undertaking. It is a long-term investment in customer relationship management in which customer knowledge is continuously fine-tuned through ongoing testing, learning, and optimization.
