In science, we work with measurable data, and the claim to results is to be verified by repeatability.
Applied social sciences - let's imagine a CRM system as an example - also relies on factually measurable information. It forms the basis of prospective data analysis, which is intended to serve as a means of monitoring success. From the perspective of the "data situation", the provider is the acting subject and the customer is the reacting object.
The ideal of quality is usually perfection. The Six Sigma system, which dates back to the 20th century and is still used by many large companies, can serve as an example. Six Sigma is a quality measure that measures the defect (or reject) rate in a flowing production line. It is immediately understandable that a reduced defect rate means not only a reduction in cost, but also a process cleanup.
In a world where the product is the focus and the user/buyer is the object, this may be fitting. Over time, once the vendor's output takes on a process character, the process component takes over for the development of the relationship and thus for success.
Measuring (and managing) relationship quality over an ongoing period of time means constantly adjusting the measurement system as the environmental constellation changes over time. The goals change as do the circumstances of use. Standards, in particular, change with the rapid development of technology and society. Therefore, the quality target must be flexible.
In the world of subjectively perceived quality, the technically defined concept of quality as perfection is of no help. Quality lies - according to efficiency criteria - in the adaptation to changing contexts and conditions as well as to changing norms. Under these circumstances, quality can be understood as a performance measure of learning ability.
Quality is the speed of adaptation in the learning cycle of a continuous flow of data. In practice, this way of calculating quality only really makes sense when AI plays a central role. We would call it "adaptive mathematics," where learning is the central task. AI can contribute a lot to this, but there are also other measures that play an important role when rolled out over times and structures.
Metrinomics has developed specialized expertise in such subjective validation methods, which also plays an important role in understanding well-being.
The goal of our analytics is to find the relevant subjective factors that are the driving factors for a Well-being concept with the "ideal" of a learning algorithm at its core.