UX data diamond • A map to design »datensparsam«

In German the word Datensparsamkeit describes a voluntary commitment to only handle and store data which is needed to fulfill a specific customer job. The term is anchored yet in the German Gemeinsame Geschäftsordnung der Bundesministerien (GGO). This political anchoring is a good sign and creates guard rails for those who shape products and services.

Efficient and satisfactory services are created with customer data. But, what is the set of data that needs to be collected in the first place to design a good customer experience?

There is no one-size-fits-all answer to this question, as the goals will depend on the specific needs and objectives of the organization designing the experience.

• Demographics (e.g. age, gender, location),
• customer behaviour (e.g. purchase history, web browsing history), and
• customer preferences (e.g. preferred communication channels, product/service preferences)

are certainly the most aggregated customer informations. That data can be used to improve experiences significantly, for example by making it easier to find the right product or by providing more personalised advice.

It is reasonable to assume that commercial data exploitation will increase by the rise of AR and VR powered experiences. In addition to these unpleasant effects, there are requirements for continuous improvement of the business, which can only be achieved through data evaluation.

We need more data-driven but also datensparsame approaches in our work, so that the required data, e.g. in service blueprints, can be presented early in the design process and made durable transparent. – We are looking for a practical way to avoid big data exploitation.

The method I want to suggest is the data diamond in extension to journey mapping to gain a deeper understanding of how customers interact with a product or service and what they need and want in a certain period of time.

The data diamond helps displaying identified data and ties. Once completed, it shows a grid of vectors and associated confidence values reflecting the impact on the experience.

We consider the following data from a set provided by the customer:

— customer needs and wants

— customer behavior

— customer preferences

— customer demographics

The model can be used by organisations that want to use data cautiously to improve customer experience. By using, hypotheses about the customer’s journey and the customer’s needs can be generated based on a smaller set of data.

With input of data analysts and market research colleagues, we shine a spotlight on the targeted customer segment. The closest related data points form a triangle and the smallest possible data set to make informed decisions.

What sounds abstract and unfamiliar at first, over time creates a shared understanding of how to use customer data mindfully and respectfully.

A guide on how to use it will follow soon. Please try it anyway and tell me how you use it.

To work datensparsam, ask yourself the following questions.

• What data is important for preferred customers?
• What conclusion can be drawn from related data?
• Can we use already consensually provided data?

We found data analytics and market research in unlinked domains at corp and SME, not integrated into the customer experience design process. This is our room of opportunity.

I’m very interested in how other UX teams handle customer data? What showstoppers are you aware of?

If you’d like more information on our project approach, drop me a message.

Thank you!