Unlocking Research Intelligence

Sabeth Wiese: What I love about design is that you are always completely immersed in new products and ways of life. At the same time, as a business graduate, I am interested in how decisions are made – and that is precisely what research helps you with. It's especially great when we send our research reports to the client, and we see how it facilitates decisions and triggers those 'aha' moments

Sabeth: An aha moment doesn't just mean reading insights and finding them interesting, but also realizing: What can I do with this now? It's about identifying potential opportunities and risks and planning the next step.

How these aha moments come about varies – it depends on the type of research and the needs of the client. For UX testing, one of the major research categories, the designers and product owners are usually present themselves. Often, a small to-do list starts forming in their heads during the test. As accompanying researchers, we then verify or question the items on that list. The approach is therefore very solution-focused and fast, which fits well with agile projects. However, overarching insights, such as the life situation of users and their fundamental needs in relation to a product category, can fall by the wayside.

Exploratory, fundamental research, such as need segmentation, is a completely different category. The aim here is to create a big picture and present multi-layered insights in an understandable way. This usually takes place in a detailed report and a presentation with many participants. The disadvantage of such data is that it takes a lot of time and space to present.

Sabeth: Reports should be quick to grasp, especially regarding key insights and their practical relevance. They must be appealing and “snackable”, i.e. easy to consume. Entertainment also plays a role, as humorous or unusual insights are more memorable.

To reuse insights, they should be held in a repository that is integrated into existing processes. It must be accessible, but also secure – trade secrets require clear access rights.

Whether research results are effectively reused also depends on many other factors. These include psychological factors: does someone enjoy starting new research more than using existing data? Or organizational structure – decentralized teams have a harder time with data reuse than centralized research departments. Processes also help for example, some clients have a system that automatically suggests searching the database for existing data when a new research project is created. Incentives also play a role, of course. If a lot of management attention is paid to new studies in an organization, that is an incentive that may prevent data reuse. And finally, skills: can POs and designers assess which data is outdated and which is not?

Sabeth: In the worst case, of course, you end up doing similar research projects on similar topics multiple times. Either because no one knows that a study already exists, or because they think it's not transferable or they don't trust the quality. This leads to the waste of scarce resources and potentially impacts consistency between touchpoints.

Too much research on similar topics can also trigger “research fatigue” in users. Another problem is that cross-project or cross-touchpoint insights don't get to where they're needed. As a result, the potential of a study is not fully exploited.

Sabeth: I think one thing has become clear: there should be a repository. And this repository should grow with the company. It's better to start small and then expand step by step or add other software solutions.

It is becoming increasingly apparent that AI solutions provide a better interface for accessing data because they can simply deliver good answers faster. They are also more pleasant to use. This means that a habit is established much more quickly, and the repository is really used. But of course, AI should be used with caution – because of data protection, biases and costs. As great as this technological lever is, the other aspects I mentioned – culture, structures, processes, incentives, skills – must be strengthened in parallel. 

And if you are already collecting data, I would recommend also documenting the associated processes. This way, you can more quickly identify best practices and optimize processes – the keywords here are design and research ops.

Sabeth: There are three major hurdles:

The first is that, out of perfectionism, you don't have a repository at all because the budget for the optimal solution is lacking. In that case, I would say: better to start small!

The second is that there is a repository, but nobody uses it. This happens when the software is not integrated into existing structures. Here you can strengthen skills, for example with workshops or toolboxes, and increase visibility in the organization by celebrating initial successes or finding promoters who will drive the topic forward in teams.

And the third problem is that a repository is used, but the data in it is out of date. This can be dangerous, especially with AI solutions, because they are often opaque. 

Our clients approach the topic of repositories in very different ways – but nobody has yet found the one perfect solution. However, we are happy to provide support for improving your approach to data reuse.

Sabeth: I think that AI makes working with existing or secondary data more pleasant. As I said, hardly any research question is researched twice with the same focus, so when looking through old reports, you must be very attentive to what is and isn't relevant.

But AI could access the underlying data directly and tailor the results much more closely to my use case. This means that eureka moments come faster – and that just feels more rewarding.

Sabeth: I see advantages on many levels. On the one hand, the efficiency of research is simply increased. The transfer of knowledge between touchpoints is improved. This increases consistency and strengthens the appeal and persuasiveness of a brand.

When existing data is used, many things happen faster. You don't have to conduct new interviews or create new studies first. It also becomes easier to see where trends are emerging over time.

In the current economic climate, companies could save on research costs by reusing data. Or they could use the freed-up budget to delve deeper into particularly interesting areas.

No matter how you look at it, insights that are used well, strengthen customer focus, increase the likelihood of innovation success, and improve quality. All this ultimately contributes to competitiveness.