One of the business cases EnBW, Energie Baden-Württemberg AG is working on, seeks to enable senior citizens to live a self-determined life within their own four walls longer in their life span. There is a system that detects irregularities in electricity consumption that can indicate emergency situations and alerts local care services.
The team around that specific business case had been put together from different areas of EnBW, Energie Baden-Württemberg AG, one of the largest energy supply companies in Europe.
They had already established research practices and conducted occasional user interviews.
Above all, the team needed qualitative feedback on the use of the product itself.
To gather this feedback EnBW’s team went for an alternated phase system. In the interview phase, they set up regular interviews with seniors for one side of the product as well as with on-call services for another. After collecting enough valuable data and insights — the team switches to the work phase.
During product development, Swenja joined the team and was faced with a large amount of previous interviews. Now, she had to find a way to compare the relevant information in all of these quotes by interviewees.
Up until now, the documentation was mainly displayed using spreadsheets in MS Excel, which meant that only a small part of the interview insights were actually documented.
Immediately after Swenja had gained an overview of the research, she realized that documenting the interviews with spreadsheets is not effective: Too many time-consuming inquiries were piling up about the meaning of the insights.
In addition, a lot of knowledge the team previously had gained in the interviews was lost because documentation was too cumbersome. Support was needed.
Thus, when searching for a tool to solve this problem the team was looking for three crucial factors:
The tool should help the colleagues to carry out the evaluation in a structured way.
Conversations and interviews with seniors are an extra challenge — even for experienced researchers.
The conversations are usually not very linear and the people testing often deviate from the topic and start to ramble.
Therefore, interview evaluation has to be as flexible as possible. The evaluation must allow for quick and flexible changes to the scope of the evaluation during the analysis.
Linking insights to interviews
A tool supporting the research has to be able to structure all results flexible and individually. The interviews with the two target groups “senior citizens” and “on-call services” differ vastly, but the findings still need to be combined and evaluated.
The focus is always on the link between insights and the raw data. Since the interviews differ that much, it often happens that in apparently irrelevant statements important findings are found much later — by chance, accident or else.
Easy onboarding experience
Introducing consider.ly managed crafting a suitable structure for data research and making the tool known within the team. consider.ly was completely adaptable to the team’s structures and proved to be very flexible.
The built-in chat function provides a direct support line connecting EnBW’s team members with consider.ly’s experts. This comfortable feature allows every new colleague to be quickly onboarded.
Optimized research with consider.ly
The positive impact consider.ly made on research practices could already be observed shortly after its introduction. Research that had previously only been evaluated in a superficial manner with MS Excel — was now analyzed in a well-structured manner. Also, the results were easily prepared for the team members and further stakeholders: Significantly more findings could now be derived per interview than before.
- The interviews themselves are stored centrally now — with consider.ly being the data hub.
- Research can be viewed by all colleagues.
- Since the distilled insights are directly linked to the actual interviews, no knowledge is lost.
- Interview parts which seem rather unimportant at the moment, can be reviewed and cross-checked with future research — with just a few clicks.
The consider.ly interface makes it easy for us to create new tags or change existing ones — while the data is still being analyzed in the background, ensuring we can start early with research analysis. Thus, we are able to develop our framework on the fly.
The handling of insights has also changed: The insights created are now directly compared with the team’s hypotheses. Directly determining whether a hypothesis is correct, if it requires further research or it can be rejected — all of this became way easier.
For the EnBW team, linking sharable results to its original data source is absolutely crucial. With such an important topic like medical emergencies, the exact wording of the respondent is important for the team to be able to implement their insight correctly.
With consider.ly, all interviews are systematically analysed and the results are processed into an ever growing Insights repository, which grows with every interview. consider.ly is one of the foundations of our product management.