Today, we speak with Yukitaka Nezu (YN), Co-founder of Datarella, about user interaction with the explore app.
The explore app provides two key elements: sensor tracking and social interaction. You are responsible for the social interaction part. Could you tell us more about it?
There are three different kinds of interactions among the editorial team and our users:
With the surveys we ask our users about common trends and their everyday behavior. Answers are collected, analyzed and instantly presented in the feedback area. Based on the Quantified Self approach every single user sees her own results compared with other users.
Then, we run different programs helping people to simply feel better. One of our popular programs, SMILE!, motivates the user to start smiling herself and to animate others to smile, too, in return. On a daily basis, SMILE! participants receive tasks they have to fulfill. SMILE! participants managed to feel better after having finished the program and were happier compared with non-participants.
Last but not least, we provide two kinds of recommendations:
– General recommendations regarding health, fitness, nutrition, etc.
– Based on the individually collected sensor data as well as the answers to the surveys we issue personalized recommendations which help the user to increase their wellbeing and happiness
Using explore for quite a while, I have seen many different interesting topics. How do you and your editorial team find these?
We don’t invent things. We listen to the people. We read what they write, and talk. Then, there are seasonal topics of interest, such as national elections, or topics which are somehow linked to special dates. These days, one of the hottest topics is the Soccer World Cup in Brazil.
Being provided with individualized recommendations seems very promising for the user. On the other hand – it sounds like a lot of work for your team. How is the user feedback on that? Do they like it?
Yes, indeed, working on the interaction side of the explore app is the opposite of a part-time job: handling this huge amount of data and interacting with our users individually is a lot of work. However, we have developed tools supporting us in our analytical work. For example, there is our core instrument, the Complex Event Processing Engine CEPE. This engine automatically triggers certain interactions based on specific events; e.g. if a user enters a shopping mall, he will be provided with a coupon from a shop nearby.
The feedback we receive from people participating in our programs is very positive. Our users like the daily tasks – they are regarded as a welcomed distraction from their everyday routines. And, most of them confirmed that they have changed their behavior in a positive way. Above all, this behavior change aspect is the most important one for us: if you realize that your users really appreciate you work on the one hand and that they are successful in changing their behavior for the better on the other – then you know that you’re doing a meaningful job. It’s about creating meaning behind the data – and social relevance, after all.
Thank you very much!