This article adds more detail to the points which I touched on in the Visual and Short version 🔗.
This is an Appendix, for reference only and linked from there.
Starting point
The initial challenges we first discussed with the Co-founder were wide and multiple, among which: problems with planning the crops and harvest; educating other farmers on other farms of how to run a farm.
The members take the food directly from the farm, which is not something the modern human is used to. The aspect of community and sustainability are important to them.
👉 Process Note: the management board were not fully aware of all these issues (slide 2, notes in orange). These discoveries were made through my initial research observations and were clarified after proposing a prototype.
User Research
The Research consisted of observation at the farm (tens of people), shadowing 1 user at home, informal discussions with many members who are either designers or operational excellence professionals and about 5 contextual interviews.
After the initial user research using contextual interviews and shadowing, a set of frustrations became clear.
Interpreting and Framing
Based on the observations above, the direction was chosen to be:
To facilitate the learning that takes place between the members of the community by building a platform which allows the users to share knowledge of tips and recipes.
This was valuable because the members take the food directly from the farm, which is not something the modern man is used to. It requires a set of skills to properly clean, store and cook the vegetables and meat. These cooking skills are not common today, because we are spoiled by grocery stores.
👉 Process Note: The challenges expressed below became clear only after framing, prototyping: after the first iteration was presented for validation (co-reflection)in the form of a rapid prototype (Adobe XD mockup); paired with an experience flow. The creation of these user research props is described in the next chapter.
Rapid Prototype – Iteration 1
The creative process and Ideation was helped by the use of tools like: scenarios, storytelling, which slowly led to functionality, wireframes and a click-through prototype.
Sometimes a bit of acting out can do wonders for the process and getting unstuck. Below you can see a set of tangible figurines which were useful in acting out and storytelling for ideation purposes; as well as for presentation through an animated video.
User Testing is not described on this page. Following sessions using the first prototype, I discovered a group of people within the community (led by Paul) who were aiming to achieve the same goal, and we joined forces. Therefore, this work was indeed desired, and realized in a collaborative manner.
Iteration 2 is different from iteration 1 in that it is focusing on original, community-created content. It puts more value on the sharing of Tips not just Recipes. Recipes are suggested based on family history. The prototype is currently running live in the real world with members contributing to it.
Machine Learning feature
The Machine Learning algorithm was prototyped in order to make possible the “recommender” behind the platform. One of the limitations for the content moderator was that they had to manually propose a selection of content – while maintaining a high level of engagement, relevance and the “new”-factor. Using an algorithm (Naive Bayes) enabled us to offload that responsibility, and paved the path toward a personalized experience.
Visitors during Demo Day had the opportunity to tangibly act out the algorithm and pretend to be in the mind of the computer. The prediction becomes more relevant in time, while being able to adjust with changing patterns.
Conclusion
In retrospect, my role as a designer became to express what the members wanted in the first place when they joined the community.
The end result was strengthening the connection between members, and helping them organize and problem solve in this decentralized organization. They have been doing it before I arrived, and would continue to do so after I left.
Gratitude:
I would like to thank Boudewijn (Co-Founder) for allowing me to apply Design in the real world, and Paul (Active member) for accelerating the speed at which we prototyped and experimented.
Note: the featured photo for this project can be found at the source.
