Planet M

Inspiration

Growing up in the generation surrounded by electronics, as a millenial smartphones have been the ultimate device to combat boredom. We spend more time looking at our phones that we spend at talking to our peers. Taking advantage of the fact of how busy smartphones keep us, we decided on creating a quick and engaging gaming service that subconsciously educates people about greener grocery alternatives whilst combating the monotonous tasks of daily life.

What it does

Planet M allows you to inherit the character of a honey bee (endangered species due to climate change) and enter the magical world of MIGROS. Your task is now to capture all the products from Migros product catalogue that are good for the climate and increase your species longevity. This game was born out the need for become accessible to all age groups. For a child the animated character of a honey bee would lure her or him to play the game with a lot of enthusiasm whilst learning about sustainability from young age. For adults it would be one of the mediums to kill their boredom whilst being made more aware of alternative green grocery choices. This would subconsciously instil in them the habit of observing the M-check value of the products thereby making sustainable purchases.

How we built it

We were give an expansive data set of product and purchase information by Migros. We made use of pandas data frame in python to prepare the data for analysis. This prepared data was then used as a reward point calculator in a game that we constructed using pygame library in python.

Challenges we ran into

One of the significant challenges that we faced was working in a hybrid team across a different time zone. We also did not have a balanced division of skill set and the team work was difficult to coordinate between team mates on-site and team mates virtually.
Another challenge that we faced was preparing the data set. Having no experience in pre-processing data sets before this was quite an exhaustive exercise given the time frame of the challenge. The team member spend a good bulk of hours how to extract relevant data from a nested json format. We also struggled with integrating our data frame in pygame. Our initial plan to host the game as a service on a front end platform was also challenged by the time constraint.

Accomplishments that we're proud of

Learning new libraries, meeting inspiring people, making new friends, agile working process and battling impostor syndrome as women in technology.

What we learned

We learned a lot about Migros sustainability agenda and that even though we have an ambitious product vision we need to identify the features that make up the MVP before implementing the product.

What's next for Planet M

Given the time constraints and a limited domain of knowledge in tools to create games, wed to refine Planet Ms functionality by using the right tools, for example, through conversations with other people we realized that the potential of a game hosting to be on AHS server.
We could also translate the reward points into partner ship with climate change NGOs to have a more tangible impact made by both Migros and the consumer. Migros could also potentially attract the consumer by offering them discount incentives on a consistently high game score.