We asked ourselves that if we were to present our product to a wide number of people, who would be listening? Who would be interested? Does the interest vary in a given time of day? This lead us to devise a solution to numerous advertisers who want to push their ads to the right media, namely the apps that the average user uses in a given time of day.

What it does

Simply put, adMOD gives viewers a bird's eye view of the typical user's day through an interactive sunburst diagram, allowing them to determine which apps are used when. Whether its an RSS or news app in the morning or Facebook time-wasting at night, our visualization gives a clear perspective on when and where to push ads.

How we built it

Our main goal for adMOD was scale, which was the underlying design principle we adopted for our platform. With the help of D3.js and JSON, we parsed the information and built a front end interactive diagram to provide a visual representation of the data.

Challenges we ran into

By trying to implement our vision in the short amount of time given, One of challenges we faced was the technical aspect of the visualization, namely manipulating the big data and inserting it into our implementation of the D3 visualization. Amongst other front-end issues that, for the most part, were ironed out, the challenges we ran into helped us grow and learn about the data space and its intricacy.

Accomplishments that we're proud of

We are proud how much we learned about team collaboration and learning how to delegate tasks to achieve an overall goal. More importantly, we are proud of actually manifesting something using the magnificently complex d3.js

What we learned

Python, D3, Tableau, R, JSON, mySQL, and how to work with little to no sleep.

What's next for adMOD

More accuracy at a larger scale. We hope to bring this information to as many advertisement entities as possible, as both a monetization tool as well as a better medium for market research.

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