On the way to VandyHacks, our group decided to eat at a restaurant. Using Yelp, we saw the dollar signs to determine how expensive the food at each restaurant was. However, when we decided on a restaurant with two dollars signs and commuted to the restaurant, the food was more expensive that we expected it to be. We didn't actually know how expensive it would be and whether it would be in our budget. In addition, one of our group members recently witnessed the opening of a Starbucks in his hometown neighborhood. He thought that it was a brilliant move since there were no Starbucks in that area for miles and there was definitely a need for a coffee shop. Unlike Starbucks, he saw that many businesses do not have the opportunity to use data insights to attract consumers and target advertising.

Noticing these two problems, we realized geolocating consumer spending data could alleviate many of these problems. As a result, we decided to create FinMap, a consumer mobile application that can estimate spending at a certain business based on past data and a web application that businesses can use for data insights to target a larger audience.

What it does

FinMap uses geo-tagged consumer transaction data to develop data insights for businesses and consumers. Businesses will be able to access heat maps and other visualizations so they can adapt their practices as market spending changes. On the consumer side, FinMap provides consumers with a mobile application that allows them to find shops based on their budget and preferences.

How we built it

We built FinMap's business facing platform using MicroStrategy's Data Visualization API, Javascript, CSS, and HTML. To create the mobile application, we used the Android Studio IDE and Java. To store the data, we used a MongoDB database with communication to a Flask server. In order to simulate the data we took a systematic approach and wrote a Python script that takes into consideration age, income level, and where customers are more likely to spend more money, etc.

Challenges we ran into

We wanted to let the data tell a story. In order to do so we had to think of out of the box solutions when simulating the data. For example, for each store that we modeled transactions for, we had to develop a weight factor in regards to the amount of money customers would spend there; customers who shop at Louis Vuitton are inherently more likely to spend a lot more money than customers who shop at Express. Developing our data insights the modeling of our data was by far the most challenging aspect of our project. Along with developing our data, visualization of the data was a challenging task. Our group had to pick aspects and attributes of data that would matter to businesses.

Accomplishments that we're proud of

One of the accomplishments that our group is proud of is the fact that we were able to implement MicroStrategy's entire data visualization platform into our project. It takes full advantage of the data we are producing and presents our information in a meaningful manner. Another one of the accomplishments that we are proud of is our modeling of transaction, store, and customer data. This aspect of the project was by far the most intensive and most rewarding when we finally got it right. However, we are most proud of the fact that not only were we able to create a web app, we also developed a mobile app for consumers to compliment the business aspect of our project.

What we learned

We learned how to use MicroStrategy's API and entire business client software in order to develop the data visualizations we implemented within our software. We also learned how to connect our MongoDB to MicroStrategy's cloud storage software in order to get live data as consumer transactions are being logged.

What's next for FinMap

FinMap can be a successful startup. We would like to ideally start collecting real transaction data by implementing various bank APIs such as CapitalOne to generate real-world data insights. Once we have data insights, we would then roll out our mobile application so consumers could take advantage of our data insights to be more cautious about their spending habits and more cognizant about their budget. With our aggregate user data collection, we could then target this data and web application at any company that wants to maximize their revenue stream.

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