A shopping mall is launching a promotion through their app, providing customers with coupons for a popular restaurant. The promotion is being advertised through billboards strategically located within the shopping mall and its surrounding areas. The primary question we aim to answer is: Are these billboards effectively encouraging customers to use the promotional coupons in the vicinity of these advertisements?

A good way to visualize this information is with a heatmap. For everyone who chose to share their location with the app, we have their latitude and longitude for the moment where they saw the coupon, and also for the moment they redeemed. This allows us to create heatmaps showing both the full picture of the campaign period, and also a day by day (and hour by hour) evolution of users' interaction with the coupon on the app. We can also see how many users redeemed the coupon off-site and if they redeemed and used it on the same day or the next day, for example.

The heatmaps offered clear visual representation of areas with high user concentration, revealing the notable influence of ads placed within the central shopping area in driving app traffic. Furthermore, they highlighted that the ad placed in the parking lot also contributed significantly to app usage. However, an ad at the shopping mall's north entrance appeared to engage far fewer people, suggesting a potential area for improvement in future ad placement strategies. These insights allow us to make data-driven decisions, enhancing the effectiveness of our advertising campaigns.

We leveraged Python libraries such as Pandas for data manipulation and Folium for the map visualizations. We collected geolocation data from the app each time a coupon was used, marked the time and location of use, and mapped it against the locations of our billboards. These visual representations provided a clear and intuitive understanding of the effectiveness of our billboard placements in driving coupon usage.