Experimenting with location analytics

I explored a new tool this week to help me better understand the value of location analytics. I created a basic map displaying the best movie theaters for Quinnipiac University students to go to based on a few factors.

For the map, I focused in on movie theaters only in New Haven County and labeled them in colors with green being the best and red-orange being the worst, based on what I found. I also marked Quinnipiac’s three campuses to show where students could be starting from.

When comparing the theaters, I factored in a few things such as the distance from Quinnipiac’s main campus, any special features the chairs had (ex: whether they reclined or not), the food service, and any special deals that the theater regularly had. I put all of this information in a spreadsheet and allowed the Google Map tool to display it very nicely visually.

Based on these factors, three theaters are in green for being the best, two are in yellow for being average and the last two are in red-orange for being the least favorable.

This helped allow me to understand the value of location analytics because by viewing the information on a map like this, I’m able to provide location-based services to my audience to help them see which movie theater option makes the most sense to them. This is a quicker and more convenient way for them to choose a theater and will ultimately help the more favorable theaters gain more customers.