The rise of e-commerce brought with it the fear that brick and mortar stores would ultimately be replaced with online shopping, however, time is proving quite the opposite. The online shopping experience isn't able to recreate the same meaningful engagements with shoppers the way brick and mortar stores have and continue to do. As retailers rediscover the importance of an impactful shopping experience, the resilience of the brick and mortar store becomes more obvious. With millions of people visiting shopping centers across the country, brick and mortar is here to stay.
Point Inside is delving into the popular centers around the U.S to bring you more information. For the next 25 weeks, we will be blogging about the top shopping center in each of the 50 states, and uncovering some interesting information on what makes them the most popular center in the state. Below you will find details on our methodology for determining shopping center popularity and the tools we use. Be sure to follow our blog and stay tuned for more information on the state of the brick and mortar retail industry.
One of Point Inside’s featured products is Retail Space Explorer. Designed to help retailers, it provides a measure of the popularity of a shopping center in two different ways: Visitor Score, and Visitor Density Score. Point Inside’s Visitor Score is based on the volume of visitors to a shopping center. Most of the time a shopping center with a larger area equates to more visitors and thus a higher Visitor Score. Visitor Density Score allows us to also distinguish the small but mighty shopping centers by accounting for square footage.
Both types of rankings use what is colloquially called “cell phone data.” This is the same data that is used by the advertising industry to figure out which advertisements to display in an app on a smartphone. Each smartphone is assigned a random identifier called a Mobile Ad Identifier or MAID. Marketers use MAIDs to identify individual devices while letting the users stay anonymous. Point Inside uses these IDs in combination with the device’s location to determine where the device has been within the shopping center. Point Inside then compiles data on foot traffic by comparing the number of unique MAID location records within a given shopping center.
The data captured does not represent 100% of the shopper population. Some shoppers don’t have smartphones (really it’s true!). Others don’t have the apps installed that are reporting this data back to the ad networks. For those that are reporting back, the device location may be too inaccurate and because it does not fall within an established data tolerance, it is filtered out. Finally, some records might be missed simply based on sample rate. If the device is reporting only every 30 or 60 minutes, it’s conceivable that a shopper was able to get in and out of the shopping center within that time window and thus their shopping activity was missed.
Despite all of this, Point Inside is able to capture a statistically significant sample of data. To prove this is true, the Point Inside popularity index is compared to benchmark data. Because no reliable source of shopper traffic is available, Point Inside uses the same methods to predict popularity of US Airports.
Using a statistical method called the coefficient of determination, the popularity index was compared using a regression model. The coefficient of determination is is commonly called R-squared or R2. When the R-squared value is 1, the data is perfectly aligned The chart below shows the R-squared value of 0.95 (0.70 is considered “strong correlation”). As seen in the chart, there is a strong correlation between the Point Inside Popularity Index and the benchmark data from the US Department of Transportation.
This blog will focus on sheer Popularity, regardless the size of the shopping center. We will start by highlighting the most popular mall in each of the 50 U.S. states, along with a little bit about each mall as well as some of the other features, besides popularity, that can be discerned from Point Inside’s Retail Space Explorer tool.
In the future Point Inside will explore what characteristics make a shopping center popular. Is it the presence of a single store, like the Apple store? Or is the presence of a series of stores? Does the tenant category mix affect popularity? Stay tuned for more and we hope you enjoy this series of blogs.
To learn more about Point Inside's Retail Space Explorer visit www.pointinside.com/retail-space-explorer