Foot traffic Analytics: Top Clothing Stores, Atlanta
The clothing store market is one of the most popular in the USA, with Big data analytics and location intelligence techniques we made an brief exploratory analysis in Atlanta city.
Friday, September 17, 2021
Case Study: Analysis of Apparel Stores in Atlanta, USA
Using location intelligence techniques it was possible to identify all the stores distributed in the city, the detailed analysis allowed us to classify the venues by market share and identify in which areas of the city they are located. Our research showed that the women’s clothing category is the one with the largest number of stores in the center of Atlanta, while the other categories are distributed in the surrounding areas.
This analysis covers not only individual stores but also those establish inside shopping malls; an example of this is the Lenox Square mall, where we identified several clothing stores and analyzed how they were distributed inside the mall.
Applying geo-marketing techniques we can visualize the foot traffic of consumers within a mall or individual store, as well as in its surroundings.
These solutions benefit any business sector. another example of this is a case study that was conducted to compare two of the most popular supermarkets in the city of Guadalajara, Jalisco Mexico. Read more about this case: “Walmart Vs. Soriana: Consumer Foot traffic Analysis.
In this section we observe the percentage distribution of stores segmented by category, the result shows that the most popular category is family clothing with 49% of the market, followed by women’s clothing with 28% and other types of stores with 12%.
This is a brief analysis of the percentage distribution of visits in the family clothing stores category, allowing us to identify which are the most and least visited brands. Marshall’s clothing brand took first place, followed by Burlington’s and Old Navy, being these the three most predominant brands in the Atlanta clothing market.
By applying location intelligence to Big Data, it’s possible to find different points of interest such as competitors’ points of sale, the concentration of people, identify the most and least crowded places, the times and days of peak visitation, consumer purchase profiles, among others. This data helps companies to optimize the expansion and marketing strategies of physical stores, which answers several questions of how to ensure the success of an expansion plan such as:
Where should I open my store?
Where are my competitors located and who are they?
The key to attracting potential customers to any new location is to determine its foot traffic potential, the use of geospatial data combined with footfall analytics makes the retail site selection process easier, faster, and more reliable.
At PREDIK Data-Driven we support corporations by optimizing their site selection strategies with 100% data-driven methodologies. One of our clients, a major regional footwear retail corporation, was able to determine in which shopping mall it was more convenient to open its first brick and mortar store, thus reducing investment risks and maximizing its revenues, having identified its potential customers and its target market in the malls.
Retail home improvement franchises need to apply location intelligence techniques and foot traffic analytics to identify consumer mobility patterns, in order to maximize sales and generate more efficient expansion models.
The correlation between foot traffic, visits, sales, and the success of hardware home improvement franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Retailers can apply location intelligence techniques and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
The correlation between foot traffic visitation, sales, and the success of retail apparel franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Analytics based on Big Data allows mall operators to maximize revenues and visits by better selecting tenants, optimizing mall design, determining rents, establishing signage and advertising campaigns, etc.
New technological tools allow mall operators to measure the number of consumers spending in and out of stores, the time they spend in and out of stores, know their relative wealth index and understand visitor behavior patterns, helping to determine the best mix of stores, site infrastructure, rent price range and implement more efficient signage and advertising.
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