Location-based services and advanced analytics are some technologies shopping malls are employing to collect and leverage behavioral data. Joy City uses membership programs and big data to collect and analyze customers’ preferences, while Macerich uses deep learning-enabled cameras and sensors to determine demographics and foot traffic.

Case Studies

  • Although there is information available regarding the methods and technologies shopping malls are using to collect customer data, information on how they are using the data to create value-added services for tenants is quite limited. The research team scoured through multiple industry-related sites, such as ICSC, market reports, news outlets, technology providers press releases and case studies, annual reports of big developers and owners, and tried to locate contractual information; still, findings were scarce. Some sources older than 12 months were used to include partnerships and strategies that are still being used by the companies.

Case Study #1: Joy City

  • Joy City, one of the largest shopping mall owners in China, uses big data analytics and mobile apps to improve its customer retention rates and mall operations management. The company launched a tiered membership program to collect data on customers’ preferences and spending capacity to assist tenants and refine its properties. It was not possible to determine when the program was initially launched, but it is constantly evolving.
  • The company offers consumers a personalized, filled with rewards, interactive membership program in exchange for their data. The company employs three big data analytics methods: market basket analysis, RFM, and the Roland Berger Brand-Values Profiler. Joy City uses these consumer insights for site selection and marketing purposes.
  • The Market Basket Analysis tracks consumer behavior based on their shopping carts to identify which products and stores should be located next to each other. The comprehensive data set also determines brand and product categories. For instance, Joy City discovered 2% of Joy City’s members bought something at Moon Kee Dessert Shop, of which 37.2% also purchased at the GAP.
  • RFM tracks Recency, Frequency, and Monetary characteristics. It is used to assess overall value, identify retention strategies, and marketing strategies. The Roland Berger Brand-Values Profiler helps develop consumer archetypes based on consumers’ opinions. Joy City then leverages these archetypes to refine its shopping mall segmentation and support continued profitability.
  • It also uses smartphone tracking tools, such as Wi-Fi and beacon technology to collect additional consumer data.
  • Furthermore, the company innovated and launched a paid membership system. It has 15,386 members. The per capita consumption of paid members is four times higher than non-paid members, and purchase frequency is five times higher.
  • The consumer data is used to power the Joy Cloud commercial system to the “whole value chain,” capturing and rendering consumer data to tenants. It includes an “intelligent POS platform, central business clearance platform, CRM3 platform, universal interface platform, data exchange platform, marketing platform, and data consuming platform.”

Case Study #2: Macerich’s BrandBox

  • Macerich is the third-largest operator of shopping malls in the United States, with 48 properties. It started the BrandBox program in November 2018, which gives digital-only brands a small space to open their first store. It is a short-term lease agreement (6-12 months).
  • It targets new brands with revenues between $15 million and $200 million, with large social media followings but no physical retail presence. The program aims to turn brands into permanent tenants and keep the malls relevant in a shifting landscape.
  • Powered by RetailNext, BrandBox uses deep learning-enabled cameras and sensors to determine consumers’ demographics and store traffic. TIVIX integrated RetailNext’s machine learning systems into the BrandBox system, along with other features, such as sales data.
  • BrandBox matches brands with dedicated teams to select the site based on customer demographic data, ideal co-tenants, average spend, among other insights. It provides all the marketing and tech, such as RFID tags, consumer profiles, shopping patterns, and others.
  • Brands have access to proprietary retail analytics dashboards that “integrates to measure in-store sales and customer engagement, as well as KPIs such as anonymized foot traffic, conversion, customer flows, and sales. The brands can even measure any increases in their local e-Commerce traffic resulting from opening a store with BrandBox.”
  • During the COVID-19 pandemic, Macerich also partnered with Waitwhile, a virtual queuing solution that allows shoppers to queue anywhere. Additionally, the tool collects data from customers and recommend improvements and strategies based on machine-learning to reduce wait times and improve customer satisfaction. Macerich is offering this tool to tenants for free for the first month, and with a 10% discount for the following 12 months.

Digital Tools

1. Advance Analytics and Consumer Spending Patterns

  • Deloitte and McKinsey both note that advanced analytics is crucial for malls hoping to appeal to shoppers and tenants. McKinsey states that armed with “robust data and advanced analytics tools, malls have the potential to revitalize and revolutionize not just their own business performance but that of the rest of the retail industry as well.”
  • Some shopping malls are using advanced analytic tools to reveal “typical walking routes within the mall and patterns of cross-conversion between categories. The tool quantifies how each store affects overall consumer spending at the mall” by examining sales data and transactional level data.
  • Deep North, a company offering AI and advanced analytics to retailers and shopping malls, recently closed a $25.7 million Series A funding round to expand its AI-powered prescriptive analytics engine that uses the camera infrastructure at shopping malls to examine consumer behavior.
  • The company does not disclose names, but one mall used its video analytics and computer vision to determine traffic patterns and optimize prices. Computer vision has the potential to change the game, as it is more precise than typical tools. Tech Crunch notes that the combination of computer vision with retail technology is a “signal of a bigger trend,” the retail industry’s use of analytics to predict consumers’ behavior.

Shopping Malls Adopting the Technology

  • For example, a mall in Asia used advanced analytics to quantify how stores affect overall consumer spending. Each store was examined as a “spend engine”, showing “whether and how much a particular tenant’s presence increases or decreases sales at other stores and whether consumers are likely to shop at certain groups of stores during a single mall visit.”
  • Applying this method revealed that an apparel-heavy anchor weakened the sales of other apparel stores and strengthened accessories and cosmetic stores. The tool also quantifies a tenant’s “halo effect” on wholesale and online sales. These insights can be used to optimize tenant selection:


  • Westfield mall is using face detection software to learn how consumers shop. Its Smartscreen Network can determine “shopper attributes such as age, gender, and even the mood of the shopper.”

2. Beacon and Wi-Fi BLS

  • According to the International Council of Shopping Centers (ICSC), one of the key technologies currently shaping the industry still is mobile apps, as they encompass a handful of emerging technologies disrupting the sector, such as beacons and other tracking technologies. These are not new technologies, for instance, beacon technology has been around since 2013, but shopping malls worldwide are increasingly adopting these tools to collect consumer data, and some make these platforms available to their tenants.
  • Beacon technology is used to develop proximity marketing strategies. Beacons are “small wireless battery-run sensors that send Bluetooth low energy (BLE) signals to nearby mobile devices.” These signals are picked by mobile apps, which then send location-specific consumer data to a server. The data, paired with details in the consumer’s profile, trigger specific actions, such as personalized ads, push notifications, and a desired item’s location. It is currently one of the most popular proximity solutions in retail software development.
  • Initially, people expected beacon technology to be universally and rapidly adopted; however, it is still not as widespread as analysts anticipated. Nevertheless, there seems to be a surge in demand for beacons over the last two years, and some expect the pandemic to drive adoption even further.
  • According to Aislelabs, it can also be used to ensure social distancing measures, as guards at key entrances can “be easily updated on the number of customers currently at the property so that when occupancy drops below a certain threshold, they can allow more shoppers to enter the mall.”
  • Wi-Fi LBS (location-based service) solutions can detect and locate Wi-Fi embedded mobile devices and track consumers’ routes and heat maps. It provides real-time statistics, enabling geofencing and advanced proximity marketing. As such, malls can send personalized and location-based messages to consumers in real-time, improving marketing activities. It also helps collect data regarding their behavior, analyzing how they move around the mall, how long they will stop at a shop, among other features.
  • Each option has its cons and pros. Beacons require less power and are more accurate, but they only work on certain devices. They also depend on consumers downloading an app and are more difficulty to implement. Wi-Fi LBS is available worldwide. It is a cheaper solution and works on more devices. It also does not require an app; however, consumers must be logged onto the Wi-Fi.
  • Wi-Fi analytics are also less precise than beacons, as they can “accurately detect device location up to 5 meters,” while beacons can detect users’ locations down to centimeters.

Shopping Malls Adopting the Technology

  • In June 2019, Hudson Yards worked with Purple to install cloud software to 2000 Cisco access points, offering Wi-Fi access for guests while collecting customer data via a captive portal. In five months, Hudson Yards collected 300,000 new CRM records, which will be used to “generate ROI from their Wi-Fi infrastructure through sponsored content and remarketing efforts.”
  • The Mall of Arabia in Jeddah uses beacons to understand traffic patterns and improve merchandising, revenue, and amenities. Simon Wilcock, chief executive officer of Arabian Centres Company Ltd, explains, “We can track clients to see where they go and identify hot and cold spots within the malls.”
  • Posnania, a shopping mall in Poland, worked with AVSystem and Apsys to employ Wi-Fi AVS. One additional benefit it perceived was the ability to track attendance at different events, as the tool differentiates between new and customary shoppers. The mall reported an increased number of customers and a surge in interest in renting spaces after the system was installed.
  • New Jersey’s American Dream Megamall also equipped its 3 million square foot mall with Cisco Wi-Fi APs. Todd Myers, CEO of GoZone Wi-Fi, explains that the mall’s developers are betting on “mall-wide Wi-Fi for market intelligence.”
  • Kimco, the largest U.S. owner of open-air centers, is piloting various mobile technologies at its open-air center in Daly City to discover whether “the complimentary service would increase the amount of time visitors spend onsite, ultimately driving higher sales per customer.” It discovered that customers that are connected to the Wi-Fi spend twice as long at the property.
  • The company is also planning to add geofencing technology to enable tenants to transmit offers and information to consumers. “When someone walks into the center, their iPhone might be pinged with an advertisement or with a message alerting them to an in-store special,” explains David Jamieson, Kimco’s executive vice president of asset management and operations.
Glenn is the Lead Operations Research Analyst at The Digital Momentum with experience in research, statistical data analysis and interview techniques. A holder of degree in Economics. A true specialist in quantitative and qualitative research.


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