The Promise of PropTech
Retail real estate developers and third-party managers bring data analytics to brick-and-mortar.
It’s a familiar experience. A customer needs an item that’s either difficult to locate or not worth the time it takes to buy in a store. Could be a red-tinted furniture stain to match a chair in her home. She drives to the nearest home improvement store and spends five minutes hunting aisles for the product, only to find the retailer doesn’t carry it. But there is an alternative solution, which is rapidly becoming universal. The customer goes to Amazon.com, where she is immediately recognized and greeted. She types in “red wood stain” and is quickly presented with two options. She makes her selection, with the knowledge that two days later the stain will be on her doorstep. A total of 47 seconds has elapsed.
Because Amazon’s entire sales volume is digital, it knows everything about the purchase history and habits of all of its customers. Yet e-commerce powerhouse that it is, Amazon and other Internet sellers are able to track less than two out of 10 retail purchases. That’s because store sales still far outnumber online sales. According to the Commerce Department, the 2018 score was brick-and-mortar, $3.6 trillion, e-coms, $517 billion.
So how does brick-and-mortar collect all the ones and zeros of consumer data intelligence in a mall or shopping center setting?
Serious headway is being made in answering that multi-billion-dollar question by so-called PropTech ventures. Best known, perhaps, is OneMarket, the former Westfield Labs unit that spun off from the big mall owner in 2017. CBL Properties took Westfield’s cue and formed an innovation division that is employing in-center technology to home in on the habits and characteristics of mall shoppers. And CBRE is employing massive mobile data to help both its retail and center-owner customers determine optimum locations and store sizes.
Through OneMarket’s Hadley customer engagement solution, retailers can give opted-in shoppers a Live Receipt following an in-store or online purchase. Delivered via the shopper’s preferred channel (SMS, email, Facebook Messenger, etc.), it lets customers engage with the retailer in real time to receive order updates, track packages, and initiate returns.
Retailers can access collected data about online and offline shopping patterns, use of branded credit cards, loyalty programs, services, and more. Data can be crunched to reveal general customer trends online and off. Depending on the services a retailer buys, Live Receipt can also track what customers viewed online and purchased in-store. When retailers join forces, it can track behaviors across multiple stores.
“You begin to understand how they develop as a customer through time,” said OneMarket CEO Joe Polverari. “A week ago, I had three Susans—online, offline, and a loyalty card member. Now I have all the history united and one Susan. Once you close the loop, the retailer better understands who customers are and can do more for them.”
Customers who choose Live Receipts tend to engage with each transaction 4.5 times, said Polverari. For example, they bought shoes and then purchased socks or wanted pants tailored. They also transact 24% more frequently than other similar shoppers and spend 29% more in subsequent purchases.
Another initiative, OneMarket’s Shopper Exchange Solution, uses proprietary shopper data to help retail clients build detailed customer profiles that drive highly targeted advertising campaigns, a service it developed as part of OneMarket.
Developer with Data
In 2017, URW introduced Insights Portal to its malls, eye-level ad displays that allow brands to track minute-by-minute campaign performance measurement, dwell time, and user demographics. The portal’s real-time analytics allow advertisers to choose which execution is served to individuals based on their preferences or demographics, according to Ghadi Hobeika, URW’s chief marketing officer and director of digital & data.
Digital wayfinders in URW malls help shoppers locate specific stores or product categories. “We’re asking Google and Apple to map our mall locations as accurately as possible and have them available in their apps,” Hobeika said.
CBL is embarked on a renovation campaign at some of the 62 malls it owns and operates nationwide. A high-priority of its initiative is to install all-new fiber optics networks, allowing it to conduct dynamic heat mapping and to track dwell time, traffic patterns, and overall customer journey. “We want to put in systems that with enough scalability and flexibility to cover our bandwidth needs for the next 15 years,” said Jim Ward, VP of innovation and new business ventures at the company.
Late last year, CBL Properties began a pilot program with a few national retailers which it powered with common area data and in-store data from a Wi-Fi platform and optical sensors. “There was a lack of meaningful, actionable data at the real estate level. If you want to enhance store performance, it’s a question of how you’re going to partner with tenants to help them leverage data and make better decisions,” said Ward.
A fourth-quarter pilot program was conducted with two national retailers. Optical sensors monitored traffic and captured age and gender data, but not personally identifiable information. There were surprises. One store skewed older and much more male than previously thought. The data, which was both unique and actionable, led to numerous discussions with the retailer on how to best adjust its product mix and window displays.
CBL has also purchased massive amounts of mobile data that indicates where shoppers are traveling from, ensuring that marketing initiatives align with what the data is revealing.
Smart Store Selection
The availability of massive mobile data is going a long way toward helping retailers determine where they should put their new stores, what rent they should pay for them, how big they should be, and how much revenue they can expect to see from them.
“For every retail chain, there’s a big empty box somewhere and that retailer wants to know if it could work for them,” mused Paul Sill, senior managing director and global retail analytics practice leader at CBRE.
Sill arrived at CBRE four years ago when the global real estate giant acquired Forum Analytics, a company Sill founded in 2001 to provide mapping solutions driven by data and analytics. Using machine learning and massive mobile data, Sill is one of a growing number of real estate professionals providing retailers with unprecedented levels of actionable data on malls, tenants, and shoppers. Information Sill and Forum can provide ranges from the demographics of passing foot traffic to facts regarding competitive businesses to neighborhoods where shoppers live and the origins of shopper traffic into the mall. Today’s PropTech masters can even predict a retailer’s chances for success and why.
“Understanding trade areas used to be an art form,” said Todd Caruso, senior managing director, CBRE, harking back to a time when store location decisions were made by tapping Department of Transportation records and housing data and drawing trade area circles around malls on maps. But retailers and developers are finally augmenting their art with science, he said.
“Technology illustrates who’s shopping a center today, something that was never a part of the art form. It has transformed how we define the customer base on the owner/investor/retailer side,” Caruso added.
Growth of WiFi, the Internet of Things, and 4G networks have made many newer mall technologies possible and feasible. Over the next year, data’s influence on the marketing of physical retail will become even more powerful when 5G applications begin to take over. Twenty-five years after the founding of Amazon, brick-and-mortar sellers are harnessing artificial intelligence and machine learning to track consumer habits, make merchandise suggestions, and efficiently process and apply big data.
Sill has been using machine learning for more than 20 years, but availability of massive mobile data—collected from smartphones in geo-fenced areas surrounding shopping centers—has given an added boost to PropTech’s capabilities. Now Forum can examine the demographics of the neighborhoods from which most shoppers are visiting a given store or center to determine that customer base’s average age, income, household makeup, and other factors.
Machine learning can serve as a crystal ball for a retail real estate manager. If a rapidly expanding retailer has 500 stores and wants to open 500 more, big data can determine why some stores will generate $10 million and others only $1 million. That real estate manager can then determine how much revenue an additional store should generate in a given market. He or she can also offer projections about proper assortment staffing mixes, sizes of boxes, and complementary tenants.
Under its ShopoGraphics banner, Forum segments markets into 37 geographic zones of retail activity with names like “Gas N’ Grub,” “Soccer Mom Pit Stop,” or “Hoagies & Hipsters.” Using machine learning, ShopoGraphics relies on more than 1.4 million data points that include factors like “urbanicity” level and presence of big-box retailers. By studying repetitive geographic patterns, ShopoGraphics can help estimate performance, identify ideal co-tenant mixes, and provide cross-shopping insights for a retail chain.
“You want to be able to tell the client they have a 70% chance of doing $2 million at a location,” said Sill.
CBRE also applies massive mobile data in Dimension, a three-year-old proprietary mapping tool. Dimension combines information from public sources like the U.S. Census Bureau and the DOT with data like grocery sales, aggregate credit card spending, and proprietary information from CBRE’s brokers and researchers. Data combinations are presented in a digital map format.
Dimension helps real estate professionals find lucrative locations and opportunities at a nationwide or local level via their smartphone or iPad, according to David Gervais, a retail location manager at CBRE. “You can drive around and have it at your fingertips rather than carry a big binder,” he said.
The service has helped retailers learn when a center is not attracting customers from where they thought it was. Hill Country Galleria in Austin, for example, used the information it learned through Dimension to tweak customer communications, moving average dwell time from 97 to 135 minutes in a year-over-year analysis. “We thought we were capturing from X miles away, but it was a larger area,” said CBRE’s Caruso.
Chances are that brick-and-mortar retail’s capacity for data analysis will never top Amazon’s. But one thing’s for sure, at least for the near future. E-commerce won’t match physical retail for the volume of sales to be analyzed.
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