Consumers have always demanded innovation from the retail industry. Shopping habits and product demands are constantly evolving, and retailers invest a significant amount of capital to monitor trends and cater to fluctuating behaviors. Recently, advancing technology has quickened the pace of change and made it even harder to win consumer attention in an increasingly crowded marketplace. More than ever, success requires financial and managerial flexibility and adaptiveness--areas where private equity can play a vital role. Below, read my thoughts on three key areas where our industry is partnering with retailers to help them keep ahead in the fast-changing sector.
Keeping abreast of shopping trends online is straightforward enough -- whole categories of startups achieve this with predictive modeling. But what about when that shopping takes place in-store? Tracking the behaviors of mall, outlet, and department store shoppers is of critical importance to physical store brands, particularly considering that the percentage of brick-and-mortar sales increased by 2% from $2.99 trillion in 2016 to $3.04 trillion in 2017. To meet this need, Miron Mironiuk founded Cosmose AI, a Shanghai-based analytics software provider that anticipates how people shop offline. Brands like Subway, Samsung, Walmart, Airbnb, Tencent, Burberry, Omnicom, Mercedes-Benz, Anheuser-Busch InBev, LVMH, Kering, L'Oréal, Gucci, Cartier, P&G, Nestle, and Coca-Cola use its tool suite to granularly track offline visitors' purchasing habits and target them with online ads via WeChat, Weibo, Facebook, Google, and over 100 other internet platforms.
Artificial intelligence or AI is changing retail in a number of ways. Using data to transform every aspect of retail from logistics to in-store customer experiences, AI at its best can enhance the most successful and enjoyable aspects of retail, and replace or improve those aspects that just aren't working. In this round-up, we cut through the clutter and give you an updated look at 34 of the very best examples of AI in retail right now. By combining AR and Livestreaming technologies, L'Oreal is enabling its customers to have a personalised makeup counter experience in the comfort of their own home. Customers can book a live-streaming appointment with a beauty assistant and have a digital makeup session. AR allows the customers to see what shade of lipstick works for them or whether they suit a dramatic eyeshadow. It is the same personalised experience that they would receive in store – and it's driven by data collection, with all aspects of each interaction captured to improve future engagements.
The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like popularity of spatio-temporal popularity of various cuisines can be analysed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.
Grocery stores are beginning to look for new options amongst the bitter pricing battles with their competitors. The constant struggle to beat competitors' prices has been going on for quite a while and shows little sign of stopping. However, new technology has given grocers an unexpected alternative to the trend of price cutting: big data and AI analytics. The potential of the new technology is so great and the competition so fierce, that small and large grocers alike are striving to master big data to stay ahead. More than any other sector of retail, supermarkets need hourly, or even real-time inventory planning and pricing management.
In 1994, soon after Jeff Bezos incorporated what would become Amazon, the entrepreneur briefly contemplated changing the company's name. The nascent firm had been dubbed "Cadabra," but Bezos wanted a less playful, more accurate alternative: "Relentless." Twenty-four years later, perhaps no adjective better describes Bezos' empire than the name he once wanted to give it. The company is known as the "everything store," but in its dogged pursuit of growth, Amazon has come to dominate more than just ecommerce. Amazon is a fashion designer, advertising business, television and movie producer, book publisher, and the owner of a sprawling platform for crowdsourced micro-labor tasks.
Tesco and Asda could install a facial recognition system at their checkouts which would check a customer's age when they buy alcohol and other restricted items. The'Fastlane' software would take a picture of the customer and approve the purchase if they are clearly old enough, meaning a staff member would not need to come and intervene. Shoppers will not have to register in advance, although regular customers can sign up to an app by linking a selfie to a passport or ID document, manufacturers said. Asda, Morrisons and Tesco are the most likely to trial the software although Sainsbury's has said it will not, the Daily Telegraph reported. If the photo does not prove a customer was old enough then supermarket staff would have to check their ID in person.
With that in mind, we've outlined 50 ways in which retailers are putting AI into action, from personalising beauty to forecasting demand. While the predominant function of Sephora's Virtual Artist app is to allow beauty buyers to try on products virtually via augmented reality, the brand recently introduced a colour match tool, powered by AI. This tool determines the particular shade of a product on a photo and suggests similar products available at Sephora that the consumer can then try on and purchase. If there's one sector where AI has been making a lot of noise, it's beauty. Olay's Skin Advisor is an online consultation platform that can tell the true age of a user's skin from a selfie. By using AI to both evaluate and determine problem areas, as well as the overall condition of the skin, it also provides personalised skincare routines and reports.
Mining consumer perceptions of brands has been a dominant research area in marketing. The marketing literature provides a well-developed rationale for proposing brands as intangible assets that significantly contribute to firm performance. Consumer-brand perceptions typically collected through surveys or focus groups, require recruitment and interaction with a large set of participants; leading to cost, feasibility and validity issues. The advent of web 2.0 opens the door to the application of a wide range of data-centric approaches which can automate and scale beyond the traditional methods used in marketing science. We address this knowledge area by exploiting social media based brand communities to generate a brand network, incorporating consumer perceptions across a broad ecosystem of brands. A brand network is one in which individual nodes represent brands, and a weighted link between two nodes represents the strength of consumer co-interest in these two brands. The implicit brand-brand network is used to examine two branding effects, in particular, positioning and performance. We use hard and soft clustering algorithms, Walktrap Clustering and Stochastic Block Modelling respectively, to identify subsets of closely related brands; and this provides the basis for examining brand positioning. We also examine how a focal brand’s location in the brand network relates to performance, measured in terms of relative market share. For this, a hierarchical regression analysis is conducted between brand network variables and brand performance. While the size of brand community on Twitter does relate to brand performance, the brand network variables like degree, eigenvector centrality and between-industry links help improve the model fit considerably.