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What’s In-Store for Brick and/or Click Retail Stores for 2017? – 17 Cool Trends

Among the major emerging trends in retail for this year are in-store price tags that can be changed with a click, mobile technology for store associates, self-checkouts and new equipment such as drones to expedite package delivery. 

Among other trends: expected rapid growth in online grocery in countries as far afield as the U.S. and China; the revival of the wearables industry with specialized devices such as Snap’s Spectacles camera glasses; and ongoing development of marketplace e-commerce formats (third-party sellers using conduits such as Amazon) in the U.S.

From supply chain to purchase delivery, digitalization will continue to remake the entire retail experience this year, and this theme dominates the “17 Retail Trends for 2017,” a new report released by Fung Global Retail & Technology.

Read the 17 Trends in This Free Report

The rise in e-commerce over the last 10 years or so has forced retailers to adapt to the changes demanded by consumers. E-commerce growth continues to accelerate and outpace growth in the brick-and-mortar channel, and online sales accounted for almost 20% of total US sales this holiday season, based on preliminary estimates. In addition, department stores have offered discounts and promotions as a key tool to drive demand and bring consumers into stores. Over time, this strategy can dilute a store’s brand and leave stores looking picked through. Also, it trains consumers to wait for discounts instead of buying products at full price.

The US is overmalled and overstored. There are currently 1,221 malls in the US, according to the International Council of Shopping Centers (ICSC), and the number of malls grew by more than 300% from 1970 to 2016. Mall operators are repurposing B malls as A malls, and closing many C and D malls altogether. Combined, B and C malls total almost 550, but represent only 28% of all mall sales. As the number of malls has grown, there has been corresponding growth in the number of retail stores.

Online marketplaces from names such as Amazon and Walmart are carving greater share of e-commerce sales in Western markets, including the US and Europe. These regions are following in the footsteps of China and India, where marketplace sites such as Tmall, and Flipkart have long dominated online retailing. Marketplaces are purely platform providers. They enable other companies to sell goods or services, but do not provide anything tangible themselves. In 2017, we expect competition among marketplaces to intensify further as the major ones gain share of consumers’ online spending.

Mobile technology has changed the way consumers interact with brands and consequently, retailers and retail companies have had to invest in digital and omnichannel capabilities to react to the changing consumer behavior. However, one area that has lagged behind this digital transformation is the management of store associates, who, in many cases, are still bound to using outdated legacy systems and infrastructure. We are seeing more retailers moving in the direction of empowering their store associates with mobile solutions, and believe the trend will pick up significantly in 2017. Integrated mobile platforms in the hands of store associates are essential for a true omnichannel customer experience. Beyond personalized services, these mobile technologies integrate hassle-free, on-the-spot payments, eliminating long checkout and waiting times from the shopping experience.

Artificial intelligence (AI) has undergone a period of rapid development over the past decade, and we expect applications using AI to become more popular in commercial interfaces in 2017.
AI is a branch of computer science that existed long before the invention of personal computers and cell phones. However, commercial application of AI had been limited until the development of machine learning since the 1990s, and in the past decade with its sub-set deep learning. Deep learning is behind key technologies, including natural language processing (NLP) and image recognition. NLP deals with analyzing, understanding and generating languages that humans use naturally. Image recognition helps to identify elements in pictures, and it is also the core technology for facial recognition software and devices.


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