Retail is entering a new era in which innovation and understanding the consumer are key to surviving in a time of rapid change. The pace is faster than it ever has been, and it’s important to remember that we will never move this slow again. That means embracing change and harnessing massive amounts of data in order to provide better context for mission-critical decision-making.
Many retailers are struggling to improve customer experiences and satisfy shareholders — while at the same time reducing costs. Gaining a competitive advantage requires keeping pace with the speed of innovation. Three imperatives in today’s marketplace are: training harder, changing the rules and proactive thinking.
Big enterprises, like elite athletes, have to be well-prepared for the competition, and for maximizing efficiency throughout the duration of a race. Training harder is a priority for most brands today. Companies are focused on streamlining processes, improving efficiencies, reducing spending and making their organizations leaner.
Staying lean and fast requires automating repetitive tasks and eliminating non-value-added endeavors, so that employees can be freed-up to focus on strategic decision-making and customer service.
Enabling faster decision-making through richer and timely insights is crucial. This is where artificial intelligence can have a particularly significant impact. Whether it is Walmart’s investment in robots to automate on-shelf inventory or Levi’s use of voice-enabled chatbots, leading retailers are embracing the use of AI.
Changing the Rules
There are physical constraints on speed. Being the fastest player in the market is no longer enough. Disruptors in any industry did not disrupt by being faster, they disrupt by changing the rules. Amazon, for example, did not disrupt by building more streamlined retail stores, they changed how products are sold. And Tesla, a disruption in progress, is changing the transportation industry.
Classic brick-and-mortar, multibranded retailers, like Target, are also moving faster than ever before and executing disruptive programs on multiple vectors. They are becoming more vertical by creating their own popular brands that can only be purchased via their channels, adding automation to the ship-from-store process and rethinking their store formats.
One way for companies to adapt to change faster is with proactive thinking that enables them to start the planning process sooner. It is important to stay up-to-date on general macro trends, technological advancements and business model transformations, reflect on how those changes will affect the retail ecosystem today, and in the future.
Three macro trends that are having a profound impact on the retail sector include the move from centralized to distributed decision-making, diversity and autonomous solutions.
From banking and computer networks to communications and organizational structures, there is a shift away from centralized command-and-control toward distributed systems. Blockchain, which relies on a distributed ledger system, is indicative of this massive migration to a more distributed paradigm.
Enterprise organizations are also embracing a more distributed approach to management. Facebook and Google, in particular, have demonstrated that decentralized decision-making can scale successfully.
A second macro trend is diversity. The demographics of both customers and employees are shifting to a more diverse population than ever before — representing a range of tastes and perspectives that retailers need to satisfy.
Diversity starts with a cultural shift that embraces a variety of thoughts, opinions and styles. It requires retailers to focus their attention on developing measurable hiring goals, which must be supported by a diversified talent pipeline.
The Retail Industry Leaders Association, in partnership with Intel and leading universities, is addressing these issues through the newly formed Collegiate Network. The new program will introduce a diverse and cross-disciplined talent pool of students to the retail world. This includes social anthropologists, data scientists, computer scientists, designers and merchandisers.
The third macro trend is the shift toward autonomous technology. This macro trend represents a maturing of the Internet of Things. The early days of IoT were mostly focused on “connected things,” and then moved to making those things “smarter.” Now, AI is being used to give smart things the ability to learn and adapt and, voila, some machines are making decisions on their own.
Autonomous things can be cameras connected to AI-controlled vision systems that automatically detect shoplifting. They can be environmental sensors that learn when to heat and cool a store in response to customer traffic, or parking sensors that enable shoppers to quickly locate the closest parking spot to the store. One job of an autonomous sensor is to help provide context to the information it gathers.
Perhaps the most important resource for driving innovation and understanding macro trends is contextualized data. As the term implies, this is information that has been analyzed in the context of a particular industry, location, goal, or problem.
Retailers have massive amounts of data available about everything from customer behavior to future population demographics. While collecting information is essential, the real need is to better understand that data, in context.
For example, statistical data may indicate that the future demographics of an area is projected to get younger. However, a richer and more contextualized insight is knowing that a local university is opening a new campus in the area, and investing in housing for international students. This context-rich insight will lead to better localized decision-making about operating hours, staffing and even product mix.
An increasingly important source of data for retailers comes from in-store sensors, such as RFID tags and computer vision systems. With the help of AI, retailers can understand RFID and computer vision data in the context of what was happening when the data was collected. As an example, knowing a product is in stock is informative, but better yet is learning that the item has been sitting on a bottom shelf without being touched for a month. Adding extra context would be to factor-in that the item is located in a highly traffic area, and that none of the other items on the bottom shelves are selling. Now, the retailer has actionable insight.
Ultimately, contextualized data means that both humans and machines will make better decisions — grounded in a deeper understanding of the customer. Aldo Bensadoun, founder of the ALDO Group, said it best when he observed that: “It is not any more a question of accounting and finance, it is now a question of artificial intelligence, analytics, sociology and anthropology.”
Stacey Shulman is chief innovation officer for Intel’s retail solutions division.
More from WWD: