Consumers can be a fickle yet demanding bunch. With more information available to them than ever before, they expect retailers to curate products and experiences to match their individual needs and lifestyle. Otherwise, they won’t hesitate to move on to another retailer that can. Given the tightening competitive landscape and the plethora of accessible digital shopping options, consumers have the right to feel a little entitled.
Retailers, on the other hand, are trying to make sense of the influx of data collected from their sales channels, digital apps, social media and IoT devices to keep up with consumer demand. Relying on traditional data gathering and analytical methods has proven to be slow and cumbersome in today’s fiercely competitive retail world.
Rapid technological advances in digitization, analytics and automation have reshaped the retail business landscape, enabling the emergence of disruptive business models, disruptive channels and disruptive technologies. At the same time, the technology itself continues to evolve, supercharging performance and bringing in a new wave of development in robotics, analytics and machine learnings.
To keep pace with the evolution of consumer behavior in the digital age, many retail organizations are exploring the best way to deploy two emerging technologies that promise to upend how retailers interact with consumers.
First, artificial intelligence (AI), a term that was coined in 1956 but in recent years has become more popular thanks to increased data volumes, advanced algorithms and improvements in computing power and storage. Today, AI capabilities are being sought by retailers looking to enhance data discovery, as well as digital transformation and the end-user experience. AI adapts through progressive learning algorithms to let the data do the programming. It can quickly digest a vast amount of data, identify data connections and produce insights that would typically take a team of data scientists much longer to emulate. With these AI-driven insights, a second game-changing technology focused on improving the shopping experience can be more effectively deployed: augmented reality (AR).
AI-Ready Or Artificially Intelligent?
Retail organizations are just scratching the surface when it comes to the impact AI can have on their customer experiences and operation. In 2018, IDC projects the retail industry to shell out more than $4B in AI technologies for services like automated customer service agents (i.e. chatbots), shopping assistants and product recommendation engines1. A recent survey conducted by Deloitte shows that most executives are looking for capabilities that can help them create richer consumer experiences, develop more personalized marketing messages and streamline their operations.
With regards to personalizing messaging, marketing teams have traditionally used A/B testing to better understand which messages work best for certain demographics. With AI, messaging can be automatically tweaked and redeployed based on responses in real time, without having to pore over reams of information.
The advantages of AI include the ability for algorithms to learn and adjust (i.e. machine learning allows retailers to test a greater number of ads in a shorter time period, helping them to increase the success rates of personalized, targeted advertising on web pages and social media). To help provide a better shopping experience, AI applications can leverage sales data and cross-reference social data, web impressions (clickstream) and reviews to increase personal engagement in stores, get ahead of trends and better manage product inventory in store and warehouses.
For AI applications to be successful, it has to be able to easily consume data — and lots of it. This data needs to be readily available and clean. Implementing AI tools over messy data is bound for failure, so before any AI applications can be deployed, retailers must focus on ensuring their data is high quality or risk wasting time and effort on a losing proposition.
Facing Reality: Real World Use Cases
AR applications can be a fun and exciting way to engage with consumers and create enthusiasm about brands and products. By 2020, revenue generated from AR experiences is expected to be approximately $122B2. The major components to adoption of AR capabilities by retailers and consumers are three-fold: 1) The problem being solved, 2) Ease of use, and 3) Mobile technology advancement.
While it may be entertaining to use AR applications, if the technology does not solve a problem, its shelf life will be limited. Beauty retailers like Ulta Beauty have deployed AR applications to help consumers ‘try on’ makeup in a variety of different shades without having to apply, clean and reapply makeup to their skin. The key benefit of utilizing this type of technology is to allow consumers to be self-served, in turn reducing operational overheard and allowing the store associates to become expert advisors — all while seamlessly augmenting the customer experience. The result? Consumers try more products and make in-app purchases seamlessly.
Lowe’s and Home Depot are also innovating with AR apps designed to make it easier for customers to find items in their stores by mapping every product to its in-store aisle location, almost like a GPS but for products, which can be accessed on inventory maps and in-store product searches. As technology (faster processors, better cameras and bigger screens) continue to develop, AR applications will gain much more traction.
One of the major benefits of developing an AR type of experience for consumers is the amount of data that can be collected. As consumers browse, test features and make purchases, they are providing retailers with an entirely new set of data points, such as the amount of time spent “trying” a specific product, facial expressions made while shopping (happy, content, dislike, etc.), and frequency of shopping using AR driven applications.
These interactions can then be used by retailers to improve product assortments and deploy target marketing campaigns to further enrich the shopping experience. Optimizing the customer experience based on data from AR will be a major source of insight for retailers. As AR applications become more popular with consumers, rather than being a one-off experiment, retailers will need to develop a more holistic strategy to collect, store and analyze the reams of data being collected.
Creating An Intelligent Shopping Experience
AI and AR applications continue to drive a lot of conversation and hype across retail. With promises to upend current practices by highly personalizing and optimizing how businesses interact with customers, retailers need to identify why these technologies make sense and how to properly leverage data to fuel success. In an environment where the attention of customers is pulled in many directions, data can be leveraged to create engaging AI capabilities, and data from AR apps can be used to develop differentiated products and services, satisfying consumer demand for personalized engagement.
Hamaad Chippa is director of Informatica’s Industry Consulting practice, responsible for identifying data management challenges, trends and best practices in the manufacturing, retail and CPG verticals. He also helps organizations understand the value improved data can have to important revenue, cost and efficiency drivers. Prior to Informatica, Chippa spent 10 years as a management consultant, specializing in providing leading-edge insights to help clients transform their businesses and attain high performance capabilities. He employed various methodologies to help clients with complexity/cost reduction, product development strategy and manufacturing operations strategy, improving operational efficiency and effectiveness. Chippa graduated from the University of Illinois at Urbana-Champaign with a bachelor’s degree in math and computer science and he has an MBA from the University of Chicago, Booth School of Business.
2ABI Research, “Augmented Reality in Retail”, 2Q 2018