How artificial intelligence is transforming customer engagement in retail
The explosion of digital in retail is uncovering many new opportunities for retailers—from digitized back-end processes that are more efficient and distribution networks that improve agility, to true omni-channel capabilities that support the modern customer journey and new innovative customer experiences that drive foot traffic in brick and mortar stores. Realizing the full value of these new opportunities requires taming vast networks of disconnected systems and solving complex technical challenges.
This is where artificial intelligence (AI) can help. AI utilizes the recent advancements in cognitive computing and cloud infrastructure to fuel on-demand, always-evolving, learning algorithms. Retailers can employ these algorithms to uncover transformative insights, automate manual processes, and power revolutionary new experiences. AI can help retailers remove technical barriers, drive innovation, and accelerate their digital transformation.
Retail trends in 2017
The themes for 2017 continue to build on the last couple of years, with many seeing a more nuanced perspective as a result of shared failures and successes.
- Personalization—Personalization has been a moving target for retailers. As a result of the innovation from proven category leaders like Netflix to early retail innovators like personal styling service Stitch Fix, consumers now expect their entire interaction with a brand to be personal, requiring brands to understand their customers at an individual level.
- Conversational commerce—As Microsoft Cortana, Facebook Messenger, and Amazon Alexa have shown, conversational commerce has moved beyond focusing only on enhancing mobile commerce to delivering “shop-by-text” and “shop-by-voice” functionality virtually everywhere.
- Unified retail—Customers have been clear; they want to engage through every channel. While current trends indicate customers are using channels differently, they expect a unified experience that delivers a seamless journey—regardless of whether they are talking to a digital customer support agent, a brand’s Twitter page, or an employee in the store.
- Convenience—2016 saw the tipping-point of push-button shopping. While Amazon introduced “1-Click Ordering” over a decade ago, 2016 saw the beginning of mass adoption of the same elements in services like UberEats, the expansion of Apple Pay to websites, and Taco Bell and 1-800-Flowers’ integration with chatbots, quickly challenging consumers’ expectations of shopping, payment, and delivery.
Evolving from mass personalization to true individualization
Let’s take a look at an all too familiar experience: your mobile buzzes with an email from your favorite brand. Because you’ve interacted with this brand directly for years, you are hoping for a personalized recommendation for a cool new product or service that’s perfect for you. However, after glancing at the email, you realize this offer wasn’t truly unique, it was simply addressed to you.
A few years ago, mass personalization was a true differentiator, but today it’s become table stakes. As consumers grow accustomed to brands and retailers building them personal catalogs and recommendation lists based off their interaction history, shared preferences, and social data, retailers need to start showing consumers that they don’t just know their name, but know them well enough to recommend relevant products and services at an accuracy that makes them feel understood.
Through the application of intelligent algorithms, retailers can more easily tackle the analysis of their vast amounts of data while tapping into external data sources in order to deliver real value. Imagine an ice cream franchise being able to easily entice customers to visit their stores on a hot day by sending them a personalized mobile offer based on their current location, ambient temperature, and favorite type of ice cream. Or a clothing retailer automating email offers that send customers a curated list of interesting clothing items to try on at a local store based on their preferences, sizes, and store inventory. Much of this is possible today.
For example, JJ Food Service is combing their customers’ ordering history stored in Microsoft Dynamics AX with information about local event schedules and applying Microsoft Azure Machine Learning to build preference profiles for each customer that anticipates their orders. As a result, their restauranteurs receive personalized recommendations especially suited to their menus.
Microsoft customer Youboox used the Azure Machine Learning recommendation engine to increase their subscribers’ book consumption by analyzing their user profiles and the pages read in individual books to build a sophisticated list of recommended books.
Tapping into conversational commerce
Picture being able to have a personal assistant to help you coordinate your vacation plans with friends and even book all your reservations. Or receiving clothing recommendations based on your preferences, body type, and location? Imagine being able to interact with full-service, 24×7 support when you need it—support that contextually understands your emotions and remembers your past interactions. Imagine all of this being done through a natural conversation on your favorite messaging platform or through your preferred digital assistant.
Conversational commerce offers a new frontier for retailers willing to venture into the uncharted territory. With consumers all too familiar with the “app overload” phenomenon and a growing preference for text as the main channel for communication, conversational commerce is poised to unleash the next shopping revolution. Early examples are proving successful, from Amazon’s “shop-by-voice” offering through Alexa Shopping to the popularity of the “shop-by-text” chatbots used in China to order food, reserve movie tickets, and purchase items.
Chatbots, the technology fueling this revolution, are software programs designed to simulate humanlike conversations with users. While not a new idea, modern chatbots are smarter and more intuitive thanks to the recent advancements in AI and machine learning, giving them the vocabulary and context needed to interact with consumers in a casual conversation. Modern chatbots can provide truly relevant and personal recommendations, helping to drive more transactions, increase basket size, and reduce abandoned carts.
Messaging-based chatbots and integration with virtual assistants like Microsoft Cortana and Amazon Alexa enable retailers to meet a new level of immediacy and simplicity for their customers’ shopping and support experiences. As these services become more and more ingrained in our lives, integrating with these services enables retailers to give their customers access to what they need where they already spend most of their time—no need to download an app or logon to a website. Using technology like the Microsoft Bot Framework, early innovators are already exploring the potential of conversational commerce.
To learn more how brands can make mobile relevant to the shopping experience, check out our blog on Conversational Commerce.
Delivering differentiated experiences with AR/VR
Imagine you are visiting a local furniture store looking for a new sofa for your living room. You find one you like, but you aren’t sure it will fit your décor. Using a 360-degree image of your living room, you’re able to use an augmented reality (AR) or virtual reality (VR) headset to view the sofa inside your own living room, confirming that it is a good fit without needing to stress about your decision.
It’s no secret that sometimes shoppers need to visit a retailer to physically interact with the items they are looking to purchase. However, more commonly these visits don’t lead to a sale for retailers since they aren’t offering much more value than a product display. And with the fall in foot traffic, it’s imperative for retailers to offer convincing in-store experiences that eliminate customer pain points, improve customer service, and offer truly differentiated, personalized experiences. In short, retailers need to transform how consumers shop.
One of the most notable technologies made possible by the recent advancements in AI has been AR/VR. For retailers, this technology offers a compelling new way to interact with customers in-store and provide value-add services through their mobile apps. Whether enabling customers to use their mobile device to view a 3D-rendered piece of furniture in their house or using a headset to test different combinations of shoe designs in the store, AR/VR represents an opportunity to connect with customers in new and impactful ways.
Microsoft customer ELSE Corp. has developed a cloud-based, software-as-a-service platform for virtual retail shopping that can be integrated into any retail apparel environment to sell customizable, made-to-order products. The platform works by using AI and virtual reality tools to scan the customer’s measurements and then enable them to see how a garment will actually look on them through a realistic, 3D virtual image of the product they’ve selected.
Meeting consumers’ desires for immediacy with robust operations
Consumers placing priority on their time is nothing new. McDonald’s business model was largely successful because of this. However, recent cloud infrastructure and AI innovations have enabled innovative companies to disrupt many incumbent markets—such as Uber in transportation, Airbnb for hospitality, and Amazon’s predicted delivery game-changer, Prime Air.
Many of these reimagined experiences rely on the optimization of backend systems, increased agility of the supply chain, and streamlined business processes. Most retailers already have the data and digitization needed. The challenge is often analyzing this data in real time to produce actionable insights that can be used to improve decision making across the entire organization—from inventory and store management to user interface and brand positioning. Creating this analytics muscle from scratch is a very resource-draining endeavor.
Fortunately, there are new, more targeted analytics capabilities to suit the specific needs of the retail industry. These tools are comprised of powerful analytics models that help retailers look through their data and find new ways to sell more products with less discounting, understand competitors’ pricing, improve product assortments to minimize gaps, and spot key trends early and capitalize on them with maximum efficiency.
Microsoft customer Arca Continental is using Azure Machine Learning to improve their demand forecasting and profitability analysis. The company is using powerful regression models to understand why a particular drink sells well at one location but not another; why sales of six-packs spike the third week of the month; and why water sales increase during the summer months. Using machine learning, Arca Continental is uncovering insights about product performance, which will help refine its portfolio and marketing strategies, and increase profits.
Microsoft’s unique approach to AI
At Microsoft, we are investing in AI and machine learning and making it the core of our strategy. We recently created a new AI and Research group of more than 5,000 researchers and engineers dedicated to developing advances in AI that will build on nearly two decades of progress in machine learning and natural language processing.
The Microsoft intelligent cloud platform offers customers a compelling partnership in their journey to adopt smart services. We take a very developer-friendly approach to our services, ensuring that they are fast and flexible, use standardized code, can scale nearly infinitely, and are interoperable across platforms.
We are just beginning to see the potential of intelligent tools and the benefits they can bring. Early leaders are successfully creating truly competitive differentiators in their understanding of their customers, exploring new frontiers in conversational commerce, developing compelling new digital customer experiences, and supercharging their operations to meet the demands of the 21st century customer.