4 Ways AI Will Impact Retailers in 2020
As we greet the new decade, it’s worth pausing to reflect on how technology has impacted retail over the last 10 years, and consider how it will continue to shape this industry over the next 10.
In 2009, e-commerce represented 3.9 percent of total retail spending. This number has nearly tripled since, and is expected to continue growing. Online retail spawned direct-to-consumer favorites, like Glossier, Parachute Home, and Warby Parker — all of which were founded in the last decade. The late 2010s saw a nod back to brick-and-mortar from these brands and their peers, many of which opened storefronts and pop-up shops. And nothing says 2019 more than Amazon.com's bot-powered stores and a resurrected version of Toys"R"Us coexisting.
The common thread? New tech. If the last 10 years were about retail’s relationship with the internet, I’m betting the next decade will have a ton to do with artificial intelligence (AI).
As of today, retailers have begun to use AI in many different ways. Much like e-commerce, the rise of AI in retail will be both incremental and transformative. Here are a few ways I imagine AI will play a starring role in 2020 and beyond.
Providing Better Recommendations, Instantly
In retail, the average employee turnover rate is greater than 60 percent — more than four times the average across all U.S. industries. Odds are, the associates you have today won’t be the same ones in a few weeks, and are likely to not have the level of experience and expertise shoppers hope for. This is especially true during the holiday season, when many brands expand their workforce to manage demand and traffic.
I anticipate retailers to train associates on AI-powered applications for on-the-spot recommendations. These apps have the ability to perform complex searches and examine data at the tap of a finger.
For instance, a hair care product rep could chat with a customer in-store about their regimen, perform a search, and offer immediate personalized recommendations. A consumer electronics associate could input preferences, specs, and device compatibility during a conversation with a shopper on the sales floor. Based on real-time learnings, both of these representatives would be able to make more informed recommendations, instantly.
Offering Effective Training at All Levels, in All Functions
Happy employees lead to happy customers. What better way to improve experiences than by personalizing onboarding and training journeys? Expect to see AI-powered apps used to train not only new sales associates, but for onboarding in support, finance, operations and marketing.
Because AI-powered apps can be targeted to uncover specific insights, individual stores will use them to inform employee training. For example, if a store is using AI to uncover trends in customer feedback and notices that its top customer complaint in January was too few greetings in a specific department, managers can tailor their own messages to how employees can be more attentive in areas of need.
Improving Global Knowledge Sharing
Suppose you just took over as the manager of a store that’s part of a global chain. You’ll likely go to a training about the basics of setting up the business, receive a guidebook that addresses commonly asked questions, and even go the extra mile to speak with others leading different locations. However, new issues come up all the time, and there’s often little you can do to learn about what other managers, let alone employees, are seeing on their front lines. Beyond the old-fashioned online forums or the occasional companywide meeting, you’re limited.
In 2020, more retailers will use AI to leapfrog the data bottleneck, building databases of tips, tricks and best practices that leaders and staff can easily search with AI-powered apps.
Mitigating Biases in Data Analysis
AI that powers solutions like text analytics, helping retailers understand what customers are trying to tell them in reviews, surveys and chats, is incredibly useful. However, since AI is created by and trained on data from people, it maintains human biases. These biases can carry harmful learnings to the products that AI powers.
Although I predict a spike in the number of retailers turning to AI-powered solutions, I see them also cognizant that they’re investing in responsible use of the technology. Brands will seek out the best providers that also prioritize building solutions that are de-biased in an effort to protect their operations and analyses — and, more importantly, their employees and customers — from the effects of harmful discrimination and prejudices.
Ying Chen is the chief product and marketing officer at Luminoso, text analytics software which works with global retailers like Officeworks on analyzing customer feedback.
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Ying Chen is the Chief Product and Marketing Officer at Luminoso, text analytics software which works with global retailers like Officeworks on analyzing customer feedback.
She leads product, design, engineering, research, and marketing to power Luminoso's applications for turning unstructured text data into business-critical insights. Before joining Luminoso, Ying led Fortune 1000 organizations and VC-backed startups, most recently heading up global product marketing for platform technologies at Pegasystems. Ying holds an MBA from Boston University Questrom School of Business and a BS from Carnegie Mellon University.