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Transform Your Omnichannel Retail Efforts with AI

Marketing Measurement by Michael Lake

The age of Amazon and e-tailers has devastated many traditional retail establishments and changed the face of retail forever. The lines between the virtual and physical worlds are becoming increasingly blurred. Retailers who want to survive and thrive in this new world order need to not only reach their customers in both worlds, they also need to provide a seamless shopping experience whether the customer is shopping online, from desktop or mobile, by telephone, or in-store. 

 “This radical, fundamentally different approach to shopping has created new challenges for retail planners and buyers who must now determine how to plan, merchandise, price and fulfill across a complex, omnichannel distribution system.” – Antuit – How Artificial Intelligence Optimizes Merchandising, Pricing, and Replenishment for Omni-channel Retailers

Companies are tackling this daunting task by implementing an omnichannel approach that will provide their customers with the merchandise they want, when they want it, from the channel of their choice. This isn’t just a convenience; it’s what customers have come to expect in a world with free two-day or even same-day shipping at every turn. 

Enter Artificial Intelligence and Machine Learning. These cutting-edge technologies are being used to home in on highly accurate demand signals, minimize carrying costs, increase sell-through, improve profit margins, personalize marketing efforts, and bridge the gap between the online and in-store experience. A BCG report found that retailers who “have implemented personalization strategies see sales gains of 6-10% at a rate two to three times faster than other retailers.”

Data is King

The movement of products through the retail supply chain and into a consumer’s home generates hundreds of millions of data points, far too much to accurately or efficiently process and analyze using traditional statistical modeling tools. Embedding artificial intelligence into a retailer’s current systems allows them to process enormous amounts of data. This integration enables precise demand signals, placing buys based on science, bridging the gaps in poor or missing data, and model demand and demand drivers at a granular level. These tools put retailers in a position to more closely align their inventory and financial goals. Antuit reports that companies who have embedded AI have seen “up to 6% improved gross margin and a 10% increase in sell-through.”

“Since customer journeys aren’t simple and linear but a series of handoffs between traditional and digital channels that can vary significantly by customer type, an effective strategy requires an in-depth understanding of what customers truly want.” – McKinsey – How to capture what the customer wants

Gaining an in-depth understanding of what customers truly want requires, you guessed it, data. Lots of data. Predictive models and machine learning can develop ideal and well-rounded customer profiles. Algorithms make it possible to create propensity models by persona that predict which customers are most likely to act on a given promotion, bundle, or pricing offer. 

AI also allows marketers to track purchase decisions back to a campaign by channel, deepening their understanding of why specific personas purchased while others didn’t. 

Personalization

AI is designed to amplify human abilities and solve problems faster, more efficiently, more accurately, and using more data than human beings are capable of, even with the best non-AI software. In the world of retail, properly utilized artificial intelligence and machine learning helps provide hyper-personalized brand/customer interactions and adds value to the customer’s experience online or in-store. AI has to wear a lot of hats, from analyzing billions of data points to acting as a computer analog of a human customer service representative. 

On the customer-facing side, an ideal AI experience is one that mimics that of patronizing a specialty wine store. Imagine you frequent such an establishment. Not only would the shopkeepers know you and be able to help you find what you’re looking for, their knowledge of your likes and dislikes uniquely positions them to recommend a seemingly unrelated product you might enjoy. They might suggest a cheese that pairs nicely with the merlot you love or a new cooling gadget for your summer whites. 

Artificial Intelligence – Real Life

The big dogs are leading the pack in this arena. Forbes reports, “Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth.” 54 percent of retailers say the main goal of their omnichannel efforts to digitize their supply chains is to provide an improved customer experience. Another 45 percent say their primary goal is faster speed to market. 

Take Amazon, for example. The digital giant realized that self-service is often inadequate; customers often still seek out a live agent on the phone. To meet customer demand, Amazon has designed an omnichannel customer care strategy with live agents available to handle complex requests, offer empathy, and resolve problems. They use AI to steer customers to the channels best suited to their preferences based on their behavior.

Sephora is another fantastic example. The beauty retailer’s investment in AI has allowed them to create a truly seamless experience for their customers across both digital and retail outlets. They have created a highly curated in-store experience by allowing consumers to interact with AI as they would a traditional store rep. They even offer different color baskets that signal to the employees whether or not they would like human assistance. 

Sephora has also partnered with specialists like Dynamic Yield to provide highly personalized recommendations to customers. Their “machine learning algorithms weigh factors like location, items previously viewed, and items purchased. This led to a return of six times on Sephora SEA’s financial commitment to the partnership.”

Taking it even further, Sephora has implemented a wide range of AI technologies both in-app and in-store to make both their online and in-store experience as seamless and appealing to younger customers as possible. Sephora Visual Artist is a 3D live experience that allows customers to try on products virtually. Their Color IQ device scans the surface of a customer’s skin. It assigns you a Color IQ number that provides scientifically precise foundation matches, a huge value-add, and timesaver for anyone in the market for makeup.

To achieve all this, Sephora made some major internal changes. They launched an innovation lab in 2015 with the explicit purpose of using AI to bridge the gap between online and in-store experience. They also merged their physical and virtual teams to look at customers from a 360-degree perspective. Mary Beth Laughton, Sephora’s SVP of Omni retail, says, “The power of using that data to better appeal to her at every touchpoint and understand her in a deeper way enables us to create these experiences that she cares about across our channels. Loyalty is a data-driven ecosystem, so that’s hugely powerful.”

Invaluable Tools

The value of AI in omnichannel efforts is hard to overstate. Propensity models driven by AI are extremely effective at increasing customer retention and reducing churn. Forbes reports, “56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences.” And Gartner predicts that “by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process.” 

As the digital and physical worlds overlap more and more, customers are becoming increasingly accustomed to getting goods and services right away. The retailers who will flourish into the next decade and beyond are those that embrace the power of artificial intelligence to transform their omnichannel efforts. 

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