Many, many years ago, I took a graduate studies course, “Marketing High Technology,” where I learned about expert systems and artificial intelligence (AI). Back then, AI was pretty conceptual. But it’s fascinating to think that even then (in the, er, … 1980s), it was already well into development. In fact, Dr. John McCarthy is credited with coining the term “artificial intelligence” back in 1956. His definition suffices even today — where machines “solve the kinds of problems now reserved for humans.”
Today AI is a reality, moving far from the conceptual and hitting the mainstream. It’s already so subtly integrated into our lives, many people don’t realize they’re using it. Think beyond self-driving cars (which sounds as daring to me as driving faster than 30 mph was considered reckless to our great-great-grandparents). Think instead how Google autocompletes search queries or how Spotify creates intuitive playlists — both common use examples of AI in action.
In fact, a recent study from Pegasystems showed that many consumers are unaware of all the AI at use in their lives.1 For example, only 41 percent knew AI was present in Amazon Alexa and just 57 percent knew it was in Apple’s Siri. The study also found that the majority of consumers worry about AI — and some even harbor an apprehension that machines will take over the world, a la H.A.L. in “2001: A Space Odyssey.”
The Future of AI
Nonetheless, businesses are forging ahead, with a $16.5 billion market for AI predicted for 2019. According to a recent study by Boston Retail Partners (BRP), “45 percent of retailers plan to utilize artificial intelligence within [three] years to enhance the customer experience.”2
The study goes on to conclude: “Stores must now encompass both worlds — the sensory experience generally available in the physical world, such as touching and feeling merchandise and personally interacting with a knowledgeable associate — whether simply human or a combination of AI and human characteristics — married with the unique and personalized shopping experience common in the digital world. The physical and digital worlds are forever intertwined as we look to the future.”
Strategy Comes First with AI Marketing Initiatives
As with all technologies that hold a great deal of promise, AI marketing runs the risk of becoming the next “new, shiny object.” So with many retailers tightening their belts, it is hyper-critical to build a strategic vision before investing in a technology like AI. Strategically and properly employed, using artificial intelligence in marketing initiatives can be a boon to CRM marketers, providing an important tool to build customer engagement and loyalty.
Keeping a strategic mindset, what follows are common retail customer relationship marketing objectives, with examples of how AI has been used to support that strategy.
Strategic Objective: To build brand affinity and sales by making shopping more frictionless for customers.
Customers love it when we make the shopping and buying process more efficient, saving them steps and time. Retailers who can make the shopping experience more frictionless than the competition will earn their customers’ appreciation and loyalty. Following are examples of AI customer experience strategies that top retailers are using today.
The retailer’s “Snap. Find. Shop.” technology allows consumers to snap a photo in the Neiman Marcus app of a product (say, a pair of shoes or a purse) that the shopper saw and loved. The AI software then uses the picture to review the store’s inventory and recommend similar items that the consumer can find at Neiman Marcus.
The digital florist and gift company tapped into IBM Watson to create GWYN, a virtual gift concierge. GWYN “intuitively guides customers through their shopping experience to help them select the perfect gift,” according to a company press release. GWYN can interpret input such as, “I am looking for a gift for my wife,” and then ask related questions about the occasion and sentiment to make reliable suggestions.
The North Face
The outdoor-gear chain launched an IBM Watson-powered digital shopping tool that presents online coat-shoppers with a series of questions, such as “Where and when will you be using this jacket?” The answers are used to generate relevant coat suggestions. Shoppers who use the tool are more likely to buy than those who do not, The North Face told Adweek. The retailer is exploring different ways to use the technology.
Strategic Objective: To increase sales by providing more relevance in marketing.
As every CRM practitioner knows, getting the right message to the right customer at the right time, a.k.a. relevance, is key to building sales and customer relationships, and reducing marketing waste.
When customers visit L.L. Bean’s website, a chatbot asks if they would like to log in with their loyalty account. This “loyalty aware” chatbot then links a customer’s loyalty and CRM data to personalize choices. Interactions with the chatbot are also recorded back to the CRM system.
L’Oréal, Aldo and Aston Martin
All three organizations are using Salesforce Einstein to integrate multiple data sources and utilize data to create predictive customer journeys — in other words, helping to predict where a potential customer is in the sales cycle and which communication channels they will respond to. These one-to-one journeys are scaled across multiple channels, including email, mobile, social, digital advertising, online and in-store.
Strategic Objective: To increase traffic and sales by increasing customer engagement.
Engaging customers with fun and useful tools keeps your brand on their radar and can often drive traffic earlier in the markdown cycle.
The maker of high-tech activity apparel recently partnered with IBM Watson to create an app that helps customers track their health and fitness activities, including sleep and nutrition. It in turn provides the users with coaching based on their data, as well as the results of other people who have similar health/fitness profiles. It also pulls from nutritional databases, physiological and behavioral data.
The food manufacturer is partnering to rollout a new ad format that allows consumers to chat to find recipes. The chat is conducted through a chatbot that will match and recommend the “perfect” recipe.
AI Beyond Customer Interaction
Although many of the examples above are customer-facing use cases of AI, there are many back-office applications that make marketers smarter and more efficient. In fact, there is some discussion that the best use of chatbots is to assist live customer service agents. In stores, this type of AI customer service tactic translates into helping store associates with their clienteling efforts.
Increasingly, AI is employed in programmatic advertising by optimizing the bidding and media-buying process. There are AI-powered websites that design and build websites. The use cases for AI are growing every day. Forrester expects that AI will drive faster business decisions in marketing, e-commerce, product management and other areas of the business by helping close the gap from insights to action.
Take AI One Step at a Time
As exciting and diverse as the opportunities presented by AI are, the most important takeaway for retail marketers is that adding this technology needs to be a strategic move. Take time to analyze how your own strategic objectives could be helped by AI. And make sure you have the infrastructure — including organized, integrated data systems — in place to support it. With these essential first steps in place, you’ll be positioned to take advantage of this promising tool for building customer engagement and loyalty.
If you’re seeking assistance integrating new technology or enhancing your customer engagement efforts, we can help. CCG’s retail marketing experts are skilled in technology sourcing and implementation support, strategic customer initiatives, and customer data and analytics. Email us or call 800.525.0313 to discuss your needs — no strings attached!
Sandra Gudat is president & CEO of Customer Communications Group (CCG), a full-service customer relationship marketing (CRM) agency that helps Fortune 2000 retailers and financial institutions improve their bottom line by improving their customer relationships, loyalty and retention.
¹ “What Consumers Really Think About AI: A Global Study,” Pegasystems, https://www.pega.com/ai-survey
² “2017 Customer Experience/Unified Commerce Benchmark Survey,” Boston Retail Partners