Customer Centricity in the Digital Age

Customer centricity — putting your customer at the center of your strategy — has long been considered the holy grail of retail marketing.

In the digital age, customer-centricity revolves around data and smart technologies like artificial intelligence (AI). With the help of AI, companies collect as much data as they can about their customers’ wants, needs, and preferences, and then apply it to customize their offerings, create personalized shopping experiences, and make the purchase process simpler and more convenient. An example of new tools available for understanding customer habits is the Personality Insights service made possible by IBM’s AI platform, Watson.

As we continue to see AI moving from the hype stage to actual implementation within organizations, retailers and marketers have new opportunities for gaining a competitive edge when it comes to customer centricity. Here are three applications of how retailers are using AI to transform their marketing strategies:

AI allows retailers to identify which customers to cultivate. A company’s marketing efforts are always at risk of being ignored. There’s a reason that John Wanamaker, the 19th-century marketing pioneer, once quipped: “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”

But AI helps companies know which customers are more receptive to their message — and, just as important, which ones aren’t. Machine learning and deep learning via AI-powered tools can parse vast troves of customer data in seconds. This helps companies differentiate between their most loyal, revenue-driving customers (high value) and those who tend to buy the least expensive products or products with the smallest margins (low value), and then create targeted approaches for each.

Asos, the U.K.-based online fashion and cosmetics retailer, applies a machine learning algorithm to predict a customer’s future worth. The algorithm analyzes customer data — including information about a customer’s demographics, purchase patterns, and returns history — to make a determination about his or her value. The algorithm then assigns a “tag” to each customer, which sends a signal to the online retailer. Asos, for instance, might nurture the high-value customers by targeting them with increased advertising or promotions, and spend less time and marketing resources on the low-value customers.

AI helps retailers provide individualized product recommendations. Chatbots, simple AI-powered apps that interact with users via text, are some of the most ubiquitous forms of AI in retail marketing. Originally, companies viewed chatbots as cost savers: By automating conversations that would ordinarily require a human employee, companies can save time and money.

Today, however, chatbots are considered key drivers of customer engagement and loyalty. From the customer’s perspective, the technology feels frictionless; chatbots mimic real human dialogue and provide user-specific content. From the retailers’ standpoint, chatbots can gather important customer insights more efficiently than human representatives can.

The best chatbots also make shopping fun. Sephora, the global beauty chain, exemplifies this approach. Customers take an interactive quiz about their cosmetics use, and the chatbot offers makeup tips and individualized product recommendations based on their answers. The chatbot then sends users to Sephora’s website to finalize purchases. The company also has a bot with a Virtual Artist feature, which allows shoppers to create a customized look after uploading a selfie — a novel approach to try before you buy. This allows the company to formulate a fuller picture of a customer’s likes and dislikes and predict which products or services might interest him or her.

AI helps create a more seamless shopping experience. Retailers are increasingly incorporating the AI-driven technologies and data collection powering their digital strategy back into their brick-and-mortar stores. This helps their operations become smarter and more streamlined; customers, meanwhile, benefit from an easier and more personalized shopping experience.

For instance, in 2018, Amazon opened a small chain of cashier-less Amazon Go stores. Upon entering the store, customers scan a bar code on their mobile app. They choose the items they want and are automatically billed once they leave. The company’s Just Walk Out model, which uses an array of sensors, computer vision, and deep learning, detects when products are moved from the shelves and follows the products in a virtual cart.

A growing number of companies are developing cashier-less technology. The reason? Data. Blending customers’ online and offline identities allows retailers to gain a deeper understanding of shopper interests and behaviors, which facilitates customized offerings and marketing messages. Cashier-less technology also helps companies become more efficient. Understanding shoppers’ online and offline activities helps retailers manage their inventories, improve their merchandising, and target shoppers based on past purchases. The increase in efficiency and customer knowledge, in turn, will allow retailers to divert critical human resources from routine activities to value-added customized service.

As AI evolves and more and more companies see its potential to transform their marketing strategies, the technology will continue to redefine retail. Companies that learn to take advantage of the innovations that optimize customization and convenience — as well as those that boost sales and profitability — will be well positioned for success.