Demographic and behavioral analytics traditionally have served as the foundation for delivering personalized, simplified customer experiences that satisfy consumers’ unique needs across multiple channels. These data sources have unlocked a trove of actionable insights into consumer interests, location and brand affinity, allowing marketers—and retailers, in particular—to better personalize the shopping experience to drive engagement and transactions. In fact, brands that offer personalization will outsell those who don’t by more than 30% by 2018, according to Gartner.
So how can brands move beyond traditional customer insights to deliver the best, most seamless customer experience? Marketing experts will soon be able to explore the true state of the customer at the time of her interaction with the brand, and to understand the emotional context of her experience. This next wave of customer analytics—emotional analytics—will help close the human experience gap.
Emotional analytics aims to identify and analyze the full spectrum of human emotions, including mood, attitude and emotional personality, allowing marketers to deliver more individualized customer services. Just as friends and colleagues read each other’s body language and tone to guide interactions, brands can use emotional analytics to identify how a customer really feels about a product, service or individual experience and then customize their interactions in response to that sentiment.
For example, consider a retailer that has incorporated emotional analytics capabilities into its in-store displays. By capturing customer facial expressions when interacting with the displays, the retailer is able to read an individual’s emotions and determine if he’s feeling confused and frustrated or delighted and happy. The retailer could then send a sales representative to assist the customer if he is in need of help.
Emotional analytics also can be leveraged across social media during a product launch. Retailers can track for language that depicts human emotions and adjust products based on direct customer feedback to better appeal to their target audience.
Emotional analytics can provide a view of a customer’s attitude ahead of an interaction, enabling brands to deliver a contextual value proposition or, most importantly, offer a more satisfying customer experience. Combining hard metrics like gender, income and purchase history with softer data such as tone, attitude and emotion will allow marketers to read between the lines and deliver truly personalized experiences that previously weren’t possible. As customer expectations of brands soar, it’s no longer acceptable to operate with blinders on. Marketers need to tap into the vast array of data (demographic, behavioral and emotional) in their arsenal to create tailored experiences that make customers feel like their needs and desires are being heard. Marketers can now think outside the box when it comes to product returns, service requests, technical troubleshooting and much more.
Customer experience matters, and brands have a new chance to uncover a seamless understanding of their customers through deep analytics. It’s time to bring emotion to the forefront of customer engagement and turn brand affinity into lifetime loyalty.
Hannah Egan is a product strategy specialist at IBM Commerce, a unit of Armonk, N.Y.-based IBM Corp. that combines software sales and consulting services.