After shoppers interact with your customer service team, do you want them to walk away thinking they simply got their questions answered? Or, do you want them to feel grateful for a personal and relevant experience?
Retailers know they need the science of big data and machine learning to improve the customer experience. Most off-the-shelf customer engagement software provides an enhanced view of the shopper based on aggregated data. More sophisticated platforms provide a 360-degree view of each customer, mining their constant signals and patterns so retailer scans provide customers with sophisticated personalized experiences.
But data and analytics alone aren’t enough to help retailers deliver great customer experiences, solve customer issues or grow revenue.
With all the data available, both internally and externally, retailers must focus on making the relevant information digestible for their customer service teams. There must be a process transformation–a digital curation–that helps tailor and optimize experiences for each shopper, seamlessly personalizing the experiences across multiple touchpoints and channels.
“It isn’t just about the shiny front-end experience with sexy websites and mobile apps,” says Melissa O’Brien, a research director at HfS Research. “It’s about an integrated back-and middle-office that supports those experiences.”
It’s a new innovation for customer service. And it combines the science of automation with the art of building loyalty.
“Retailers can differentiate themselves with customer service, but they need to do it profitably”
Retailers face two big challenges.
The first is that today’s customers come with rising expectations. They expect issues to be resolved quickly and seamlessly. They expect to be recognized and understood. And they expect personalized service, regardless of their location, their preferred device or even the time of day.
Retailers can differentiate themselves with customer service, but they need to do it profitably. Which leads to the second challenge: as retailers fight to maintain profits, they also must defend their business against pure play online-only sites.
Take a look at department stores–one of the few retail segments that had declining sales in 2016, as reported by Bloomberg. The category’s total sales dropped by more than 6 percent year-over-year, yet online sales skyrocketed by almost 12 percent.
If retailers conquer the first challenge, they also will tackle the second one.
The science of data and analytics means that when a shopper calls, the customer service team sees a screen that outlines a customer’s purchase history. Of course, the customer service team still must solve issues as soon as possible to help with first call resolution (FCR). But once the team has a platform that offers access to a 360-degree view of shoppers, there are other ways to use it.
This is where the customer service team needs to take the data and analytics and translate that information into a relevant and personalized experience at every customer touch point.
In other words, this is where the team takes the science and adds the art.
The art starts with a human-centric, design thinking approach supported by seasoned experts. At Sutherland, for example, there’s a team of world-renowned psychologists, anthropologists, ethnographers, interaction designers, documentary makers, software engineers and product managers. They use observation, interviews and ethnographic skills.
Hands-on research, journey mapping, co-innovation workshops, and solutions testing. After analyzing and optimizing Sutherland’s own customer service unified desktop (which is customized for each retail client), this team trains the customer service staff to use enhanced customer information. That includes loyalty, engagement, channel preferences and analytically driven next best customer actions–all of which both delight customers and drive business outcomes.
“This is an important element that retailers need to consider right now–drawing information from disparate sources in order to generate insight to have a more proactive customer engagement strategy,” O’Brien says.
Perhaps a shopper calls a fashion retailer with an issue, for instance. Using predictive analytics based on historical customer data, the customer service agent might be able to identify the issue before the shopper does much explaining. In turn, this provides the type of in-store experience the customer expects.
That science helps with the standard conventional customer satisfaction metric (C-SAT). But that measure becomes less helpful as the standard of retail customer service becomes more differentiated. Today, after the customer’s issue is quickly resolved, the customer service agent needs to focus on a more important measure.
The agent must shift from science to art.
Maybe the agent notices that the caller is a frequent shopper who often buys a certain clothing item. Because of that, the agent might offer a 20 percent discount on that clothing item and add it to the shopper’s cart, driving both loyalty and incremental sales.
In time, this will be done not only with humans, but will be automated across digital channels. And it will spark a better way to measure customer satisfaction via the Net Promoter Score (NPS).
Customers become promoters when they appreciate the commitment shown by retailers. It makes shoppers so loyal; they spread the word to family and friends. And this loyalty should translate into increased purchases and shopping frequency.
Delivering that loyalty and, in turn, that revenue, requires retailers to combine science and art. Retailers must build customized strategies to grow and retain the right customers through rewards and incentives that are both non-monetary and monetary. And retailers can wisely decide which customers warrant an investment, how much to invest and what that investment should entail.
Consider what we found for one retailer:
• Pushing just 6 percent of its “engaged customers” to become “very engaged” would cause total revenue to jump by more than 5 percent.
• Preventing only 5 percent of its “very engaged customers” from lapsing would avoid a loss of 1.5 percent of the total revenue.
• Improving customer satisfaction for as many as 1.5 million shoppers would spur $7 million in incremental annual revenue.
The results of those efforts then get added to the next batch of data. That leads to updated science that will also fuel better art.
Combining complex data and analytics with design thinking can be hard, so the key is finding the right partner and the right processes to make this transformation easy.
That’s how you meet the challenge of satisfying the rising expectations of today’s customers. That’s how you meet the challenge of preventing your customers from fleeing to online-only sites.
And that’s how you celebrate growing your business