Good businesses have always acknowledged that the customer is king—or queen. And in today’s digital world, those kings and queens are more powerful than ever. They’re powerful because they possess information and have the tools to use it. And they’re becoming more and more demanding.
Customers know they’re in the driver’s seat, and technology is making them increasingly picky. They no longer settle for choosing from what’s on broadcast TV or what’s listed in a catalog. They want to schedule their own program-viewing times, they rely on their peers rather than brands for product advice, and they want to use multiple channels at the same time for making purchase decisions. Plus, they expect relevant, targeted interaction that leads to a compelling value proposition.
Businesses, meanwhile, are finding that the old rules no longer apply. Intuition-based customer management is failing. The old business rules are designed for the so-called average customer. Only
a combination of human and machine intelligences can provide the deep knowledge of today’s— and tomorrow’s—customer that businesses need in order to be most successful at growing sales.
Now, successful companies are moving from traditional marketing methods to digital ones to engage these savvy customers. Those companies, too, have an array of tools at their fingertips. They can use decision sciences to select and target the best customers and then offer those customers multiple—and consistent— real-time purchasing channels that also provide great customer experiences in the form of microcuration.
The most-effective, most-digitally-transformed companies have mastered how to understand,predict, and interact with customers. That’s one of the three primary levers—along with information- based decision making and digital automation— that digitally transformed companies are pulling to improve performance. Those levers are especially effective when supported by the right business models and enterprise architectures, coupled with a business- focused mind-set for investing in and governing technology-related decisions (figure 1).
Each customer makes a journey from being a prospective customer to becoming a new customer who could be up-sold and cross-sold, to evolving into an established customer who could be made loyal, and then to a fading customer who could possibly be won back (figure 2).
The first step in maximizing performance with customers is to understand where each customer is on the customer’s individual journey. That understanding should be augmented by additional information
such as payment history, buying patterns, previous interaction experiences, demographics, and lifestyle.
Both customers and prospective customers should be strategically segmented by using clustering algorithms that help the business spot patterns and relationship groups it wouldn’t otherwise have been able to see.
Simplistic segments based only on age or location fail to create groups that are homogeneous. For instance, not everyone living in Florida wants those khaki Bermuda shorts for $89. Bottom-up, behavior-based segments provide clusters of customers who actually behave in similar ways despite significant differences in age, income, and ethnic background. A nationwide company that recently completed a bottom-up segmentation found that customers who were buying only once, prompted by a specific promotional sale, formed the fastest-growing customer group. With that insight, the company became able to reattract many of those customers without a promotion and engage them in additional transactions.
Underpinning all of this is a critical understanding of just how profitable customers really are. Many businesses pursue the wrong customers, chasing volume and taking on too much loss in the mistaken belief that all revenue is good revenue. In fact, a thorough look at customers after a full assignment of costs offers a very different view of profitability.
As an example, say a consumer products company gained almost all of its profits from just 500 of its 3,000 customers. A further 1,800 contributed nicely to profits and revenue, but those profits were offset by 700 customers who were unprofitable—and who caused the company to lose money.
Understanding customers also depends on a continuous feedback loop: e-mails, surveys, comment boxes on Web sites, and listening to social media. Without all of that, businesses won’t know what’s important to their customers or how customers use and rate their products and services.
Once a company really understands its customers, it can use that vital knowledge to predict how customers will behave. A predictive model can tell you the likelihood of a customer’s buying another product or staying loyal—and how much effort your company will have to put into the relationship.
Predictive analytics evaluate likelihood of purchasing, price sensitivity, communication channel preferences, product preferences, behavioral changes, and lifetime value. Companies that apply predictive analytics effectively and that next take the best actions are increasing customer value and winning in today’s low-growth environment.
The retail industry has achieved some impressive results using predictive analytics. For example,
a national retailer recently improved its catalog marketing program. Using predictive models to determine which customers would buy more if they were sent catalogs—rather than sending catalogs only to the highest-value customers—the retailer added $1 million in monthly incremental earnings before interest, taxes, depreciation, and amortization.
Analytics informs the entire customer journey. As new customers get brought on board, the business can monitor their activities and engage with them to enhance the relationship and maximize value.
Predictive models score each customer from high to low based on likelihood of acquiring, cross-selling, loyalty, and recovery. Interaction models are developed based on those predictions of how to best proactively and reactively deal with each customer, from solicitation to providing customer service. Predictive likelihood scoring sets forth the amount of effort and investment that should go into those interactions. The score dictates that more should be done to attract and satisfy customers who are more likely to be of high value. Results of interactions are scored continuously and then refined so as to develop the best predictive model and to capture ways of best interacting with each customer.
Once they understand their customers thoroughly and it has become possible to predict how those customers will buy, successful companies have highly effective methods of interacting with customers. The best results of how to best interact with each customer have accrued to targeted marketing; multiple sales channels with frictionless, seamless, and consistent transaction experiences; and the systematic guidance of front-line personnel (sales force and customer service team).
Customized customer campaigns are executed through digital and physical marketing methods that come with next-best- action features based on customer reactions. The impacts of such campaigns are then evaluated so companies can take steps to win customers who respond to the initiatives. These methods get reevaluated and improved constantly, and they can be customized to specific segments based on the company’s now improved understanding and predictive ability.
One of today’s biggest misconceptions among companies is that using multiple marketing channels is actual omnichannel marketing. But there is a tremendous difference between using multiple sales channels and truly being omnichannel. Many companies have physical locations as well as e-commerce functions through Web sites where their products and services are available to customers.
However, the most successful companies are truly omnichannel by providing a consistently favorable customer experience that is supported by integrated back-office operations—not by sales channels that are operated separately. Great omnichannel companies greet their customers the same way regardless of the chosen channel, and they provide heavy integration between the channels so that, for example, customers can use the online channel and the physical channel at the same time for fully informed purchase decisions.
As digitally transformed companies successfully predict how to get potential customers to buy, how to get new customers to buy more, how to keep good customers loyal, and how to rank customers based on the likelihood they will do those things, what do they do next with all of that information? They make sure their sales forces and customer service team have the information readily available, and they train employees on how best to use it. They develop customer interaction methods, train personnel in how to execute interactions, and measure them on their performance.
Digital channels that are succeeding with business-to-consumer companies are making rapid transitions to business-to-business applications, and they now threaten traditional business models. All consumers regardless of the industry they’re interacting with expect the straightforwardness, simplicity, speed, and transparency available through digital interaction. Digitalization has eased customer switching and eroded margins at companies using traditional models. And historical revenue pools have disappeared, as their underlying services become rendered obsolete by digital capabilities. In a digitally ascendant world, it’s the new entrants, unshackled by historical cost structures, that define the new and alternative value chain configurations.