Gathering and understanding customer experience data — and rapidly acting on that information — is essential for any organization to survive in an increasingly complex and competitive business environment. Nearly two decades ago, Richard Owen’s team led the development of the Net Promoter Score methodology, which created the most widely used approach to customer experience metrics. Today Richard, who serves as CEO of OCX Cognition, is still one of the most highly regarded thought leaders in the customer experience industry.
I recently connected with Richard through Frontline Summit, an event both of our firms are co-hosting, about the early days of NPS, how customer experience metrics are evolving, and why he’s moving beyond customer surveys.
A Higher Standard for Customer Service Metrics
NPS came on the scene in the early 2000s as companies had grown increasingly skeptical of the return on their considerable investments in customer satisfaction. At the same time, business leaders were adamant that their relationships with their best customers — and that treating those customers exceptionally — absolutely paid off.
“There had to be a logic break somewhere because there simply couldn’t be a universe where customers didn’t reward you at all — and yet that seems to be the observation,” Richard says.
The key insight that led to the development and widespread adoption of NPS, Richard says, is that customers do, in fact, reward companies for good customer experience — but the standard is just much higher than most organizations realize.
The consumer landscape had evolved considerably from the post-World War II environment, where merely having a product that didn’t break was seen as a competitive differentiator. By the 90s, Richard says, businesses of all types were trumpeting 90 or 95-percent customer satisfaction in their advertisements. “That should have been a clue that in a contemporary, modern, contested economy, anything where everybody is running around saying we’ve hit 90 or 95 percent was probably not a competitive differentiator,” he says.
NPS offered a recalibration of that old customer experience model, Richard says, by shining a light on customers that love a brand enough to sing its praises to other potential customers. “You can win at this game, but the bar is much higher than you thought,” he says. “It’s no longer just about satisfying customers; it’s now about creating promoters. These guys are going to be hard to find, but they are economically much more rewarding than you probably realized.”
Richard says when NPS first hit the market, nobody had ever encapsulated customer experience in a truly measurable way. Companies that rigorously adopted this new methodology enjoyed a real competitive advantage, particularly before the widespread adoption of customer experience programs. These days, he says, too many companies take a “simplistic and cosmetic approach” to NPS that doesn’t yield meaningful results.
For example, he says, a company that wants to get an honest perspective on satisfaction would make sure it’s polling all of its customers to understand what they think about the business. A less effective but common approach would be to rely on the sales team to designate who is sampled in the poll, which would select for people who have a positive bias for the business.
“Selective use of samples is a really simple trick, and a lot of companies do it without even realizing it,” he says. “They rationalize their decision because their north star isn’t getting to an honest result. Their north star is getting to a number — and often a number that pays out a bonus to people.”
The Next Frontier
One of the complicating factors in any contemporary NPS program is that survey response rates have declined in recent years, Richard says, which has, in turn, prompted a steady drop in the quality of customer service data. The issue will only accelerate in the coming years as a larger share of customers are digital natives who are more comfortable providing feedback via social channels.
“A survey is a 1960s technology, and its origin is in someone standing on a street with a clipboard and asking you questions,” he says.
For Richard and his company, the solution is machine learning. “I no longer want to ask you whether you’re a promoter. I want to predict whether you’re a promoter,” he says.
The benefits are two-fold. First, this prediction approach eliminates the problem of getting customers to respond to survey questions. “I’m going to predict whether you’re a promoter whether you respond or not,” he says.
Machine learning also allows companies to predict whether a person is a promoter on a more timely basis, he says. “If something happened to you yesterday that changed your likelihood to recommend me and I didn’t send you a survey at the exact right moment, I don’t know it happened,” he says. With machine learning, “instantly, I’ve got a revised score for you, and that buys (your business) time.”
For example, he says, consider a scenario involving a complex business with large B2B customers. Suppose the firm’s largest account had a fifth consecutive technical support call that failed to resolve the customer’s problem. That piece of data could prove extremely valuable in intervening with that customer and preventing a drop in satisfaction — if it’s properly identified.
“If I can capture that data, I can adequately figure out whether it’s material or not, whether it’s tipped the customer from passive to detractor, or promoter to passive — and I can tell you the instant it happened.”
Capturing the Impact of Content Marketing
Every interaction your customer has with your brand plays a role in customer experience, and it will be reflected in your customer experience metrics.
That means content can play a key role in customer satisfaction. “Content to me talks to a lot of different problems,” Richard says. “It enables the buyer to understand what the company is about; it expands on the definition of the product and service to help the customer to get value from it. It informs and educates and conveys the additional value of the brand relationship in that regard.”
The correct design of the underlying analytics is key to capturing that impact, Richard says, and organizations should start by looking at all the underlying data on how customers respond to your value proposition and brand. “Customers don't think narrowly — they think expansively.”
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