Can AI reduce customer friction on every step of the onboarding journey?
In the COVID-19 era, consumers are more eager than ever for direct solutions to their online experiences
Even before the pandemic, companies and customers were looking for more authentic AI.
More than sixty years ago, management guru Peter Drucker admonished, “There is only one valid definition of business purpose: to create a customer… The customer alone gives employment.”
It is therefore perverse to find that generations later, the task of onboarding customers to a new provider or a new product seems to be a peripheral concern for most enterprises: an administrative garnish on their main dishes of devising new offerings, as they fight what’s often presumed to be a zero-sum war for market share.
Onboarding, the moment of truly creating a customer, deserves more thought—and may now be able to get that attention, affordably and at scale, thanks to AI. After more than six decades of effort, AI is yielding deliverable tools and practices for effective, efficient customer onboarding.
When AI is seen as a means for creating power tools that augment human expertise, it can readily yield assistance at all points on the customer journey—from discovery, into engagement and ultimately on to faster and more sustained adoption. For this to be most fully realized, though, the enterprise must accept a humbling reality: that customers are demanding a reinvention of the experience they get from their vendors and their service vendors, and that only the customer gets to define what it means to be “served.”
There is, however, a reward to be had from acts of service. IBM research has shown (as early as 2014) that well-served customers, sharing candid and credible reports of their experience in channels such as social media, overshadow the influence of both old and new forms of paid-for messaging.
The onboarding of new customers is therefore not an after-sale expense that comes out of profits. Rather, it’s a forward-driving investment. Onboarding should be seen, and treated and prioritized, as the recruitment of the next cohort of the customer ambassadors who have become a company’s most important marketing asset.
Failure to make this investment has devastating costs, even if they are the kind of opportunity costs that fail to appear on a P&L statement. When customers experience high-effort interactions, they have a 96% likelihood of becoming “disloyal” customers who discourage others from becoming new customers, as well as reducing their own volume of ongoing business.
Further, those high-effort interactions actually are more expensive to provide: they take their toll in a doubling of repeat calls, escalations and channel switching such as a customer turning from web-based to telephone-call interaction. These frictions and redundancies waste the energy of all participants.
AI has begun to turn some of these interactions around, and even more promise lies just over the horizon.
AI insights supported by customers
Consider the real value already being realized from relatively simple chatbots. At Winston-Salem State University, a chatbot developed in consultation with student-onboarding workers was able to accomplish a 37 percent improvement in immunization compliance, and a 74 percent improvement in completing financial arrangements prior to the arrival of new first-year students. This removes entirely non-value-adding costs from a crucial stage of customer creation.
Curating such initial stages of onboarding offers comparable potential leverage in finance. At Ireland’s Ulster Bank, Damien Judge, the head of business commercial excellence, has described the creation of a “next best product” engine that generates insights into cross-selling opportunities by analyzing a business customer’s activity with other financial institutions.
“By combining analytics with machine learning, we can predict what products and services customers are most likely to need,” said Judge. In later stages of customer onboarding, Judge has plans for a “next best action” engine that will calculate whether a customer has the optimum products for their business, and can suggest alternatives.
The current pandemic has turned this challenge—and opportunity—up to 11. Customers are less able to make personal visits to a place of business. They’re more concerned about aspects of offerings that once might have been considered outside the bounds of a transaction. For example, getting information that helps customers plan a safe visit to a place of business, or seeking accommodating payment terms for customers dealing with pandemic disruption to families and jobs, are suddenly notable factors in a potential sale.
Usefully, though, customers in the time of COVID-19 are demonstrably more eager to experiment with digital engagement tools, even in demographic groups who have previously shown less likelihood of adoption.
Growing AI acceptance amid COVID pandemic
The label “Generation N,” a coinage of Brian Solis, global innovation evangelist at Salesforce, encapsulates many of the concerns and behaviors now so suddenly and noticeably common among consumers across all demographics. The “N” in Generation N stands for “Novel Coronavirus” and builds on Solis’s 2012 identification of a “Generation C” (for “connected”). Solis now observes that “the stressors, feelings and consequences of a global pandemic” are now inflecting the content and focus of behavior for a cohort whose lives were already increasingly lived online as much as “IRL.”
These sensitivities have created an opportunity for the efficiencies and cost reductions of digital modes of service. It’s a rare opportunity for acceleration, particularly with the addition of personalization at scale. The judicious application of AI-derived techniques of pattern recognition and prediction to proactively guide shoppers to simpler actions and more immediate needs is unlocking levels of customer attention heretofore inaccessible for the majority of consumers. Everything becomes a boutique experience with mass-market affordability.
Practitioners will do well to anticipate, though, the intensifying scrutiny on AI-aided assistance. Legislative proposals already contemplate requirements for public description, risk assessment and privacy impact analysis of systems that may affect life-changing decisions and that work with sensitive personal information.
The opportunity created by technology to address the rising expectations of customers, intensified by the new demands of the pandemic, must therefore be pursued in a way that leads to a governable and socially accepted outcome as well as to superior financial results.