The flurry of activity surrounding bots strongly resembles the rush by mobile app developers as IPhones and Android based devices achieved primacy and ubiquity. With messaging apps on hundreds of millions of phones, the question arises “Why should I have to leave the messaging app to order an Uber, book a table for dinner or make a payment.” Just as the glass displays of most iPhone 6s or Samsung Galaxies are littered with over a dozen icons (most of which are never invoked), users of Messenger, WeChat, WhatsApp and other messaging apps are about to find a gaggle of bots listening in on conversations poised to suggest a movie, hail an Uber or schedule an event on their calendars.

In the age of bots, apps, interactive text response and other Conversational Commerce vehicles, a top challenge for customer service professionals has been anticipating and serving each customer’s fast-changing expectations for self-service. To a large degree, customer expectations have kept pace with, and assimilated each new approach that technology advancements have made possible. While individuals can add new devices, apps (passwords for that matter), each entails incremental investment in development costs and staff time that tends to span multiple business units among brands and businesses.

Let’s take a closer look. Ten years ago customers craved robust search. Google had set their expectations that something akin to a natural language query should bring instant, satisfying results. Whether shopping online or looking for details about their insurance policy, customers wanted at least a “google like” search experience or better. The expectation was that they could go to a brand’s website and use a search function to quickly locate relevant information to answer their questions.

Making this experience possible required a joint effort on the part of IT departments, customer service organizations, product and marketing groups, and possibly other divisions within a company. Someone had to write “knowledge articles” or other content that encapsulated relevant information about the company’s products, services, and applications. Another group had to categorize this information in a way that made it searchable. Maybe a third group worried about the overall customer experience.

None of this work was easy. It underscored the difficulties and limitations of enterprise knowledge management. It put a spotlight on the inefficiencies resulting from the way most businesses separated IT functions from other areas of the business.

Where Intelligent Assistance Began
Fast forward five years. Apple launched Siri, which opened up a new world of self-service possibilities. Even though the actual experience was often disappointing, people quickly grasped the possibilities of using natural language to ask for and get answers to lots of questions. Around the same time, Google was on the way to implementing their Knowledge Graph technology. They’d acquired Freebase, a metadata repository, in 2010 and were using it to understand relationships between real world entities and to quickly find reliable answers to lots of natural language questions.

The age of intelligent assistance was born. Brands who had committed to staying ahead of customer self-service expectations were early adopters of virtual agents, typically including them on their websites alongside of, or even in place of, traditional search boxes. These brands leveraged existing investments in knowledge bases and taxonomies. Those that truly excelled at exceeding customer expectations included predictive technologies that could even anticipate the customer’s need.

In 2016 we’ve seen the eruption of hype around the “conversational interface.” Though it’s still unclear whether the hype outstrips customer expectations, there’s no doubt that a huge shift has occurred away from desktops towards mobile. Natural language in the form of messaging and texting has proven to be the dominant form of communication on mobile devices. At the same time, Amazon Echo and its Alexa assistant are showing customer acceptance of voice input, at least in the right environment.

Once again forward-thinking brands are jumping into the world of conversational interfaces, whether it means experimenting with customer-facing bots, launching voice-based “skills” for customers with an Amazon Alexa device or adding natural language understanding to Web chat or Interactive Voice Response (IVR) systems.

In conjunction with the shift toward natural language, machine learning technologies have matured to a point where they are not only improving speech recognition, but also enabling better predictive capabilities, more reliable answers, and even transaction completion. The most innovative brands are experimenting with all of these capabilities.

Keeping pace with customer self-service expectations requires vision, commitment, and probably a bit of daring. When customers get even a glimpse of a technology that makes their lives easier, they quickly embrace it and want even more. Self-service is now the preferred type of customer service. Brands that have stayed ahead of expectations are well positioned to meet and exceed customer expectations.

Categories: Conversational Intelligence, Intelligent Assistants, Articles

Tags: Natural language processing, machine learning, intelligent Assistants, Customer service, CX, Bots