Artificial Intelligence ringing in a new age of personalized marketing and commerce.
Artificial Intelligence ringing in a new age of personalized marketing and commerce. PHOTO: Unsplash

We are entering a new era of commerce fueled by younger consumers, mobile devices and high demand for more personalized shopping experiences. Millennials are now the largest generation in the U.S. workforce, and their income and buying power are rising. This generation, along with the next tide of digital natives, is putting pressure on enterprises to deliver customer service with the usability and simplicity of consumer apps, and without the inconvenience of phone calls. Companies are investing in artificial intelligence (AI) technology that can provide these experiences through bots, thus ensuring that customers get what they need — when, how and where they like.

Although AI is a solution to customer service problems, intelligent automation is difficult and expensive to develop. To understand and resolve customer requests, bots require many capabilities, including intent prediction, response generation, conversation management and process automation functionality. The proliferation of new channels adds another layer of complexity. While contact centers focus on traditional channels such as phone, chat and email, new customer touchpoints have emerged, such as social media (company pages, forums, trending topics), messaging platforms (like Facebook Messenger and Apple Business Chat) and personal assistants (Alexa, Google, Cortana, Siri). Unfortunately, these new channels also create more opportunity for fragmentation and data siloes.

Companies can’t afford to duplicate AI investments by building disparate systems for different channels. Instead, companies should consolidate platforms and integrate data sources to leverage AI across all customer experiences. This approach will improve the power of AI through enhanced learning that spans channels, business functions and data sources. The resulting customer experiences can then lead to increased sales and brand loyalty.

Intent-Driven Engagement Improves Customer Experience

Intent-driven engagement is the concept of managing customer experiences by predicting and responding to consumer intent. Intents are associated with the “journeys” consumers take as they research, choose, checkout, consume and engage with the brand. By processing large amounts of phone and chat conversations, it’s possible to understand the language signals that indicate a specific intent. However, natural language processing by itself is only a basic form of intent prediction. The words spoken or typed are obviously relevant, but in some situations customers may not be able to describe their issue clearly. In other situations, the company should be able to anticipate the issue even before the customer has to find the words to express the problem.

By combining language data with huge volumes of behavioral and transactional signals, AI models can more accurately determine a consumer’s current journey and anticipate the next step in that journey. This comprehensive data set includes interaction events from web visits, phone calls, mobile app sessions and in-store visits, as well as transactional events from sales, billing and other enterprise systems. Using AI to predict what a customer wants or needs enables companies to act by providing the next-best action. The resulting experience is more personalized and efficient than a lengthy conversation guided by a generic script.

A Unified Approach to Customer Engagement

Consumers want the flexibility to interact with companies in different ways. A customer journey may include multiple interactions, conducted over time and using different devices based on preference or accessibility. These journeys require the choreography of a consumer’s experience within and across channels. Examples include an online account that requires a phone call to cancel the subscription (web to IVR), a hotel booking bot that requires live assistance to extend an existing reservation (virtual agent to chat) or a call to unblock a credit card that displays suspicious charges on a mobile web session (IVR to web). One large North American telecommunications company found that by enabling an IVR-to-chat feature, it was able to drive increased digital adoption. By offering consumers the ability to chat immediately with a live chat agent (as opposed to waiting for a phone agent) it was able to deflect 25 percent of live agent calls.

To build loyalty (and also save millions of dollars), companies should provide a seamless experience from one interaction to the next, while advancing customers along their journeys. Companies need to remember the context of each customer journey and resume where each one left off, regardless of changes in the device or channel. This eliminates one of the biggest and most annoying customer pain points — the need to “start over” and repeat their problem each time they switch channels or get transferred to a new agent.

One barrier to unified customer engagement is the partition between digital and voice (or telephony) channels. Today, chatbots and IVR systems are built on separate architectures and are typically managed by different departments. However, the two rely on many of the same components — process flows, business rules, natural language processing, intent prediction models and backend integration. Dual efforts are a waste of resources and create a broken experience when consumers switch from digital channels to the phone, or vice versa. Furthermore, it hampers the ability of chatbots to truly deflect and reduce phone calls.

Another barrier to unified customer engagement is the separation between automated service and live service. Some bots do not support a transition to a live chat agent and will force consumer to place calls on their own. Other bots will hand off to a human agent if required but then fail to track the agent conversation to understand the reasons for the escalation. A more effective AI system would continuously learn from agent interaction data to improve the prediction of intents and the treatment of those intents.

Conclusion

AI represents a major investment for most companies, but the technology could also serve as the driver for unifying customer engagement. The impact of AI is amplified when digital and voice are connected, and when automated service and live service are connected, thus increasing the accuracy of predictive models and the reach of automation. If done correctly, AI-driven experiences will become the norm for customer interactions for millennials and others.