How is AI changing the game for nextgen CX

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Examining the phases of the CX evolution and how AI is likely to push it into next orbit.

When Captain Kirk gets into an “elevator” of the USS Enterprise, holds a “lever,”and shouts “Bridge!”, the elevator authenticates the captain (fingerprint?) and takes him to the Bridge of the spaceship. Many such ‘common events’ in the 60s TV series, Star Trek has served as inspiration for CX interfaces and several recent technology inventions, including AI and conversational interfaces. Instead of starting this discussion with Artificial Intelligence (AI), Machine Learning (ML), or other buzzwords in the industry today, let’s start by looking at how customer experience has evolved during the last few decades. In other words, let’s examine “A Brief History of CX” while understanding the underlying technology changes and how they have evolved.

Phase One: Struggling to understand customer needs. The earliest form of CX may well be large monolithic systems, such as the ones found in banks in the 80s. These systems were designed to meet the needs of a bank’s in-house processes and had little or no inclination towards understanding or addressing CX. Their audience or customer was the banks IT department – and the mission was to make their job easier. The actual end customer had to be satisfied with what was thrown at them.

Phase Two: Meeting customer needs. The 80s and 90s were about brand loyalty being a synonym for product quality. Most organizations focused (and competed) on launching better quality products to drive up their profits. The driving force of CX, and its allied technologies, was to find out what was important for customers, understand how enterprises were delivering against those (quality) expectations, and fix the products or services accordingly.

Phase Three: Making it easy. The 80s and 90s also saw the dawn of the CRM industry. Attempts were made to understand the customer better and to be able to transact with them at a more personal level. Early forms of personalization were experimented with, and customers finally began to feel that they were being treated like “individuals.”

Phase Four: Enjoyable CX. By around 2010, customers had started to take quality for granted and were quickly getting used to being treated personally. Now, when choosing a product worthy of their loyalty (and their money), they look for something more. In this era of social media connections, it’s fun to talk about a product with friends, so connecting emotionally with the customer is critical to delivering that enjoyable CX. Content creation, digital marketing, and personalized offers lead to better personalization and emotional connect with the customers.

Phase Five: Intelligent CX. In today’s era of instant gratification – where we want things “here and now!” – enterprises are turning towards AI and machines for help. While there are some good deployments, namely chatbots and virtual assistants, there is a growing focus on predicting what the customer is likely to want tomorrow and launch that as an offer before the customer can finalize their choice. This proactive approach is likely lead to true customer delight and AI will very soon play a crucial role during this phase.

AI will be instrumental in providing that much needed push into the next orbit of CX. A recent Capgemini reportThe Secret to Winning Customers’ Hearts with AI: Add Human Intelligence suggests that customers are already getting a taste of AI and beginning to ask for more intelligent, human like conversations as long as they get better value. They are willing to pay more for such better experiences.

It is also interesting to see, from a technical view, how the phases were supported and how the underlying technical architecture evolved. During phase one, we would typically have either a one or two-tier architecture, using “dumb” terminals to query monolithic systems. The operators would have to run a complex query, even to get a single piece of information with any relevance to CX.

Phases two and three were built on the three-tier architecture of enterprise IT systems. From a CX perspective, this phase was like phase one, except that the information was distributed across the multiple tiers, so that more precise, accurate information was stored across these multiple tiers. Also, this is the phase where Internet became prominent.

During phase four, enterprise IT systems started their migration to the cloud and saw the emergence of “APIfication,” where most of the services for enabling CX were just an API call away. More importantly, with the prominence of mobile, which we can think of as most important CX device, we saw the emergence of a fourth tier in the guise of mobile interfaces. In the near future, we may see this as the emergence of a “CX tier” in the IT architecture.

Phase five will be built on microservices architecture. Microservices essentially serve customers’ demand for instant gratification. When you ask Amazon’s Alexa for a weather report, you are essentially getting that one focused piece of information in the format you want, without having to open your computer, write a query, or make any deductions based on what your computer throws out. Microservices also mean easier integration to AI, ML, neural networks, computer vision, deep learning, etc. to provide a predictive and intelligent customer experience.

Conversational interfaces are here to stay, and more customers are welcoming this form of interfacing with the machines for better CX. Another Capgemini reportConversational Commerce: Why consumers are embracing voice assistants in their lives” predicts that commerce transactions will become more popular.

Soon – a day not too far away – enterprises will use AI to ‘sense’ customer behaviors, actions, locations and associated patterns and will calculate customer’s need. IoT may further enable it with human implants. Based on this prediction, enterprises will aim to achieve true customer delight; before they are even at the store (physical or virtual). Exciting times, isn’t it?

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