The promise and power at the intersection of AI and CX (Part 1)

By Paula Courtney,
CEO – The Verde Group

Artificial intelligence (AI) has been a fascination for many at least since HAL, the soft-spoken computer that begins killing astronauts aboard the Jupiter spacecraft in 2001: A Space Odyssey.

Today’s AI hasn’t (yet) reached HAL’s level of sentience or psychopathy but, in the last five years, it’s certainly matured enough for it to become a small, growing and convenient part of our lives. The advent of self-driving cars is an obvious albeit still controversial example but so are AI engines we now take for granted, like Apple’s Siri and Amazon’s Alexa. They hear you, respond back as a person and interpret your desires — within limits. Even Google Search is a remarkable AI accomplishment, instantly doing natural language processing to understand what your search request really means.

On the business side, organizations are becoming really enthusiastic about AI, with text and sentiment analyses particularly hot topics relevant to customer experience (CX). Businesses, for instance, are increasingly using chat and auto bots to interact with customers to address easier customer issues without putting expensive live bodies online or on the phone. And other AI technologies like geo-tagging and facial authentication are being quickly incorporated into business models to help interact with customers.

In short, AI is beginning to have radical implications for CX as a business strategy. Internally, that’s kicked off interesting conversations and questions like: Why use customer surveys anymore to understand customers’ needs when you have dynamic and robust new AI data sources?

Surveys, after all, are expensive to conduct and customers are increasingly tired of doing them, so response rates are dropping. In this article, I’d like to offer a few thoughts in three key areas to help companies understand the promise and power of AI at its intersection with CX:

  1. Can AI help you listen more effectively to your customers and hear what they need?
  2. Can AI help you evaluate what you hear to understand customers better?
  3. Can AI help you act on what customers require from you?

Listening to customers and growing your data lake

Given the advances of AI, both in monitoring and analysis power, there’s now a multitude of tools and methods to listen to customers and collect data in centralized repositories called “data lakes”.

Your data lake can include a wealth of structured, semistructured and unstructured data points to better understand customers, including social media posts and emails or voice recordings, operational data, sales data, customer service interaction data, visits to websites, economic data, as well as data from third-party overlays you can buy for demographic and sociographic information.

To dive into this metaphor, the thing about data lakes is they can get rocky and deep, which means you’re going to need to know how to swim. So, while the complexity of these kinds of data can be an asset over time, in the initial stages of listening to customers, if it’s not managed and considered intelligently, your data lake can be a big liability.

One common CX issue with AI in B2B environments, especially relationship-driven ones, is that data lakes may not be robust enough for AI to have a big impact — yet. That’s because, many B2B organizations usually don’t have the same sort of interaction or purchase velocity that B2C ones do. Take farmers as an example. They may buy products once a year and, while some may occasionally gripe about service or products on social media, they don’t do so nearly as often as customers whose shoes don’t fit or whose blender doesn’t work. This means that B2B companies may need to think of new ways to augment their data lake for AI to truly work its magic in CX.

Keeping with our metaphor, whether you’re a B2C or B2B enterprise, a growing data lake means you’ll need to decide where to fish and what to fish for. This is where surveys can still be extremely useful. That’s because surveys can have a key role in defining the analytic focus of AI — assuming the customer service survey research you’re doing is well designed. After all, a big reason customers often prefer AI in place of customer surveys is because many surveys are truly awful. They’re often not specific enough, they’re a lagging indicator, they don’t explain the financial outcomes of a customer experience and they don’t focus on the key drivers of performance that lead to superior market outcomes — all legitimate criticisms and reasons why Verde Group designs its surveys to address these kinds of issues.

Effective surveys can do a great job of uncovering and understanding the customer experience, which can then be augmented with AI and, in turn, further augmented with more surveys.

Once you’ve grown your data by listening to customers, however, you need to be able to evaluate it. That leads us to the second and third ways CX and AI can intersect to benefit businesses and we’ll take a look at those in Part 2 of this article.  Stay tuned.