By Paula Courtney,
CEO – The Verde Group
In Part 1 of our look at the intersection of artificial intelligence (AI) and customer experience (CX), we saw how AI has begun to shape businesses’ quest to understand customers and their needs. And we reviewed how AI can help companies listen more effectively to customers to hear what they need. Our next key question is: Can AI help organizations evaluate what they hear to better understand customers?
Evaluating AI and survey data to improve CX
Fifty years ago, there were more straightforward customer service interaction points where, companies tried to maximize customer value and narrow the decision set based on their history with them. That meant the odds of making the right customer service choices were higher. In traditional pre-AI environments, that wasn’t always easy but companies still did a great job delivering customer service or sales thanks to human beings with amazing non-artificial intelligence who evaluated how best to service customers.
Today’s challenge is whether, using AI and collected data, companies can make better bets on how to evaluate their data and interact with customers to maximize satisfaction and economic value, minimize service costs and more easily anticipate problems.
Relationship or transactional surveys are, again, a good place to start. They can lead your AI efforts to better understand what to focus on and they can also be used at the back end to augment insights revealed by AI. In other words, when it comes to surveys and AI, it’s not “either/or” but “both/and”. In short, companies that win the AI game will be those that properly collect and understand voice of the customer data and intelligently incorporate it into their AI evaluation strategy.
Acting on AI knowledge to give customers what they want
It’s not hard to understand why AI is so exciting for companies. If you can use AI more and humans less, you lower the costs to serve, lower the cost of sales, and raise customer satisfaction, loyalty, customer equity — in theory.
First, it may be a generational and cultural issue that evolves over time, but a lot of customers still simply don’t want to have anything to do with AI interactions like chat bots. They still don’t want to give your company eight data points to correctly direct their call. They want to talk to a human being now — or at least they’re not ready to completely replace human interaction.
Nevertheless, there are really interesting ways AI is being used to benefit customers. As recently as 10 years ago, fraud algorithms were relatively rudimentary and, if you were travelling internationally, you had to call your credit card company to let them know so they wouldn’t freeze your card or refuse to process your transactions. Today, credit card companies’ AI-supported infrastructure, including GPS on your phone and business travel and other data lake information, enable them to make better bets about whether a credit card charge is fraudulent or not without giving you hassles.
Airlines too are particularly good at using their AI-supported data lakes to improve customer service and get ahead of problems. For instance, taking pre-emptive measures when a flight is cancelled to pre-identify and deliver options to customers and even push make-good offers like flight credits to customers who are worth the investment.
Four questions and a few final suggestions
As good as AI is getting, it won’t be replacing humans or customer experience surveys anytime soon.
For thoughtful and effective CX research, a survey process that supports timely and accurate research, that’s specific and actionable about performance drivers and expressly linked to financial outcomes, can complement AI but it won’t be replaced by it — at least not yet.
As for humans, AI can augment what humans do but it won’t replace them either. In low stakes categories, for relatively trivial issues to resolve, AI can do the trick. But in higher stakes issues, where problems are more complicated or there’s an emotional investment — like an expensive medical claim — most customers simply won’t want to hand over decision-making to robots. (Interestingly, however, while high-grade support might need to be handled by humans, that support may still be driven by AI decision-making and evaluation under the hood.)
So, where does AI make the most sense in CX today? Here are four key questions your customers will be asking as you try to deploy it:
- “Does AI make my life easier?” If it leads to faster and better service, use it.
- “Do I trust the guidance from AI?” Your AI may be able to give customers the perfect recommendation but they may not trust it. In many instances, humans can explain things and convey a sense of confidence and trust that a chat bot or Alexa simply cannot – again, at least not yet.
- “Do I feel like my privacy is intact and respected?” You may find that, in many instances, you know so much about a customer that you can go straight to the answer. But doing so might set off Big Brother alarm bells, as they wonder how you know so much about them. AI is based on a deep understanding of customers that they may not yet be all that comfortable with.
- “Can I still talk to a human?” If your objective in deploying AI is to erase all human interactions then you’re likely misplaying AI as a business tool.
And, finally, if you’re ready to truly dive into AI-supported CX, here are few principles to adhere to on your journey:
Start simple and build: Use the information you know you can rely on and if you have good customer experience measurement protocols in place, don’t throw them out. Think of how you can build on them as you try to incorporate AI to make your listening and evaluation processes more robust.
AI has to be a cross-functional exercise: AI doesn’t care about silos, it looks at the data for non-obvious relationships that are useful in terms of curating customer experiences.
People first, then money: The power of AI to reduce costs and drive revenue is immense. But my strong advice is to focus more on reducing customer effort and solving customer pain. If you do that and build AI strategies from a customer-first perspective, the money will come. The horse is reducing customer effort. The cart is reducing costs. Ultimately, the horse should be pulling the cart and not the other way around.