Why focusing on customer effort is the key to maximising AI and automation in CX

In 2013, customer experience innovator Matthew Dixon authored a book that forever changed the way contact center leaders look at customer service. In his book, The Effortless Experience, Dixon argued that customer loyalty is earned when companies deliver on their basic promises. In other words, effortless customer experiences produce repeat buyers and greater ROI much more than moments of above-and-beyond service or shiny, new engagement tactics ever will.

Ten years later, I find that these words are more relevant than ever. While automation has carved out an important role in the modern customer experience strategy, many of the most common contemporary use cases increase customer effort rather than reduce it – jeopardising ROI in the process.

 What do I mean by this? Let’s explore a quick example.

The initial customer and problem identification questions at the start of most customer service queries are ripe for automation. By handing these repeatable tasks off to a chatbot before funneling customer topics to the right live voice agents to handle the job, it stands to reason that you could save a significant portion of your upfront labor costs.

But here’s the catch. This workflow would also create a customer experience that required a friction-filled transition to a new channel mid-conversation. Ultimately, customers choose the channel they choose because they think it is going to help them achieve the outcomes they want with the least amount of effort. For this reason, it’s just as likely this project would result in customers changing their behaviors to circumvent the new chatbot altogether – reducing your operational cost efficiency rather than boosting it.

This is just a hypothetical example, and there are probably a handful of simple adjustments this company could make to solve its problem. But it’s easy to imagine how this could happen in a CX environment where CX transformation was focused on cost containment first and customer effort second.

How to balance cost containment and customer effort in CX transformation

To identify potential CX transformation initiatives that deliver the intended ROI without increasing customer effort, organizations should look first at customer interactions where complexity is low.

Leading CX organisations have already been doing this for years. By targeting high-volume, low-complexity interactions – like password lookup or order tracking – they’ve managed to slowly chip away at their costs without damaging customer loyalty and satisfaction in the process.

Here’s where things get trickier.

In the AI era of customer experience, the traditional automation formula (prioritising high-volume, low-complexity use cases) is changing. The conversational skills of generative AI separate it from previous iterations of automated solutions. Now, this next-gen automation can tackle higher complexity tasks, without dramatically increasing cost or raising customer effort.

For starters, this might look like using a chatbot to handle more of the load in a customer conversation. Or in some cases, the AI chatbot might be able to tackle the entire interaction from start to finish.

These incremental gains are worth celebrating on their own, especially in a market where even the smallest change in margins can provide a big competitive advantage in the race to deliver exceptional customer experiences.

But what if I told you AI not only helps drive incremental ROI into your existing automation strategy, but actually changes the rules altogether?

Unlike automated tools before it, AI isn’t just equipped to automate inbound requests from customers. Rather, it opens the door to new strategies that eliminate these requests completely.

AI as a tool to eliminate customer effort—not just minimise it

If you recall Dixon’s hypothesis that customer effort is the single most important factor in customer loyalty and retention, eliminating inbound customer requests in the first place would essentially be the Holy Grail of customer experience automation. Not to mention it would greatly reduce contact volumes and operational costs as well.

Pre-AI, automation focused on solving customer queries more quickly once a customer took the first step to reach out. But the customer still had to reach out in the first place – and that required a lot of effort.

In the AI era, organisations have now unlocked new capabilities that make the leap possible.

First, the natural language skills of modern AI models enable organizations to process, transcribe, and summarise all of the conversations taking place across the entire customer experience – regardless of channel or medium. Second, advanced AI models can be pointed at these treasure troves of conversation data to search and analyse them for trends and insights. And finally, rapidly improving access to data visualisation via AI-enabled dashboards helps close this loop, bringing real-time insights to the people who can act on them.

Together, we call this type of solution conversation intelligence. And it’s changing the game for organisations who are committed to removing customer effort from their customer experience.

So, how can these capabilities be used to eliminate or reduce customer effort?

Let’s pretend you work for a bank. After digging into your customer conversation data, you discover two common reasons for inbound customer requests: checking deposits on payday and looking up forgotten account login information.

Identifying these two problems as experience initiators enables your bank to now take a new approach.

In this case, it might be worth changing the account enrollment process to allow for simple two-factor authentication or password retrieval questions to make it easier for customers to solve their password issue at the login screen. Now instead of calling your helpdesk to retrieve the password, your customer can resolve the issue without calling.

As for helping customers track paydays, imagine you took this insight and used it to set up new alert features that allowed your customers to be notified via email when a specified transaction appeared in their checking accounts. No call or painful login to the account portal necessary.  

You can imagine how nuggets hidden deep within the trends of daily customer support conversations could help solve any number of different initiators of customer effort:

• Product issues like malfunctioning parts or software bugs.
• External changes like new regulations or announcements that will leave customers questioning how the updates will impact their situation.
• Channel issues like a customer portal that is down or broken links or forms on the company website.

Preparing for the next wave of automation innovation

Over the last decade, automation has offered incremental opportunities to directly replace live agent workloads. In the generative AI era, this process is moving faster than ever. But it’s up to you whether these capabilities help drive ROI or create new customer frustration where none exists.

The key is customer effort.

With customer effort as your North Star, you can help build customer-centric CX transformations that deliver never-before-seen ROI.