Earning an “A” for Effortless: Uncovering Insights in Customer Effort


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When it comes to brand loyalty, customers punish companies that fail to live up to their promise, but reward companies that deliver the best experience. Increased effort – whether that means waiting on the phone for over an hour, explaining a problem to multiple agents, or failing to resolve an issue independently – frustrates customers and encourages them to find alternative options. The amount of effort customers exert as they interact with a company’s products and services is therefore a crucial driver of overall satisfaction and loyalty. In addition to being a strong predictor of loyalty, effort can also reveal pain points within a customer’s journey, even if overall sentiment towards the brand is positive. For companies looking to reduce customer churn and improve overall experience, measuring effort cannot be overlooked. In fact, a Gartner study suggests that customers are four times more likely to become disloyal to a brand after an effortful experience with customer service.

It may seem intuitive that a laborious interaction between a consumer and a brand would leave a negative impression on that customer and impact her future decisions. But when looking at vast amounts of customer feedback data spread across a wide variety of channels, it is often harder for brands to identify the level of difficulty in customers’ experiences, as well as when and where in the journey these difficulties occur. This is where the effective use of artificial intelligence can provide clarity.

Through the use of text analytics and natural language processing (NLP) techniques, brands can analyze immense amounts of customer feedback across various channels for the words and phrases that indicate effort. By training these algorithms to understand context, brands can also quantify and track the level of effort of each experience and interaction through the eyes of the customer in their own words rather than asking for retroactive feedback that relies heavily on recollection (ie through a survey days later). Not only will this allow for insight into granular problems, but it can also be aggregated and tracked over time to reveal larger, systemic issues that require attention.

For example, let’s imagine a car rental service has recently implemented this kind of analysis in its contact center. Because the company has invested in the right AI technology to surface issues with high effort scores, it immediately learns that multiple customers have complained about how difficult it was to change their reservation. Phrases that are highlighted could include “jump through hoops,” and “was on hold for 30 minutes.” This insight provides an opportunity to assuage a customer pain point while continuing to track whether the effort scores around changing reservations decrease over time, thus indicating a more seamless experience for customers.

To gain a more complete understanding of its customers’ concerns, let’s say the car rental service then analyzes the feedback it is receiving over its social media accounts. The company quickly sees that the effort score around returning cars is very low. Customers indicated the process was “super convenient,” and “a total breeze.” There were a few instances, however, where a specific location was getting higher effort scores – customers reported issues such as “waited in line forever” and “nearly missed my flight home.” Now our imaginary company does what we hope all brands do when they are alerted to an ongoing issue – they intervene and track for improvement over time.

By measuring effort through machine learning technologies, brands are directed to very specific points of friction within a customer’s journey, and this better informs them on how much they need to change to improve the overall customer experience.This becomes especially important for customers who still express an overall positive sentiment towards a brand despite a bump in the road. Keeping brand advocates on your side is essential.

Brands have more opportunities to engage with customers than ever before, so why keep asking them to put in the effort to tell your business how it’s doing with surveys and feedback forms? If brands want to know where they can improve the journey and keep customers loyal, they must take a closer look at the mountain of data that exists in their back pockets – hours of phone call transcripts, miles of social media feedback, and seemingly endless logs of web and SMS conversations (with agents and chatbots alike). Through the power of artificial intelligence, we now have the ability to analyze these sources at scale. By putting in the effort to measure customer effort, brands will be amazed at what they can uncover.

Fabrice Martin
Fabrice Martin is Chief Product Officer at Clarabridge. Fabrice has 20 years of experience in entrepreneurship, product management, marketing, and enterprise software sales, with specific domain expertise in SaaS/PaaS, data visualization/discovery, Business Intelligence, and analytics for marketing and contact center operations. Prior to Clarabridge Vice President of Program Management at MicroStrategy. Fabrice holds a Computer Engineering degree from ITESM CEM in Mexico and an MBA from Georgetown University.


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