Getting to the “Why” of CX Design: Measuring Effort to Drive Change
July 18, 2018
In September 2009, Simon Sinek stood in the middle of a stage in a cozy theater in Puget Sound, Washington and delivered one of the most iconic TED talks to date. In his presentation which has been viewed online over 39 million times, he argues that “why” is a more compelling and powerful question than “what.” He frames his argument by describing why and how certain CEOs, politicians, civic leaders and inventors were more successful than others. Although customer experience (CX) was a nascent space back in 2009, it is easy to extend his argument to the customer experience space today.
In the CX space, understanding why customers feel a certain way or why they want certain features is more valuable than understanding what they say that they want or what they think they want. After all, any good designer or product manager will tell you, what you want is not necessarily what you need! By understanding why, CX practitioners can empathize with their customers and design stronger solutions to customer issues. This logic is encapsulated in the Design Thinking approach which establishes “empathize” as the initial step in the design process, a full 4 steps (if not more) before implementation. Design Thinking and empathizing isn’t just altruistic though; research has shown that for every percentage of sales invested in product design, profits rise on average 3-4% every year for the first five years.
Why is closely tied to the concept of effort, i.e.: how easy or how difficult was it for a customer to conduct business with you. We’ve all seen a customer review online that made us physically cringe as we read about the effort she was asked to exert as she jumped through hoops trying to resolve her issue. Looking at your customer feedback in search of the why is looking in hopes to not just to make that journey cringe-free, but to make it easy and effortless.
On paper, this approach sounds appealing and doable. The challenge historically, though, is that why has been difficult to measure leading many to fall back into the comforts of reporting on what. For one, in the world of data, the “whys” tend to be more qualitative than quantitative which can make them unfit for many traditional data analysis techniques. Articulating why is also a difficult task. You can blame evolution for this unfortunate hurdle. The part of our brain responsible for processing why, called the “reptilian brain”, developed hundreds of thousands of years before humans developed the neurological components necessary to produce language. We lack the vocabulary to express why our body acts or responds in certain ways. Think about a “gut feeling” you had recently. No words could explain why you felt that way but you could explain what you were or weren’t going to do in response to that feeling. When we don’t have the words to express why, we lean on our neocortex to articulate what.
We recently released our new Clarabridge Effort Score as a tool to overcome the challenges in measuring why and to cultivate the opportunities in analyzing it. If empathizing with your customers wasn’t enough of an incentive on its own, effort is a proven leading indicator of loyalty – far stronger than NPS or satisfaction scores. According to Gartner, customers are four times more likely to become disloyal to a brand after a difficult, high effort experience with customer service, but are 94% more likely to repurchase when their experience is effortless.
You may be surprised by what you find when you look at your feedback starting with the why (effort) instead of the what (satisfaction). For example, in my latest webinar, I looked at a half million reviews of kitchen appliances from sources like Lowes, Home Depot, Sears, and Best Buy using our CX Analytics tool. When I approached that data in the traditional way, leading with what via analysis of the star rating and basic topics, the analysis lead me through multiple steps and presented me with a conclusion that sound was a significant issue for one particular refrigerator model.
However, when I approach this exact same inquiry seeking the why first by using our new Clarabridge Effort Score and our proven Emotions models, the analysis lead me in a very different direction. While the same refrigerator model stood out, it was the design of the door latch rather than the sound of the ice maker that revealed itself as the most challenging and frustrating aspect of the product. This lead was further validated by comparing the impact to the review rating for these two aspects of the refrigerator. When customers describe their effort opening and closing the door, their satisfaction was nearly 20% lower than when they were simply complaining about the sound.
While the manufacturer probably should address the complaints around the sound of the refrigerator, fixing those issues will not have the most significant impact to loyalty. Had I started with what instead of why, I would have incorrectly advised this company to prioritize the less impactful improvement.
Analyzing effort helps us return to the principles of Design Thinking. By understanding that customers were having difficulty open and closing the refrigerator door due to a faulty latch, I can easily empathize with the user and immediately forward the issue along to my design team for improvement. If I only knew that ratings were low for this product or aspect of the product, I still don’t know what to fix. Effort helps us pinpoint problems and quickly conceive empathetic solutions.
The power of why, empathy, and starting with customer effort can really transform the way you look at your customer’s journey. If you’d like to see this in action, you can see a demo in this webinar. You can also talk to your account team if you’re interested in participating in our Beta program for the Effort Score. We are anticipating a full launch in the Fall.
Ellen Falci is a Principal Product Manager at Clarabridge. She is currently responsible for Clarabridge’s NLP and Data Enrichment strategies. Throughout her time with Clarabridge, Ellen has worked with 35+ customers, many among the Fortune 1000, to develop innovative solutions to language, sentiment and social media data challenges within the context of linguistics and technology theory. She is currently focused on leveraging Clarabridge’s NLP engine to enrich data in creative ways that deliver high value content for the purpose of customer experience management. Ellen is an alumna of the University of Virginia where she studied Cognitive Science, Spanish Linguistics and Computer Science. She also recently completed her M.A. in Communication, Culture and Technology at Georgetown University.