Behavioral Science and UX and CX Research

Editor's Note: Concluding this series focused on closing the gap between what people say they do and what they actually do, known as the say-do-gap. Each article will look at a traditional research method (e.g., a brand tracker or a concept test) and show how researchers can overlay behavioral science to the method to get a deeper understanding of the why, as well as the what, uncover unconscious drivers and motivations, and close that say-do-gap. Bring on the learning! Meanwhile, in case you missed it, check out Part 5 of this series focused on behavioral science and conjoint and price testing: Behavioral Science and Conjoint and Price Testing (Part 5 of 6)

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Behavioral Science is often used to answer big complex questions; how do we define value? What role does sustainability play in our purchasing decisions? But to only look at behavioral science through this lens misses the true value of the approach.

Research participants don’t always tell us the truth. That’s not because they’re making an active or conscious decision to withhold, but more likely because we’re terrible at articulating our behavior and motivations.

This creates a gap between reported behavior and actual behavior that can lead to poor outcomes for brands, products, and services. However, there is an answer; behavioral science. In this series, I’ll be sharing how you can apply behavioral science techniques to traditional research methods and close the gap between claimed and actual. 

This week we look at UX and CX Research

How are customers experiencing your product and category? Are they satisfied with the product or service they’re receiving? Understanding how customers experience their purchase journey and the final product is vital in helping you tackle any issues that will affect their propensity to purchase. This is even more important in hyper-competitive categories where most products are fairly similar and feature similar claims.

There is a survey, of course – a continuous customer satisfaction study gathering data every time a customer fills it out. And a few old studies on the user interface of the company’s website and how they experience the company’s products. But can you trust these methods?

Once more the say-do gap is in play. Firstly your sample is distorted. Generally, only people who respond are those who feel strongest, whether negative or positive, about the product will complete a survey. Many brands also allow web designers to create the survey form, which – despite their excellent visual skills – is not the best way to get a good research experience.

Finally, we hit on the sticky measure of the Net Promoter Score, a single metric that can be comparable with the rest of the industry. All the data of your report is based on a 10-point recommendation scale flattened out into three groups; Promoters, Detractors and Passives. 

What can you do?

Traditional customer experience surveys are no way to collect data to make major strategic decisions. If you want data you can make better-informed decisions with, you need to make some changes:

•  Random sampling to get a real cross-section of customers – you can’t just focus on the lovers and haters, but the vast majority in between those two polls.
• Immersive research to recreate the context of usage, of both product and website, with intensive customer listening to help customers open up verbally about their experiences. 
• Monadic nudge testing, trialing new psychological design tweaks to the product and website, and measuring the real changes in customer behavior that result.
• Redesigned satisfaction surveys including System 1 and System 3 word associations, and narrative data collection techniques will get to the non-conscious truth about how customers feel.

Like the brand tracker, we know deep down that NPS doesn’t work for us. There have been several suggestions at conferences from dozens of agencies providing new ways to look at customer satisfaction. Yet still, overwhelmingly, brands still use it. Here the say-do gap is present because of sampling and design issues, and a single metric that doesn’t reflect how we as humans behave with each other.

As a now-famous meme of a customer satisfaction survey has demonstrated; “I need you to understand that people don’t have conversations where they randomly recommend operating systems to one another”.