You cannot read about any trends in customer experience, without reading about the huge potential of artificial intelligence (AI). There is not a CX tooling company that is not promoting its AI capabilities. Nebu Data Hub also offers support in creating AI on top of your Nebu Dub InterViewer questionnaires. 

Is AI the way to go in CX research?

In this blog, Zanna van der Aa, for 20 years in the customer experience field, shares her views on why most current AI applications in CX research can lead to wrong conclusions on what your customers really need. And what you can do to maximize the value of AI by tweaking where and when you use it. Let’s start with the CX in a 1 Minute video summary of this blog:

In our recent blog, Improving Satisfaction by Closing the Feedback Loop, we discussed loops to improve the overall customer experience. There are three ways your customer satisfaction measurement program can support a closed feedback loop:

• First, identify your moments of truth and integrate measurement across them.
• Second, automate the process from start to finish.
• Third, act on the information to improve customer satisfaction.

Many companies become unhappy with their customer satisfaction programs because they don't get information that leads to improvement. Using the closed feedback loop approach avoids this situation and helps to build a customer-focused approach to continuous improvement.

Most businesses understand that customer satisfaction is a key metric of organizational success. But many businesses also struggle with making those customer satisfaction metrics actionable and meaningful - internally and externally. Actionability has been a challenge that businesses have struggled with for years, and it is only becoming more challenging as the digital environment facilitates - and also confuses - the consumer's path to purchase.

In the early days of marketing and marketing research, we described the path to purchase as AIDA (awareness, interest, decision, and action). And that worked fairly well for marketers who controlled the information and distribution channels to the consumer. However, in our more complex digital world, consumers can take many paths to purchase, some of which we control and some we don't, and businesses need to understand and monitor all of those paths.

In Part 1 of this topic (Testing the Overall Product/Service Concept), we looked at the very first stage of testing a product or service concept quantitatively: Testing its most rudimentary parameters, and gauging interest in that basic product or service among its target audience.

So let’s assume that you’ve tested the various new cornflakes flavors your client has been considering, via an online survey administered to 1,000 male and female adult consumers across the United States who currently eat cereal at least twice a week. According to your research findings, there is a definite interest in peanut butter-flavored and – surprise! – bacon-flavored cornflakes among these consumers; however, they express very little interest in the chocolate cornflake cereal that was being considered by your client. You’ve presented these findings to the client, and they’ve subsequently stated their willingness to consider manufacturing and marketing both the peanut butter and bacon flavors, given the high interest in both.

Before a new drug is launched, a new variety of cereal is manufactured and stocked on grocery shelves, or a new television sitcom is produced, each of these new market entries will, ideally, go through multiple phases of concept or product testing with the target market, or markets, for the product. And just as some approaches to the conduct of Awareness, Trial, and Usage (ATU) research or Pricing Sensitivity research are more effective than others, there are also ways of constructing concept or product testing that will yield richer, more reliable findings.