“We don’t know our customers…well enough.”

We first heard this phrase in 2012 from the then CEO of Schindler, one of our main customer companies.

It was the spark that ignited our curiosity about the world of customer experience.

It is a common mistake to assume that you know your customers.

Losing customers or lack of repeat purchases cannot come as a surprise.

How can we solve this problem?

The answer exists but it is hidden in the data–and companies are often overwhelmed by the enormous amount of business data collected.

We are all sitting on a treasure trove of business data, but it has no value unless we process it properly to enable us to make better decisions.

Identifying the reasons why customers buy or change their minds is necessary to enable management to make informed decisions about what action to take, and discovering these reasons is the result of qualitative research.

Quantitative data are based on numbers and are measurable. Qualitative data are based on interpretation, description and language. Quantitative data tell us how many, how much or how often certain events happen. Qualitative data help us understand the why and how of certain behaviors.

Operating mostly in B2B, we prefer telephone interviews based on open-ended questions that do not influence the respondent, conversations and focus groups, while I do not think web or digital surveys can be useful, when trying to learn more about customers, because they report information about the how much but not the WHY of behaviors.

Of course, when you are dealing with thousands of unstructured data you need the help of text analytics software based on intensive use of artificial intelligence to shape them and be able to identify customer stories hidden in the data, but here is finally some good news: this is now possible thanks to the introduction of Natural Language Processing (NLP), which has made huge strides in the analytics world.

We very much believe in the power of Visual Reporting tools, which enable the identification of organizations’ weaknesses and strengths and allow for easy shared identification of actions to improve the Customer Experience from huge amounts of data that are not easy for management to read.

Finally, we must never forget that we are all human beings (even managers!) and data, by its very nature, fails to elicit emotion. Where data scientists often fail is in finding emotional ways to communicate the customer stories derived from the great work of analytics.

Data in this case must become storytelling.

Here is the importance of story telling, both inside and outside the organization.

It is not data, but the story of an individual customer that you can empathize with, to create emotional connections.

This is the perfect way to get the word out about your brand and sustain the Brand Promise, because the ultimate goal of CX is to make sure the Brand Promise is kept!