What are the Best Practices for Customer Feedback Analytics Tools?

New business intelligence tools (BI) like Power BI and others have made it even easier for you to understand Customer Feedback data in real-time. Unfortunately, with almost any new and highly effective tool, there are often better ways to use them. Let’s discuss the best practices for Customer Feedback Analytics tools to get the most out of your data.

Please note that while our work at The Daniel Group is focused exclusively on customer experience, much of what I say is helpful for other business intelligence applications we well.

Best Practice – Begin with the End in Mind 

Design your surveys with clearly defined outcomes. One of the benefits of a well-designed survey is the opportunity to collect unstructured customer feedback. But all this unstructured feedback comes with a problem—how to get it structured to identify clear customer messages? In addition, it is challenging to read through comments from even a relatively small number of surveys and determine what signals the customers are sending. Nevertheless, we’ve found ways to provide structure to this unstructured customer feedback data. 

If you are doing phone or digital surveys:

      1. Allow the customer to provide additional feedback
      2. Structure the feedback to be effectively analyzed

To do this, after a scale response question (e.g., Overall Satisfaction), we ask: 

(1) What went well?” (transaction)

(2) What could have been better?  

To do this, in a digital survey, we provide predefined choices for the customer to click and add comments. And, in a phone survey, our researchers tag the verbatim comments with predefined categories. With these tags, our business intelligence tools we use (Power BI), we can quickly and easily identify the critical customer messages. See CX Dashboard example below.

Best Practices Customer Feedback Analytics - Power BI Example

Some of you may ask, “Why not use an AI tool to identify messages and sentiment?” While the AI tools are good and improving, they are still not good enough to be deployed in an industrial B2B environment. The terms are often more technical or specific to a market or industry, making these tools less effective. However, I expect that AI tools will continue to improve and, one day, allow users to apply structure to the unstructured effectively. 

Best Practice – Focus on Customer Behavior

It is helpful to ask customers what they think (even better, feel) about an experience or product. We have found it very powerful to ask about customers’ behaviors. Actions always speak louder than words. While the results from the question we asked are simple, with a business intelligence tool, you can learn even more:

Examples: Referrals

On a number of our surveys, we have asked customers about their referral behavior.

Best Practices Customer Feedback Analytics - Referral Example

The results showed that farmers liked the product, and there was vigorous referral activity. We took the analysis a bit further and used the BI tool to identify territories where the referrals were strongest and those where they were less intense. Since referrals play a significant role in a farmer’s purchase decision, our client used this information to target their marketing plans more finely.

The typical question is, “Have you referred (X company or product) to someone over the past six months?

The response options are Yes, No, Can’t Recall, or Won’t Say.

We found that, depending on the market, between 30% and 40% of customers interviewed said they had referred the company to others. Further, of those referring, more than 90% gave a 9 or 10 on the Net Promoter question.  

The above analysis does not necessarily require a powerful business intelligence tool. However, a more recent example does highlight where a BI tool helps. 

This time, we asked the respondent, a farmer, about a new product and whether they had referred. The results showed that farmers liked the product, and there was vigorous referral activity. We took the analysis a bit further and used the BI tool to identify territories where the referrals were strongest and those where they were less intense. Since referrals play a significant role in a farmer’s purchase decision, our client used this information to target their marketing plans more finely.  

Best Practice – Focus on the data not the Customer Feedback Analytics Tools

Don’t fall prey to the “one additional analysis problem.” Most BI tools are easy to use, even for the less-experienced user. That flexibility and power are great, but it can also sometimes cause users to continually search for the “perfect” way to display information or design one additional view to answer your Customer Feedback questions. Unfortunately, obtaining the “perfect” answer is likely unachievable, and trying to do so may distract you from using the data you have. 

Remember, business intelligence tools are just that, “tools.” According to Merriam-Webster, a tool is something (such as an instrument or apparatus) used in performing an operation or necessary in the practice of a vocation or profession. These tools do not make decisions. BI tools, just like other business tools, help inform us so that we make better decisions.

Summary

Business intelligence tools add significantly to your repertoire of Customer Feedback Analytics tools. They provide quick and powerful ways to identify opportunities and problems. However, best practices for using them require us to design our surveys intelligently first. Make sure you are asking all the right questions to get actionable data. And don’t let yourself become overly focused on perfecting the business analysis tools. Instead, work on getting the data you need and so you can act on it.

For more information on building your surveys and analytics, check out our EBook: Why B2B customer feedback programs fail (and how to make yours succeed).

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