Thomas Hawk

Using Data To Understand The Cause Of Negative Customer Feedback

Every brand faces negative feedback on occasion. It’s impossible to create a perfect customer experience 100% of the time for 100% of your customers. In fact, many complaints or negative reviews may appear to be serious to the person making the complaint and not so important to other customers – everyone has a different view based on their expectations of the brand and expectation of service more generally.

But how do you understand the true cause of any negative feedback? In some cases the cause may be something that requires little change in your customer experience (CX) strategy, such as when the customer complaint appears to be focused more on the customer fishing for compensation, or the complaint is exaggerated by anger. It’s important to be able to recognize these times, when the customer may not actually be right, and the more general assumption that a complaint is valid. But how do you really get to the heart of why you are receiving negative feedback?

In some cases it can be extremely hard to determine the cause of negative feedback. You might see that there is a negative social comment or an escalation to a supervisor in the contact center, but the actual trigger may not be defined or clear. Machine data specialists Splunk recently published an excellent article exploring this exact question of locating the underlying cause of negative feedback. They focused on measuring the customer journey and using Net Promoter Score (NPS) as the main metric to determine whether a customer was satisfied or not.

The Splunk example focuses on a customer using a subscription based service. They are sent an upgrade offer by the brand, which could easily be rejected by customers who have no interest, but if the customer accepts the upgrade then there are three possible negative outcomes to the journey that follows:

  • Complaint (process ended with a complaint);
  • Not eligible (customer tried to apply for their new offer, but during the process it was established that they did not meet the eligibility criteria, or they failed a credit check); and
  • Error (customer experienced an application or system error).

By systematically analyzing each negative outcome so the type of customer, product, and purchasing situation is compared to the outcome it is possible to use the data to determine what is going wrong with the customer journey. This has significant implications for delivering an improved customer experience:

  1. With the data it is possible to pinpoint the exact paths (and with further drill-down, individual elements of a path) that are most likely to cause negative customer experience – which can be monitored to ensure optimum performance.
  2. Alerts can be created so the known types of customer experience dips can be anticipated – when an alert is created the problem can be swiftly addressed while it is still live.
  3. It is possible to identify which customers have had a negative experience (even when they have not provided feedback), and this then allows for proactive activity. Even if a customer has a poor experience, the insight into why they didn’t have a good experience can allow your team to turn that negative into a positive by stepping in proactively and fixing the issue.

This data-driven approach can deliver more than just insight into why you are getting negative feedback – it creates the opportunity for negative CX to be reversed by letting your team know which customers need attention, and why. That’s not just insight, that’s a powerful customer retention tool that has a measurable value.

Let me know what you think about analyzing negative feedback and how it’s possible to determine what customers really mean when they complain. Leave a comment here or get in touch via my LinkedIn.

Photo by Thomas Hawk licensed under Creative Commons.