Plenty of companies have their ear to the ground and their eyes on the stats that drive customer success. After all, insight into external opinion is key to improving all aspects of your products and process. Opinions that come in from customers, and even prospects, help businesses decide next steps, cultivate new offers, make improvements, and decide what is or isn’t working.Positive accolades give the go-ahead to continue down the current path. They assure companies that good choices are being made throughout by marketers, product developers, and support teams. They support the mentality of “if it ain’t broke don’t’ fix it” and encourage forward movement. However, as gratifying as it might be, hearing nothing but praise for how well your organization’s products and services are doing can be as damaging as hearing nothing at all.
While negative feedback is not the commentary that businesses typically seek out or crave, it can act as a helpful warning sign for bigger issues. Negative feedback signals that something is amiss. You might be hard pressed to find a company that gives “Atta boys” for falling customer service rankings, poor reviews or high complaint rates. But, negative feedback and customer complaints can serve as a catalyst for positive change.
Where Does All That Feedback Go?
According to Forbes, “Collecting, correlating and analyzing data from customer interactions across channels is the key to transforming the customer experience from nightmare to nirvana.”
It’s important to develop a strategy that mines consumer feedback from more than just online comment boxes or the more animated calls made by particularly vocal users. Consider the value of responses (or criticism) contained in your social media channels. Picking up on the nuances of seemingly minor grievances posted on social media can lead to bigger picture themes. Good insight can even be gleaned from a gripe or challenge overheard by an associate in marketing.
The challenge is getting these disparate sources of information to paint a complete picture.
What do your support teams do with customer complaint data after they hear it? Do these complaints get circulated for review just to end up in the “negative feedback” folder? What about feedback gained by those not necessarily on the front lines of customer support?
While collecting positive - and negative - feedback is a first step, the real value comes in seeing themes and patterns so you can positively act on the data.
Using Data to Paint a Complete Picture
Getting genuine insight from external opinion helps organizations get a sense of when things are starting to go off the rails. An even better approach is having a process in place that lets you take action before the derailing begins.
Imagine looking at each piece of positive or negative feedback as a historical data point. Then, feeding each data point into a computer, which analyzes the data as it comes in. As more and more data points are gathered, the machine learns from the data and identifies patterns to predict future behavior.
This process of using feedback as data points to learn from and act on is the underlying power of machine learning. Machine learning is a predictive analytics approach that allows computers to automatically learn by example, continuously adapting to new information to provide a proactive worldview of customer needs, desires and actions. It analyzes historical data – like negative reviews, comments or feedback - to look for patterns of behavior like:
- Likely customer churn
- Typical customer support issues
- Possible transactional problems
Machine learning lets you leverage the ability of analytics to make connections between customer groups, demographic segments, their needs and their omnichannel journeys to provide a complete context within which to inform decisions.
The more inputs, the better the predictive outcomes, which makes complaints from customers as valuable as praise.