Understanding the Power of Feedback Analytics 

Feedback analytics is the process of collecting, analyzing, and interpreting feedback data from various sources, such as customer surveys, social media comments, online reviews, and customer support interactions. When used effectively and in the right context, feedback analytics can transform your ability to make data-driven decisions—as long as it’s integrated and balanced against other input. Critics of feedback analytics might argue that it’s possible to rely too heavily on customer feedback. They’ll say things like: 

• Doing this will stifle innovation and create a conservative, risk-averse culture (because you don’t want to upset customers). 
• We’re focusing too much on incremental improvements rather than pursuing breakthrough innovations. 
• A myopic view of customer feedback is stopping us from developing a holistic understanding of the market. 

None of that has to be the case, though. The power of feedback analytics lies in using it contextually, appropriately, and with external validation. 

Five ways to use feedback analytics to drive innovation and growth 

Personalization and customization 

91% of consumers are more likely to shop with brands that recognize, remember, and provide relevant offers and recommendations. You see this in practice everywhere. When you open YouTube, Netflix, or Amazon, your homepage is filled with suggestions tailored to your preferences. You rarely have to invest effort in finding the next show to watch or item to buy, because each of these businesses already knows your likes and interests. 

Feedback analytics typically focuses on things like analyzing unstructured text. While capturing data on a customer’s past behavior or product usage don’t quite fit that category, these things are like the two wheels on a bicycle: lose one and you’re stuck. Say your customer bought a sweater from your ecommerce store. Great! You now know they like sweaters, they wear a women’s medium, and they like the color green. But then a month later they leave a review: “This sweater was the perfect Mother’s Day gift! My mother thinks it’s super cozy, and she loves the shade of green it comes in.” 

Oops. 

You’ve just learned—through feedback analytics—that all of the “personalized data” you gathered on this customer was inaccurate. Each of those things apply to their mother, not to them. ”Analyzing what your customers have done and what your customers have said together is the only way to get the full picture of who your customers are.” 

Predictive analytics 

Feedback analytics can help you anticipate customer needs and behavior. It’s powerful, but predictive analytics isn’t an exact science and usually requires leaving room for error. Combining customer feedback with purchase info, browsing history, or engagement metrics can fill in the blanks. If you’re a SaaS company and a customer shows behaviors that indicate decreased product engagement. Not good. Then—in a totally different tool—you also see they’ve reached out to your support team multiple times about product issues. 

Really not good. 

Combining and analyzing these two sources of data through feedback analytics can give you the information you need to make changes proactively and retain that customer.

Real-time feedback 

Agile environments thrive on fast and frequent iterations. Sometimes that means it’s valuable to stop the roll-out of a new feature because of a technical issue that wasn’t caught during testing. Other times that means capitalizing on a great opportunity to produce more of a product or extend an event because your customers have received it so positively. The only way to react quickly enough in these situations is to use feedback analytics. But analyzing feedback manually is incredibly time-consuming. By the time you know how to react, it’s often too late. Gathering real-time insights through a modern customer feedback analytics tool enables you to make those impactful decisions quickly and confidently. 

Data-driven decision-making 

There’s a huge correlation between data maturity and business success. McKinsey suggests that data-driven organizations are: 

• 23 times more likely to acquire customers. 
• 6 times more likely to retain customers. 
• 19 times more likely to be profitable. 

And it’s easy to understand why—data is the ultimate objective validation of anything you want to do. Feedback analytics enables you to access a giant pool of data that is otherwise only shared as individual anecdotes or in a broad statistic that doesn’t teach you anything. Imagine the wealth of information in your customer surveys, support inbox, or online reviews. 

If you aren’t combining and analyzing that data effectively, you’re forced to make decisions without the complete picture. It’s easy to talk about wanting to be “data-driven” as an organization. But the only way to tap into the power of feedback analytics is to invest the time, effort, and money to building out a great analytics program.

Idea generation and validation 

While it’s true that customer feedback might not provide you with that innovative and unheard-of solution that will make or break your business, customers are very good at telling you what isn’t working. our customers are experts in their own paint points. And feedback analytics teaches you how to listen. 

Ten customers might send you ten different feature requests explaining how that feature would help them. At first glance, these seem unrelated. If your support team manually categorizes feature requests, they might list them all as different features. But what if there was an overlap between the pain points each customer experienced that made them send in the request? 

Feedback analytics can easily show you these connections because it helps you identify patterns in your huge amount of customer feedback. Your team can use it to generate new ideas and validate customer interest, enabling you to make smarter bets. You might say it’s the difference between playing roulette—a game of total chance—and playing poker, which involves both chance and skill. 

Feedback analytics unlock deeper customer insights 

Your organization is probably already comfortable working with data and tracking essential business KPIs. Once you’ve laid that foundation, you’ll find that feedback analytics can provide a depth of understanding that’s hard impossible to achieve without it. It helps you dive into the wealth of unstructured data available to you, enabling you to understand nuances, identify emerging patterns, and uncover root causes. 

Feedback analytics makes you customer-centric by giving you access to your customers’ thoughts in their own words. It removes bias and lets you combine quantitative data with qualitative insights. 

Ultimately, feedback analytics gives you the “why” behind the “what.”

About the Author

Ryan Stuart is the co-founder and CEO of Kapiche, a new wave insights and analytics platform for customer insights and research teams. With more than 15 years experience in Natural Language Processing and its application in Customer Experience, Ryan is deeply passionate about using data and technology to make better business decisions.

Kapiche is revolutionizing customer intelligence to create a world where organisations make human-centric decisions & every voice is heard. Kapiche has grown into one of the most in-demand technologies in the Australian customer insights space, and has recently launched into the North American market with customers that include Zappos, Nextdoor, Cox Communications, Toyota, Target, Wolverine Worldwide, Colorado Tech University and Basalm Brands. 

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