“Listen to your users” is a piece of advice that you’ll hear everywhere these days thanks to a sea change in modern business canon. The lean methodology for building a great company (that has more or less dominated the entrepreneurial zeitgeist) is to start small, listen to feedback, and iterate from there until you reach product-market fit. This is a really important concept to get your head around because you don’t want to spend time, effort, and resources building something that the market doesn’t want.

However, practically implementing this step into your operational workflow is something that often gets overlooked – and it’s where a lot of companies get tripped up. It’s all very well taking in lots of input, but if you aren’t doing nuanced feedback analysis, then it’s just noise.

In this article, we’ll explore what it looks like to do feedback analysis right, the types that are available, and how AppFollow can help you perform feedback analysis successfully for your mobile app.

What is Feedback Analysis?

User feedback is the communication you’re receiving from your users about their experience with your product or service. It can be in written form like in user reviews or messages, transcribed from customer service calls, or captured from focus groups – and it typically is collected in a rather unstructured format.

Feedback analysis is all about collecting the feedback you’re receiving and organizing it in a way that allows you to spot patterns, trends, and commonalities that might point you to a more effective business response or product development.

This analysis is designed to cut through the noise and break things down into tangible insights that can be acted upon – allowing you to prioritize what matters and ignore that which would just be a distraction.

Why is Feedback Analysis important?

Feedback analysis is important for a myriad of different reasons, some of the most important ones being:

Prioritization

When you take listening to your users seriously you’ll receive a wide array of different comments, requests, and frustrations that you have to deal with. However, with limited time and resources, you have to be able to triage them effectively. Good feedback analysis helps you identify the most common and pressing pain points so you can allocate your resources appropriately – and serve your user base better.

Improves decision-making

A robust feedback analysis process gives you consistent insight into how your offering is being perceived on the ground and provides quantifiable data that can help you make better decisions. You’ll often see market trends or expectations popping up in your customer data well before you see it in your financials – so you can use this process to get ahead of the curve and be proactive rather than reactive.

Increases customer loyalty

When a customer feels that they are being heard, they are much more likely to continue buying from you. Good feedback analysis does the hard work in the background so that you can show tangibly that you listen to users and strive to make their experience better in every way.

Unlocks new revenue opportunities

Customer feedback is a priceless treasure trove of new feature ideas just waiting to be discovered. Feedback analysis helps you quickly comb through them and find those with the most opportunity, so you can unlock new revenue streams and ideas to further your differentiation.

These are just a few of the reasons why feedback analysis is so important – and the companies who take it seriously are the ones who are going to thrive in the fast-changing digital landscape.

How can you analyze user feedback?

There are two main ways you can do feedback analysis, and we’ll deal with each one in turn.

Manual Analysis

The most grassroots way to do feedback analysis is doing it manually. Transfer all your feedback from the source into one place such as a spreadsheet. From there, you can read through each piece of feedback and categorize it into different topics and types. For example, you might separate new feature requests, UI complaints, bugs, comments on customer support, and so on. Each topic cluster can then be analyzed by the appropriate employee or team to assess what action should be taken and prioritised.

You can take this one step further by doing a rudimentary form of sentiment analysis where you score each piece of feedback on a scale of 1-10. Calculating an average here can then give you some quantifiable measurement of how your company is performing based on your qualitative customer data.

Automated Analysis

A more savvy way to do feedback analysis at scale is to use feedback analysis software that can automate this collecting and categorizing process. Such tools use AI and machine learning to evaluate thousands of pieces of feedback in seconds and then categorize them appropriately using tags and topics. This is incredibly valuable when you have hundreds or thousands of reviews or feedback and doing it manually would simply be too time intensive and costly.

Feedback analysis software also offers various quantitative scores surrounding sentiment, commonalities, and semantic trends that can be invaluable in planning the next steps for your digital product offering. It can be fully integrated into your current workflows, providing insights without disrupting the technology stack that you’ve already built. This makes it easier than ever to start getting results.

The types of automated feedback analysis

There are three main types of automated feedback analyses, which unearths different types of user feedback:

Sentiment Analysis

This is a quantitative score referring to the attitude that your users have towards your product and your company. By analyzing how positive or negative the language is in a piece of feedback, you can get a good sense of how customers perceive your product. We’ve written about sentiment analysis  in more detail here.

Phrase Analysis

Looking at specific keywords used in reviews or feedback can help you to understand the language users connect with your product and create a deeper understanding of what they think. By listening to the words that are used to describe their pain points and frustrations, you can better serve your user base through more savvy marketing and better product development.

Semantic analysis

Clustering feedback into topics demonstrates what is most important to address and what can be left for later. It also helps to reroute specific feedback to different departments and individuals to maximize the impact across the business.

Each of these offer a wealth of information and context that can make the world of difference as you seek to continually improve your company’s offering.

Feedback analysis with AppFollow

Here at AppFollow, our feedback analysis tool helps you to process and categorise all your app reviews. You will also be able to analyze them in a way that makes finding crucial insights and responding to feedback easy and effective.

Understand your users’ perceptions through detailed sentiment analysis, monitor your reviews as they come in, and tackle them effectively through our app review management suite of tools, or transform your entire operating strategy through savvy workflow automation.

If you’d like to explore how this could work for your app, be sure to get in touch with us. We’d love to hear more about your vision for the future and see how we can play our part in turning it into a reality.