What is Feedback Analytics?

What is Feedback Analytics? A Guide To Get Started

Running a company without clear customer insights is like driving a car to a new destination without a GPS. You’ll be moving forward, but it might not be in the right direction. You can make educated guesses and might even get lucky, but the odds aren’t in your favour.

That’s where feedback analysis comes into play. When you’re building a business and serving customers, feedback analytics keeps you on the right path. A crucial part of this process is the 'customer feedback loop,' which helps in continuously improving products and services based on feedback.

Before we dive in, here’s a quick primer on feedback analysis.

What is Feedback Analysis?

Feedback analysis is the process of collecting, analyzing, and understanding customer feedback to uncover insights into what your company, product, or service is doing well and what could be improved.

Doing this analysis is crucial for understanding customer satisfaction, identifying areas for improvement, and making data-driven decisions to enhance your customer experience. Measuring customer feedback often starts by looking at NPS and other CX survey data, but it’s far more than that. Your feedback could also include product reviews, the data in your support team’s inbox, and comments on social media platforms.

The ROI of Feedback Analysis

All of this customer feedback can have a big impact on your business—if you can learn from it. And that’s why feedback analytics is so important. Every business that’s serious about growth needs to invest in building out processes to consistently analyze feedback data.

A customer feedback loop can enhance the ROI on any feature release, or market test by enabling continuous feedback and improvement. Key benefits include:

  • Enabling you to deeply understand your Voice of the Customer (VoC) data. You’re likely already collecting mountains of data. Implementing dedicated feedback analysis software to analyze it will lead to a better understanding of your customers. Rather than surface-level impressions, you’ll be able to understand what’s most meaningful to your customers. You’ll know why your NPS score is down or why your customers aren’t as satisfied as they once were.

  • Ability to analyze everything in-house. When you analyze customer feedback in-house, you benefit from the three C’s of a great feedback analysis framework: control, customers, and context. Relying on a third-party for feedback analysis increases the risk of misinterpretation. External feedback analysis can also lead to slower turnaround times and less nimble decision-making. These factors can seriously hamper a growing business.

  • Identifying actionable data. Data is always good to have, but it’s no use if you can’t determine what action to take from it. For example, if your CSAT score has been dropping consistently over the past few months, you’ll never be able to determine what to fix simply by looking at high-level scores. Feedback analysis empowers you to quickly and effectively analyze the open-ended feedback you’ve received and easily investigate concerning trends amongst themes and topics customers are referencing, along with their verbatim responses. This means you’ll be able to find out exactly why your CSAT dropped and what to do about it.

The risks of not implementing feedback analysis

Your business depends on your customers. If you don’t implement effective feedback analytics, your customers will be the first to experience the negative effects. Ignoring the 'customer feedback loop' can lead to missed opportunities for improvement and customer dissatisfaction:

  • Losing trust. Your customers spend their precious time writing reviews or contacting your support team. When you ignore their input or fail to resolve their concerns, you lose trust. Your customers never want to feel like you’ve turned a deaf ear to them.

  • Poor product. It’s next to impossible to build a truly valuable product for your customers if you’re not listening to them. You’ll end up with a crummy version of exactly what they need or a great version of something they don’t. Either way, if your product isn’t solving the right problem for your customers, you can bet they’ll churn quickly.

  • Losing money. Speaking of churn, the obvious impact of high churn is lost revenue for your business. That means your future revenue goes up in flames, but since customer acquisition is far more expensive than customer retention, it also means you’ll be burning more money on marketing and sales.

  • Bad word-of-mouth. The more you don’t listen to your customers, the more likely they are to tell others about their experience. You may not see this negative sentiment in your product reviews, but that doesn’t mean it’s not happening. 96% of unhappy customers don’t complain directly to the company, but simply leave and never come back. This “quiet abandonment” can damage your reputation through negative word-of-mouth.

At the end of the day, choosing not to invest in customer feedback analysis is foolish. It’s the business equivalent of burying your head in the sand. While there are certainly different strategies and tools you can use to analyze customer feedback, every business ought to be investing in understanding their customers better.

Where to find the customer feedback for analysis

An effective feedback analytics framework helps you make sense of the never-ending volume of feedback data you get from your customers. But where do you actually find customer feedback?

With a tool like Kapiche, you can pull data in from virtually anywhere. Here are some great places to start looking for meaningful data on the customer experience:

  • Customer surveys (NPS, CSAT, CES, in-app surveys)

  • Customer reviews (Google reviews, product reviews, mobile app reviews)

  • Support tickets (including call and chat transcripts)

  • Social media

  • Sales interactions (sales call transcripts, internal customer notes)

  • A dedicated feedback form or email inbox

  • Customer research (user interviews, beta testing)

With a little bit of creativity and a healthy dose of curiosity, you can discover customer feedback in many different places.

Challenges to customer feedback analysis

Let’s be real: customer feedback analysis has its challenges.

The ROI of doing it right is fantastic, but it can take real effort to get your organization to a place where the data you’ve collected is meaningful. Here are the five biggest challenges you’ll face, and how to solve them so you can get the most out of your qualitative data.

Feedback analysis tools

1. Using inconsistent feedback analysis tools

Customer feedback data can be all over the place. Your product team may use one tool or have their own process, your support team may use a different tool or process, and your mobile app team might use a completely different approach. Siloed data is incomplete data, and incomplete data makes effective feedback analytics next to impossible.

There are two possible solutions to this: Get all stakeholders to adopt the same tools and processes. Or set up an analytics tool that brings it all together for you.

Either solution can be effective, but using a tool designed to analyze feedback from multiple sources is often far easier. The key is to have a central location—a single source of truth—that all areas of your business can rely on for complete access to ALL customer feedback, not just some.

2. Feedback analytics with complex languages

Language is complex. People can communicate one idea in many different ways. You probably have 10 different customers that find your product hard to use, but they all say it differently. And if you add multiple languages on top of that, analysis can become messy.

When analyzing customer feedback, identify common words or phrases that you see throughout the data. Search for these, and then categorize all similar responses together. Tools like Kapiche do this automatically for you, but you can also dump all the data into a spreadsheet and spend time analyzing feedback manually.

3. Quality issues with data analysis

The feedback you get from customers will always be a mixed bag. Some feedback is specific, making it easy to immediately determine what action you could take to satisfy that customer. But often feedback is too vague to be very helpful.

One way to improve feedback quality is to get smart with your customer survey design. If you find that a specific question in your analysis consistently yields little to no usable feedback, tweak or replace the question to see if it drives more useful input.

You’ll never be able to completely eliminate unhelpful or vague feedback. That means it’s also a good idea to create a process—manual or automated—that weeds out feedback you can’t use, cleaning up your overall data quality.

4. Slow-to-action feedback analysis recommendations

One of the biggest challenges when collecting and analyzing customer feedback is prioritizing action. Analysis paralysis is a real thing, especially when there’s so much data to sift through.

Implementing a customer feedback loop can help in quickly identifying and acting on feedback trends. When you identify a common trend in your feedback and then don’t take action, it’s like you’re ignoring your customers. So start with low hanging fruit. Identify one or two actions your company can take quickly, things that would require little effort. Help your company knock those things out quickly, then look for bigger challenges.

Action begets action. Once you’ve started responding to customer feedback you’ll find it easier to continue moving with that momentum.

5. Negative customer feedback analytics

While it can be painful, negative feedback is a golden ticket to deeper customer insights.

We’re all human. We can logically understand that negative feedback can help us understand where we need to improve—but we still don’t like hearing it. It’s painful to pour time and energy into building a product and then have a customer spit venom about it.

Feedback for customer insights

How negative feedback benefits customer insights

If you learn to embrace the critique, negative feedback is like your customers handing you insights on a silver platter. They’ll often tell you exactly what you need to do to make your product better. And while it might be clothed in frustration or angry language at times, negative feedback is incredibly helpful. A customer feedback loop can help in systematically addressing negative feedback, ensuring continuous improvement. It benefits your customer insights in four key ways:

  • Product evolution. You’ll never be able to build the perfect product out the gate. You need constructive feedback or your product can’t improve, which means limited growth potential for your business.

  • Identify areas for CX improvement. Your product improves from negative feedback, but so does your customer experience (CX). It can be hard to know what changes to make to craft a more meaningful and positive customer journey. Negative feedback makes it obvious where you can do better.

  • Identify “bad fit” customers. Your product, service, or brand won’t be a good fit for everyone. And that’s okay. “Bad fit” customers take your team away from selling to and supporting customers who love what you do. Negative feedback helps you understand who your product isn’t meant for, enabling you to be more targeted in everything from marketing and sales to customer success and service.

  • Humanize your brand. Considering negative feedback forces you to be more empathetic and introspective. When you take negative feedback seriously, it shows your current and future customers that your company is a group of humans that are genuinely trying to solve their pain points. It also helps your company to see your customers as human as well…and that’s a reminder we all can use regularly.

How negative reviews help your business grow

Research shows that 93% of customers say online reviews makes an impact on their buying decisions. But here’s the catch: when your customers consider purchasing from you and see negative reviews, it can actually enhance your company’s credibility.

Sound counterintuitive?

Think about how you feel when you see a product or service that only has five-star reviews. It’s too good to be true, right? And it almost always is. You don’t know for sure what’s going on, but it’s easy to become skeptical and assume that the company is doing something unethical to only solicit positive reviews.

But when your customers see a mixture of reviews on a review site—and your company responding positively to negative reviews—they will think, “This product may not be perfect, but that company cares.” This invites them to continue looking and gives your product the opportunity to shine.

Paying close attention to negative feedback also allows your CX team to turn bad experiences into good ones, which can create brand advocates. Free marketing!

One feedback analysis solution for all 5 challenges

An easy solution to all five of these issues is to use technology to solve them.

Technology can enhance the feedback loop by addressing various feedback analysis challenges, making it easier to gather, analyze, and act on customer feedback efficiently.

Software can help with each of these areas: centralizing your customer data, weeding through language complexities, discarding unhelpful data, preventing human biases, and prioritizing possible action based on things like volume, customer sentiment, or impact on satisfaction scores.

It’s not magic, but finding the right tool to help with your data analysis is like making a new best friend.

When you’re doing everything manually, the volume of feedback can be overwhelming. Your ability to identify patterns is limited by human coding consistencies and error. Once you put a great process in place using data analysis software, the volume of feedback is no longer a barrier. The more data you have, the more confident you’ll be in the insights you generate and any next steps you recommend.

Feedback analytics strategies and methods

There are several analysis methods you can use to determine what actions your customers want you to take. Each of them brings different pros and cons to the table.

By integrating different strategies into a feedback loop, you can ensure continuous improvement based on real-time customer insights.

Manual feedback analysis

Tagging, categorizing, and coding are all similar solutions designed to solve one problem in analysis: easily understanding open-ended feedback.

Open-ended feedback is one of the most valuable types of data to an organization. Traditional methods of analyzing feedback rely on tagging, categorizing, and coding to make open-ended feedback more digestible. A real-world example of this is when your support team categorizes a ticket based on the type of issue or the product it’s related to—making future analysis of those issues or that product easier. Manual analysis can be a useful part of your feedback process, providing detailed insights that help improve products and services.

Pros of tagging, categorizing, and coding

Categorizing customer data brings organization to the chaos that can sometimes occur with open-ended feedback. The more you categorize around key themes, the more you’ll get to specific insights. When you add a code for something like “slow mobile app” or “iOS 15.5” you can then use traditional quantitative methods to analyze qualitative data.

Cons of tagging, categorizing, and coding

The cons of tagging, categorizing, and coding are pretty apparent. For one, it’s typically a very manual process, which means it’s time-intensive and therefore expensive. Customers don’t consistently use the same language either, so maintaining and updating your tags or codes becomes a concern.

You can also have disagreements between employees or leadership about which codes to use or how many codes to have.

More importantly, there’s a meta-question that is hard to solve: Is tagging or categorizing customer feedback even valuable work? Is it worthwhile to know that 30% of your customers think your app is slow, if you don’t have a way to dig deep into the “why” behind it?

Sentiment feedback analysis

Humans love to express emotions in writing. That’s where sentiment analysis comes in. It uses Natural Language Processing (NLP) to automatically detect customer sentiment within text.

Pros of sentiment analysis

Sentiment is a great way to measure how your customers feel about your brand and track how those feelings change over time. Sentiment is especially useful when analyzing social media data, where high-quality structured data is hard to come by.

Knowing when to use sentiment is the key to benefitting from most of the positives in this approach, especially if you decide two things up front:

  • Is sentiment analysis appropriate for your data?

  • Is sentiment analysis a primary or supplementary reporting measure for your data?

Sentiment can be a great complementary CX metric to other metrics like NPS and CSAT. It can provide a little more perspective into customer satisfaction, and why customers feel the way they do.

Cons of sentiment analysis

Sentiment analysis can be misinterpreted, so you have to be extra careful when analyzing the data. Some sentiment analysis programs can be thrown off by things like double negatives (or other linguistic oddities). Make sure you’re double-checking your analysis for things like Detractors with positive sentiment and Promoters with negative sentiment.

Customer feedback analysis through keywords

Keyword analysis is the process of extracting keywords or phrases from customer feedback to help determine actionable next steps. Some keyword examples can be “issue,” “glitch,” “price,” or “love” (as in “I love this product”).

Pros of keyword analysis

By finding specific keywords within feedback, you’ll know what your customers are talking about most. Looking for specific keywords can help you identify tags or categories to pay attention to if you’re combining two methods. Keyword analysis can also play a key role in finding actionable insights on social media.

Cons of keyword analysis

Keyword analysis doesn’t provide the full picture.

If you’re not careful, using keywords in your analysis can introduce bias into the analysis process. For example, if you’ve already convinced yourself that price is a key factor in consumer decisions around your product, you may put more importance on pricing keywords even when that isn’t the whole story. You may also face disagreement within your organization about which keywords matter.

Another con of keyword analysis is that you usually have to do some manual work after the analysis is complete to verify and get action items from the insights you receive. This can impact your conclusions, as the initial keyword analysis might not accurately reflect customer sentiment, leading to misguided decisions.

For example, if you have a key phrase of “price too high” it’ll identify every time that phrase is mentioned, but it won’t catch the sentiment behind it. Consider this scenario:

  • Customer 1 says, “The price is too high and not worth it for me.”

  • Customer 2 says, “I thought the price was too high until I started using it. Now I can’t live without it!”

A careless keyword analysis would catch and group both of these items together, even though one is negative and one is a rave review.

Topic feedback analysis

Traditional topic analysis, also called supervised topic analysis, is when you create a set of topics and then have machines search for those topics in your feedback data.

Pros of topic analysis

Topic analysis was created to fix many of the challenges associated with manual analysis. It successfully mitigates issues like human bias, time to results, and codebook maintenance. It also allows you to generate insights from under-utilized unstructured data.

Cons of topic analysis

Like other manual processes, topic analysis is not very scalable. Set-up time can be significant, and you have to manually update processes every time there is a new topic to discover and analyze.

And that brings us to the big limitation of supervised topic analysis: it only catches the topics it’s been given. Since you’re relying on humans for topic creation, it’s impossible to catch everything.

Manual feedback analysis vs using software for feedback analytics

Doing manual analysis using any of these methods can uncover valuable insights, but it’s also time-consuming, costly, and hard to sustain. Modern feedback analysis software has introduced a new capability called unsupervised text analysis that overcomes all three of these challenges.

Unsupervised analysis can quickly identify topics without you needing to manually identify them. This means you can see everything customers are talking about, not just the topics that you’re aware of and that you have manually indicated are important enough to monitor.

While manual analysis can deliver results and may be a necessary starting point for some organizations, the benefits of using software for analysis far exceed the costs. Software allows you to deliver more insights far faster, enabling you to dig deeper into your customer feedback.

Your Best Feedback Analytics Software

Feedback analytics software is quickly becoming a necessity in order for businesses to effectively turn their customer feedback into actionable steps. It’s no exaggeration to say that it makes analysis incredibly quick and easy. The right software can enhance your feedback strategy by providing accurate and timely insights. But there are some requirements to keep in mind when choosing a text analytics solution:

  • Level of accuracy and insights. Find a software solution that won’t just show you topics you know you need to look for, but can also identify the emerging trends your customers are just starting to talk about.

  • Time to results. Consider how long it’ll take to get insights out of the solution on an ongoing basis. But also take a step back and think about the initial set-up. How long will it take to configure and start using the solution? Both initial setup and ongoing analytics efficiency should be taken into consideration when thinking about how quickly analysis software can impact your business.

  • Easily consumed output. Choose software that has a great user interface and easily digestible reporting. You don’t want your analytics expert to be the only one who can read the analysis findings.

  • Trial using your own data. It’s smart to find software that provides a trial period. Ideally, you should find one that allows you to use your own data during the trial period to see how it works for your particular use case.

Related: 5 questions to consider before purchasing a feedback analytics platform for your VoC program

Kapiche Analytics DashboardKapiche Analytics Dashboard

Driving Action from Customer Feedback Analytics

The end goal of your analysis is to drive action from the insights. Otherwise, what’s the point?

In order to drive action, you need actionable insights. We define an actionable insight as feedback information that provides value to your business and that you can act on. Actionable insights share six attributes:

  • Alignment. Actionable insights will help your business achieve your business objectives if you take action on them.

  • Context. The best insights have relevant supporting data that contextualizes what the insight means to your business. This background helps you and your organization understand and take action on the insight.

  • Relevance. Is the actionable insight in the hands of the right team? A technical issue uncovered from analytics probably won’t be relevant to your marketing team.

  • Specificity. An actionable insight should tell you the specific levers you’ll need to pull to get results.

  • Novelty. Information like “fast support responses improve customer satisfaction” are actionable but not useful because you most likely know that already. Actionable insights are new discoveries.

  • Clarity. Actionable insights are communicated in a way that can be easily understood by all necessary stakeholders.

Understanding what makes insights actionable is important for two reasons: It enables you to weed out irrelevant feedback and helps you steer clear of analytics tools that can’t deliver the desired results.

Kapiche Customer Feedback Analysis Software is Here to Help

Kapiche was created to help you uncover actionable insights from any data source within minutes and with zero setup time. If your customer feedback program isn’t delivering the insights you need, it’s a virtual guarantee that Kapiche can help. Kapiche enhances your customer feedback program by providing comprehensive feedback analysis, ensuring you get the most out of your data.Watch a demo here.

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