Collecting and analyzing customer feedback provides valuable insights for product improvement and helps you better understand your customer’s needs and expectations. And when you monitor feedback on a regular basis, you can measure customer satisfaction over time to see if your business is going in the right direction.
According to a recent survey of leading product managers, over half say their new products and features are mainly inspired by customer feedback, yet only one in ten teams say they successfully capture feedback from all available sources, and one in three say they have no process for collecting customer feedback, whatsoever.
In this guide, you’ll find everything you need to start gathering, analyzing, and visualizing customer feedback. Then, we'll explain how you can use machine learning to simplify the customer feedback process with no-code software like MonkeyLearn.
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Customer feedback is the information and opinions provided by your customers about a product or service. Whether it’s in the form of survey responses, social media mentions, data from online product reviews, or text from chats with your customer support team, listening to your customers should be a central part of any business strategy.
By gathering and analyzing customer feedback, you can find out which aspects of your business are working well and which may require improvement. Then use those insights to make data-driven decisions to align your product or service with your customers’ needs.
Customer feedback is important because it can help drive business growth. We all know how it goes: the happier your clients are, the more likely they'll stay loyal to your brand and help you attract new clients through recommendations.
Analyzing your customer feedback helps you understand what drives customer satisfaction. Often, customer service plays a very important role in this. According to a Microsoft report, 56% of customers say they have stopped doing business with a brand due to a poor customer service experience.
Finally, making improvements based on consumer feedback allows you to create products and services that really solve your clients’ needs, and monitoring your customer feedback over time will keep your finger on the pulse of your customers.
Before collecting customer feedback, you’ll need to identify what your goals are. For example, do you need to improve products or services? Do you want to launch a new product feature? Which particular aspect of the customer journey do you need to improve?
You’ll be able to tailor surveys around a specific goal, or gather specific data on social media to help you find the answers to your questions. Gathering feedback that specifically answers your goals will help you gain relevant and actionable insights.
Some of the most popular methods for collecting customer feedback are:
This is one of the simplest and most popular approaches to measure customer satisfaction. Basically, NPS surveys consist of asking customers to score a product or service on a scale of 0 to 10 based on how likely they are to recommend it to a friend or colleague. This rating allows you to classify customers as Promoters (9-10), Passives (7-8), or Detractors (6 or less). The ‘Net Promoter Score’ is the result of subtracting the percentage of Detractors from the percentage of Promoters.
However, while the score may indicate how high or low customers rate your business, it doesn’t provide any extra information about the customer’s opinions and experiences with your product or service. That’s why follow-up, open-ended questions are often included, inquiring about the reasons for customer scores. These responses can deliver valuable insights on what drives customer loyalty, what causes customer churn, and various other pain points.
The advantages of NPS survey analysis is that they’re extremely simple to understand and require little effort from customers. That’s why they generally get high response rates. Quantifying results from the closed-ended question is also easy: you just need a spreadsheet, and you can immediately identify clusters of customers. Many businesses consider NPS a key metric, as it’s strongly related to customer loyalty and business growth.
However, scores can be ambiguous and a 6 or an 8 may not mean the same for every customer. An NPS score alone doesn’t offer enough information: it is necessary to dig deeper with a follow-up question. But categorizing the follow-up responses may be difficult unless you’re using machine learning tools.
Collecting and analyzing online reviews from different sources (from review websites like Capterra, G2 Crowd, or Google App Store to specialized forums, or even your Facebook business page) can provide valuable insights on what your customers think about your customer service.
Online reviews are honest opinions that reflect customer experiences and offer detailed explanations to support their statements, allowing you to understand exactly what customers like or dislike about your service.
Qualitative feedback from customers in online reviews allows you to get more actionable insights. You can understand the reasons behind a client’s opinion and the situation that motivated them to reach out to your customer service team. However, since reviews are unstructured, they are harder to analyze.
Unstructured text data needs to be organized in a certain way before you can detect patterns and classify opinions, and sorting them manually can be extremely time-consuming and unproductive. In this case, you’d need to automate your customer feedback system, something we’ll cover in later on in this guide.
Keeping track of social media mentions (and analyzing them periodically) can shed some light on the challenges and pain points of your business. What are your customers’ most frequent complaints? What are the most urgent issues to fix? Listening to your customers on social media can alert you of product issues and allow you to handle them before they escalate.
Social media mentions are spontaneous, so they provide you with instant and honest feedback without having to ask for it, but you will need the right tools to collect and analyze them in real-time.
Asking for feedback inside an app is a great way of getting to know what your users expect from your product. It’s a fact: understanding your customers is the key to creating outstanding products. You can ask them about their favorite features, inquire if the app is making things easier for them at any point, and encourage them to suggest features they would like to see.
In-app surveys can be as flexible as you want: you can tailor questions to your product or service (maybe you’ve just launched a new feature and you’d like to know how it’s working, or set priorities for your product roadmap). You can also decide which users get to see the survey (those who’ve recently made a specific action within the app, for example), and when the survey will be displayed.
Once you’ve decided which is the most suitable method to collect customer feedback, it’s time to design the questions you are going to ask your clients.
Here are some tips:
Listening to the voice of the customer (VoC) is one of the cornerstones of long-term business growth. To take full advantage of its potential, it’s key to develop and automate a customer feedback strategy that involves collecting feedback, analyzing it, and transforming it into actionable insights. This is where a customer feedback loop comes into play.
A customer feedback loop is a three-step procedure to gather, analyze, and act on feedback data. It’s a regular process that will ensure you’re in constant communication with your customers – that you’re engaged with them 24/7 – using their feedback to improve your business and your products and services.
What are your objectives and what kind of feedback do you need? Do you want to get feedback related to customer service? Or find out what customers think about your product and identify what feature needs to be improved?
Perform regular CSAT or NPS surveys in-app, in emails, or in-store. Applications like SurveyMonkey make survey data analysis easy and can integrate directly with text analysis and visualization platforms.
There’s also plenty of customer service feedback from CRM systems, surveys, emails, live chats, and more. Email or in-app follow-ups after purchase, onboarding, and other major customer journey touchpoints can be helpful to make sure customers are consistently happy or discover pain points.
Customer feedback examples are available all over the internet. Performing social listening on Twitter, Facebook, YouTube, and more can find out what customers are saying about your business 24/7, and in real time. You can even use text analysis to find relevant customer comments and opinions from blogs, forums, app stores, Amazon, Capterra, and more.
Once you’ve collected your feedback, it’s time to make sense of all that information. Whether you have quantitative or qualitative customer feedback, you will need to set up a system to categorize the data and gain meaningful insights.
SaaS text analysis platforms like MonkeyLearn can gather and analyze your customer feedback in just a few steps. Sentiment analysis, for example, can automatically read surveys, social media comments, and online reviews for opinion and emotion to understand how your customers feel with almost no human interaction.
This pre-trained sentiment analyzer automatically understands this tweet as negative feedback:
SaaS tools allow you to get even more fine-grained with techniques like aspect-based sentiment analysis to classify customer feedback first by topic or aspect, then sentiment analyze it.
Now it’s time to close the feedback loop. Now that you’ve analyzed customer feedback, it's time to put the results into action by:
Whether you analyze customer feedback manually or with machine learning, you’ll probably receive an Excel spreadsheet or a CSV file with rows and rows of results that are neither engaging nor easy to understand. Think of something like this:
Daunting, right? Luckily, you can share the results with different teams within your company using reports or data visualizations, and collectively decide what actions to take based on the data.
Data visualization tools, like MonkeyLearn Studio bring all of your results together for an easy-to-understand overview to spot patterns and fine-grained analyses.
Take a look at this aspect-based sentiment analysis of customer feedback of Zoom.
Each review has been categorized as Usability, Support, Functionality, etc., and then analyzed by sentiment: Positive, Negative, or Neutral.
Imagine running this on your customer feedback data from internal sources and all over the web.
Once your teams have had time to look at the results, they’ll need to define a clear strategy on how to act on that feedback. The most critical issues (for example, UX flaws and bugs that may affect your customer retention) should be addressed right away.
Then, you should come up with a list of priorities, by calculating the ROI of improvement (the impact it will have on your business), and the costs of implementing a given solution (considering your budget and resources), among other factors.
There are different ways in which customer feedback can drive business decisions. Listening to your customers can shed some light on existing features that require improvement (and new features that you can add to your roadmap), for example. Also, you can leverage customer feedback to redesign your marketing strategy. You might want to target more customers, detect customers at risk of churn, or invest in more training for your customer support representatives
Finally, closing the loop of your customer feedback process involves following up with the customers that shared feedback.
Finalizing interactions with your customers, not only to thank them for completing a survey or writing a review, but also to let them know about changes or improvements you made or that you are currently working on based on their feedback, shows them that you care about their opinion.
Addressing your customers’ feedback is also a great way of building trust and loyalty. There are several ways you can finalize interactions, from sending personalized emails to your customers, to publishing a report explaining how you implemented their feedback.
And, you should always follow-up with customers that have raised complaints or had an issue they needed to solve, as well as dissatisfied clients at risk of churn. Of course, you should respond to these clients as quickly as possible, and proactively reach out to disgruntled customers.
There are several online tools that can help you at different stages of the customer feedback process. These are some of the most relevant:
Artificial intelligence makes it possible to analyze large sets of customer feedback in just seconds. Even though you can build an AI solution from scratch using open-source libraries, it takes time and resources. SaaS APIs, on the other hand, are a great choice if you need to get started right away, with little (or no) coding, and just a few steps to get you up and running.
You can create AI-powered solutions to identify relevant topics in online conversation or determine sentiments in product reviews or social media posts, among other tasks. These are some of the smartest tools on the AI block:
As we’ve seen earlier, these are the most popular data visualization tools you can use to create killer reports and presentations out of your customer feedback results:
Having a consistent customer feedback strategy is key for companies to learn what their customers think about their product, but also to understand how their needs and expectations evolve over time. Regularly asking customers for feedback is also the best way of coming up with fresh ideas, detecting opportunities for improvement, and discovering new market trends.
The first step to set up a successful feedback loop is to define your objectives. Knowing which aspects of your business you’d like to focus on can help you figure out which type of data you’ll need to collect and decide on the best methods to gather feedback. Depending on your goals, you can send an NPS survey, check out online product reviews, or implement a feature request board, among many other alternatives.
Then you’ll need to turn raw data into a customer feedback report, full of actionable insights, that you can share with the different areas of your business. While analyzing quantitative feedback doesn’t entail much complexity, processing qualitative feedback (like open-ended survey responses) requires an extra effort: you need to categorize each piece of feedback, providing a sort of structure that enables you to find patterns, identify frequent topics, etc.
Even though the concept of machine learning can seem a little hard to understand, getting started is actually very simple.
Sign up to MonkeyLearn for free to easily create AI models that automatically analyze your customer feedback, and discover meaningful insights about your product or service in next to no time.
Or, if you want to see how MonkeyLearn can help you with your customer feedback data, schedule a personalized demo.
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