If your organization has thousands of customer reviews regarding everything from your products to your customer service support, you should count yourself lucky.
Reviews contain invaluable information that you can use to improve your products, sell more, and grow as a business.
However, processing large numbers of reviews -- the actual comments, not the ratings -- can be overwhelming.
They can come from a wide range of channels and the information is often unstructured and disorganized.
Sometimes important information can get lost. And, while you know these reviews could help you meet your business goals, you're not always sure where to begin.
In this guide, we'll make it really easy for you to get started with review analysis.
First though, let's start with what it is and why it's important. .
Review analysis is the act of going through customer and product reviews from a number of different channels and uncovering insights. These insights can then be used to improve products and services, create new ones, or enhance the overall customer experience.
Review analysis can also help catch any bugs or customer complaints that could escalate and be damaging for your brand.
With a small number of reviews, analysis can be done using tools like Microsoft Excel.
However, when analyzing a large quantity of reviews you'll need to perform your review analysis with AI-powered text analysis tools.
These tools can automate your review analysis process and get you results, quickly.
Reviews tell you what your customers are thinking and what they need from your organization.
If you don't know what your customers think about their experience with you, it's hard to create products, and experiences that will make them want to keep purchasing from you. You also won't know when something goes wrong or when you're about to lose their business for good.
Reviews also signal to potential customers whether they should or shouldn't start doing business with you.
According to a study from TrustPilot, almost 9 out of 10 consumers consult online reviews before making a purchase.
In other words, reviews help you grow.
Here are the number of different business areas that review analysis can be applied to.
Product development. Rather than creating products randomly, reviews help you steer the creation of new products in a way that makes sense for your customer. When you create products according to their needs, they're more likely to purchase, repurchase, and recommend you to their friends.
Detecting product bugs. Reviews are an essential way for your customers to tell you about any issues with your product. It's important that you process and take these reviews on board. This means you can give your customers a product that is functioning as it should. By acting on their reviews quickly you can avoid any customer frustration and make sure your customers enjoy using your products.
Improve customer experience. Reviews tell you what your customers like and dislike about their overall experience with your company. They can also provide insights on a more granular level. These insights let you isolate problem areas within your customer experience journey and quickly put measures in place to improve it.
Prioritize product roadmap. What you think is the most pressing new product or feature might not actually be what your customers think is the most urgent. Reviews help you to make a list of priorities, or a product roadmap, according to real demand. When you prioritize according to feedback and reviews, you are more likely to sell more products.
Competitive analysis. Comparing the reviews your customer gives you, and the reviews that your competitors get can help you assess how you rank in the market. It can also help you to improve by showing you what your competitors are doing well and what you need to work on.
Making informed business decisions. To gain more business you have to put your customers at the center of every business decision you make. A great way to do this is by using the insights from reviews to inform these decisions.
Reviews immediately tell you what it is your customers expect from you. When they hear you listening to them and implementing changes based on their reviews, they'll be more satisfied and easier to retain. This in turn will help grow your business.
In conclusion, to create a successful, customer-centric company that puts your customers' wants and needs at the heart of all major business decisions, you need to analyze your reviews.
By analyzing your reviews effectively, with the right analysis tools, you can gain tangible insights from your data that you can then use to improve your business outcomes.
So, let's dive into how to actually do it.
Your first step needs to be gathering your reviews so that you have something to analyze in the first place.
This is not always an easy task as reviews are everywhere, so you'll need to go out and find them. To help know where to look, we've grouped reviews into the following two categories:
These are reviews that you don't actively ask for but which customers give anyway. They exist on the internet and you normally have to actively seek them out. Examples include:
Review sites. Sites like Trustpilot, G2 and the App Store allow any consumer to leave a review regarding your products and services. They are independent so you can trust that the reviews are genuine and not heavily biased. They often contain quite a bit of detail so there is more chance of getting insights.
Social media. Customers regularly leave reviews on social media sites which makes social media monitoring a must. These reviews can go viral and can potentially be seen by a lot of people. They can also heavily influence potential customers. Where your customers leave their reviews will depend on the demographic, however common sites include Facebook, YouTube, Instagram, and TikTok.
These are reviews which you specifically ask your customers for, such as:
Review requests. There are a large number of ways that you can request reviews from your customers. These can include requests via email, your website, over the phone.
It's easiest for your customers if you let them leave the review in the format that they are already using. I.e. if they are in the app send the request there or if you have to email them letting them know their order has shipped you can add a review request at the end.
Once you have gathered your review data, review analysis is your next step.
You're probably already familiar with the star ratings in reviews. They're easy to manage and help you quickly understand whether you're on the right track.
Sites like TrustPilot provide a simple dashboard that shows you your star-rating trends over time, allowing you to filter by 'sources' and 'ratings' -- and get an overview of your company's performance.
Here are Trustpilot's results for Apple:
However, to go further, you'll need to analyze the actual comments, below the star ratings.
This is not always easy because reviews can come to you in a number of different formats.
So, you'll need to standardize the data before you analyze it. This means amending some parts so that details like dates, product names, etc. all follow the same format.
You'll also have to clean your data before analyzing it. This is because a lot of customer reviews are noisy, full of irrelevant data, spelling errors, and special characters.
Added to these challenges, review data is unstructured. This makes it hard to quantify and make sense of open-ended comments in a spreadsheet.
This is where MonkeyLearn can help you.
MonkeyLearn offers a range of no-code review analysis templates which are set up to automatically run different kinds of text analysis techniques like sentiment analysis, topic extraction, and keyword extraction on your data.
Here we'll go through a step-by-step example using our review analysis template.
First, sign up for your free trial with MonkeyLearn, then go to the App Review Analysis template.
Next, you'll need to upload your data. For this particular tutorial we've used public app store reviews for Twitter. You can use any data you like but it needs to be in CSV file format.
After that you'll need to match columns to the following fields:
created_at: Date that the response was sent.
text: Text of the response.
rating: Score given by the customer.
Simply name your workflow and wait for the data to be processed.
Then, you'll be able to explore your dashboard and view your reviews according to topic, sentiment, and keywords:
Here's an example of what your dashboard could look like if you decided to filter by 1-star ratings:
And here's what it would look like if you wanted to filter by the topic 'Usability':
Review analysis can seem overwhelming, especially when you have a large amount of reviews to get through.
However, it's essential for the success of your products, and your business as a whole, that you effectively analyze the comments in your reviews -- and not just the star ratings. This is because you'll gain more in-depth insights that can have a meaningful impact on your business.
Customer and product review analysis doesn't need to be difficult. With the right text analysis tools, it can be fast and easy.
If you have over 10,000 reviews MonkeyLearn can help you analyze them using a variety of machine learning analysis techniques. This gives you results in seconds that you can then visualize in the user-friendly interactive dashboard you saw above.
Book your free demo today to see how you can step up your review analysis.
March 1st, 2022