Customers have been leaving feedback more than ever before, and businesses amass that customer feedback data for numerous purposes. However, most companies do not capitalize on customer feedback analysis, making the data useless.
The analysis of customer feedback data enables you to draw insightful, actionable information from your customers. And when you do it the right way, feedback analysis can be valuable for your company’s development.
This article will be a comprehensive guide on customer feedback and how to analyze it. Let’s scroll down to explore!
What is Customer Feedback Analysis?
Customer feedback analysis refers to extracting insights from customer responses to online reviews or surveys. Getting a great amount of data to process is a good start, but you’ll have to classify, tag, and manipulate the data to discover the concealed information. That useful information will help you make informed, data-backed decisions for your business.
Without customer feedback analysis, companies might probably become “data-rich but information-poor” – a new business adage in a number of industries.
Why Is Customer Feedback Analysis Important?
It is obvious that customer feedback analysis plays an integral role in the growth of your e-commerce business. So, what exactly can the analysis benefit your organization? Explore now.
- Improve customer experience
Listening to your customer refers to gradually forming a better customer experience by putting positive changes in the right places.
You will understand where it hurts, identify where to improve and how to revamp it. And since you will analyze your feedback in the proper way – you will gain these insights fast.
- Enhance products and services
Accurately analyzing customer feedback data means understanding your customers more deeply. That helps you make your products and services better based on your customers’ opinions. Therefore, your beloved clients will highly appreciate your efforts and become more loyal to your company.
- Boost business growth
According to Gartner, listening to your customers and analyzing their feedback will escalate upselling and cross-selling success rates to 15-20 percent. This also leads to higher customer retention, less churn, and a better customer lifetime value, in addition to fewer expenses to retain shoppers.
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There are two main stages in customer feedback analysis, including micro and macro.
In the micro stage, you will find out how to tag each data piece and implement the tagging as precisely as possible. Next, in the macro stage, you will uncover what all that tagged customer feedback data mean for your business
Types of Customer Feedback Analysis
Businesses receive a huge amount of feedback every day and converting the data from that feedback into meaningful insights can be challenging. Even with rich data, the analysis is still not useful enough as organizations don’t know how to analyze feedback data accurately.
There are several popular types of customer feedback analysis.
Type 1: Verbatim Analytics
With verbatim analytics, you can implement analysis on input that is given verbatim, like the customer said or wrote it. Instead of limiting customers to responses in surveys and other data collection forms, you can include more open-ended questions that bring you much more valuable input.
Type 2: Text Analysis
- Sentiment analysis
Sentiment analysis is the involvement of identifying positive or negative sentiment in text.
As customers reveal their opinions and feelings more openly and comfortably than ever before, sentiment analysis is becoming a crucial tool to track and understand that sentiment.
- Keyword or aspect analysis
A keyword or aspect analysis uncovers specific “things” in the text.
For instance, if a buyer includes the word “discount,” it will label or regard the feedback as being about discounts.
A keyword analysis mostly relies on the language used by customers, making it susceptible to errors and inaccuracies.
- Topic analysis
A topic analysis is a type of AI-backed analytics that learns and analyzes like a human but with a faster speed.
A topic analysis not only just find a keyword and name the piece of feedback. It considers the context of that word and the meaning of the text. Precise categorization will not depend on any specific words, leading to more accurate results.
For instance, a topic analysis tool can discover that a customer is dissatisfied about “free shipping not working” even if they say “the offer didn’t apply at the checkout step”.
How to Analyze Customer Feedback Data?
The process of customer feedback data analysis takes place almost the same as any other type of data analysis. You collect the data from trusted sources, convert the data from qualitative to quantitative, and then analyze and evaluate the data to uncover trends.
Step 1: Collect Customer Feedback Data
The very first step in customer feedback analysis is to collect their responses. For most businesses, there are three primary sources of feedback data.
Online Reviews: There are a bunch of online view sites designed for various industries. For instance, CNET is a reliable platform for checking electronics products, while Yelp is a popular platform for restaurants and destinations. You can manually garner customer data from these websites or leverage a data-collecting tool to save time and effort.
Social Media Mentions: You can take advantage of social media to get customer feedback. Like online reviews, this source will depend on specific industries. Since B2B customers engage more on platforms like LinkedIn, meanwhile lifestyle and product-based businesses work more on Instagram. You can gather customer data from direct messages, posts, and comments to your own accounts.
Feedback Surveys: For more targeted feedback, a lot of companies send customer feedback surveys via emails to clients who recently interacted with the company, such as a customer service call or an order.
Step 2: Categorize the Feedback
Now you have your data in your hands. Next, you need to seek a way to convert the qualitative, open-ended feedback into quantitative points.
For instance, you cannot numerically compare the responses ‘The price is fair to me for what you get’ and ‘The price is too high, but the quality is almost worth it.’ When you transform these feedbacks into “Price: 5/5” and “Price: 2/5”, then you can have a better data comparison.
Method 1: Manual Coding
Changing qualitative data into quantitative data involves coding. This is not coding in the computer programming but the process of appointing codes, otherwise known as tags, to every piece of data so it can be categorized appropriately. With manual coding, you will need to consider every customer feedback and attach it to a category (quality, price, customer service, etc.) and a sentiment based on your defined scale.
Method 2: Rule-Based AI
The rule-based approach consists of human-crafted and curated rule sets. Rule-based approaches search for linguistic terms, like “like,” “dislike,” “love,” and “hate.” The appearance of positive and negative words decides whether a sentence is positive or negative.
Nevertheless, various word meanings make it challenging to set rules. The most popular reason why rules fail comes from “polysemy,” when the same word can have multiple meanings. Consider the word “hot” in terms of taste and temperature.
Method 3: AI + Sentiment Analysis
If manual coding seems to be strenuous, many companies invest in AI-backed software that can perform this process. With sentiment and topic analysis backed by deep learning algorithms, these programs can analyze your customer feedback data and tag and group every piece of feedback, automatically transforming your data from qualitative to quantitative. What used to last days can now be completed in minutes.
Step 3: Code the Feedback Data
The following step is to begin going through the data and carefully generate feedback codes for every row of data. Learning to analyze customer feedback also covers learning to code that data. And the first thing you need to understand is every code related to the product that got the feedback.
Here are several analysis codes for feature requests you can employ:
- Attaching complicated HTML to some tasks
- Including and deleting teammates from screens
- Appointing one task to different customers
Step 4: Search for Patterns
After coding the data, it’s about time for you to define how much feedback each code contains. The simplest way to identify patterns is to filter the feedback data according to the theme, type, and code and then emphasize the repeating patterns. If you do this, you can recognize the most popular type of feedback and figure out specific patterns.
Step 5: Invest in Automated Tools
Even though you can learn how to analyze customer feedback data without using tools, it’s way easier and more effective to leverage data analysis automated tools. These tools utilize machine learning for customer feedback analysis.
Here are some recommendations for you:
- Adverity: This is a data-driven marketing analysis tool that reduces manual data collection and uncovers actionable insights.
- Zoho Analytics: This tool provides numerous visualization options and a custom-themed dashboard.
- Looker: This cloud-based platform offers a role assignment, drag-and-drop for elements, and precise charts that list the data in detail.
Tips For Analyzing Customer Feedback Data
Those steps draw out the basic process of customer feedback data analysis, but any type of data evaluation can be tricky.
The last part of this post will provide some excellent tips, so you can ensure your time and effort results in ideas and initiatives that have an effect on your bottom line.
Tip 1: Share Insights Throughout Departments
It’s crucial to understand the customers’ voice in each department of your company, not just for C-level executives. Distribute your data, analysis, theories, and insights via dashboards so that every member can access the information they want.
Tip 2: Consider Root Problems to Understand Small Complaints
If you’re getting many niche complaints about a specific product or service, you had better dig deeper into that product or service to find out whether it needs to be revamped. While customer feedback analysis aims to discover overarching trends and themes, sometimes, a number of small problems can lead to a detailed issue.
For instance, if different customers leave reviews about a product criticizing different things, such as “My computer gets lagged whenever I open this,” or “I couldn’t get the import function to work,” you may research the product and know that the onboarding manual file is broken. Customers cannot learn how to install the software accurately in the first place, resulting in a variety of complaints. Keep in mind that you don’t know what you don’t know.
Tip 3: Account for Demographic Differences
As you look at numbers on a spreadsheet every day, you easily get lost in the data. Thereby, it’s essential to keep your customers in context and contemplate the obvious answers according to demographic differences.
For example, suppose that you’re considering the percentage of your university-aged customer group that either did or did not buy sweaters this winter.
To have a more precise representation of this percentage, you would need to sort out the locations where people aren’t inclined to buy sweaters during this period because it’s too hot. You can account for similar demographic differences relating to seasonality, income, lifestyle, etc., to ensure you’re collecting data that covers these variables.
Solutions for Customer Feedback Analysis
Now you already have an overall look at customer feedback analysis and its benefits to your business. So how can you perform customer feedback analysis the right way?
Here are several solutions to save you time and money.
Use an Outsourcing Agency
The first solution is outsourcing all your customer feedback analysis to a third party. Traditionally, organizations would utilize market research agencies and only gain the results in the form of PowerPoint reports.
Today, some agencies offer dashboards using excellent built-in tools like Tableau and Power BI. And Synodus offers numerous data analysis services using these tools, plus Qlik and Google Looker. Have a quick chat right here!
Great agencies use people experienced in market research. They can show you how to build your customer surveys and how to concentrate on the right questions to ask. In addition, the agencies can suggest people who helped other businesses with similar projects. Therefore, you can learn from others’ trials and errors.
Hire a Data Analyst
Data analysts, who may have stayed with you for several years, usually know your business thoroughly and get any insider knowledge needed to analyze customer feedback. Whoever you hire new for this role would need to be trained first.
Moreover, with all the advancements in AI, people still excel at truly understanding natural language and its nuances. This is particularly true for sarcasm. For instance, one customer writes ‘yeah, awesome service’, and the algorithm assigns it positive.
Utilize SaaS Customer Feedback Analysis Tools
Saas has transformed the world of business. For every business, there is a solution for you to buy. And in terms of analyzing customer feedback data, there are also some effective solutions for your company. Here are three types of SaaS tools that can aid you in customer feedback analysis.
- Customer feedback management solutions
- Customer insight solutions
- Text analytics tools and APIs
Handling customer feedback analysis can be challenging at first, particularly if you don’t have any experience. Nonetheless, building an analysis plan is essential to your long-term customer response strategy because it will help you come up with clear-cut objectives and approaches.
Or you can reach out to us for our dependable and excellent data analytics services. We will take care of your hard work and leave you meaningful and valuable insights for your business growth.
More related posts from Big data blog you shouldn’t skip:
- What Is Behavioral Analytics? Definition, Examples And Tools
- Data Analytics Services For Small Business: Pricing Review
- 6 Best Practices For Small Business Analytics
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