In e-Commerce and CX, feedback is a potent tool for service enhancement, but it wouldn’t work without the right analysis. Every day, organizations worldwide collect millions of user comments, but either don’t realize their value or don’t know the methods of user feedback analysis. In this article, we’ll tell you how to create an efficient user feedback analysis system.
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Why Is User Feedback Analysis Important in E-Commerce and CX?
Customers are the main driving force behind any e-Commerce and CX enterprise. Feedback analysis is the tool to understand client sentiments, needs and desires. In comparison with cold statistics, real customer comments are useful for more in-depth analysis because they:
- explain positive and negative trends in production and sales processes
- contain roots of problems and clues on how to solve them and find new working approaches
- provide a profound vision of customer needs and support strong connection with end users.
Manual processing of user comments is ineffective, and it’s almost impossible to turn them into valuable data. In case there are dozens of messages a day, you’d better automate the process using AI techniques such as Natural Language Processing (NLP).
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User Feedback Analysis Methods and Techniques
Several methods can be implemented for the analysis of customer feedback in CX and e-Commerce. Here are the most commonly used techniques.
Brand Name Extraction
Monitor social networks and relevant media sources to pick out the name of your company from messages. Then, compare the number of your brand mentions in the media with previous periods. This method helps you see the general position in the market: whether your company’s popularity is increasing or declining.
The previous method doesn’t work in isolation and needs to be complemented with a strategy that helps determine the real situation. Messages concerning your company are likely to be mostly negative, and urgent measures may remedy the problem.
Therefore, you should identify the sentiment of messages, whether it is positive, neutral or negative. Only the general user mood allows you to assess customer loyalty.
Knowing your user sentiment is not enough. You need to be aware of the exact issues people are mentioning. In this regard, pick out the most commonly used words — keywords — in all comments and put them together. They will serve as hints, revealing problematic issues, as well as successes.
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Five Steps of User Feedback Analysis
Besides the methods mentioned earlier, business leaders should create a strategy to perform effective and agile analysis of customer feedback for their online commerce and customer experience operations. We suggest five steps that will help you achieve positive results.
1. Choose a Proper Tool
First and foremost, start with choosing an efficient automated tool for customer feedback analysis. There is a plethora of AI-based algorithms developed, especially for processing large amounts of data.
Machine learning–based systems can be taught to extract keywords, categorize data by different criteria, generate reports and perform other tasks. They significantly reduce the manual workload, cost and time of analysis. These systems are flexible, so you can create custom tools by adding the criteria you need.
Microsoft provides a set of tools and APIs, known as Microsoft Azure Cognitive Services, for developing AI applications that process unstructured data. Among them, there are language APIs able to analyze texts and define utterance sentiment.
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2. Categorize User Feedback
The scope of responses any business receives always falls into specific categories. In general, they may concern points such as:
- Price, quality and range of products
- Staff professionalism
- Delivery service
- Digital availability
You may sort the feedback into many more categories and subcategories, depending on your niche. As a result, you get a broad picture of how clients view your products and services, and what issues should be addressed. Categorization proves to be a handy customer feedback analysis tool. It highlights some less noticeable features that can play a significant role in the overall process, making the company perform better.
An easier way to divide comments into categories is revealing their sentiment. Thus, you will get only three categories:
Focus on negative comments most of all, as they reveal real problems and show you changes that you need to make. Neutral feedback is quite dangerous as it can turn into a negative attitude toward your company — don’t neglect it.
3. Find Root Causes
It is not enough to find out issues based on customer comments; it’s vital to get to the root causes. Only by doing so will you be able to provide an adequate solution to the issue. Analyze the scope of comments as a system, trying to extract common threads.
When the analysis shows that hundreds of customers are not satisfied with, for instance, the delivery service, you must take notice of the situation. The first solution that comes to your mind is, perhaps, to hire new couriers. But when you try to look at the root of the problem, you may realize that the delivery management system is insufficient and requires improvement.
4. Plan Further Actions
Once you have realized issues that have to be addressed, it’s time to make an action plan. Think about feasible activities you can perform to improve your status. Be careful: innovations should not disrupt those parts of the system that work well and get positive feedback.
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5. Inform Teams
Each relevant team and individual involved in the process should be aware of the needed changes and should be ready for them. Share the results within the organization and discuss plans with employees.
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Automate Your User Feedback Analysis
Listening to your customers and considering their wishes is the only way to flourish in the competitive e-Commerce world. That is why qualitative feedback analysis is a must-have for modern enterprises. Remember that you can always turn your weaknesses into strengths, as long as you are flexible and open to changes.
SaM Solutions can highlight your strengths using emerging technologies, including machine learning. Contact our specialists for detailed information.