How To Do Feedback Analysis: A Guide For Marketing Managers
How to do feedback analysis is a decision most companies have to deal with once they realize that their customers have a lot to say. Perhaps it also is, to a great extent, because companies themselves realize that there is value in listening to what customers have to say about them. The feedback analysis process thus can give you important insights into the minds of customers and help marketing teams decipher market trends that can be instrumental in better sales and revenues.
What Is Feedback Analysis?
Feedback analysis involves the study and scrutiny of the voice of the customer data to identify customer sentiment around different aspects of a business. When deciding on how to do feedback analysis, some companies do so manually, while others choose automated platforms because they can effortlessly analyze thousands of comments and reviews through an AI-based voice of the customer analysis.
These insights gathered from the feedback analysis process give businesses critical information about customer grievances, motivations, product deficiencies, and other important areas. This vital information thus, in turn, influences a company’s marketing strategy, product innovations, service improvements, content strategy, and much more.
How Is Feedback Analysis Done?
The feedback analysis process comprises many steps, all of which are instrumental in extracting hidden insights from within myriads of feedback comments and reviews. Let’s review these steps in detail.
Step 1: Gather & Study Your Feedback Data
In this step, you must first gather a large stack of customer comments and reviews for the feedback analysis process. The important thing to note is that the more data you gather, the more accurate your analysis and insights will be. Most often people think of customer feedback only in terms of reviews like Google. But you must also consider social media listening data.
User-generated videos and comments can give you intelligent TikTok insights that you can use for advertising and Influencer marketing programs. Similarly, Instagram social listening can give you indications of emerging trends in the industry or customer sentiments related to your competitors. The same goes for YouTube comments analysis as you decide on how to do feedback analysis for further market research.
Once you have gathered enough data for analysis, you must study it to see the patterns in the comments. This will give you an overall view of what the main themes in customer comments are and what the overarching sentiment is about your brand.
Step 2: Analyze & Categorize Your Data
Now that you have gathered relevant data that is of an optimal size, you need to study and scrutinize it. This step is important because it is during this stage of the feedback analysis process that you will categorize the data based on aspects, themes, and topics as precisely as possible.
The categorization of data will depend to a large extent on what industry you are in because your feedback is related to business. For example, if you are a bank, your aspects and categories could be “internet banking”, “deposit management”, “checking account”, “teller”, as well as general issues. If you are a restaurant owner, the categories could be “food”, “drinks”, “cleanliness”, “service time”, etc.
Once you have made these categories, you need to analyze related feedback and review them for finer details. For example, in the category “food”, what are the main aspects emerging? Is it “quantity”, “taste”, “spice”, “variety”, or “appetizers”, etc.?
These subsets of categories are equally important because they will give you minute information about things that perhaps you have overlooked in your daily operations.
Step 3: Identify & Calculate Sentiment
Once you have segregated your data into different categories, you need to now identify what kind of sentiment is associated with them. Do people love your product? Or do they feel that you are competitively priced even though your product is the best in the market? Has the sentiment declined or increased over time?
One thing to keep in mind is that since you are manually analyzing the feedback data, you need to be as objective about it as possible. Customer feedback is dependent on many factors such as the time and inclination of the person giving the feedback, the medium of the feedback, the subject on which the feedback has been received, and more such factors. And therefore, you need to view them as such.
Given these elements in a manual feedback analysis process, you also have to be honest while segmenting the responses as positive, negative, and neutral. This segmentation itself can be further classified into the polarity of the sentiment. For example, “good”, “the best”, “excellent”, “okay”, and “value for money,” are all positive sentiments but the sentiment polarity is different in each case.
Step 4: Condense & Report
Once the data has been processed and analyzed for sentiment based on your feedback categories, it is time to condense them so that you can create a coherent report with actionable insights. This is a critical part of the whole process of how to do feedback analysis.
These insights derived from the feedback analysis process are very important for marketing teams. It is so they can understand what it is that they can change in their strategies, and what it is that other departments in the organization can do in order to better customer experience and thus increase sales revenues.
Oftentimes, the entire burden of creating a sales pipeline falls on marketing managers, whether it is the Go-to-Market teams, digital strategists, content marketing teams, PR departments, and so on. The feedback analysis process can reveal how a company can include these insights for an overall, holistic approach to nurturing a customer-driven brand experience and growth strategy that involves all departments and stakeholders.
Customer insights reports can tell a company that its supply chain is slow and not able to meet customer demands. Or that the website has bugs or user interface issues. It can also give important insights to the product development team about issues they were unaware of. An intelligently made report can thus help a business in these and a hundred different ways.
How To Do Feedback Analysis: An AI-Driven Approach
The customer feedback analysis process is most effective when it is done objectively and takes into account all the factors and sources of customer feedback. An AI-driven approach to feedback analysis ensures that a business can analyze hundreds of customer reviews and comments from across a variety of sources including social media so that it gets the most accurate results possible.
Machine learning capabilities such as natural language processing, video content analysis, named entity recognition, semantic clustering and classification, text analytics, sentiment analysis, neural networks, and a variety of others, make sure that customer feedback is broken down to its most basic format in order to extract precise insights.
Precise breaking down of comments into categories as mentioned in the above section is necessary for the perfect analysis of feedback data but it is not humanly possible to do so. This problem is compounded when there are hundreds of comments, and if they are in different languages.
Machine learning algorithms thus help a business overcome these challenges easily while giving you incremental returns on your investment because the platform keeps learning from your data and thus evolving the more you use it.
Why Do We Need Customer Feedback Analysis?
The feedback analysis process gives critical information to marketing teams and businesses about how their product or service is performing and what can be done to improve customer satisfaction and profitability.
Below are the 10 most important reasons why businesses need customer feedback analysis.
- Track and measure customer satisfaction
- To create better customer experience
- Identify your ideal customer
- Nurture customer loyalty
- Bridge product-market gaps
- Improve products and services
- Increase business credibility
- Boost overall marketing and growth strategy
- Build better customer relationships
- Increase sales and revenues
Learn more about the importance of feedback analysis.
Conclusion
The feedback analysis process is an important element that needs to be part of a data-driven, intelligent marketing strategy. How to do feedback analysis does not need to be a complicated process. That’s why we have machine learning-based solutions that can seamlessly translate feedback big data into actionable business intelligence.
For example, Repustate’s Customer Experience Analysis solution, built on cutting-edge AI tech analyzes 1,000 comments per second. More importantly, the CX solution is highly customizable and comes in pre-built aspect models that are based on your industry.
It is infinitely scalable and has a no-code technology that gives you complete control over your data even if you want to add new metadata for your future needs. The solution is available on Cloud and as an on-prem installation.