AI Customer Feedback Analysis: The Key to Unlock Customer Sentiments

Traditional methods of feedback analysis have enabled businesses to process feedback, fetch trends, predict behavior, and even assess pain points for several years. However, these platforms struggle to keep pace with the sheer volume and complexity of the feedback data pouring in from different data sources.

Imagine automatically analyzing vast amounts of unstructured feedback and uncovering hidden gems. AI-powered customer intelligence platforms make this a reality. 

And that’s where artificial intelligence, natural language processing, and machine learning come into play.

AI goes beyond just processing words.  Survey platforms, driven by AI, can identify recurring themes, emotional undertones, and the root causes of customer frustration.  

This allows businesses to understand not just what customers say, but also why they say it.   

Many feedback management tools are offering AI models that can be trained on large datasets and deliver a 360-degree view of the customer journey along with the sentiments associated with feedback at every touchpoint. 

This blog will help you unlock the power of AI-powered feedback analysis. 

Discover how you can use an AI-driven platform to gain a deeper understanding of your customers and make data-driven decisions that drive results. 

AI Customer Feedback Analysis: Uncovering Sentiments In Feedback Data 

In the past, organizations would take upon the laborious task of manually analyzing customer feedback, often requiring time-consuming methods. But with AI, businesses can revolutionize how they interact with and understand their customers.

AI technology, equipped with advanced AI and ML technologies for sentiment analysis and efficient feedback categorization, enhances businesses’ capability to detect patterns and gaps in customer feedback analysis. By choosing to automate customer feedback analysis, companies can save time and resources while gaining valuable insights.

And not just that, artificial intelligence can offer a number of other strategic advantages to companies using it for feedback analysis. 

• Analyze customer feedback collected across the globe in seconds: Whether you collect customer feedback on a national or global level, using multiple languages, or having hundreds of branches, using AI for feedback analysis can process it in seconds.

• The more unstructured data, the better: Businesses need not shy away from unstructured feedback data. AI algorithms can be trained to analyze open-ended comments, feedback, and reviews effectively to give more context to every quantitative data or CX score achieved. The more data you get, the more accurate its results will be.

• Manual errors in tagging or categorizing comments; not anymore!: Artificial Intelligence identifies themes, sub-themes, topics, and more in every comment or text data to give accurate customer feedback analysis without any error.

• Human bias, what’s that?: Humans can have biases that can cloud their judgment while analyzing feedback. AI algorithms have a standardized approach to feedback analysis that eliminates any bias.

• Efficient analysis of large volumes of feedback data: It is easy to train Machine Learning algorithms on large datasets to identify patterns and indicate the sentiments accordingly.

• Scale your feedback scope unhindered: Scaling with AI algorithms is a cakewalk. Without adding any resources, businesses can easily scale their perimeter of feedback collection or add more locations without worrying about resource expansion. 

How AI Transforms Feedback Analysis?

Using AI and ML for customer feedback analysis can offer multifold benefits to businesses across every industry. 

But how does it actually work?

How does incorporating AI in customer feedback analysis methods change the way businesses unlock customer insights?

Here’s how. 

Traditional methods of analyzing customer feedback can be time-consuming and prone to human error. AI steps in as a game-changer, automating many of these tedious tasks with machine learning muscle. Imagine algorithms that can swiftly categorize and group feedback based on sentiment, and key themes, and even automatically labeling them. This translates to actionable insights delivered directly to product managers and engineers – all at lightning speed.  The benefits are twofold: significant time saved and a boost in both the accuracy and consistency of feedback analysis.

Let’s quickly go through the different ways AI is transforming feedback analysis. 

1. Automated Sentiment Analysis 

Humans possess the ability to identify sentiments. Now imagine, assigning a team of professionals to go through the hundreds and thousands of feedback comments received on your system to analyze the sentiments hidden behind them. 

Seems like an impossible feat to achieve, right?

But if someone did try to achieve it, they would need to invest a huge amount of resources, time, energy, and effort to make a group of people sit around, go through every comment one by one, scan through the text data, and feed the sentiments alongside. 

Enter Artificial Intelligence. 

Today, AI algorithms can be trained on large, multiple datasets to quickly analyze the sentiments expressed in customer feedback. Based on the data fed into the algorithms, it can identify patterns and indicate text as positive, negative, or neutral. 

Our sentiment analysis tool continuously feeds on the data collected by our clients so that it can deliver more accurate results. 

Not only can it generate sentiment scores, but it also gives sentiment breakdown, compares with previous periods, and highlights the sentiments over time to identify trends and changes. 

2. Identification of Customer Intent & Its Analysis

Oftentimes, businesses rely on customer’s demographics and 

Customer, user, or buyer intent is the determining force in any business success. It helps identify the underlying reason for the feedback and the intention behind that feedback. 

Feedback can have different intents- praise, complaint, suggest, recommend, question, inform, and much more. What an AI survey tool does is, it recognizes that intent automatically from the responses received and understands what the respondent really is trying to say, and moreover, their underlying intention of how they want to engage. 

With intent analysis, businesses can identify the underlying purpose behind feedback and take action to increase the chances of conversion, retention, and delight. 

Let’s say, Stephen has left the following feedback on your store:

"These sneakers looked amazing online, but the quality is terrible! They ripped after just one wear. Really disappointed."

Using AI for customer feedback analysis, we can know that the customer's intent is to complain and show dissatisfaction with the product quality. 

This can be further streamlined when viewing all the responses with similar intent.  

3. Prioritizing Tasks Based on Urgency 

An AI-driven survey platform can instantly help your teams prioritize tasks based on urgency. In any crisis, AI can tag it as a high-urgency task and intimate users to take action on it. With human intervention, these issues can be resolved easily. 

For instance, you are taking product feedback and a user has reported a bug in one of the features. AI would tag it as High Urgency, marking it in red, so that it can be taken into consideration quickly. Further, by pairing it with CX automation, the customer feedback analysis tool can also send alerts to the required team or team member, create a ticket in the integrated systems, or even send out an automated email to close the feedback loop. 

4. Automated Identification of Entities From Unstructured Feedback

Unstructured data from open-ended questions is one of the biggest challenges faced by businesses aiming for standardized feedback analysis. With Natural Language Processing algorithms, businesses can train their AI models on large unstructured datasets to extract actionable insights. 

NLP can then identify the key entities through a process known as Named Entity Extraction. It can detect and categorize important entities in text data such as name, location, organization, person, product, service, amenities, competitor, etc. 

For instance, you’re performing hotel surveys, and here’s some feedback that you’ve received:

"I love your store on Main Street!  Always find something new and exciting. However, Stephen at the store wasn't very helpful today. He could not assist me when I asked for a different size."

A standard customer feedback analysis would’ve flagged this as a negative review without giving specific details of what’s negative about it. 

Here’s how NLP would’ve done entity recognition:

Location: Main Street

• Sentiment: Positive
• Urgency: Medium
• Emotion: Satisfied
• Intent: Praise

Staff: Stephen

• Sentiment: Negative
• Urgency: High
• Emotion: Anger
• Intent: Complain

This way, one can easily get a quick glance at what the customers actually like at the store and what areas of the store require more attention.

5. Automated Tagging & Topics Assigning to Discover Hidden Patterns

Manually skimming through each and every comment to identify similar issues could take up days and weeks for any particular campaign. With an AI engine powering your feedback tool, you can expect nothing but automated tagging and topic assigning along with themes and sub-themes to organize your feedback into similar groups. 

These AI survey tools are powered by topic modeling. This technique helps discover main themes and sub-themes in large data of text feedback. 

For example, a business collecting hotel feedback would have several topics like-

• location
• staff
• amenities
• facilities
• cleanliness
• price
• food
• services

What topic modeling does is it clubs together feedback talking about these themes and even goes further to bifurcate them into different sub-themes. 

So, here the large topics would be facilities, cleanliness, location, staff, amenities, services, price, food, etc. And every sentiment around that topic would be displayed in front of it. 

Our feedback software not only automatically detects the major recurring themes, topics, sub-themes, and tags, but also allows them to manually add tags based on the categorization they desire. 

You can easily put your model into learning mode to view suggestions of new tags, themes, sub-themes as more and more reviews come in that can be added to the company’s custom AI model. 

Even for tagging, there can be an option where it would match feedback with existing tags, suggest new tags based on text analysis, or automatically generate new tags. 

6. Highlighting Text Based on Emotions Detected

Not every feedback would be white or black (read positive or negative). There would be a lot of gray space. This is where neutral or mixed sentiments come in.  

Any customer feedback can have multiple negative or positive statements. With emotion detection technology in AI-driven feedback analysis software like Zonka Feedback, you will also get all the negative as well as positive statements within the text highlighted in red or green. This would be in addition to the overall sentiment that would be shown alongside the feedback. 

The image below showcases how a feedback comment would look once AI has worked its magic on analyzing the sentiments and highlighting statement-based sentiments. 

Business Benefits of AI Feedback Analysis

Now that we know the different insights that can be generated by powering a feedback management tool with AI, let’s understand how businesses can benefit from it. 

1. Increased Customer Satisfaction & Improved Experience

Most businesses collect customer feedback to measure customer satisfaction and improve the overall customer experience. 

By using AI for customer feedback analysis, businesses can not just facilitate faster identification and response to negative feedback but also help them address customer concerns proactively. The AI engine can quickly analyze the negative sentiments expressed in the feedback, extract entities connected to it, and help identify areas that require improvement. 

Moreover, AI can detect emerging issues or trends in customer feedback before they escalate into larger problems. This allows businesses to address issues proactively, preventing dissatisfaction and showing customers that their concerns are taken seriously.

2. Enhanced Customer Insights

Normal feedback analysis gives a picture of the different scores that the customers share, their comments, and whether they are satisfied, dissatisfied, had to put in high or low efforts while getting issue resolution, or their likelihood to recommend the business to others. 

However, today’s CX leaders believe that these insights are not enough. 

To get a complete picture of the customer’s insights, one needs more than just the surface-level scores. With AI-driven tools like Zonka Feedback, businesses can get a comprehensive picture of customer insights that go beyond sharing their sentiments, emotions, intent, and urgency. It also identifies entities, themes, trends, and emerging topics from feedback data to offer actionable insights. 

It can uncover areas of improvement, pain points, and even opportunities to help serve the customers better. 

3. Eliminating Errors in Repetitive Tasks

Any manual task is prone to human error. Artificial Intelligence and Natural Language Processing eliminate various types of errors in customer feedback processing and interpretation, including:

• Inconsistent categorization: AI algorithms can categorize and tag feedback consistently and accurately, reducing the likelihood of inconsistencies or misclassifications that may occur with manual categorization by different individuals or teams.
• Missed Insights: AI can analyze large volumes of feedback datasets quickly and comprehensively, ensuring that no valuable insights or trends are overlooked due to human limitations in processing vast amounts of information within a limited time frame.
• Biases in Analysis: AI feedback analysis can mitigate biases that may inadvertently influence human analysts' interpretations of feedback data, such as confirmation bias or subjective judgment. AI algorithms process data objectively based on predefined criteria, reducing the risk of biased analysis.
• Delayed Response Time: AI-driven real-time analysis enables businesses to respond promptly to customer feedback, minimizing delays in addressing issues or concerns raised by customers and preventing dissatisfaction resulting from slow response times.
• False Positive/Negative: AI algorithms can reduce the occurrence of false positives (misidentifying non-issues as issues) and false negatives (failing to identify actual issues) by applying sophisticated pattern recognition and natural language processing techniques to accurately detect meaningful insights from feedback data.
• Language Barriers: Most AI-driven feedback systems work in multiple languages and would instantly translate feedback to analyze sentiments and overcome any language barrier.

4. Refine Products & Services with Precision

AI goes beyond basic sentiment analysis. It identifies recurring themes, frustrations, and desired features within customer feedback. This allows you to refine products and services based on precise customer needs, not just assumptions.

AI feedback analysis also helps categorize and prioritize customer concerns, enabling you to focus product development efforts on the areas with the most significant impact. Product managers can also aim for targeted improvements by analyzing feedback on specific product features or customer segments to identify key areas, leading to more effective product iterations.

5. Simplify Data Transformation

AI automates the analysis of vast amounts of unstructured customer feedback received from different data sources like open-ended feedback, reviews, and social media comments. This eliminates manual effort and reduces the time needed to extract valuable insights.

With its real-time analytics, AI can analyze customer feedback as it comes in, providing instant insights into customer sentiment and emerging trends. This allows for quicker identification of issues and faster response times. Moreover, AI can use topic modeling, tagging, themes, and sub-theme identification to categorize and organize feedback. This helps streamline the process of transforming raw data into actionable insights.

6. Enhanced Efficiency and Scalability

AI is boundless. The more data it is fed, the smarter its algorithms become. 

So, without a doubt, scalability is never an issue when you are using AI for feedback analysis. 

With an AI-driven tool for customer feedback management, your feedback collection process is automated, along with faster preprocessing of data. 

So, no matter how many campaigns you want to run, locations you want to cover, or even span globally, AI can help do it in seconds. 

7. Proactive Issue Resolution

Artificial Intelligence holds the power to identify the changing sentiments of users and showcase trends and customer concerns. 

AI algorithms can quickly identify and flag emerging issues or concerns raised by customers in feedback. This allows businesses to address problems proactively before they escalate, mitigating potential damage to brand reputation.

Zonka Feedback’s AI-driven Feedback Analysis

Theoretical knowledge of AI and NLP technologies isn’t enough for a business like yours to gain a competitive advantage. You need to get into the practicality of things. 

With the abundance of customer feedback analysis tools available, it's crucial for businesses to select the right one. 

Zonka Feedback is an AI-powered customer experience and feedback management software that turns your customer feedback data into actionable insights. 

From feedback collection to reporting and analysis and closing the feedback loop, it incorporates AI in every aspect. However, the major business advantage lies in its AI feedback analysis. 

The tool’s Analytics dashboard is lined with the latest reports including text analysis, sentiment analysis, and topics that will share a drill-down report of how the customers are feeling. 

For every open-ended question that you include and get responses from, Zonka Feedback will showcase them in the response inbox. On the dashboard, you can view the sentiment score, statement-based sentiments, urgency, and intent. However, upon clicking the response, a drill-down report appears. This shows the intent, entities, highlights, and much more to give context to your feedback. 

All through AI!

You can also try Zonka Feedback’s all-new AI-powered feedback analysis. Sign up for a free 14-day trial or schedule a demo to get a detailed walkthrough of how this tool can align with your CX efforts.