As a content marketer, I’m constantly experimenting with new ways to incorporate AI into my workflow — to save me time and energy to focus on what I’m best at. I’ve found a ton of interesting use cases in my research, especially how you can use AI for customer feedback analysis.

In our State of AI Report, we spoke to customer service experts to learn more about how they are using AI in their workflow. It turns out 28% of customer service experts use AI to collect and analyze customer feedback, making it the second most popular customer service use case for AI/automation. (First place goes to routing requests to reps, at 29%.)

To learn more about the process, I speak to folks already walking the walk within their businesses. We discuss the tech’s benefits, limitations, and practical applications below.

Table of Contents

• Why use AI for customer feedback analysis?
• How to Use AI for Customer Feedback Analysis
• AI Feedback Analysis: Is It Worth It?

Why use AI for customer feedback analysis?

It’d be all too easy for me to praise AI without a second — let alone critical — thought. (Isn’t that the cool thing to do now?) And I genuinely believe customer support is an area where the pros of AI can outweigh the cons — if you apply the technology thoughtfully within your operations.

But here’s the thing: For you to make an informed decision about how, or even whether, to apply AI to customer feedback analysis, I need to go beyond the benefits. So, for each benefit, I share a corresponding limitation. The cherry on top? Benefit or limitation: I’ve paired each with a real-world example.

Benefit: Analyzing Vast Amounts of Data at Speed

There’s no two ways about it: AI can gather, segment, and analyze an amount of data that’s simply unfathomable to our human brain. What’s more, it can do so in mere minutes — seconds, even. It all depends on the complexity of the task.

If you struggle to wrangle all the customer feedback data you receive across multiple channels, AI could help you analyze it and reap the benefits.

Real-World Example

“I recently worked with a SaaS company that was struggling to keep up with the volume of customer feedback they were receiving,” says Richard Dalder, a business development representative at Tradervue.

To address this issue, Dalder’s team implemented an AI-powered feedback analysis tool. The tool automatically collected and categorized feedback from all channels, using natural language processing (NLP) to understand the context and sentiment behind each comment. “It then generated reports that highlighted key insights, such as common pain points, feature requests, and positive experiences,” Dalder recalls.

Limitation: Fewer Face-to-Face Interactions with Customers

Yes, AI can reduce the time and resources needed for customer feedback analysis. The downside? You can develop a bias toward gathering written customer feedback. We’re talking email, social media, and customer surveys — all of which are helpful.

But in the long term, you risk having fewer face-to-face interactions with customers. Face-to-face customer interactions are a chance to build a genuine rapport, ask follow-up questions, and gain honest insights you might not have gathered otherwise.

Real-World Example:

Justin Silverman, founder and CEO at Merchynt, says their company uses AI now for every step of the customer journey.

That has given the ability to provide their customers with “a near-instant personalized strategy plan based on information we gather during their sign-up process,” says Silverman.

The trade-off? According to Silverman, Merchynt has experienced fewer interactions with our customers. That means they are missing valuable opportunities to gather feedback on how we can continue to improve our offerings.

Benefit: Sentiment Analysis

In 2024, 69% of consumers “would feel positive about using a business if its written reviews describe positive experiences.” Most consumers expect to see responses to their reviews within two to three days (34%) or a week (22%). Further, 11% expect a response the same day — yikes!

Through sentiment analysis, AI can help you understand the emotional context and, thus, the level of urgency behind the customer review (aka feedback.) Here’s how.

Let’s say 100 customers have left you product reviews in the last week. AI can analyze and segment those reviews into categories based on language sentiment. It can also prioritize the reviews in order of urgency. That means you can respond to the most negative customer reviews first and de-escalate the situation before it worsens.

Pro tip: Scale customer support, drive retention, and improve response times with Service Hub.

For a bonus, the faster you respond with a customer service solution, the more likely customers are to withdraw the negative review. Even if they don’t, it shows other customers that you’re timely in your responses.

But wait, there’s more. AI sentiment analysis can also help you improve your products and services.

Real-World Example

“One key application is sentiment analysis, where we use AI to analyze the emotions expressed in customer reviews, emails, and social media mentions,” says Sam Speller, the founder and CEO of Kenko Tea.

“But rather than simply detecting whether something is positive or negative, we can deploy AI to detect more subtle emotions — anger, confusion, delight — in customer comments,” Speller notes.

For example, Speller thinks back to a few months ago. Through the AI sentiment analysis tool, Kenko Tea started seeing more reviews mentioning “inconvenient packaging” in relation to loose-leaf matcha.

Speller adds, “Nobody complained about the quality of the matcha, just the resealable pouch, which wasn’t easy to reseal. We were able to find and implement a new pouch design within a few months, after which the number of negative reviews about packaging dropped by half, and customer satisfaction scores increased by 10%.”

Limitation: Understanding of Nuance

Don’t get me wrong; AI is getting better at understanding the broader context and nuance behind human language. Hence, “sentiment analysis” being added as a benefit of using AI in customer feedback analysis.

That said, sometimes AI might struggle with more challenging issues like high frustration or cancellation intent. And if I’m honest, many folks brought this up as their primary limitation with AI feedback analysis.

I think this is where you need human intervention within the process. By all means, you can use AI to speed up data gathering, analysis, and segmentation. However, a human agent or team member should be involved to review and validate the insights.

Or as Sam Speller puts it: “AI isn’t yet capable of context and nuance. Our human reps are still vital for understanding the ‘why’ behind the sentiment and for adding the personal touch.”

Real-World Example

“AI’s limitations include potential misinterpretation of nuanced feedback and a lack of creativity and emotional understanding,” says Sally Bannerman, director of product marketing at ICUC.Social. According to Bannerman, human analysts complement AI in this area.

“Once our human analysts review these AI reports, they interpret the insights to double-check accuracy and identify more nuanced responses. This is key to connecting the dots between raw data and actionable advice because a robot simply won’t have the contextual knowledge needed to interpret certain trends properly,” Bannerman says.

Bannerman adds that this misinterpretation can lead to inaccurate sentiment analysis and skewed data.

“This is when it is important for human analysts to step in and review the data to provide context, as they’re more likely to piece this information together and make sure the feedback is correctly understood,” Bannerman says.

How to Use AI for Customer Feedback Analysis

Here are eight ways folks use AI to fuel their customer feedback analysis processes.

1. Analyze customer reviews at scale.

Matthew Franzyshen, business development manager at Ascendant, has been a driving force behind the implementation of AI solutions into business processes.

“One major benefit I’ve experienced is the ability to process vast amounts of feedback data in record time,” says Franzyshen. “We once analyzed over 10,000 customer reviews in just a few hours, a task that would have taken our team weeks to complete manually.”

Although AI excels at quantitative analysis, like Speller and Bannerman, Franzyshen also warns us about nuance: “I’ve found that it sometimes struggles with nuanced language or sarcasm, potentially misinterpreting the true sentiment of feedback. To mitigate this, we always have human oversight to validate AI insights.”

2. Determine client satisfaction.

Next up, VP of Analytics Services Ben Vaughan, shares two ways Brewster Consulting Group uses AI.

“Within Brewster Consulting’s clients, we leverage an AI Notetaker (fireflies.ai) to record our meetings and transcribe them. Fireflies.AI has a Chat-GPT-like tool embedded into their platform that allows you to query based on the transcript of a conversation.”

Vaughan adds: “So we’ll ask it things like ‘Based on the conversation, do you believe that Client X feels they are getting a good value for their money?’ or ‘Does Client X exhibit any indications that they may end our relationship in the near future?’ These insights provide a bias-free opinion on client satisfaction.”

3. Facilitate text mining.

“When we do analytics work for our clients, the primary area that we leverage AI in is text mining,” continues Vaughan. “When a customer has a large number of, say, survey responses, we leverage AI to create sentiment analysis and find common words/phrases in the text responses to better tailor the customer’s product.”

Vaughan notes that AI analyzes the responses much faster than a human could and can provide customers with tangible feedback they can use.

“A couple of example phrases we‘ve pulled out are: ‘This event needed more activities’ or ‘The product doesn’t taste like [its competitor],’” says Vaughan.

4. Develop a product roadmap.

Lucas Carval, growth specialist at Mention, shares their use case for AI in customer feedback analysis.

“We recently used AI at Mention to analyze all our reviews from G2 and Capterra, our NPS scores, and feedback from churned customers to better position ourselves in the social listening market,” says Carval.

Carval AI helped the team condense hundreds of reviews into a 10-minute report, which highlighted what customers liked about the software and areas for improvement. Carval categorized the customer feedback into three groups (positive/neutral/negative) using sentiment analysis.

Carval continues, “Based on this information, we’ve developed a product roadmap to address the main issues customers were encountering (mainly to improve the ease of use of our software). This analysis will guide our development for Q3 and Q4.”

5. Forecast trends and opportunities.

Founder and CEO of Kenko Tea Sam Speller, shares more about their experience with AI-driven customer feedback analysis.

“AI can also help us forecast trends and opportunities by analyzing the key topics and keywords that appear most often in customer feedback,” says Speller.

For example, Speller recalls, the company learned from reviews that customers were increasingly looking for recipe inspiration.

“This led us to develop a thorough recipe section on our website as well as our email campaigns with ever-expanding collections of healthy and delicious ideas for incorporating matcha into meals, snacks, and drinks,” Speller says.

The result? “We are able to anticipate and meet many of our customers’ needs, increasing brand loyalty and encouraging repeat business.”

6. Analyze customer emails at scale.

“We use AI to process and understand thousands of customer emails every day,” says Chad Gouws, founder at FDB Analytics. In doing so, FDB Analytics hopes to achieve three things:

1. Understanding how the customer feels about their current problem and the insurance company’s way of handling it so far. If the email is flagged as negative, it is prioritized so that the customer success team can solve the problem, Gouws notes.

2. Identifying trends in the issues that customers face. “We want to find patterns or parts of the business that are having issues so we can solve these issues at their root cause, improving the overall customer experience,” Gouws says.

3. Identifying which competitors customers are mentioning and dealing with to gain an understanding of the insurance landscape.

Gouws warns that data is the main limiter to this approach: “If customers are not communicating through consistent channels or the data is not available in an accessible manner, this method struggles to produce results.”

7. Automatically categorize and prioritize feedback.

“In my experience, one powerful use case for AI in customer feedback analysis is automatically categorizing and prioritizing feedback,” says Jon Gordon, managing partner and co-founder at Sheer Velocity.

At a previous company, Gordon says, there were thousands of customer support tickets and product reviews pouring in daily. Manually sorting through all that unstructured text data was incredibly time-consuming and error-prone.

According to Gordon, they implemented natural language processing models to “automatically tag feedback as relating to specific product areas, surface high-impact issues based on sentiment analysis.” They could then route items to the appropriate teams.

Gordon adds: “This AI-driven workflow allowed us to be incredibly responsive to customer needs, quickly addressing pain points and requests for new features. The AI streamlined what would otherwise have been an unmanageable deluge of data into clear, actionable insights.”

8. Anticipate customer issues before they escalate.

Alari Aho, CEO and founder of Toggl, cites their use of AI in customer feedback analysis as pivotal in enhancing Toggl’s suite of productivity tools. But how are Aho and the team using AI to achieve that?

“We employed AI-driven predictive analytics to anticipate customer issues before they escalate,” says Aho. “By analyzing historical feedback data, AI models can predict which features or aspects of our tools are likely to cause user frustration or delight.”

According to Aho, this proactive approach helps refine Toggl’s product development strategy and customize its customer service responses.

“We specifically use AI here because it allows us to anticipate and mitigate potential problems, ultimately leading to a proactive rather than reactive customer service approach, thereby increasing customer loyalty and satisfaction.”

Despite the success, Aho warns that the initial setup and training of AI systems require “substantial time and data to function effectively.” That said, “the long-term gains in customer engagement and operational efficiency are well worth the investment.”

AI Feedback Analysis: Is It Worth It?

Short answer: Yes. However, it really depends on your situation and setup.

If you have a TON of customer feedback from reviews, emails, and survey responses and struggle to wrangle the data manually, then yes, an AI-driven feedback analysis is worth it.

If you’re a freelancer with a client roster of five? The juice from AI feedback analysis probably won’t be worth the squeeze. You’d be wiser to spend that time on delivering great work for your clients.

That said, even if it makes sense to use AI to speed up and improve customer feedback analysis, it isn’t a “perfect” solution. That’s not to say you shouldn’t benefit from AI’s strengths (volume, speed, segmentation). But remember to pair this with human input and intuition.

When left to its own devices, AI often struggles with nuance. For example, it might completely overlook the sarcastic tone of a review and misinterpret it as the hallmark of a positive customer experience. (Awkward!)

Nuance aside, AI still can’t deliver that personal touch quite like a human can. So, remember to gather face-to-face customer feedback and use your intuition to analyze it occasionally.

Managing a customer’s experience is now a requirement to succeed – in fact, 90% of businesses are making it a priority.

Throughout my career managing various customer-facing teams and consulting for numerous clients, I have learned two critical factors that shape a customer’s experience:

• Do your products/services fulfill an unmet need in your target market?
• Does your team create moments of customer delight?

Once you have a handle on those questions, the next step is to consolidate all of the interactions a customer has with your business. But what functionality and features do you need to address your organization’s use case?

The answer lies in a customer experience (CX) platform. This platform centralizes all touchpoints within the customer journey – from interacting with your product to engagements with your team pre- and post-sale. It provides you and your leadership with a comprehensive view of customer sentiment and loyalty.

Table of Contents

• What Is a Customer Experience (CX) Platform?
• Types of CX Platforms
• How to Choose the Right Customer Experience Platform
• Customer Experience Platform Examples
• A CX Platform is Crucial for Success

A CX platform either natively records or integrates data to deliver actionable insights for your team. By leveraging this platform, organizations can better understand their customers needs, behaviors, and preferences – enabling their teams to enhance satisfaction and retention.

The Importance of a CX Platform

In today’s competitive environment, delivering exceptional customer experiences can be a competitive differentiator.

A CX platform allows your organization to:

1.  Gain a holistic view of the customer journey. By consolidating data from multiple touchpoints, businesses can see the complete customer journey and identify areas for improvement.
2.  Enhance decision-making. With real-time insights, companies can make informed decisions to boost customer satisfaction and loyalty.
3.  Drive personalization. Understanding individual customer preferences allows organizations like yours to tailor experiences, making each interaction more meaningful, impactful, and personal.

Implementing a CX platform correctly streamlines all customer interactions, while also empowering your team to deliver outstanding service on a consistent basis. This is an ideal way to follow through on the promises made in the sales process, and can be codified in an overall strategy. Keeping your customers happy and retaining them by even 5% can increase profits by 25-95%.

The CX platform translates your strategy into real actions your team can use to realize customer satisfaction, retention, and growth. (If you want to dig more into developing your CX strategy, check out our free Post-Sale Playbook.)

That said, there are numerous types of CX platforms to consider what is right for you, and I’ll explore some of them below.

Types of CX Platforms

CX platforms come in various forms, each designed to address different aspects of the customer journey and provide unique insights. While some tools employ numerous features and address an organization’s customer experience strategy, others are highly specialized and concentrate on one distinct part.

Nonetheless, these platforms are barometers your organization can use to quantify the impact their teams are making on their customers, identify pain points to increase satisfaction and retention, and employ strategies to encourage growth.

Here are some types of CX platforms:

Customer Relationship Management (CRM) Systems

CRM systems manage and analyze customer interactions and data throughout the customer lifecycle. They help organizations visualize the customer journey, improve relationships, retain customers, and measure growth.

Examples: HubSpot, Microsoft Dynamics 365, Salesforce

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Customer Feedback and Survey Platforms

These platforms collect and analyze feedback from customers through surveys, reviews, and other mechanisms. They help organizations gauge customer satisfaction, identify pain points, and gather actionable insights.

Examples: Medallia, Qualtrics, SurveyMonkey

Customer Support and Helpdesk Software

Customer support platforms consolidate functions used during interactions with customers – logging issues, troubleshooting, routing, live chat, and knowledge management – ensuring timely and effective resolution of customer issues.

Examples: Intercom, Freshdesk, Zendesk

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Voice of the Customer (VoC) Platforms

VoC platforms aggregate and analyze customer feedback from multiple sources. These provide deeper insights into customer sentiment and help organizations prioritize improvements.

Examples: Forsta, NICE (Satmetrix)

Customer Analytics Platforms

These platforms analyze customer data to uncover trends and behaviors, driving predictions around customer retention, growth, and churn. Moreover, these platforms provide queues around behaviors customers are taking, and help inform data-driven insights to enhance the customer experience.

Examples: Amplitude, Google Analytics, Mixpanel

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Loyalty and Rewards Platforms

Loyalty platforms manage customer loyalty programs, offering rewards and incentives to encourage repeat business and retention.

Examples: LoyaltyLion, Smile.io, Yotpo

Understanding the value add with these customer experience platform types, organizations can gain a deeper understanding of their customers, deliver personalized and consistent experiences, and drive customer satisfaction.

How to Choose the Right Customer Experience Platform

Ever since the early days of my career, I constantly thought about how I could quantify my actions to demonstrate my contribution and better collaborate with others in my organization. That mentality helped me consult better with customers, which is an important part of the customer experience.

When taking the criteria to choose the right CX platform, consider the following.

Quantify Actions

Understand the actions you need to perform and inform your team’s metrics and contribute to your organization’s business goal. Examples of this include customer calls, feedback surveys, renewal conversations, and onboarding milestones.

Quantifying your actions can show the correlation on your performance, as well as for your organization – improvement on customer sentiment, revenue retention, upsell/cross-sell opportunities, and referrals for future business.

Enhance Collaboration with a Customer-First Mentality

Ensure the platform facilitates collaboration within your customer-facing teams and across the organization, aligning with sales, marketing, customer success, product, and others. This can help promote and tie your actions (and others) towards embracing a customer-first mentality.

Enhanced collaboration and demonstrating a customer-first mentality can elevate your entire organization culturally. This fosters improved and positive relationships both internally and with customers, helping everyone understand other’s needs and desires, while aligning customer issues with their business goals and use-cases for context.

Customer Experience Platform Examples

1. HubSpot

HubSpot is a customer platform with an overarching mission to help millions of organizations grow better. This mission was certainly felt by me during my years working in the customer success department as a customer success manager and a consultant on their professional services team, before leading both sets of teams.

The platform has grown in numerous ways to embody the capabilities of a CX platform.

HubSpot is best for organizations wanting to scale their go-to-market and operations teams together in one system. From a one-person business to a 2,000+ employee enterprise, HubSpot customer platform has the marketing, sales, customer service, operations, and content management features you need to build the best experience for your customers.

There are also numerous ways you can translate your business goal, metrics, and initiatives through the platform so that you can record, store, automate, and view insights of how your customers are interacting with your business.

While there are numerous pricing tiers for HubSpot, there are also options to utilize a specific number of features to summarize your customer interactions, and how your team interacts with them in response.

Worth noting: HubSpot’s AI capabilities have been a highlight in the past year, and will only enhance their functionality for customers going forward.

2. SurveyMonkey

SurveyMonkey is a popular customer feedback and survey platform, and has a vision of “amplifying individual voices.” They certainly embody that through their user interface and product, the education curated on how to attract survey submissions, how to respond to that customer feedback, and connect feedback with your go-to-market motion.

The customization goes far beyond the core measurements of customer feedback like NPS and CSAT. SurveyMonkey’s product allows you to customize and structure your survey questions to balance the quantitative with the qualitative. For those interested in the data management aspect, SurveyMonkey’s answers can be entered into fields in numerous forms – numbers, open line text, enumeration, single/multi-checkbox, and more.

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SurveyMonkey’s value adds for me are simplicity and scalability. Moreover, their extensibility is demonstrated by their vast array of integrations to connect with other systems – especially other types of CX platforms – to seamlessly transfer their data and further inform the rest of the organization on customer’s feedback and sentiment.

Due to their emphasis on prioritizing the user experience, many of their pricing plans provide a progress check on how far a user is in a survey (and provide that in subsequent reporting on the backend as well).

Additionally, their AI capabilities feature greatly here to aid in the user interface, branded as SurveyMonkey Genius, to create customized surveys based on a prompt.

3. Zendesk

Zendesk has been providing solutions in customer support and helpdesk functionality for years in an effort to make the customer experience “extraordinary.” This mirrors part of a larger trend CEOs are reporting to invest more in customer service teams to gain a foothold in better understanding their customers.

They evolved from their initial customer support tool to incorporate sales and CRM functionality to align customer service and sales teams better.

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The core of this functionality, though, speaks to consolidating all types of interactions between a service team and customers. It gives service teams the ability to receive tickets, categorize issues, route and escalate, and view themes of common issues to inform better decisions for a management team. To automate repeated motions that a service team experiences, triggers help with scaling a service team’s operations.

Zendesk’s tools are a foundation that prioritize a customer service team’s importance on understanding and managing the customer experience. The industry would not exist without customer platforms like this powering the channels to receive, manage, triage, and respond to customer issues effectively and at scale.

Their expansion into CRM shows their willingness to embrace the alignment that is required to incorporate the customer experience to their service and go-to-market teams going forward. All the while, Zendesk has proven to be a platform that grows with you – their AI capabilities enhance decision making on where to invest in terms of your customers.

4. Google Analytics

Google Analytics can give you a complete understanding of your customers’ interactions in your organization, across brands, devices, and products. Renamed Key Events, this platform can measure user action taken on your website and can be pulled in numerous ways to provide insights to drive action for keeping users engaged with you.

The advantage of Google Analytics, and any Google application for that matter, is that its native connectivity to Google gives it the ability to seamlessly integrate with the suite of tools your organization uses. Furthermore, there are many customization options on the reporting side of things to give yourself full control of what insights you need to see, when, how, and how often.

Google Analytics also provides numerous recommendations and insights on user behavior to clarify your takeaways. Their flexibility in integrating with a myriad of systems seamlessly allows their analytics to show up against that of your other systems, so you are able to come up with a shared understanding of what your analytics mean for taking action and improving your organization’s acquisition and retention of customers.

A CX Platform is Crucial for Success

Customer experience, as IBM notes, will continue to evolve, especially with the rise of generative AI. Managing interactions and behaviors of your customers requires a system that drives actionable insights and aligns your organization to uplift the customer experience. Considering a customer experience platform is a necessary and important task to devote time and resources towards.