Top 8 AI Tools That Read Customer Reviews for Actionable Feedback

Customer reviews can reveal what your customers love - and what they don’t. But manually analyzing thousands of reviews is time-consuming. That’s where AI tools come in. These tools help businesses quickly extract insights from reviews, identify trends, and make data-driven decisions.

Here are 8 AI tools that can transform how you analyze customer feedback:

• BuildBetter: Consolidates feedback from 100+ platforms, offers AI transcription, and prioritizes product updates.
• Qualtrics: Uses AI to analyze sentiment, predict customer needs, and integrates with tools like Salesforce.
• Chattermill: Tailored AI for deep insights, real-time monitoring, and predictive analytics.
• MonkeyLearn: Build custom models to analyze text and extract key data points.
• Clarabridge: Focuses on emotions, intent, and effort in customer feedback, now part of Qualtrics.
• IBM Watson NLU: Advanced sentiment and emotion analysis with customizable features.
• Lexalytics: Industry-specific solutions for text analysis with multi-language support.
• Revuze: AI-powered VoC platform for extracting insights from reviews, surveys, and social media.

Quick Comparison

These tools save time, improve accuracy, and help businesses act on customer feedback faster. Choose the one that fits your needs and integrates seamlessly with your current systems.

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1. BuildBetter

BuildBetter is an AI-driven platform designed for B2B product teams to analyze unstructured customer feedback. Using advanced natural language processing (NLP), it gathers feedback from both external sources and internal communication tools. BuildBetter integrates with over 100 platforms, including Zoom, Slack, Jira, Salesforce, Zendesk, HubSpot, and Intercom, allowing teams to consolidate feedback in one place.

Key features for analyzing customer reviews include:

• AI transcription for customer calls and support tickets
• Automated insights generated from scattered data
• Real-time AI chat for instant insights
• Smart meeting summaries to organize feedback
• Custom workflows to prioritize product updates

Pricing Options

BuildBetter provides several plans to suit different needs:

Data Protection Standards

BuildBetter complies with GDPR, HIPAA, and SOC 2 Type 2 standards, ensuring secure handling of sensitive customer data. By focusing on organizing feedback into actionable insights, the platform helps teams quickly identify trends and prioritize updates that align with customer needs.

Next, we’ll take a closer look at Qualtrics and its AI-based review analysis tools.

2. Qualtrics

Qualtrics is an Experience Management (XM) platform that uses AI to turn customer reviews into actionable insights. Here's a breakdown of its standout features for review analysis.

Key Features for Review Analysis

Real-World Implementation

In 2023, Hilton rolled out Qualtrics across 7,600+ properties. The platform analyzed feedback from multiple sources - call centers, chatbots, emails, in-app responses, and surveys - to enhance overall performance.

Next, let’s look at how its advanced AI tools and integrations take feedback analysis to the next level.

Advanced AI Capabilities

Qualtrics has invested $500 million in AI to create more personalized customer experiences. Brad Anderson, President of Product, UX, and Engineering at Qualtrics, highlights this focus:

"Fueled by our $500 million commitment to AI innovation, Qualtrics is making it easier than ever for customer experience teams to rapidly and meaningfully deliver the superior, personal, and human experiences their customers are looking for."

This investment is timely, as 80% of customers change brands after poor experiences, and businesses stand to lose $3.7 trillion due to inadequate customer interactions.

Integration Capabilities

Qualtrics connects seamlessly with tools like Salesforce and Zendesk, giving teams a full view of customer feedback for quicker, informed decisions. Key integrations include:

• Salesforce for managing customer relationships
• Marketo for automating marketing efforts
• Adobe for managing digital experiences
• Microsoft Teams and Slack for team collaboration
• Zendesk and Intercom for customer support

These integrations create a unified feedback system, helping teams resolve issues faster.

3. Chattermill

Chattermill uses deep learning AI to turn feedback into actionable insights. Its tailored AI model provides detailed insights, especially useful for product teams.

Key Performance Metrics

These metrics highlight Chattermill's ability to deliver effective analysis.

AI-Powered Analysis Features

Chattermill's AI engine evaluates feedback with these ratings:

• Text Analysis: 4.7/5
• Sentiment Analysis: 4.4/5
• Customer Experience Management: 4.7/5
• Reporting/Analytics: 4.6/5

Real-World Applications

HelloFresh serves as a great example of Chattermill's impact. Steve Crolic, Associate Director of VoC at HelloFresh, shares:

"Chattermill has transformed our focus on customers. We can measure the impact of customers complaining about packaging, quality of ingredients, or recipe instructions, on retention and revenue, and take steps to improve."

Advanced Capabilities

The platform offers several standout features:

• Real-Time Monitoring: Alerts teams to important metric changes and new trends
• Impact Analysis: Pinpoints issues affecting metrics like NPS and CSAT
• AI-Generated Summaries: Automatically analyzes main feedback themes
• Predictive Analytics: Anticipates customer needs and potential challenges

Harsha Gavarna, Former Strategy & Ops Lead at Uber, highlights:

"Chattermill unifies our customer experience and customer feedback data, examines it at scale using deep learning AI, and provides insights that aid us in understanding customer experience at an incredibly granular, actionable level."

With a 4.5/5 rating from 169 reviews, Chattermill stands out as a leader in feedback analytics.

4. MonkeyLearn

MonkeyLearn is a platform that uses machine learning for text analysis, turning raw customer reviews into useful insights.

Key Features

MonkeyLearn uses two main types of models:

Build Your Own Models

With its user-friendly interface, MonkeyLearn allows teams to create and test custom machine learning models using their own data - right away and without needing technical expertise.

Pre-built Models

For quicker results, MonkeyLearn also offers pre-trained models designed for tasks like sentiment analysis and topic identification.

How It Works

By combining classification and extraction tools, MonkeyLearn processes customer reviews to deliver insights that help product teams make informed decisions.

5. Clarabridge

Clarabridge, now part of Qualtrics XM, uses natural language understanding (NLU) to analyze customer feedback from platforms like social media, product reviews, and support conversations, turning it into actionable insights.

Advanced Language Processing

The platform leverages over 150 industry-specific NLU models to examine customer feedback across three main areas:

Automated Insights

Using its conversational analytics system powered by iQ™ technology, Clarabridge processes feedback to:

• Monitor channels for early detection of issues and trigger workflows
• Highlight critical pain points in support interactions
• Spot key opportunities for improvement

These insights are then used to drive quick and strategic responses through its xFlow feature.

Real-time Action Management

The platform enables teams to act immediately on insights. With the xFlow feature, product teams can quickly address customer feedback.

"The Qualtrics XM platform allows you to understand what people are saying about your company, wherever they're saying it. From the contact center to social media to online reviews - and everywhere in between." - Qualtrics

Integration Capabilities

Clarabridge integrates seamlessly with tools like Microsoft Dynamics 365 and Customer Voice, offering:

• Automated updates to customer management systems
• Efficient closed-loop feedback management
• Enhanced profiles for better product strategies and reduced churn

6. IBM Watson Natural Language Understanding

IBM Watson Natural Language Understanding (NLU) uses deep learning to turn unstructured reviews into useful insights for businesses.

Advanced Text Analytics

Watson NLU analyzes customer feedback across several dimensions:

These insights provide a foundation for data-driven decisions that can directly impact business strategies.

Measurable Business Results

Companies using Watson NLU have reported impressive outcomes:

• A 50% reduction in the time spent analyzing customer feedback
• $6.13 million in benefits over three years
• 383% ROI for businesses adopting the solution
• A 5% annual revenue increase through better product decisions

Tailored Intelligence

Watson goes beyond standard analytics by allowing teams to customize its capabilities. With Watson Knowledge Studio, companies can train the system to understand specific industry jargon or technical terms. This ensures more precise analysis tailored to their unique needs.

"IBM Watson® Natural Language Understanding uses deep learning to extract meaning and metadata from unstructured text data. Get underneath your data using text analytics to extract categories, classification, entities, keywords, sentiment, emotion, relations and syntax." - IBM

Flexible for Enterprise Use

Watson NLU offers features designed for large-scale operations:

• Supports 13 languages, enabling global feedback analysis
• Can be deployed securely behind firewalls or on any cloud platform
• Handles millions of feedback items monthly
• Pricing starts at $0.003 per item for the Standard Plan

Its real-time processing and detailed classification system allow teams to quickly address issues and prioritize updates effectively.

Next, we’ll take a look at another tool that can enhance review analysis even further.

7. Lexalytics

Lexalytics helps product teams turn raw customer feedback into actionable insights. Using text analytics and natural language processing, it transforms unstructured data into clear business strategies.

Industry-Specific Solutions

Lexalytics tailors its tools to meet the needs of various industries. Here are some examples:

Advanced Text Analysis Features

The platform combines machine learning with natural language processing to analyze feedback from sources like social media, surveys, online reviews, and support tickets. This approach extracts meaningful insights that businesses can act on.

Lexalytics also supports extensive customization and can be integrated across a wide range of industries.

Customization Options

With a 4.3/5 customization rating on Gartner Peer Insights, Lexalytics offers features such as:

• Multi-language support
• Emoji interpretation
• Custom dictionaries for industry-specific terms
• Configurable sentiment analysis rules

Performance Metrics

Gartner Peer Insights provides the following ratings for Lexalytics:

• Overall: 4.0/5
• Product Capabilities: 4.1/5
• Ease of Deployment: 4.3/5
• User Recommendation Rate: 67%

Real-World Applications

One global food and facilities management company used Lexalytics to analyze thousands of client interviews. This led to measurable improvements in customer satisfaction and retention. However, larger datasets may require additional processing time and initial training.

8. Revuze

Revuze takes a fresh approach to analyzing customer feedback using AI-powered tools designed to deliver actionable insights.

Revuze is a Voice of Customer (VoC) platform that uses AI to analyze feedback with impressive accuracy - achieving 90% precision and recall in sentiment analysis and topic extraction.

How It Works

Revuze collects data from a variety of sources, including product ratings, customer reviews, social media discussions, surveys, and customer support interactions. This comprehensive data collection ensures a well-rounded view of customer sentiment.

Tailored Solutions for Different Teams

The platform offers specialized tools, known as ActionHubs, for teams in Product Development, Marketing, Competitive Intelligence, eCommerce, and Reporting. These hubs are designed to meet the unique needs of each department.

Helping Brands Make Smarter Decisions

Revuze has helped major brands streamline their processes and make informed decisions.

One user shared how the platform changed their approach:

"Since we partnered with Revuze, so many people in our organization now get true consumer insights when we do a new product development. It really impacts so much of what so many teams do, whether it's product, market research or consumer insights … now everyone understands that they have a piece of it."

L'Oréal used Revuze to speed up decision-making and bring products to market faster, thanks to the platform's adaptability and reliable data. Char-Broil also shifted to data-driven strategies through Revuze’s automated analytics.

Seamless Integration with Your Tools

Revuze works with popular BI tools like Power BI and Databricks, supports secure cloud storage sharing, and includes built-in chat features via CoPilot. It also offers Delta Share data access, making it easy to incorporate insights into your existing workflows.

Simplifying Data Analysis

Revuze’s AI engine cleans and processes data, removing irrelevant information and highlighting key trends. This has enabled companies like Pilot Pen to move from manual review tracking to automated analysis.

"Before we had Revuze, we were doing screenshots of our Amazon reviews and manually putting them into an Excel spreadsheet. That was not the most efficient way to garner information."

Revuze provides a comprehensive solution for businesses looking to turn customer feedback into meaningful action.

Choosing the Right AI Review Analysis Tool

Picking the right tool means finding one that fits your needs and helps you turn data into useful insights. Here's what to focus on:

Integration and Workflow Compatibility

Make sure the tool works smoothly with your CRM and data storage systems. Seamless integration ensures it fits into your existing processes without causing disruptions.

Data Processing Capabilities

The tool should be able to handle:

• Unstructured support tickets
• Reviews from various channels
• Survey responses
• Social media discussions
• Internal company data

Accuracy and Insight Quality

Look for features like:

• Detailed data tagging for in-depth insights
• Precise sentiment analysis
• Real-time data processing

Relying on manual analysis can leave you with outdated insights.

User Experience and Team Adoption

A user-friendly interface with clear data visualization is key. It helps your team adopt the tool quickly and encourages collaboration across departments.

"Finding ways to get qualitative feedback and surfacing qualitative trends, in my experience, has been the most valuable for actually making changes to a product or an operational process to improve the experience." - Megan Bowen

Key Features to Compare

Measuring ROI

"It drives the right focus on product direction and digital services. Most importantly, it allows leaders to pivot customer service from being a low-value cost center to a higher value, commercially focused, business-aligned unit."

Efficient tools can deliver measurable ROI. Metrics to consider include:

• Time saved on analysis
• Accuracy of insights
• Speed of delivering insights
• Team adoption rates
• Costs for integration and maintenance

Privacy and Security

Don't overlook data protection. Ensure the tool meets all necessary security standards to safeguard customer information.

The best tool should help you turn raw data into meaningful insights that improve your products and create a better customer experience.

Related posts

• How AI Analyzes Customer Sentiment in Real Time
• 10 AI-powered tools for analyzing the voice of the customer
• 6 AI Tools That Read Customer Reviews (& How to Use Them)
• Use AI To Make Customer Feedback Analytics More Actionable