AI in VoC: Scaling Customer Insights

AI is transforming how businesses handle customer feedback, making it possible to process massive amounts of data, identify patterns, and act on insights faster than ever. Traditional Voice of Customer (VoC) methods often fail due to manual processes and fragmented data, but AI-powered systems solve these issues by automating analysis and delivering actionable insights.

Key Takeaways:

• Why AI Matters: Companies with strong VoC programs see 50% higher revenue growth and 55% better customer retention, but only 8% of customers feel they get great experiences.
• Challenges with Manual VoC: Bottlenecks, survey bias, and missed patterns are common issues.
• AI Benefits: AI tools like NLP and machine learning process unstructured data, detect trends, and adapt to customer needs.
• Real-World Impact: Faster resolutions, better customer sentiment analysis, and smarter decision-making are driving results for enterprises.
• Top Features to Look For: Integration with tools like Salesforce, real-time data processing, advanced analytics, and strong security measures.

AI-powered platforms like BuildBetter streamline VoC processes, helping businesses save time, reduce costs, and improve customer satisfaction. With flexible pricing and features tailored for enterprises, these tools are becoming essential for staying competitive.

How AI Improves VoC System Scalability

Automated Data Processing and Analysis

AI is revolutionizing the way businesses handle massive amounts of customer feedback by automating tasks that were once manual. For example, speech recognition technology can turn phone calls into searchable text, while sentiment analysis and natural language processing (NLP) quickly extract key themes and emotions. Imagine an international retail company scanning call transcripts for phrases like "order status" to improve its FAQ section - this is the kind of efficiency AI brings. It also tackles the complexities of multilingual communication, accurately interpreting diverse accents and dialects, which is a game-changer for global enterprises. Beyond these functions, machine learning takes automation a step further by identifying subtle patterns in customer interactions that might otherwise go unnoticed.

Machine Learning for Pattern Recognition

Machine learning algorithms are particularly skilled at detecting trends and recurring themes in unstructured data - something human analysts might overlook. Through text mining and classification, these tools automatically highlight common topics. For instance, an insurance company might use NLP to sift through call transcripts and identify frequent concerns like "policy renewal", "claim status", or "premium calculation." This not only streamlines operations but also helps train agents to address issues more effectively. Predictive analytics adds another layer of insight. Take a banking call center as an example: by monitoring spikes in calls about online banking issues, machine learning models can predict future increases, enabling the company to proactively update systems and communicate with customers in a timely manner. Liz Miller, VP and Principal Analyst at Constellation Research, sums it up well:

"We can implement granular processes that continuously extract real-time insights from customer conversations, feeding directly into product development and operational decision-making."

These capabilities don’t just uncover patterns - they lead to smarter, more efficient operations.

Efficiency Benefits of AI in VoC

AI’s ability to work around the clock, processing feedback instantly, is a major advantage. It reduces human error and bias, ensuring more accurate insights. Companies that excel in both customer and employee experience can even see revenue growth rates up to 1.8× faster than their competitors. Another strength of AI is its ability to categorize feedback into actionable insights, operational improvements, and long-term strategies. This ensures that the right information gets to the right decision-makers without delay. Finbarr Begley, Senior Research Analyst at Cavell Group, highlights the value of this approach:

"A single customer's sentiment with broader trends across all customer interactions, offering a more accurate and scalable approach to understanding customer needs."

Leveraging Generative AI for Customer Feedback | AWS Events

Must-Have Features in Enterprise AI VoC Platforms

When choosing an AI-powered VoC (Voice of the Customer) platform for enterprise use, the right features can dramatically enhance how businesses understand and act on customer feedback. These capabilities address the common challenges of scalability and integration that traditional VoC systems often face.

Integration with Business Tools

An enterprise-grade VoC platform needs to seamlessly connect with your existing tools. The best systems integrate directly with CRMs like Salesforce and HubSpot, communication platforms such as Zoom and Slack, and project management tools like Jira and Linear. This kind of connectivity ensures that customer insights flow effortlessly to the places where they’re needed most.

For example, integrating with CRMs like HubSpot and Zendesk can automate tasks like lead routing, streamline operations, and improve predictive analytics, saving time and effort for your teams.

Opt for platforms that include pre-built integrations and robust API capabilities. These features eliminate the need for manual data transfers, which often cause delays. Imagine a system that can automatically send customer sentiment data to your CRM or create support tickets. This kind of automation can significantly reduce response times and improve customer satisfaction.

Unified Data Integration

Beyond tool compatibility, a top-tier VoC platform should consolidate data from all customer touchpoints. Modern businesses collect feedback from a wide range of sources - support tickets, surveys, call recordings, chat logs, and more. The most effective platforms bring together both structured data, like NPS scores, and unstructured feedback from conversations, emails, and internal discussions.

Real-time data ingestion is another game-changer. For instance, if a customer expresses frustration during a support call, that sentiment should instantly update their profile. This immediate integration allows teams to identify broader patterns that might otherwise go unnoticed when looking at individual channels in isolation.

The platform should also handle diverse data formats without requiring extensive preprocessing. This ensures that all feedback, regardless of its source or format, contributes to a comprehensive understanding of your customers.

Advanced Analytics and Reporting

Basic sentiment scoring isn’t enough for enterprise needs. Look for platforms that deliver automated summaries of key themes, trend analysis to catch issues early, and in-depth insights that explain the "why" behind customer behaviors.

For example, in 2025, Melodics used Thematic's AI to uncover app performance issues that were negatively affecting NPS scores. This insight allowed the team to take immediate action and improve their product.

Reporting tools should cater to various stakeholders. Executives might need high-level trend summaries, while product teams require detailed feedback on specific features. Customer success teams benefit from real-time alerts about at-risk accounts, and support managers need operational metrics to address recurring problems.

Security and Compliance

Given the sensitive nature of customer data - personal details, conversations, and competitive insights - security is non-negotiable. Enterprise VoC platforms must include robust measures like data encryption, strict access controls, and continuous monitoring to protect this information.

Compliance is equally critical. Platforms should align with major frameworks like GDPR, CCPA, SOC 2, and ISO 27001. For instance, under GDPR, companies can face fines of up to €20 million or 4% of global annual revenue for noncompliance. Similarly, the EU AI Act, effective February 2025, introduced penalties of up to €35 million or 7% of annual turnover.

Permission management is another key feature. The ability to control who can access specific data ensures privacy while still enabling collaboration across departments. Clear audit trails also simplify compliance reporting. With nearly 70% of companies planning to increase investment in AI governance over the next two years, these controls are more important than ever.

Finally, platforms should include human oversight for AI-generated insights. Automated analysis should support, not replace, human judgment, especially in critical customer situations. These features help platforms like BuildBetter deliver secure, actionable insights that enterprises can trust.

BuildBetter: AI-Powered VoC Platform for Enterprises

BuildBetter is an AI-driven platform designed to scale Voice of Customer (VoC) processes for enterprises. With five years of experience in AI solutions, BuildBetter has earned the trust of 28,000 organizations and boasts an impressive 98% retention rate.

What sets BuildBetter apart is its ability to merge internal and external data sources into one cohesive platform. While many VoC tools focus solely on external feedback like surveys or support tickets, BuildBetter takes it a step further by analyzing internal communications, call recordings, and even Slack conversations. This approach provides a more complete understanding of customer and team interactions.

BuildBetter Platform Features

The platform offers native integrations with tools like Zoom, Microsoft Teams, Google Meet, and Webex, including a "no bot" local recording option to meet enterprise VoC needs. Its mobile app also allows teams to capture in-person or impromptu recordings.

Key features include:

• Workflows: Automatically tags and labels calls to generate project briefs.
• Chat: Delivers AI-powered assistance using company-specific data.
• Team Controls: Ensures proper permission management and compliance with privacy standards.
• Documentation: Converts unstructured data into structured reports with customizable templates.

BuildBetter supports over 100 enterprise platform integrations and operates in more than 37 languages, making it a globally viable solution.

"Congratulations on rolling out an AI product that actually works", says Seamus Smyth, AI Director.

The platform also includes CustomContext, which embeds company knowledge to enhance AI-generated insights, and scheduled digests that provide regular, structured updates. Product teams, in particular, benefit from highly accurate call summaries tailored to product-related discussions. These tools transform raw data into actionable insights, streamlining documentation processes.

From Data to Documentation

BuildBetter excels at turning qualitative data - like calls, tickets, and messages - into fully cited reports that teams can act on. By making every interaction searchable, the platform helps businesses extract real value from their data.

The documentation process is highly automated, allowing teams to generate reports such as Customer Issues Reports, User Stories, and Product Requirements Documents (PRDs). Auto-tagging and labeling make it easy to locate relevant information when needed.

"It wouldn't be possible to do my job at this scale without BuildBetter", says John Strang, Product Operations.

The platform significantly boosts team efficiency, cutting meetings by half, reducing decision-making time by 30%, and lowering operational work by 40%. It also shortens the time from customer insight to action by 50%. On average, teams save 18 hours per project, allowing them to spend 43% more time on revenue-generating activities.

"We don't operate without BuildBetter. This is the only platform that we use religiously", shares Aditya Goyal, Product Lead.

Pricing Plans for Different Enterprise Sizes

BuildBetter offers flexible, usage-based pricing with unlimited seats across all plans, ensuring organizations only pay for the time they save. This structure makes the platform accessible to teams of all sizes and adaptable to their needs.

The Starter plan at $7.99/month is ideal for small teams, while the Growing plan at $400/month suits mid-sized organizations. Larger enterprises with extensive data needs can opt for the Scaling plan at $1,600/month. For the largest organizations, the Enterprise plan offers custom pricing with unlimited usage, advanced privacy controls, custom SSO/SAML integration, and dedicated support. Discounts are available for startups, non-profits, and organizations with unlimited usage requirements.

With teams saving an estimated $21,000 per person annually (based on an average hourly rate of $45) and reducing meetings by 26 per month, the platform delivers substantial cost savings that often outweigh subscription fees.

"It's insane how calming it is to have BB sit in on meetings", notes Tash Keuneman, Head of Design.

Implementation Guide for AI VoC Systems

Implementing an AI-driven Voice of the Customer (VoC) system requires careful planning and a structured approach. Organizations that take the time to strategize and coordinate their efforts see much better results.

Setting Clear VoC Goals

The success of any AI-powered VoC initiative starts with setting clear, measurable objectives. Without well-defined goals, even the most advanced systems can fall short of expectations.

Begin by evaluating your current customer support operations. Identify areas where AI can make the biggest impact - such as repetitive tasks, slow data processing, or gaps in customer insights. Studies show that organizations targeting these areas can cut operational costs by 47% within six months through call deflection and automated workflows.

A good starting point is to classify customer queries by complexity. Routine questions can be routed to AI systems, while more complex issues should still go to human agents. Some of the best AI implementations in this space achieve impressive results, including 89% first-contact resolution rates, average handle times of just 2.1 minutes, and customer satisfaction scores of 4.7 out of 5. Use these benchmarks as a guide to set realistic goals tailored to your business.

Next, create a detailed implementation plan. This should include timelines, budgets, and resource allocation. Companies that incorporate AI-driven sentiment analysis into their VoC strategies often see customer satisfaction scores improve by 20-25% within the first six months.

Once your goals are in place, the next step is to build a collaborative team to drive the implementation.

Building Team Collaboration

Achieving success with an AI VoC system hinges on teamwork. Treat the implementation as a company-wide transformation, not just a technical upgrade. Cross-functional collaboration ensures better adoption and smoother execution.

Involve stakeholders early in the process. This helps avoid bottlenecks and ensures workflows are designed with input from all relevant teams. As Jerome M. Austin, Intelligent Automation Lead at Coca-Cola Beverages Florida, puts it:

"Stakeholders are focused on driving their day-to-day activities and whatever's important for their responsibilities or jobs. Really getting their head around what's possible, what's doable, and how does this translate into what I do day in and day out - that's the real key for engagement."

Take a business-first approach. Use insights to design workflows and let the technology support their execution. Comprehensive training is also essential - customer service representatives should not only learn how to use the tools but also how to interpret AI insights to improve their interactions.

To ease the transition, start small. Test AI systems with micro-workflows focused on one or two tasks. This allows teams to see immediate results, build trust in the technology, and identify any issues before scaling up.

Tracking Results and Making Improvements

After setting goals and aligning teams, the focus should shift to continuous monitoring and improvement. Regular tracking ensures that AI systems perform well and deliver a great customer experience.

From the start, establish governance processes to oversee AI performance and ensure responsible use. A lack of oversight is one reason why only 25% of companies see substantial returns on their AI investments. Assign clear roles for monitoring performance and maintaining accountability.

Create feedback loops to refine the system. Allow agents to flag incorrect AI responses and provide input to improve prompts. Incorporate VoC data with human feedback to fine-tune AI models over time.

Track metrics that reflect real business outcomes, not just technical performance. Focus on indicators like customer satisfaction scores, resolution rates, and operational efficiency. Regularly benchmark your results against both internal goals and industry standards to measure ROI. Key performance indicators to monitor include:

• Sentiment Accuracy Rate: Aim for 85-90%
• Reduction in Customer Complaints: Target a 30-40% decrease
• Increase in Agent Productivity: Look for a 20-25% improvement
• Faster Escalation Response Time: Expect a 50% reduction in handling critical issues

To stay ahead, integrate AI with live data sources like CRM tools. This ensures that insights are actionable and up-to-date, enabling immediate responses to customer concerns.

Flexibility is key. Be ready to adjust your approach based on what you learn during implementation. View technology as a way to enhance human roles rather than replace them. As Daniel Fernandez, RPA/AI Strategy & Product Leader at Equifax, explains:

"When you do automation right, it becomes a strategic driver to the enterprise. We've seen from our leadership that every time they need to approach a new problem, it's now like, 'Okay, can we go automation first?'"

Finally, prioritize data privacy and compliance at every step. Make sure your AI systems meet regulatory standards and protect sensitive customer information. This builds trust and ensures long-term success.

Conclusion: AI-Powered Customer Insights for the Future

AI is revolutionizing Voice of Customer (VoC) systems, making it easier for businesses to understand and respond to their customers. This guide has explored how AI tackles the limitations of traditional VoC methods, like handling overwhelming data and reducing reliance on manual processes.

Key Benefits of AI in VoC Systems

Organizations adopting AI-powered VoC solutions are seeing real, measurable results. For instance, automation driven by AI can cut call volumes by up to 30% and reduce response times by as much as 50%. Virtual agents are also helping resolve complex issues 40% faster. Additionally, many companies report a 10-15% drop in customer churn rates.

AI's potential doesn’t stop there. By leveraging algorithms for targeted recommendations, businesses have improved sales conversion rates by up to 15%. Sentiment analysis tools are boosting customer retention by as much as 25%. Self-service platforms powered by AI have also reduced the need for direct customer service interactions by up to 70%, allowing human teams to focus on more nuanced, high-impact tasks.

Nick Mehta, CEO of Gainsight, captures the transformative power of AI:

"AI simplifies operations and enhances decision-making with actionable client insights. What's predictive of churn? How do you drive a better upsell or advocacy in your client base to help you do a better job with your customers? Spending less time on that annoying, mundane work that takes you away from your clients, your family, or folks outside of work. AI is going to radically make customers and customer success better."

Other advancements include AI-powered chat summaries, which improve follow-up accuracy by up to 40%, and customer journey mapping tools that drive up customer engagement by as much as 30%. With these tools, companies can deliver personalized experiences at scale, even when managing thousands - or millions - of interactions.

What’s Next for Businesses?

The time to act is now. Companies that embrace AI-powered VoC systems are positioning themselves for long-term success. A recent survey found that 64% of SaaS respondents believe AI will enhance customer relationships and improve productivity. By the end of 2024, over 60% of businesses are expected to integrate AI into their operations. Among Customer Success and Support teams, more than 85% are already adopting generative AI, with nearly half anticipating significant time savings and efficiency boosts through task automation.

Solutions like BuildBetter are leading the charge, offering tools specifically designed for product teams. These include call recordings, automated summaries, AI chat capabilities, and seamless integrations with platforms like Zoom, Slack, Jira, and Salesforce. By turning unstructured customer data into actionable insights, BuildBetter helps businesses stay ahead of the curve.

Meenal Shukla, Senior Director of Customer Success at Gainsight, emphasizes the importance of collaboration:

"When you think about AI in the customer journey, the collaboration between CS and your product, as well as digital scale teams, is imperative. CS teams are the eyes and ears of the organization and can pay close attention to what your customers are saying and looking for from AI capabilities in their experiences. Look for opportunities where your product can bring AI to your customers and bring those recommendations back to your product team. The only way to ride this AI wave is to be nimble and responsive to your market."

The future of customer insights isn’t just on the horizon - it’s already here. Businesses that act quickly to adopt AI-powered VoC systems will be able to deliver the kind of personalized, efficient, and scalable experiences that set market leaders apart. Waiting too long could leave enterprises struggling to meet rising customer expectations in an increasingly competitive world.

The real question isn’t whether AI will reshape VoC systems - it’s whether your business will lead the way or fall behind.

FAQs

How does AI help businesses scale their Voice of Customer (VoC) programs more effectively?

AI is revolutionizing how Voice of Customer (VoC) programs operate by making it possible to analyze massive amounts of customer data quickly and with precision. Traditional methods often depend on manual analysis, which can be time-consuming and resource-intensive. In contrast, AI can process millions of customer interactions in real time, eliminating the need for additional staff.

With automated data analysis, AI provides businesses with actionable insights into customer trends, preferences, and challenges. This efficiency allows companies to respond more promptly to customer needs, make smarter decisions, and ultimately boost customer satisfaction.

What features should an AI-powered VoC platform have to integrate effectively with existing business tools?

An AI-driven Voice of Customer (VoC) platform works best when it integrates effortlessly with the tools your team already relies on - like CRM systems, helpdesk software, and communication platforms. Key features to prioritize include real-time data analysis, automated workflows, and compatibility with platforms such as Zoom, Slack, Jira, and Salesforce.

With these features, a VoC platform can streamline your workflows while providing insights that fit seamlessly into your existing processes, helping your team work smarter, not harder.

How can businesses maintain data security and compliance when using AI for Voice of Customer (VoC) systems?

To keep data secure and compliant when using AI-driven Voice of Customer (VoC) systems, businesses need to stick to key practices. This includes encrypting data during processing, setting up strict data retention policies, and following regulations like GDPR or ISO 27001. Regular audits and well-defined governance frameworks play a crucial role in staying compliant.

Additionally, companies should implement AI-specific security strategies. These might involve threat detection, continuous monitoring, and risk assessments. Such measures not only protect sensitive information but also minimize vulnerabilities and ensure customer data remains intact and secure.

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