To make people happy using your product, you’ve got to understand what’s working for them, and what’s not. That means you’ve got to capture as much feedback as you can from them — but then, how do you make sense of all that data and turn it into next steps?
This is where voice of customer analysis comes in — it provides actionable insights into customer pain points, preferences, and expectations. When you use voice of customer (VoC) analytics tools, you can make data-driven decisions that improve the overall customer experience.
What is voice of customer analytics?
The content from surveys, reviews, and other feedback channels is what we call the voice of the customer. So, voice of customer analytics refers to the process of analyzing customer feedback from various data sources, such as customer surveys, reviews, and social media, to gain insights into customer sentiment, behavior, and expectations.
These insights help companies optimize customer support, product development, and customer retention strategies.
Main aims of voice of customer analytics
- Identify product issues: VoC analytics helps uncover recurring product issues based on customer feedback, which makes you more nimble and able to respond (and fix) problems faster.
- Understand customer expectations: By analyzing feedback across all channels, you can gauge how well you’re meeting customer needs.
- Measure product performance: VoC data provides valuable insights into how customers view your product, helping you assess its overall effectiveness (sometimes in the form of online reviews).
- Enhance customer experience and drive retention: Insights from VoC analytics allow companies to make data-backed decisions that improve customer experience (CX), resulting in reduced customer churn.
When combined with customer behavior analysis, VoC analytics gives you a holistic picture of the customer experience based on both quantitative (things like heatmaps and click maps) and qualitative (feedback forms, surveys, focus groups, etc.) data analysis.
How to gather and analyze voice of customer data at scale
If you’re operating at a smaller scale, reading through everyone’s feedback or survey responses may be feasible — but what happens when you’re successful (yay!) and those numbers start going up and to the right?
With the right tools and approach, you can scale your VoC program and remain a customer-centric company, regardless of how big you grow.
Here are some key steps to scaling your approach to VoC analysis.
Segment customer data to identify key trends quickly
Breaking down customer insights into specific segments — such as demographics, purchase history, or behavior — enables companies to identify trends and patterns within their feedback.
Tools like Sprig's AI Recommendations help automate this process by continuously analyzing your data as it comes in, and categorizing it based on themes, helping you gather actionable insights from vast datasets without overwhelming manual analysis.
Leverage automated feedback collection tools
When you use automation, you’ve always got an ear to the ground to and gauge how people are feeling. Gathering real-time customer feedback continuously through in-app surveys and automated data collection tools can provide timely insights — and help you resolve issues before they become big problems.
Sprig’s feedback features help companies collect large volumes of customer feedback efficiently, giving them the power to monitor how customers feel about product updates and other changes.
For example, after launching a new product (or even , automated tools allow you to gather customer sentiment (and use AI to conduct a sentiment analysis) quickly, so you can adjust based on feedback.
Measuring customer satisfaction: CSAT vs NPS vs CES
- CSAT (Customer Satisfaction Score): Measures how satisfied customers are with a specific interaction or feature. Note: Sprig offers a free, customizable CSAT template to kickstart your analysis.
- NPS (Net Promoter Score): Gauges customer loyalty by asking how likely they are to recommend your product to others. Sprig’s guide to conducting an NPS survey offers more insights.
- CES (Customer Effort Score): Tracks how easy it was for a customer to complete a task, like making a purchase or using a feature. Note: Sprig offers a free, editable CES template to help you launch your research faster.
Each approach has unique benefits that allow businesses to gather data, measure, and compare customer sentiment across these different parameters.
The results page of a CSAT survey in Sprig — Sprig AI automatically summarizes responses to give you actionable learnings in real-time.
Implement in-app surveys for real-time responses
Targeted surveys allow companies to collect feedback in context, directly during the customer journey. With Sprig, you can set up your surveys to trigger based on specific actions or demographics.
For example, if a particular feature is generating mixed feedback, you could survey users based on their stage in the customer lifecycle to get more accurate insights. Sprig’s AI-powered in-app surveys make it easier to act on feedback quickly, turning customer preferences into actionable insights faster.
Survey results page in Sprig — Sprig AI analyzes all responses to generate a study summary and key takeaways.
Use AI-driven analysis for faster insights
For small teams, analyzing vast amounts of customer feedback can be daunting. AI solutions, like those from Sprig, offer the advantage of rapid, data-backed decision-making.
With AI, companies can process massive amounts of VoC data in seconds, surfacing key pain points, positive or negative feedback, or sentiment (like from customer reviews) that directly inform product changes or improvements.
Here again, Sprig’s AI Recommendations feature allows you to scale your VoC analysis, as well as collaborate more effectively, by automatically categorizing feedback based on themes, making it easier to address and build solutions across marketing, customer success, and development teams.
Ask anything about your product—Sprig AI analyzes all of your experience data to uncover product optimizations and generate recommendations.
Integrate data from multiple touchpoints to get a holistic view
In order to gain comprehensive insights into customer behavior and sentiment, it’s essential to gather data from a wide range of touchpoints. This ensures you’re not just hearing from customers who respond to surveys but also capturing feedback from customer reviews, social media platforms, and even in-app interactions.
When you combine feedback from various sources, you can paint a clearer picture of your customers' needs, expectations, and pain points, which helps drive more informed decision-making.
Here are 4 tools for gathering data across touchpoints:
1. Sprig AI Explorer
Capture experience data at scale via Replays, Heatmaps, Surveys, and Feedback—Sprig AI instantly analyzes all data to uncover trends in your product experience.Sprig’s AI Explorer is a powerful feature that aggregates feedback and behavior data from multiple sources and uses AI-driven sentiment analysis to extract actionable insights. It helps you track customer behavior, satisfaction, and sentiment over time by analyzing data from in-app surveys, feedback forms, and user reviews.
It’s especially valuable for understanding customer pain points in real-time and identifying key areas for product development and improvement.
- Key takeaway: With its AI capabilities, it can categorize customer data into segments and help prioritize feedback for immediate action.
2. Brandwatch
Brandwatch is a listening tool designed to gather insights from social media platforms and other online spaces where customers may be discussing your brand.
It monitors platforms like X (Twitter), Instagram, and Facebook, analyzing conversations around your product or service. When you integrate data from social media, you gain a deeper understanding of customer sentiment and can stay updated on emerging trends or issues affecting your customer base.
- Key takeaway: Brandwatch excels at sentiment analysis, giving you a detailed look into how customers perceive your brand across social platforms.
3. Mention
Mention is another excellent tool for tracking brand mentions and customer sentiment across the web. It monitors blogs, forums, news sites, and social media platforms, offering real-time data on what’s being said about your product.
This can be particularly helpful when tracking the success of a new product launch or monitoring responses to recent updates.
- Key takeaway: With Mention, you can identify where conversations are happening, gauge customer sentiment, and track how it evolves over time.
4. Trustpilot
Trustpilot is a widely used review platform that collects customer feedback in the form of online reviews. Trustpilot’s review system also allows customers to rate your product or service, which you can use to track overall customer satisfaction. When combined with feedback from other sources, these reviews provide additional layers of customer insight.
- Key takeaway: Trustpilot reviews let you gather valuable insights into customer satisfaction, common pain points, and potential areas of improvement.
The value of a omnichannel approach
When you integrate data from multiple touchpoints — such as social media, product reviews, and in-app surveys — you're getting a well-rounded view of your customers.
This omnichannel approach allows you to track sentiment across the entire customer journey, from initial awareness on social media platforms, all the way to post-purchase feedback. Also, by using a combination of listening tools, you can make sure that no important data source is left out of your analysis.
Integrating feedback and metrics from these diverse data sources gives you the power to deliver a more customer-centric (and satisfying) experience. Through tools that specialize in data collection and sentiment analysis, companies can make faster, more informed decisions that lead to enhanced customer retention and improved product performance.
Prioritize high-impact feedback for immediate action
Not all feedback holds the same weight, and businesses must learn to distinguish between minor suggestions and critical pain points that can significantly impact customer satisfaction.
Sprig AI Explorer allows companies to automate the process of prioritizing feedback based on several key factors, such as urgency, sentiment, and frequency.
This targeted approach ensures that businesses focus their resources on the issues that have the most immediate impact, allowing them to address the most pressing concerns swiftly.
Using AI to prioritize feedback:
- Urgency - Urgent feedback often stems from major customer pain points that need immediate attention. For example, if a new feature is causing frustration or a crucial product issue arises, AI tools can flag this feedback as urgent. Sprig AI Explorer and similar tools analyze the language used by customers to detect emotions, such as frustration or dissatisfaction, which often signal urgent problems. This allows businesses to react quickly and resolve issues before they escalate, potentially saving customer relationships and improving customer retention.
- Sentiment - Sentiment analysis plays a crucial role in prioritizing feedback. By evaluating whether customer feedback is positive, negative, or neutral, AI-driven tools can help businesses identify patterns in customer sentiment and prioritize negative feedback that requires immediate action. Sprig AI Explorer uses natural language processing (NLP) to assess sentiment in real-time, helping businesses detect customer frustrations or concerns as they arise. For example, if multiple customers express negative sentiment about a specific feature or even something like pricing changes, the business can prioritize addressing that issue to prevent customer churn.
- Frequency - Frequent feedback on a particular topic suggests a widespread issue that could affect many customers. In this use case, Sprig AI Explorer can track how often certain feedback themes appear, giving businesses insight into which problems are more pervasive. For instance, if new customers report difficulty navigating a feature, the frequency of this feedback will flag it as a priority, prompting immediate action to resolve the issue with something like an improved onboarding experience.
Immediate action for enhanced retention
Taking quick action based on prioritized feedback is one of the best ways to maintain a customer-centric strategy. Addressing critical pain points not only enhances the customer experience but also helps build trust and loyalty.
If a company can swiftly resolve a significant issue, customers are more likely to feel heard and valued, leading to greater customer retention. When you prioritize high-impact feedback and act on it quickly, you can create a proactive approach to customer support, reducing negative experiences — and even anticipating them before they arise.
Prioritizing feedback with the help of AI tools ensures that businesses can focus on what matters most — delivering value to their customers. Whether it's addressing urgent complaints, fixing a widely reported issue, or resolving problems that impact customer satisfaction, AI-powered prioritization allows for faster, data-driven decision-making.
Maximize your customer experience through actionable voice of customer analytics
Voice of customer analytics gives businesses the tools they need to understand customer expectations and improve satisfaction.
AI-driven analysis, when combined with automated feedback collection and integrated with data from multiple touchpoints, allows companies to turn feedback into action much faster.
Rather than being lost in a sea of data from across your customer lifecycle, machine learning gives you a leg up by not only collecting but also processing and analyzing your data in real-time, so your team can focus on delivering a better customer experience.
Book a demo of Sprig today to get to full value of your voice of customer analytics program by leveraging Sprig’s powerful suite of products, all backed by AI.