Voice call sentiment analysis for growing businesses

Ever wish you could tell what your customers were thinking? Or at least get a sense of whether they’re happy with your business? Mind reading (sadly) has yet to be perfected, but AI-powered customer sentiment analysis can get you pretty close.

AI-powered sentiment analysis helps you understand how callers are feeling so you can prevent customer churn and negative reviews. And thanks to rapidly developing AI technology, every business can access sentiment analysis tools. In other words: you don’t need deep pockets to deliver top-notch customer experiences.

Thinking about giving sentiment analysis a try? Let’s dive into what voice call sentiment analysis is, how it works, and how you can use it to strengthen customer relationships.

What is voice call sentiment analysis? 

Voice call sentiment analysis uses natural language processing (NLP) and machine learning to analyze tone of voice, speech patterns, and word choices in customer calls. It identifies whether a caller is happy, frustrated, or neutral and assigns a sentiment score (like ‘positive’ or ‘negative’) so you can quickly gauge their mood.

For example, imagine you’ve just launched a new product and want to track customer reactions. Sentiment analysis can show you how many customers are praising the product versus those reaching out with issues. This helps you spot trends, resolve problems faster, and proactively address customer needs — without relying on gut instinct.

Sentiment analysis itself isn’t new — tools for call intelligence have been around for years. But in the past, these were expensive solutions built for call centers. Today, modern sentiment analysis tools are far more affordable (some costing as little as $35 per user per month).

What are the benefits of sentiment analysis? 

Thousands of small businesses have adopted voice call sentiment analysis tools, and it’s not hard to see why:

• Helps businesses understand customer emotions at scale. One unhappy customer might not seem like a big deal, but what about a hundred? Or a thousand? Sentiment analysis tools can track customer emotions in every call so you can get a better idea of common pain points across  teams and locations.
• Saves managers time — no need to manually review calls. You could assign sentiment scores or call disposition to calls manually, but this becomes less doable as your business grows. Plus, if you only rely on anecdotal feedback to determine sentiment, you might be missing key context. Artificial intelligence can help you assign objective sentiment ratings within seconds of ending a call. This can help you keep an eye on repeat problems and provide struggling reps more personalized coaching.
• Enables data-driven decision-making. Thirty-two percent of customers will stop buying from brands (they love) after a single bad experience. And yet, most won’t even tell you about it — they’ll simply stop doing business with you. This is what makes sentiment analysis such a powerful tool: everyone on your team can see dips in customer sentiment and diagnose the cause before the situation gets worse. That way, you can work together to adjust the approach and avoid bad customer experiences.

How voice call sentiment analysis works in 3 steps

Curious how sentiment analysis actually works?

Here’s how AI turns customer conversations into actionable insights:

1. Phone system captures calls and creates transcripts

The first step is capturing the call itself. This is usually done by recording it on your phone and then uploading it into a third-party tool.

But you can do this a lot faster with a VoIP system like OpenPhone, which manually or automatically records calls and  generates post-call summaries for quick context.

2. AI tools analyze transcripts for sentiment 

Next, your AI system uses pre-input prompts to tag call transcripts for customer sentiment.

Depending on your tool, it might factor in elements like:

• Tone: AI can detect emotions based on voice patterns, like frustration, happiness, and hesitation. For example, clipped speech could be flagged as a frustrated customer, even if they don’t express their complaint outright (more on this below).
• Word choices and keywords: AI detects phrases that signal positive or negative sentiment. Sentences like, “I love this product!” generally signal satisfied customers. But phrases like “I’ve already spoken to three different reps” usually flag dissatisfaction.

For more accurate sentiment tagging, be as specific as possible in your AI prompts — define clear criteria, include examples, and differentiate between similar sentiments like frustration versus a general problem.

3. Calls are automatically organized with sentiment tags

Using a dashboard with an overview of call data, you can easily sort calls by sentiment tags and start noticing patterns. .

In OpenPhone, you can also use AI call tagging to categorize conversations by referenced topics, such as billing, cancellations, and feature requests. This makes it easier to find the root cause of customers’ frustration or satisfaction. 

💡Related: Getting started with AI call tags

Want more use case suggestions?

Jump to the section below.

6 key ways to use sentiment analysis to improve the customer experience

Here are six simple ways to get the most out of sentiment analysis:

1. Spot unhappy customers before they churn

AI tools can flag frustrated customers even if they don’t explicitly say they’re unhappy. This takes the pressure off your team to read between the lines and prevents you from getting blindsided by a cancellation.

Let’s say you have a customer calling about a recurring software issue. They don’t outright say they’re frustrated, but your AI tool picks up on their long pauses, sighs, and sharper-than-usual tone.

This insight might prompt you to take action before they quietly cancel, like offering a free upgrade. 

2. Identify satisfied customers to create more opportunities

Sentiment analysis isn’t just for identifying unhappy customers — it can also help you spot brand ambassadors: loyal customers who love your product and are likely to recommend it. 

You can use this insight to:

• Prioritize specific customers for upsells or referrals
• Ask for testimonials or case studies for social proof
• Find examples of top-notch customer care for your team

When your AI tool flags a highly positive call, do a little digging to see what went right. Then, start thinking about ways to replicate their experience.

3. Improve customer support processes

Sentiment analysis helps identify common emotions and pain points, making it easier to refine customer support scripts, enhance service, and boost retention.

Let’s say your sentiment analysis tool shows that customers frequently call with pricing questions. You can improve your approach by simplifying explanations and sending clear written summaries to customers after calls.

You can also train reps to adapt based on emotional cues. For example, reps might prioritize speed for frustrated customers or add thoughtful gestures — like a refund or discount — to turn neutral experiences into positive ones.

4. Turn customer feedback into an improved product offering

Since sentiment analysis tools categorize emotions and topics, everyday conversations can become fuel for new ideas. You’re not just learning what your customers like (or don’t) — you’re uncovering opportunities to stay ahead of competitors.

Thinking about adding a new feature? In OpenPhone, you can create call tags for recurring feature requests. This helps you prioritize updates based on trends in customer feedback.

Curious about what customers think after launch? Sentiment analysis can detect negative spikes and highlight common frustrations, like bugs or usability issues. It also helps track positive feedback so you know when to ask for reviews.

5. Improve rep performance with more efficient coaching

Maybe a rep is struggling with revised call scripts or a new team member is feeling stressed about high call volumes. Either way, you can use sentiment analysis to find those who are struggling and offer support.

If you notice a rep has calls with worsening sentiment, you can swoop in to help with a review of quality assurance best practices. You can also use sentiment analysis to give real-time feedback. For example, you might sit down with a sales call recording and walk through the transcript with a customer service quality assurance checklist.

6. Analyze trends for your fallback options

When your team isn’t available, you may also have an answering service or AI agent taking your team’s calls. With the right business phone system, you can easily review high-level trends and then look at specific conversations to identify areas for improvement.

In OpenPhone, you can easily add a virtual assistant or answering service rep to your shared inbox so you can review call transcripts and call tags. Plus, use OpenPhone’s AI agent, Sona, that can handle calls 24/7, answer common questions, and take messages for faster follow up.

Implement voice call sentiment analysis with OpenPhone today

Sentiment analysis is like giving your customers mood rings — you never have to guess how they’re feeling on the other end of the line. AI detects emotions in rea -time, so you can focus on delighting and surprising customers at scale.

With OpenPhone, small businesses can access sentiment analysis tools starting at just $35 per user per month. We make it easier to build better customer relationships by:

• Automatically applying call recording AI tools so you can categorize recordings, identify call drivers, and track customer sentiment
• Converting audio into text with AI transcription to capture insights and generate instant action items
• Providing a centralized view of call recordings, tags, and contact notes — without the hassle of switching between tabs

But seeing is believing. So why not give us a try?

Sign up today for a seven-day free trial and experience the power of AI-driven sentiment analysis firsthand.

FAQs