Every customer call holds valuable information about what they really want and how they feel about your company. Now that we can improve customer experience with artificial intelligence (AI), manually reviewing hundreds of sales calls to analyze the most or least effective talking points is a thing of the past.
Analyzing phone calls with AI reveals what resonates with customers and what doesn’t. If your call center AI tools highlight frequent inquiries about billing issues or service plans, you need to refine your service plan documentation and improve billing transparency.
The global AI market in the call center segment is projected to exceed $4 billion by 2027. This means many businesses are using AI call analysis to save time and improve customer experience. Whether you’re new to AI call analysis or looking to improve your existing strategies, this blog offers valuable insights to help you succeed.
How does AI call analysis work?
You can use AI to analyze phone calls and find valuable insights into customer needs and preferences. Here are some key approaches:
Transcribe audio to text
An AI call analysis tool transcribes conversations from audio to text in real time. This lets you search through call transcripts for specific sentences or phrases without listening to every call.
Say you notice a sudden spike in complaints about a product or service. You can quickly surf through call transcripts and search for specific words to pinpoint customer complaints. This way, you derive meaningful insights from customer calls in less time.
Sentiment analysis
An AI-based call analysis tool goes beyond words. It finds trends in conversation, including repeated keywords, speech patterns, and even customer sentiments and tone.
The tool extracts emotions from various sources, including customer emails, chatbot histories, customer service call transcripts, comments on returns and refunds, and customer feedback or surveys.
Keywords and topic detection
Phone call analysis with AI automatically identifies key topics and keywords discussed during calls. This helps you spot trends, like recurring questions about a particular feature or a product, allowing you to update FAQs, training materials, or even product documentation.
The benefit? It significantly reduces call handling times and pushes agents to provide accurate and timely responses, automatically improving customer service.
Predictive analysis
AI call analysis has the power to analyze past interactions and predict future behaviors. So if a customer frequently asks about a product upgrade, you might proactively reach out with customized offers or share new product updates with those customers.
Using AI for customer service also facilitates market segmentation. Analyzing data on customer behavior, preferences, and demographics helps you identify segments that are likely to be the most profitable.
This approach lets you focus on high-value customer segments, leading to lower costs for acquiring new customers.
Benefits of using AI to analyze phone calls
Analyzing phone calls with AI offers numerous advantages that can transform customer service operations, including:
Learning customer sentiments
Talking to hundreds of customers each day, it’s easy to misinterpret a good call for a bad one and vice versa. An AI-powered sentiment analysis helps understand how customers feel during interactions. It assesses the tone and emotion behind their words for you to pinpoint areas of frustration or satisfaction and tackle them head-on.
For example, if AI detects a high level of frustration in calls about a specific service, it prompts you to inform your marketing team or provide additional training to agents who may lack the necessary information to effectively address customer concerns.
Identifying trends
AI for customer service uncovers emerging trends in customer inquiries and concerns. Analyzing call data over time reveals recurring issues or topics, such as a rise in questions about a new product feature.
Use this insight to adapt your offerings, enhance marketing strategies, or update training materials to better equip your team.
Improving training and development initiatives
AI call analysis also identifies common challenges agents face. You can apply these insights to tailor training sessions for addressing specific skills or knowledge gaps.
For instance, if calls reveal that agents struggle with handling billing-related inquiries, you can help them understand the pricing structure and discount options to improve their confidence.This ultimately leads to a more competent team.
Step-by-step guide to implementing AI for call analysis
Here’s a step-by-step guide to using AI to analyze phone calls.
1. Choose an AI-powered communication tool
When it comes to AI call analysis, several popular options stand out. These are a few platforms and key features to consider when setting up your audio streaming platform:
- Gong: Provides an in-depth analysis of sales calls.
- CallRail: Focuses on marketing analytics and monitoring call performance and customer interactions.
- Telldesk: Offers real-time call analysis and helps identify trends and customer sentiment.
Depending on your objective, choose a platform that best suits your business. Consider these features when making your choice for an AI-powered communication tool:
- Ease of integration with your existing CRM system
- Detailed analytics and visual dashboards
- Real-time call analysis
- Sentiment analysis
- User-friendly interface
2. Categorize calls
Choose call centers AI tools that categorize calls in real-time based on issue types, product inquiries, and customer satisfaction levels. This feature provides managers with better insights into common customer concerns and agent performance, making it easy to identify trends and training needs.
For example, Plivo creates an AI-driven call summary within a few seconds, as opposed to manual note-taking, which could take minutes or more, depending on the complexity of the call.
Plivo segments calls by:
- Subaccount
- Geo-location
- Hangup details
- Carrier network
- Device metadata
3. Speech-to-text transcription
Choose an AI call analysis tool that captures user input with real-time transcription to perform sentiment analysis.
Plivo, for instance, offers support for speech recognition in 27 languages and their regional variants. It also offers pre-built use-case-specific IVR models with pre-answer that lets you specify what happens after a call is transferred but before it’s picked up.
Additionally, it filters profanity, detects and masks inappropriate content in transcriptions, and improves speech recognition accuracy with a list of hint words expected from the speaker. These features lead to better AI call analysis and save your business from legal risks and penalties due to profanity in content.
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4. Perform qualitative call analysis
Poor call quality directly impacts sales and customer satisfaction. When customers can't effectively communicate with your team, you risk losing sales opportunities and existing clients.
The solution is to choose a platform that performs — Plivo.
Tools like Plivo’s call debug UI quickly find the root causes of these issues, helping businesses mitigate these risks, improve overall call quality, and enhance customer experience.
With this feature, you get answers to:
- Who initiated the call?
- How and from where was the call initiated?
- Where did it terminate?
- Which device, operating system (OS), browser, and mic was used?
- Who hung up the call and why?
- What other legs were involved in the call flow?
Moreover, it collects feedback from end users using the Call Quality Feedback API. This process determines the root cause through correlations between audio quality metrics and various attributes, including network conditions, geolocation, and device metadata.
5. Generate comprehensive reports
A detailed summary of key call quality indicators such as latency, MOS, and packet loss makes complex data more accessible. You can tailor reports to suit different stakeholders, ensuring the information is relevant and easy to understand.
Plivo lets you access all or any of the call insights data within the console using existing views, or even push data to third-party AI tools/platforms.This makes the call not only more accessible but also actionable.
6. Implement continuous KPI performance monitoring
Key metrics provide insights into customer satisfaction. Regularly collecting feedback will help you stay in tune with customer needs and expectations.
Keep an eye on the following metrics:
- Net Promoter Score (NPS): Measures loyalty by asking how likely customers are to recommend your product to others on a scale from 0 to 10.
- Customer Effort Score (CES): Assesses how easy or difficult it is for customers to interact with a company.
- First Contact Resolution (FCR): Indicates the percentage of customer issues resolved during the first interaction.
For example, tracking NPS reveals how likely customers are to recommend your service, while CES measures how easy it is for them to get their issues resolved.
Incorporate Plivo’s VoIP solution for improved customer experience
Plivo easily integrates with existing AI tools/systems. It’s really a simple five-step process:
- Sign up: Create an account on Plivo.
- Purchase number: Acquire a number through the Plivo console or API.
- Attach number: Link the number to your application.
- Establish connection: Use the following XML instruction to start streaming:
<Response>
<Stream bidirectional="true" keepCallAlive="true">wss://yourstream.websocket.io/audiostream</Stream>
</Response>
- Send audio back: Stream audio back to Plivo using the websocket connection for real-time conversational interactions.
Once integrated, you can facilitate real-time call analysis and action on live audio data. With automated call transcriptions, you can easily dive into sentiment analysis, allowing you to understand customer emotions instantly. This capability ensures that you maintain high-quality calls, even during peak times, so your customers consistently receive top-notch service.
Plivo also offers a feature called Queueing, which enables AI analysis to capture and process all calls in the queue. Combining detailed debug logs with AI insights helps identify recurring call patterns and issues to encourage proactive problem-solving before escalation.
All of these powerful features come at a competitive price of just $0.003 per minute per stream for VoIP and audio streaming. Use AI to analyze phone calls with Plivo and improve your customer service experience.
Contact us today to learn more about how Plivo can improve customer experience with AI.
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