By CloudTalk

| 6 Nov 2024

Call Center, Callcenter

A Comprehensive Guide to Customer Sentiment Analysis (+ How to Use it for Training)

The outcome of a call can hinge on a single moment, with even a small change in tone making the difference between success and failure.

If you’ve ever been in this position, you’ll know how frustrating it can be when a promising interaction goes south—seemingly out of nowhere. So, how can you work out what went wrong to refine your approach?

Research has found that 93% of the success of a cold call comes down to using the right tone of voice*. With that in mind, it’s crucial to understand the “why” behind the subtle shifts in mood that can guide conversations toward successful conversions.

This is the essence of customer sentiment analysis, a key pillar of conversational intelligence and something that every sales and ops team can embrace through easy-to-use software tools.

Keen to learn more? Read this guide to understand what customer sentiment analysis is and how your teams can harness it effectively.

Key takeaways:

Customer sentiment is a crucial metric for assessing customer experience. If you want to understand and address customer pain points, you should be collecting this data.
Sentiment analysis can help train your agents. Analyzing customer feedback and emotional sentiment will highlight areas where your agents could refine their approach.

Automation is the key to efficiency. With customer intelligence tools like CloudTalk AI, your sales and support teams can save time and harness performance insights in real-time.

Gain AI-powered call insights.

Use CloudTalk’s customer sentiment analysis to understand and improve performance.

What is Customer Sentiment Analysis?

Customer sentiment is a metric that indicates your customers’ opinions of your business. It highlights what you’re doing right and what you’re doing wrong when it comes to customer service, sales, and support, enabling you to understand your audience’s emotional state when interacting with your brand.

The process of collecting and assessing sentiment data is known as customer sentiment analysis. Organizations gather this information from multiple sources, including online reviews, social media posts, chatbots, customer surveys, and call recordings.

Then, using modern conversation intelligence software, managers can generate reports and identify patterns—highlighting weaknesses and opportunities for improvement.

As a result, your agents will gain a more accurate, end-to-end understanding of their prospects’ attitudes and preferences. These insights can help you optimize call scripts and can significantly enhance key customer sentiment metrics, such as:

Net Promoter Score (NPS)
Customer retention
Customer lifetime value (CLV)

Key Benefits of Customer Sentiment Analysis

Customer sentiment analysis enhances basic quantitative metrics to reveal more about your audience. With these valuable insights, your ops professionals can improve experiences and address changing customer expectations throughout the customer journey.

Here are three areas where customer sentiment analysis can add value for your business:

Agility

Your customers are humans with changing opinions and emotions. As such, your approach to sales and customer service can’t stay the same; you need to evolve your messaging. Customer sentiment analysis provides the insights required to track customer sentiment over time, enabling you to stay responsive and proactive and deliver more personalized experiences.

Understanding

These days, businesses interact with customers across multiple platforms and formats every day. And yet, only 46% of consumers say the employees they interact with really understand their needs. So, as customer experience is a top differentiator for buyers worldwide, effective customer sentiment analysis can play a pivotal role in informing your agents’ approach.

Loyalty

“Consumers seek frictionless experiences in a world of disruptions.” That’s according to PwC’s February 2023 Global Consumer Insights Pulse Survey. Unhappy customers are unlikely to stick around when there are plenty of other options available, so actively addressing feedback is crucial to managing your brand’s reputation and reducing churn.

Plus, by combining sentiment analysis with your existing call center systems and support processes, you can help your agent improve their performance across metrics like:

Customer satisfaction score (CSAT)
First-call resolution (FCR)
Average handling time (AHT)
Call quality scores

How Does Sentiment Analysis Work?

Customer sentiment analysis tools use natural language processing (NLP) and machine learning (ML) to analyze conversations and determine the overall tone of every interaction—simply put, whether they’re negative, positive, or neutral.

This is typically classified based on three key sentiment analysis categories:

Fine-grained analysis: This ranks interactions on a scale of very negative to very positive sentiment, helping to add more nuance to simple polarity-based reports. It’s often associated with aspect-based analysis, which focuses on conversations about specific topics (like a product or service).

Emotion detection: Using ML to analyze lexicons (in this context, the vocabulary associated with each customer profile), emotion detection defines the emotional connection between your audience and brand.

Intent analysis: Intent analysis identifies the motivations behind customer feedback. Are they looking to make a purchase? Do they need additional support? Have they got in touch to make a complaint? This type of analysis goes beyond emotional sentiment to understand purpose, which can help your team take action.

The main advantage of using sentiment analysis software is that it automates the process of collecting, reviewing, and categorizing customer interactions.

Instead of relying on human operatives to sift through call recordings, customer feedback, and chat logs, AI-driven performance evaluation will do it for you—saving time, increasing customer data volumes, and improving the accuracy of your findings.

Example: CloudTalk’s Sentiment Analysis Tool

Sentiment analysis is part of our growing suite of AI-powered conversation intelligence features. It’s integrated with our omnichannel platform, enabling users to view sentiment analytics alongside call history in a Real-Time Dashboard.

So, how does it work?

CloudTalk Sentiment Analysis software showing visual timeline of shifting sentiments
(Source)

Our conversation intelligence tool provides immediate insights into conversational tone, helping agents and supervisors shift marketing strategies on the go.

It assesses every call to determine the mood of each participant. Then, it automatically generates a timeline of mood shifts for both the agent and caller throughout the conversation before assigning an overall sentiment category—positive, neutral, or negative.

You can review these findings in our desktop Phone app or the Analytics dashboard, enabling you to see color-coded sentiment scores for both sides of the conversation transcript.

Agent Training: How to Enhance Performance with Customer Sentiment Analysis

So, now you know the fundamentals of customer sentiment analysis, you can put it into action with AI-driven software.

Sentiment analysis models allow users to harness automation with ease, reducing the amount of manual work involved in gathering, processing, and interpreting customer feedback.

With a platform like CloudTalk, you won’t need to navigate complicated tech to get the information you need. Instead, it’ll be presented to you in clear, actionable insights you can use to inform agent training and optimize scripts.

Wondering how you can use customer sentiment analysis to achieve these results? We’ve broken down the steps you need to take to get started.

Step 1: Identify Training Gaps Through Sentiment Data

How good are your operatives at reacting to customer needs and tailoring their approach on the go? Customer sentiment analysis data can tell you.

Sentiment analysis reveals the strengths and weaknesses of every call and customer interaction. This allows you to adapt your agent training material to reflect the current mood of your target audience and address service issues before they impact sales performance.

You can begin by using your analysis tool to assess a range of feedback from your customers. Then, break it down into categories to identify key areas for improvement.

For example, aspect-based sentiment data might highlight that your customer support services are inefficient on mobile devices. This might indicate that customer support agents need additional training to navigate the system and handle common inquiries to resolve support tickets.

Top Tip

Monitor key sentiment trends over time to stay competitive. Not only can this help you determine whether adjustments to your customer service approach are paying off, but it’ll reveal emerging issues before they impact performance metrics.

Step 2: Create Personalized Training Modules

Customer sentiment analysis offers real-time, data-driven insights into how your customers respond to various scripts, tones of voice, and sales strategies. As you can probably imagine, this data is a goldmine for training development.

Let’s say an agent’s performance metrics are trending downward. CloudTalk’s AI-powered conversation intelligence tools can generate overviews of customer emotions during their recent calls and pinpoint the exact moments where issues occur.

This could reveal that the agent struggles with handling frustrated customers or misses opportunities to build rapport during critical points in the conversation.

With this information, you can provide targeted soft skills training that addresses specific areas for improvement, such as active listening. As a result, your teams can manage customer interactions with greater consistency and effectiveness. This can be achieved through mentor matching, where experienced team members are paired with newer employees to provide guidance and support.

Remember, every interaction is unique. But by analyzing user sentiment and identifying recurring themes, you can empower your customer service teams with the knowledge they need to succeed.

Step 3: Enhance Role-Playing Scenarios With Sentiment Data

Nothing has an impact quite like a real-world example. So, when it comes to agent training, sentiment analysis data is your best friend.

Sentiment analysis can help you identify the type of interactions that cause your customers the most grief, providing a bank of data that you can use in role-play scenarios.

Perhaps your agents are dealing with a lot of product complaints that are taking a long time to be resolved. In this case, gather relevant call transcripts and look for any trends that your sentiment analysis software has found.

Determining what language contributed to positive and negative sentiment outcomes will give your support team a better understanding of how to navigate these sensitive situations.

You can integrate this into training by creating a scenario based on a common issue flagged by sentiment data. For example, one agent plays a frustrated customer, while another practices responding with empathy and active listening. The facilitator can pause at key moments to highlight phrases or tones that escalated or de-escalated the situation, allowing the team to learn in real-time.

There are two main benefits here:

Your agents can practice empathetic communication to address and resolve customer frustrations quickly and calmly.
Your target audience will feel more valued and understood by your company, increasing customer loyalty and retention.

Step 4: Implement Real-Time Feedback Loops

A real-time feedback loop is a process of continuous data collection and processing, with the intention of supporting ongoing performance improvement.

In a customer service center, this might involve gathering and analyzing caller sentiment data to help agents adjust their approach in real-time during conversations.

For instance, emotion-based sentiment analysis can provide immediate feedback on the fast-changing mood of a call, helping operatives to respond appropriately, empathetically, and professionally. This capability is especially valuable when the caller seems frustrated or confused and one wrong word could lead to a negative customer experience or escalation of the issue.

To implement a real-time feedback loop successfully, you must first define the performance metrics you’ll be tracking during every call. By programming your unified communications platform to flag specific indicators like keywords, speaking speed, and tone, your agents can guide conversations in a positive direction.

Top Tip

Integrate your customer sentiment analysis tools with the rest of your customer relationship management (CRM) system to ensure all findings and feedback are stored in customer profiles. That way, your agents can always view up-to-date caller preferences and deliver more personalized follow-up interactions.

Tackle Pain Points Head-On with Customer Sentiment Analysis

“CloudTalk’s AI conversation intelligence drastically improved our efficiency. We’ve cut call quality analysis time from 2 hours to 20 minutes a day and reduced wrongly targeted leads by 24%, enabling us to focus on strategic growth while maintaining consistency across markets.”
Sara Konickova, Sales Ops Manager at Capitalo

It’s not enough to guess or assume how your customers feel about your brand; you need to know.

For salespeople and support agents, the only way to gain this understanding is to do the research. But, with the sheer volume of data available, this is a complex task—unless you integrate automated solutions that manage that work for you.

For this reason, customer sentiment analysis software is essential for business leaders wanting to better understand their customers and improve decision-making.

Conversation intelligence can unlock new insights about caller behavior and agent performance while reducing the required workload. Using this technology facilitates more personalized, targeted agent training and ensures you’re making continuous improvements to the customer experience.

CloudTalk AI can offer all of these capabilities with omnichannel integrations and advanced AI-powered algorithms to provide reliable, scalable, end-to-end service support.

The result? Smoother interactions, more productive employees, and, most importantly, happier customers that keep coming back.

*Sources:
Zippia, 2023

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Frequently Asked Questions about Customer Sentiment Analysis

What does customer sentiment mean?

Customer sentiment is a simple measure of how your customers feel about your brand. It’s sometimes referred to as “user sentiment” and is all about understanding the emotions and intent behind every business-customer interaction.

How do you analyze customer sentiment?

To analyze customer sentiment, you must gather feedback data from a range of touchpoints, such as survey responses, emails, live chats, and call transcripts.

You can then sift through all this data by hand to identify patterns and rank emotional tone, but this is incredibly time-consuming. Alternatively, use a customer sentiment analysis tool that uses AI to automate these processes. This software will provide live call insights and generate accurate reports to help you track and improve customer sentiment metrics over time.

In a call center, these metrics might include:

– Average response time
– Average handling time
– Call abandonment rate
– Customer reviews
– Customer satisfaction score
– Total resolution time

What are the types of customer sentiment analysis?

Customer sentiment analysis will determine whether an interaction is considered negative, positive, or neutral. However, modern software takes this one step further by classifying conversations based on three key categories:

Fine-grained analysis ranks interactions on a scale of very negative to very positive. It’s often associated with aspect-based analysis, which focuses on specific topics.

Emotion detection analyzes language to detect the emotions of the agent and caller throughout their dialogue.

Intent analysis identifies the motivations behind customer feedback to help sales teams take positive action.