How to Use Customer Sentiment to Improve CX

Why is contact center sentiment analysis important? Well, listening to calls is one way to learn about your customers, but how do you know you’re listening to the right ones?

What if there was a way to extract highlights and learn exactly which calls should be reviewed for quality management, to gather product insights, or to gauge agent performance?

And what if it wasn’t manual and time-consuming? Sounds pretty good, right? Well, that’s precisely what sentiment analysis does.

What Is Contact Center Sentiment Analysis?

Sentiment analysis evaluates interactions to determine customer emotions (positive, neutral, or negative). Using artificial intelligence (AI), machine learning, and natural language processing (NLP), it can detect and flag things like keywords, raised voices, tone of voice, and even the context within a conversation.

In a contact center setting, you can use sentiment analysis to identify customers who need help in real time, flag supervisors when agents are struggling with issue resolution, and identify pain points with products and services.

How it works

Sentiment analysis works as a three-step process:

• Collection: Collect data from calls, chats, emails, and surveys.
• Processing: Process inputs and cross-referencing algorithms using AI and NLP to assess tone, linguistics, context, and emotional cues.
• Analysis: Assign sentiment scores, correlate them with metrics like Net Promoter Score (NPS) and call duration, and flag any real-time issues that need supervisor intervention.

Measuring Negative and Positive Sentiment Across Channels

When it comes to customer sentiment analysis, we often get sidetracked into thinking we can only analyze calls. While calls still make up the majority of customer interactions, you can use sentiment analysis across any channel in your contact center.

Call recordings

By transcribing calls in real time, sentiment analysis tools detect and rate the tone, pitch, and language of every call you choose to record. For most call centers, this means managing the impossible task of analyzing all inbound calls at scale. Traditionally, quality assurance teams rely on a more random process, selecting a small number of calls per agent or department to review manually.

But here’s the challenge: Most quality management teams review up to only 2% of calls manually. With such a small sample size, it’s nearly impossible to get a representative view of agent performance or customer experience. Imagine if only 1% of your work was reviewed and assessed — how could you get an accurate picture of your strengths, areas for improvement, or overall impact?

Let’s break it down further:

• Small contact centers (<50 agents): Typically review 5-10 calls per agent per month.
• Mid-sized contact centers: Usually review 3-5 calls per agent per month.
• Enterprise contact centers: Rely heavily on automation, with manual QA focused on a smaller subset of calls.

This is where call center sentiment analysis changes the game. It provides managers with real-time tools like live dashboards, email alerts, and audible alarms that flag when a call in progress needs attention. For instance, this could be an irate customer, someone whose tone changes throughout troubleshooting, or any negative emotion detected during the interaction.

Instead of waiting for the call to end, supervisors can join the call immediately to defuse the situation and turn it around. Additionally, sentiment analysis helps prioritize post-call reviews, and grading calls so you can focus on those that need the most attention. Calls flagged for negative sentiment, non-compliance, or unresolved issues can be reviewed without the need to sift through random samples manually. From here, managers can improve training programs, enhance agent performance, and ensure the customer experience is top-notch.

Chat and email analytics

Conversation analytics aren’t limited to phone calls. In fact, any form of written communication in your contact center can benefit from sentiment analysis.

When customers are on live web chats, sentiment detection highlights tone shifts, negative phrases, or recurring issues within the conversation. It’s just like what happens during a live call.

For other written mediums, like email, the post-interaction analysis comes into play, too. When there is frustration in email threads, these are the ones that get flagged for supervisor help — instead of random checks every month.

Social media and reviews

Outside of your contact center, customers may be saying things about your company in public, especially on social media. This makes it vital to monitor public sentiment about your brand not only across platforms like Twitter, Instagram, and Facebook but also sites like G2, Trustpilot, and Yelp.

You can get ahead of any potential public issues by processing the conversations and analyzing what needs attention from contact center agents, marketing teams, or even C-level executives. This might be a simple case of remedying the issue online, offering a refund, or taking the query offline to find the root cause.

At the other end of the scale, you can leverage valuable insights to amplify positive customer feedback. When you get a glowing review but your customer didn’t tag you, you can take online brand monitoring to the next level and gain natural customer testimonials ready for retweeting and embedding in sales material.

Key Benefits of Sentiment Analysis for Contact Centers

Proactive issue resolution

The main use case for (and benefit of) sentiment analysis is remedying live issues rather than waiting for calls to get flagged when it’s too late.

Sentiment analysis identifies unsatisfied customers during interactions, flags what type of problem has occurred, and allows supervisors or senior call center agents to address issues immediately.

Sentiment analysis also highlights positive interactions, helping managers identify and reinforce successful behaviors that drive exceptional customer experiences.

This has a direct correlation with higher customer retention. The faster you deal with customer problems, the happier they are with your company.

Enhanced agent performance

When you know which calls to listen back to (or join in real time), agents get the training and coaching they need — rather than standard group training where they might already excel.

Take Joe, a customer service rep of four years. He knows the ins and outs of broadband faults but has just started dealing with VPN issues for the first time. If you listened back to five random calls, you’d hear that Joe is a model agent and doesn’t need any help. However, using sentiment analysis, you’d get the timestamps of where he struggles with first-line VPN troubleshooting, leading to frustrated customers and low first-call resolution (FCR).

As an industry, we’ve been talking about AI in customer experience for a decade. The best use case is to enhance agent performance with educated insights from sentiment analysis.

Voice of the customer amplification

When you conduct a customer survey, you can be inundated with data, some of which will fall by the wayside. When you supplement that data with insights from sentiment analysis, however, you can better understand customer emotions without missing a single component.

What’s more, you don’t need to wade through every response to find vital feedback. Instead, it’s flagged by detecting the most emotional responses or extreme ratings (e.g. 1 out of 10 or curse words in response to questions).

To improve customer experience, we must not just ask our customers how we’re doing but also evaluate their responses. Sentiment analysis speeds up this process and prioritizes which customers need to hear from you the most.

Marketing and product insights

How long does it take to extract insight from customers? A typical process involves identifying customers who will talk about your product, incentivizing them to do so, formulating questions that provide good answers, and evaluating what it all means.

When constantly analyzing your calls, emails, and chats, you uncover trends to refine messaging and improve product offerings.

For example, when a customer says they love the new interface and it’s sped up their workflow by 10 minutes, that’s good feedback that your product team needs to hear. Likewise, if changing how customers log in to your tool means it’s now a four-step process, you need to know how frustrating a simple process has become.

Improved CX metrics

How we measure customer experience has changed from just time-based metrics like average handle time (AHT) and abandonment rate. Today, we can track sentiment against KPIs like FCR and customer satisfaction (CSAT) for comprehensive performance evaluation.

Combining these metrics and insights means you get a holistic view of how customers feel about your service and how long it takes them to contact you.

As you see a downward curve in FCR and AHT, you should see an upward trajectory in your CSAT and NPS.

Best Practices for Implementing Sentiment Analysis

Invest in AI-powered tools

• Choose tools with real-time analysis capabilities.
• Opt for a tool with CRM integration to analyze data cross-platform.
• Ensure solution scalability to accommodate business growth or acquisitions.

Integrate across channels

• Implement sentiment analysis across both inbound and outbound calls.
• Embed written communication channels like live chat and email.
• Analyze social media interactions where you get mentioned both directly and indirectly.

Link sentiment with quality assurance

• Use sentiment scores to guide quality evaluations.
• Prioritize interactions with negative sentiment for detailed reviews.
• Adhere to call center best practices and measure script adherence with added insights.

Train agents on sentiment insights

• Share sentiment data to help agents understand emotional cues.
• Prioritize low-scoring calls to inform and continuously adjust communication strategies.
• Combine monthly/quarterly learnings and share them with wider product and marketing teams.

Refine through feedback

• Take preventive measures to stop repeat issues from occurring.
• Continuously improve sentiment models using customer and agent feedback.
• Learn what matters most to customers and communicate to the wider business.

Why Nextiva for Sentiment Analysis?

Adding a stand-alone sentiment analysis solution makes no sense. You need an integrative platform that shares and analyzes data across all your supported channels.

Nextiva provides sentiment analysis among a range of other market-leading contact center functionality. When used to their full effect, contact center leaders simply can’t get enough of Nextiva.

Unified communication and sentiment insights

All your communications channels live in one clean interface and stem from one back-end technology. Not only does this make agents’ lives easier, but it also enables more efficient tracking and integrating of customer data.

Be it calls, emails, web chats, social media, or even messaging apps and review sites, everything gets managed, analyzed, and proactively set up for review in one place.

Real-time monitoring

AI-driven dashboards provide live updates on sentiment, enabling immediate action. You get a live view of calls in-flight, metrics showing you how today compares with an average Thursday, and alerts when calls get out of hand.

This combination of real-time monitoring and sentiment analysis allows for proactive attention when customer conversations need it the most.

Scalability and ease of use

Nextiva is designed to grow with your business and is suitable for small teams and enterprises. There’s no hardware to install and nothing to maintain. Not only do you save on storage space and implementation time, but you can also add new users in just a few clicks. Plus, Nextiva seamlessly connects with your existing business systems, ensuring smooth integration into your workflows.

If you need help, our award-winning 24/7 customer support team is on hand to walk through any issues or troubleshoot setup queries.

Advanced analytics

If you’re investing in sentiment analysis, you’re serious about improving the customer journey and giving customers what they need when they need it.

If you can correlate sentiment with other KPIs, you get comprehensive insights that can inform business decisions, design communication strategies, and power agent training.

When combined with customer analytics like the number of automated live chats, the number of positive reviews per month, and cost savings based on AI bot efficiencies, you can make a genuine positive change not just to your contact center and customer base but to your entire business.

Serious about customer engagement? Take Nextiva’s AI-powered contact center for a spin.