Call center sentiment analysis is a process used to interpret how customers feel when they engage with agents in a call center, via email/text, chat, or voice contact. The customer’s words, attitudes, and even emotions can be analyzed and rated.

Sentiment analysis is used to identify and resolve recurring issues, train agents on areas of weakness, and guide the improvement of call center best practices. But if call sentiment analytics data is not understood or used correctly, it can lead to skewed interpretations or incorrect business decisions.

Call Center Sentiment Analysis Doesn’t Tell You Everything

In the old days, call center managers looked at how fast calls were answered or other KPIs (Key Performance Indicators) to determine agent—and call center—success. With the advent of sentiment analysis, call center leaders suddenly have a different data set to gauge call center performance.

While sentiment analysis does reveal more about the feelings and intentions of customer interactions (which can help guide decisions), it isn’t a perfect standalone solution. Sentiment analysis, while pretty good, still often fails in correctly analyzing customer meaning and intent in certain areas.

  • Complex emotions: Sarcasm and irony are often missed. For example, a customer may say “Yes, that’s a great solution” as a way to indicate sarcastic disagreement with an agent’s suggestion. Sentiment analysis may categorize that as a positive interaction since the words yes and great were used. This skews both customer satisfaction and the agent’s performance rating.
  • Ambiguous phrases: When a customer says something that isn’t entirely clear from the words used, sentiment analysis can misinterpret intent and mischaracterize the nature of the interaction as a result. Both satisfaction and agent performance can be adversely rated.
  • Body language: Sometimes people say one thing, but their body language indicates the opposite. If you can compare both, you make better assessments. Since sentiment analysis is limited to what it reads or hears, it can be tricked if a person’s calm tone and voice belie their opposite body language of crossed arms and furrowed brows.
  • Language and cultural differences: Even when people are speaking the same language, the words they use can have very different meanings. Sentiment analysis has limitations that can miss important nuances.

In all these scenarios, the sentiment analysis results will likely be inaccurate. Customer satisfaction ratings may be falsely inflated. Agent performance may also be incorrectly rated. These scenarios result in business decisions made on inaccurate data.

To help mitigate this, it’s essential to use sentiment analysis as one of many tools in the manager’s toolkit. Other important KPIs should be studied, too. The totality of all results is what should guide management decisions, especially when those decisions relate to agent evaluation and performance.

How to Use Call Center Sentiment Analysis the Right Way

Find the Right Tool

There are many different types of call centers, including inbound (agents receive calls from customers), outbound (agents place calls to customers, leads, and prospects), and offshore (agents are physically located in a different geographic region or country than the business). Even among similar call center types, no two call centers are identical.

That being said, when considering the addition of a sentiment analysis tool, there are some basic steps every call center should take.

  1. Identify business needs: Determine what you want sentiment analysis to help solve. Improve agent performance? Identify underlying problems with your products or services? Get a clear picture of how customers feel about your brand? Each is a unique goal and picking a sentiment analysis tool that offers features and functionality tailored to your goal is the key to success.
  2. Gauge technical ability or resources: Some sentiment analysis tools come to you ready to go, with minimal effort on your part. Others require more heavy lifting, like training data sets. If you’re a small call center with limited technical resources, you’ll want a solution that is easy to set up and use.
  3. Find out how the tool will fit in with your existing tech stack: Chances are your call center is already using a customer relationship management (CRM) tool, telephony system, and other call center software. You want to choose a sentiment analysis solution that plays nicely with your existing systems and will integrate without causing you tons of headaches.
  4. Consider costs: While you probably shouldn’t decide based on cost alone, the reality is that your budget will impact the sentiment analysis solution you choose. Keep your budget in mind—both now and as your call center needs increase—when deciding what works now and down the road.
  5. Determine scalability: If you have plans to grow your call center, you want to make sure the tool you select is poised to match that growth and can scale up without drama.

Now that the basics are out of the way, it’s time to turn to unique call center needs. Depending on the type of call center you’re running, look for sentiment analysis tools that speak to the particular needs of that type of call center.

Inbound call centers: Customers are proactively contacting your agents. You’ll want features and functionality that include:

  • Automated agent routing so the right type of calls go to the appropriate agents
  • Real-time analysis so agents can be prompted on the spot with suggestions to optimize the interaction
  • Real-time agent feedback so agents can pivot to improve the customer experience while on the call

Outbound call centers: Your agents will likely be tasked with sales responsibilities, so you’ll want to look for tools that offer things like:

  • An autodialer to optimize agent efficiency
  • Integrations with your CRM to stay on top of lead nurturing
  • Multi-channel support to provide a comprehensive analysis across all outbound channels, including email, social media, and other communication channels.

Offshore call centers: Given the nature of using an offshore call center, there are some functions that are essential:

  • Multilingual analysis to ensure accurate results no matter the language a customer speaks
  • Cultural sensitivity feature to ensure the tool considers variations in how diverse cultures express thoughts and ideas
  • Regional integration so that the tool will work well with the remote call center’s own tech stack

Once you select the right tool, it’s go time. Here’s a high-level look at next steps to give you an idea of how to get your sentiment analysis tool up and running. It will require cross-team collaboration, especially from your IT department.

  1. Create a dataset for training your system. You can create your own or find a pre-built dataset to use.
  2. Test and evaluate initial results against sample data.
  3. Integrate the tool with your existing tech stack.
  4. Test and evaluate based on real-time data.
  5. Monitor for continuous improvement opportunities.

Choose the Right Approach

The sentiment analysis tool you choose should be based on what you plan to do with the data. Sentiment analysis tools run on algorithms, which vary depending on what you are trying to measure.

The most common approaches include:

  • Emotion-based analysis: Reveals customer feelings like happy, sad, mad, etc. This is a good choice for call centers that want to resolve customer issues in real-time and boost customer satisfaction scores
  • Aspect-based analysis: This identifies specific products or services that were part of a customer response. This is a good approach for quickly identifying trending products or services that have an issue, so product improvements can be made.
  • Real-time analysis: As the name suggests, this analyzes customer interactions as they occur to produce immediate data, allowing for quick action. This is good for call centers that want to agilely address issues and give agents real-time tools to improve the customer experience as it’s occurring.
  • Voice-based analysis: This studies a customer’s tone, pitch, volume, and other vocal characteristics to gauge feelings and intent. This is a good strategy to use for call centers that are measuring customer satisfaction with agent performance and to identify agent training opportunities for improving soft skills.

Use Sentiment Analysis With the Right Metrics

Call center sentiment analysis is not a standalone approach. It should be paired with other important call center KPIs to provide a 360º view of call center performance.

Customer Satisfaction (CSAT)

This percentage represents how satisfied a customer is with a company’s products or services. It’s measured by customer feedback, usually via a post-sale survey.

You can supplement this information with call center sentiment analysis to dive deep and determine the specifics about what is driving your CSAT scores and how to improve them.

Net Promoter Score (NPS)

This measures the likelihood that your customer will recommend your business to someone else. It’s usually measured with a one-question survey.

Use call center sentiment analysis to quickly analyze large volumes of NPS responses and free up your customer service team for other tasks. This strategy also removes the subjectivity that naturally occurs when multiple individuals are tasked with tagging a response as positive, negative, or neutral.

First Call Resolution (FCR)

This tracks the percentage of calls an individual agent resolves without escalation, transfer, or calling the customer back. This is based on measurable data captured by your call management system, and it’s an important trend for any call center to track.

You can incorporate call center sentiment analysis to quickly identify the pain points that are driving lower FCR ratings, then provide adequate training to resolve them and boost your FCR number.

Average Handle Time (AHT)

This measures the average amount of time it takes an agent on each call, from answering the call to disconnection. It’s calculated on data captured by your call management software.

Use call center sentiment analysis to better understand the “why” behind longer calls. While lengthy calls may suggest a negative customer experience, it is also possible that long calls resulted from very complex issues where the customer was actually quite satisfied at the conclusion. Sentiment analysis will debunk assumptions.

As you can see, relying on traditional KPIs alone results in data that isn’t always what it seems. Using sentiment analysis together with these KPIs gives you a deeper understanding of what is driving call center performance. Managers can then make better, data-driven decisions.

Have a Designated Quality Monitoring Team

Every call center should have a dedicated team of people continuously monitoring performance. This is how process improvements are made, leading to a higher-performing call center team. Call centers with quality assurance teams in place have higher customer satisfaction rates than those without.

When you layer in sentiment analysis to the mix, you need to have a quality monitoring team on hand. If you don’t, all that data analysis is likely to be underutilized or misinterpreted

But if you do take the time to combine QA with sentiment analysis, your QA team hits the data jackpot. They can more quickly identify issues and recommend process improvements, implement targeted training where needed, and better understand the overall customer experience.

Implement Quality Assurance Training

Pinpointing problems and uncovering trends is just the first step. Call center sentiment analysis makes this a lot easier, but you can’t stop there.

You also have to do something valuable with the data. One focus should be on continuous call center improvement, and that comes from appropriate agent training. When you focus on creating a positive and nurturing environment for your call center workforce, you elevate the overall customer experience.

A rock-solid quality assurance training program does that, and it’s backed by data. As a result, you see improved customer satisfaction, better customer retention and stronger customer loyalty.

A good QA training program should set clear goals, provide regular feedback, deliver consistent agent evaluations, and produce data that is easily shared and understood across your organization.

Sentiment analysis helps build stronger, more successful training programs.

Traditionally, much of the data used to create these training programs came from manual analysis of a sample size of customer interactions. That meant you never really got a full view of the customer experience, just a glimpse. As a result, training efforts could be skewed.

Call center sentiment analysis ensures more precise results since the entirety of call center data can be analyzed.

5 Benefits of Call Center Sentiment Analysis in the Call Center

When you incorporate sentiment analysis into your call center, you reap a number of rewards.

Improved Customer Satisfaction

When you can identify problems in real time, agents can proactively respond on the spot and resolve customer issues right away. This boosts customer satisfaction levels.

When managers quickly identify recurring pain points, they can offer solutions before the issue escalates. Whether it is targeted agent training, process improvements, or even product changes, the data gleaned from sentiment analysis is invaluable.

Better Call Routing

For call centers that use an interactive voice response (IVR) system, sentiment analysis can help send calls to the agents best equipped to handle the customer. For example, if a customer is upset, they can be sent to an agent especially skilled in handling irate people.

This approach streamlines call center operations and reduces things like escalation or transfer situations.

More Effective Agent Training

Sentiment analysis can reveal pain points faster than other methods of analysis. Managers can look at the data and quickly identify areas where agents are doing well and where they could use a boost.

Managers can quickly implement agent training to address identified gaps. This creates more skilled agents, which improves the customer experience.

Better Customer Retention

Satisfied customers tend to stick around. Unhappy customers disappear without a trace. It takes a lot more effort to win a new client than keep an existing one satisfied. So you want to keep customers and avoid attrition.

Sentiment analysis helps agents improve and provides actionable insights that managers can use to improve the overall customer experience. Together, these strategies boost customer satisfaction and retention rates. In turn, this boosts the bottom line, too.

Improved Quality Assurance

Sentiment analysis data helps your QA team quickly identify trouble spots and customer attitude issues early on. They can then ramp up training and process improvements to fix the situations.

This helps keep customer satisfaction high.

Call Center Sentiment Analysis Gets an Upgrade (and Why it Matters)

Artificial Intelligence (AI) tools have dramatically enhanced sentiment analysis in recent years. Larger and larger data sets can be analyzed faster than ever before, using a variety of techniques.

Machine learning uses algorithms and models to teach a computer how to make predictions on its own. Natural Language Processing bridges the gap between computers and people, so computers can understand and interpret human languages.

Together, these tools and others in the AI realm help turbo-charge sentiment analysis. The results are more accurate and consistent, scalability is increased, and businesses can use these reliable insights to make data-driven decisions and pivot faster than ever before.