Call Center Sentiment Analysis: Ultimate Guide [2023]

Today, more than 60% of customers say that they expect higher levels of customer service than they did before. Call center sentiment analysis can help businesses grasp their customers’ evolving needs, concerns, and sentiments accurately.  

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However, conducting call center sentiment analysis presents some challenges, including the breadth of data analysis it requires and being able to pull actionable insights from this data. To help you overcome the stumbling blocks, this article reviews the importance of call center sentiment analysis, how it works, and how to conduct proper analysis using the best call center software. 

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What is Call Center Sentiment Analysis?

Call center sentiment analysis is a valuable technique used within customer service and experience management to gauge and understand the emotional tone and attitude of interactions that occur between customers and call center representatives. It involves using natural language processing (NLP) and machine learning algorithms to analyze the content of conversations, whether they're conducted over phone calls, live chats, emails, customer reviews, or other interactions. 

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The primary goal of call center sentiment analysis is to determine whether customers' sentiments are positive, negative, or neutral during their interactions. This information can provide organizations with actionable insights to enhance customer satisfaction, identify training needs for agents, and uncover areas for process improvement. 

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How Sentiment Analysis Works

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The process of sentiment analysis involves several key steps:

1. Data collection: Conversational data from call recordings, chat transcripts,  emails, and other sources is collected and pre-processed to remove noise and irrelevant information.
2. Text processing: The collected data is cleaned, tokenized, and transformed into a format suitable for analysis. This step often involves removing stopwords and punctuation and converting text to lowercase.
3. Sentiment classification: Machine learning models, such as sentiment classifiers, are trained on labeled datasets to understand the context and nuances of language. These models then categorize text into positive, negative, or neutral sentiments.
4. Feature extraction: Features that represent the linguistic and contextual aspects of the text are extracted. These may include keywords, phrases, and syntactic structures that contribute to sentiment.
5. Sentiment scoring: Each piece of text is assigned a sentiment score based on the identified sentiment category. This score quantifies the emotional tone of the conversation.
6. Aggregation and analysis: The sentiment scores are aggregated across interactions or time periods, allowing organizations to identify trends, patterns, and shifts in customer sentiment. This analysis can reveal recurring issues, peak sentiment periods, and areas of improvement.
7. Actionable insights: The analyzed data provides insights into customer emotions and experiences. Organizations can use these insights to optimize agent training, modify scripts, streamline processes, and proactively address customer concerns.

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Sentiment Analysis Example Sentences

Here are some example sentences to illustrate the concept of sentiment analysis in a call center:

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Positive sentiment:

1. "The agent was incredibly helpful and patient in resolving my issue. I'm really satisfied with the support I received."

Neutral sentiment:

2. "I had to wait for a few minutes before connecting to an agent. The service was average, not exceptional but not terrible either."

Negative sentiment:

3. "I'm really frustrated with how my problem was handled. The agent seemed uninterested, and the solution provided didn't work."

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Identifying and tracking these sentiments offer businesses valuable insights to tailor their approaches to meet customer needs, thereby building a stronger relationship with their audience.

Benefits of Conducting Sentiment Analysis In Contact Centers

Sentiment analysis uses text and speech analysis to offer powerful capabilities for understanding customers. Some benefits of sentiment analysis in contact centers include: 

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1. Makes It Easier to Identify Customer Pain Points

Customer pain points can broadly be defined as the areas where customer needs have not been met. They are specific problems along the various stages of the customer journey that cause negative sentiments. 

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Sentiment analysis makes it easier to decipher the undertones in customer interactions and determine when they express positive, negative, or neutral sentiments. This provides contact centers with a nuanced understanding of where customer experiences might be falling short. 

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With a great sentiment analysis tool like Operative Intelligence, this insight is further refined. Operative Intelligence is a customer service analytics software that allows organizations to not only gauge sentiment but also to pinpoint the specific types of inquiries that generate the lowest sentiment scores at scale. 

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By analyzing 100% of inbound interactions, the platform can provide additional context into what customers say, directly in their own words. This proves more scalable than using surveys or only the Net Promoter Score because it captures the sentiments of all your customers— instead of recording a few responses that may not accurately reflect the needs of the majority. 

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2. Helps Organizations Stay on Top of Negative Customer Experiences

Sentiment analysis acts as an early warning sign for negative customer experiences. When conducted in real-time, it empowers agents to promptly address low customer sentiment during, and not after interactions. 

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This immediate feedback loop enables agents to quickly learn and adapt their approach to provide a more frictionless customer experience.

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3. Provides Agent & Call Center Performance Metrics

A call center's success depends on the performance of its agents. Sentiment analysis provides a comprehensive lens through which agent interactions can be evaluated. It sheds light on the effectiveness of agent responses, empowering managers to identify top-performing agents and implement call center coaching programs for low-performing agents. 

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Challenges of Call Center Sentiment Analysis 

In call center operations, sentiment analysis can help provide valuable insights into the emotional nuances of customer interactions. However, this approach is not without its fair share of challenges. 

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Let’s take a look at some of them:

1. Requires Large Amounts of Data Analysis

Call center sentiment analysis presents a challenge due to the need for a large volume of diverse data for accurate insights. Gathering data from various communication channels like calls, chats, and emails is complex and time-consuming. Cleaning, labeling, and preparing the data for analysis require large infrastructure and manual efforts.

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 Also, variations in language and tone, especially across different demographics, further complicate the accurate interpretation of sentiment. The total amount of resources involved in this process makes it especially difficult for businesses with smaller budgets to scale.

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2. Focuses on Individual Words

While focusing separately on each word may be convenient, call center sentiment analysis often fails to capture the full context and nuances of customer interactions. Language use and semantics are intricately tied to the overall flow of conversation, making it important to consider the entire dialogue. 

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For instance, a customer might use a negative word in a neutral or positive context, resulting in an inaccurate sentiment classification. This oversimplification can lead to misjudgments and provide a skewed understanding of the customer's true emotional state.

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3. Challenging to Determine Sentiment by Inquiry Type & Agent

A basic sentiment analysis tool is only able to identify keywords but cannot categorize interactions based on the type of inquiry or agent performance level. For example: 

• What particular inquiry always receives negative sentiment from customers? 
• Can it be classified into a product, service, or process-related issue? 
• Are the agents effective enough in answering the inquiries? 
• How can agents utilize the feedback from analysis to improve their skills? 

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Prioritizing sentiments with the largest volume helps contact centers make the most significant impact in improving customer satisfaction. However, it would require years of investment and analysis to do this manually. Understanding sentiments within these contexts requires sophisticated tools that can automate the entire process without compromising service quality.

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4. Doesn’t Provide the Root Cause of Customer Issues

Sentiment analysis sheds light on whether a customer interaction was positive, negative, or neutral. However, it does not directly reveal the underlying cause of customer dissatisfaction. The lack of insights into the root cause hinders the ability to implement targeted solutions through sentiment analysis. 

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Finding these root causes involves examining how products, messaging, or the organization itself contributes to customer friction. To effectively address these concerns, contact centers have to collaborate with other departments in the company. This enables the entire organization to eliminate the occurrence of these issues rather than just managing them. 

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Solutions may include:

• Process adjustments
• Adopting new technologies like automation or self-service options
• Enhancing inter-departmental communication
• Providing improved training for customer service agents

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Fortunately, Operative Intelligence takes care of all these limitations with: 

• Data analysis: Operative Intelligence assesses optimal opportunities for contact center automation, including ROI analysis. The solution is also able to pull and analyze 100% of customer data within just two weeks compared to years of manual processing. 
• Sentiment keywords: Operative Intelligence identifies customer pain points by analyzing every inbound interaction verbatim, and not just individual words or key phrases.  This approach helps call centers paint a whole picture of customer sentiments in the right context they express them. 
• Sentiment matching by inquiry type and agent: Operative Intelligence evaluates agent effectiveness in terms of query resolution and customer satisfaction across all query types. It empowers businesses with targeted improvements by pinpointing reasons for increased handle time and negative customer sentiment.
• Determining the root cause: Operative Intelligence facilitates in-depth insights into the root causes of customer pain points at scale. The platform also factors in volume, cost, sentiment, satisfaction, and resolution for all inquiries. 

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Improve Your Call & Contact Center Sentiment Analysis

Call center sentiment analysis is a great approach for understanding customer interactions and shaping the customer experience positively. However, it can be infinitely improved with the Operative Intelligence platform. 

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Operative Intelligence’s automated software provides actionable insights into the root cause of customer contacts, and then organizes them by volume, cost, and sentiment. This powerful data enables service leaders to build a compelling case for fundamental change across the organization. 

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Book your demo today

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