Your customers are talking, and they have a lot to say.
As a contact center leader, there’s no question you’re at the heart of the conversation. But do you know how your customers really feel about your brand, products, services, and support? If you’re not analyzing customer sentiment, you’re only getting part of the story.
The whole story—the one necessary to make informed decisions about measurable CX improvements—doesn’t come from multiple-choice answers in a customer survey or comments shared after a single interaction. Rather, public opinion unfolds over time, and it’s expressed in the nuances of language, hidden patterns, and the emotions of people.
The Crucial Role Of Customer Sentiment In Data-Driven Decisions
The most valuable customer insights for improving CX come from the emotions behind positive feedback, negative feedback, and every other customer communication—across all interactions and on all channels. In an omnichannel contact center, that may include email, text, chat, social, voice-based interactions, and more.
To be truly meaningful, the information you gather across channels and customer interactions should consider what is behind the words, to reveal customer sentiment—the human emotion reflected in linguistic nuances and language patterns.
Creating context around the words, and following a more analytical approach to decoding customer sentiment, provides a finely tuned understanding of customer satisfaction, consumer preferences, and overall public opinion. The meaningful insights from consumer sentiment analysis are an essential component of a customer-centric strategy because they indicate the actions needed for improvement.
What Is Customer Sentiment Analysis?
A deep dive into customer emotions and how people feel about what you’re delivering requires customer sentiment analysis, or opinion mining. It’s a powerful tool for CX measurement and optimization. When done correctly, it goes beyond traditional key performance indicators (KPIs) to provide a clearer understanding of strengths and areas for improvement in how your contact center provides CX.
Accurate analysis of customer sentiment takes thought and effort. In an omnichannel customer service environment, in particular, there’s much to consider. But making sense of the data for business intelligence and actionable insights can play a pivotal role in creating a better customer experience, and even in growing your business and improving its bottom line.
Starting your analysis of sentiment, and ultimately getting it right, will provide a treasure trove of valuable insights. With those insights, you’re empowered to make smarter, data-driven decisions that boost long-term customer loyalty and spending.
Decoding customer sentiment to get to the key insights it brings is a critical tool for businesses. That’s because human emotions and sentiment over time directly influence whether someone buys from you or chooses an alternative to products and services.
Negative experiences and the negative emotions they generate are the primary threat to building long-term loyalty. Positive sentiment, though, is likely to be shared and drive revenue for your business.
How To Transform Customer Feedback Into Deeper Insights
Customer sentiments are like breadcrumbs, revealed bit by bit, throughout the customer journey. It’s up to you to pick up those clues by listening to and analyzing how people communicate about your products, services, and support, and uncovering critical insights behind the words.
Customer sentiment is a qualitative measure of how a customer feels emotionally about your business, including their thoughts, opinions, and attitudes. There are three types of sentiment:
- Positive sentiment
- Neutral sentiment
- Negative sentiment
According to the Zendesk CX 2024 CX Trends Report, 70% of consumers spend more with companies they feel provide positive experiences. Earlier Zendesk research shows 61% of customers would switch to a competitor after one bad experience. That’s a compelling reason to start using sentiment analysis in your contact center, or tune up the sentiment analysis techniques you’re already using.
Accurate sentiment analysis in a digital world is complex because human language is nuanced. In digital customer communication, we lose the benefit of eye contact, tone of voice, body language, and other cues we use during in-person dialog. As a result, it’s more difficult to gauge how customers really feel and identify the true customer feelings behind the words.
For example, comments like these, which were pulled from customer surveys, online product reviews, and social media platforms, leave much to interpretation:
- It works fine.
- Support is okay.
- I waited five minutes to talk to a live person.
- I spoke to someone who tried to help.
Are these the words of happy customers? Would broader context reveal they are indifferent, or even dissatisfied? Getting those answers—decoding customer sentiment—takes accurate sentiment analysis.
Best Practices For Best Results
There’s no one-size-fits-all approach when it comes to how contact centers should approach customer sentiment analysis. But there are proven methods for success. That said, it’s important to remember that sentiment analysis is measuring opinions, and the root cause for opinions does vary. Sentiment is a great metric to use as directional data to make people and process improvements that will help create stronger experiences.
Here are five best practices for starting and maintaining a successful sentiment analysis program:
- Establish goals early. Before you begin any in-depth analysis, set measurement goals that align with your overall contact center goals and the goals of your business. For example, are you measuring sentiment to gauge the effectiveness of marketing strategies, reduce customer churn, improve CX, or increase positive experiences in the contact center?
- Include all channels. To increase the level of reliability, don’t limit the data. Your analysis should include all channels your customers use to communicate with you, including social platforms, online community discussions, phone, email, surveys, review platforms, and more. Each provides important additional context and insights into customer sentiment throughout the customer journey.
- Ensure proper tagging. Proper tagging is essential to the accuracy of sentiment analysis. Tagging helps you categorize customer feedback and communications for better analysis and more helpful insights. While manual tagging may work for smaller customer support centers, medium to large ones should implement automated tagging solutions.
- Elicit direct customer feedback. While collecting and synthesizing data across channels is necessary, it’s also important to use direct customer feedback to ask customers their thoughts, feelings, and opinions directly. Add online surveys, comment fields, and customer satisfaction surveys (CSAT) to provide additional color to your sentiment analysis. The key is to not rely on one input.
- Apply the insights. Whether it’s providing training and coaching moments for customer service teams or sharing product reviews and usability data with business partners, the point of sentiment analysis is to act on it to make improvements over time based on your established goals.
Can AI Help Decode Customer Sentiment?
While the benefits of sentiment analysis are compelling, the process may initially sound like it requires mind reading. Without the benefit of in-person interactions, how do you “read the customer” in online messages, text, and chats?
That’s where artificial intelligence (AI) comes in.
To decode customer sentiment, manual analysis has given way to automated sentiment analysis solutions powered by AI. AI can quickly transform customer sentiment into valuable customer insights.
AI can automate the process of detecting tone, intent, and feelings in human language. Using natural language processing (NLP) and machine learning algorithms, AI-powered sentiment analysis tools go beyond words to interpret attitudes and emotions from text, in addition to context.
They analyze mountains of data from sources like email, chat, text, social media platforms, and online customer reviews to accurately identify and categorize customer sentiment as positive, negative, or neutral. With NLP, automated sentiment analysis goes beyond simple praise or criticism to interpret attitudes and emotions from text.
It assigns sentiment scores to customer opinions with values ranging from +1 (extremely positive) to -1 (extremely negative) based on certain words or phrases customers might use. But accuracy takes AI sophistication. For example, a customer review that says “I love my new laptop,” is easily interpreted as positive. But it’s more difficult to accurately discern sentiment in a review that uses slang like “This new laptop is straight fire” or sarcasm like this, “I love waiting five minutes for my new laptop to start.”
A tool without the right sentiment analysis algorithms to consider context and nuance will miss a lot in translation. AI with more sophisticated sentiment analysis models will learn to interpret the language your customers use so you can accurately measure customer opinions no matter how they convey them.
Eliminating Human Bias
Manual customer sentiment classification is a much slower process than an AI-driven approach. It is also problematic for another important reason: human bias.
People look for words or phrases that confirm what we already know or suspect. For example, you’ve probably heard something like this from your agents, “All our customers hate the new version of our product.” Is that negative sentiment true, or has the agent just experienced confirmation bias and chosen an interpretation that aligns with his or her personal beliefs?
In this example, you can leverage data to get to the unbiased truth. With historical sentiment analysis and machine learning, AI can find trends and predictors of success or failure that aren’t limited by human interpretation or bias. You’ll eliminate absolutes like “all,” “none,” “always,” and “never,” to get to the unbiased truth, and understand what customers really mean and feel.
Without human bias potentially muddying your analysis of customer sentiment, you’ll have better data, to make smarter decisions, for greater impact.
What Are The Benefits Of Customer Sentiment Analysis?
With accurate customer sentiment analysis, you can spot sentiment trends, patterns, and correlations that allow you to quickly make changes to help optimize your contact center’s performance and the experience you deliver.
Use data on trends over time to identify and understand issues, then make changes to address them. This might include scheduling differently or adding more support channels, or staffing channels differently.
If there are certain days or times associated with an uptick in negative sentiment, for example, consider scheduling changes at those times to ensure sufficient staffing. Similarly, you can flag customers who repeatedly display negative sentiment and ensure they are routed to agents who are best equipped to handle them.
Detailed insights from analysis of customer sentiment often reveal areas where small changes can have a big impact on customer experience—agent training, call scripts, or agent reward structures, for example.
When you carefully listen to what your customers are saying—and act on it—you’ll quickly see meaningful results. That’s the power of the valuable insights you get from customer sentiment analysis.
How Playvox Can Help
Happy agents boost customer happiness. The research is clear about the correlation between happy customers and happy employees. McKinsey found that happy, engaged agents are three times more likely to feel capable of delivering exceptional service.
You’ll probably find that’s the case in your contact center. Effectively engaging and motivating agents is a good first step in boosting positive sentiment. Read our ebook, Celebrating Success In The Contact Center: Top Tips For Engaging And Motivating Agents to learn more.
While customer sentiment may not be among the KPIs you track now, customer sentiment analysis deserves serious consideration. In fact, leaders of top-performing contact centers tell us the positive impact of sentiment analysis is clear and quantifiable.
It’s an especially valuable tool for businesses with large amounts of data from multiple channels. Done right, you’ll gain a deeper understanding of needs, issues, and preferences throughout the customer journey. Success, though, requires the right sentiment analysis tools and best practices.
Playvox can help you understand public sentiment and use it to your advantage. Contact us to learn more about Playvox AutoQA, a collection of functionality that lets you get the full picture of what is driving customer interactions with your business and also identifies the underlying sentiment.