Feature
The Gist
- Emotional intelligence. Sentiment analysis enriches CX with nuanced emotional insights.
- Data-driven personalization. Real-time sentiment feedback allows for tailored customer interactions.
- Strategic decisions. Voice of Customer data informs content strategy and competitive benchmarking.
Customer feedback is a treasure trove of insights waiting to be unearthed, but combing through the deluge of reviews, feedback, social media posts and customer surveys is no easy task. Enter sentiment analysis — a sophisticated tool that interprets the emotional tone behind words, giving brands a lens into how their services or products resonate in the hearts of their customers. It's more than just classifying comments as "positive" or "negative"; it's about a business deepening its understanding of customer sentiment to fine-tune the customer experience. This article explores how to leverage sentiment analysis and Voice of Customer data across channels to obtain actionable insights.
Understanding Sentiment Analysis
Michelle Tilton, VP of marketing at Gryphon.ai, a call center platform provider, told CMSWire that a growing trend in customer experience today is how CX pros are relying on sentiment analysis to inform more personalized conversations with customers. "Instead of relying on automated prompts or having to put customers on hold to find an answer, conversation intelligence powered by sentiment analysis provides real-time recommendations with an AI-powered analysis of the customer’s responses, therefore equipping CX pros in real-time with the emotional intelligence and awareness needed to personalize the conversation and help fulfill the customer’s needs."
Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique that programmatically identifies the emotional tone behind words and language. It works by applying advanced text analysis algorithms and machine learning (ML) models to scan through unstructured text data and determine whether the sentiment is positive, negative or neutral.
Key capabilities that enable sentiment analysis include NLP, text analysis, emotion AI and aspect-based sentiment:
- NLP enables systems to "understand" human language by recognizing parts of speech, sentence structure and other linguistic nuances.
- Text analysis extracts critical topics, themes and patterns from customer feedback using statistical models and rules.
- Emotion AI takes this a step further by inferring the sentiment of words and phrases based on context, using ML trained on extensive human-annotated datasets.
- Aspect-based sentiment looks beyond just positive or negative and dives into sentiment toward specific aspects of a brand’s products or services.
Raghu Ravinutala, CEO and co-founder at Yellow.ai, a global leader in conversational AI, told CMSWire that advanced AI can analyze customer sentiments and provide appropriate responses or escalations for queries. "Sentiment analysis and feedback have led to a new wave of worker productivity and an enhanced user experience. In-depth sentiment analysis can detect emotions and identify areas for growth by analyzing word choice, speech patterns, and tone," said Ravinutala. "Speech analytics can improve customer experience and business outcomes by providing seamless service across voice-enabled chatbots, human-agent contacts, and quality control in agent-customer engagements."
To get an idea of how sentiment analysis works, you can try an online demonstration at MonkeyLearn. It allows a user to enter some text, and then analyze it for sentiment. Note that it does not allow bolding or italics, two text styling elements that can often be indicative of sentiment.
Another example of sentiment analysis can be seen in Grammarly reports, which highlight the sentiment that is used in a subscriber’s writing throughout the week, as seen here:
The true power of sentiment analysis stems from its ability to parse massive volumes of unstructured text that would be impossible to analyze manually. By programmatically scanning thousands of customer reviews, social posts, surveys, call transcripts and more, sentiment analysis can reveal major themes and trends in customer perceptions that might otherwise be overlooked. The actionable insights derived from sentiment analysis allow brands to benchmark sentiment over time, pinpoint areas needing improvement, and quantify the impact of changes. With its scalability and precision, sentiment analysis offers an invaluable emotional pulse check on the full customer experience.
Praveen Satyanarayana, senior director of customer experience at Tredence, a global data science and AI solutions provider, told CMSWire that just knowing sentiment alone isn't enough — it's also essential to consider customer sentiment throughout their journey to identify areas of frustration or dissatisfaction. "Combining sentiment analysis with VoC scores and operational data can provide a more complete picture of customer sentiment," said Satyanarayana. "This approach helps uncover hidden issues behind seemingly positive customer engagement and sentiment. Additionally, it's crucial to monitor competitor sentiment, not just your own."
Tilton explained that AI-powered conversation intelligence is also able to analyze the intricacies of human speech patterns, emotions, and intent to retrieve valuable customer insights during and after the conversation. “This helps reps provide more relevant and personalized assistance,” said Tilton. “Even more, when reps have access to real-time sentiment analysis, they can adjust their responses according to shifts in customers’ tone and emotions effortlessly, leading to more tailored customer service.” Tilton suggested that this also helps boost customer engagement and satisfaction, eventually increasing loyalty in the long run.
Related Article: Sentiment Analysis Improves the Customer Experience
Voice of Customer Data: Don’t Put Words In Your Customers’ Mouths
The quality of the insights derived from sentiment analysis depends greatly on the data that is fed into it. As such, brands need to be strategic in gathering a representative sample of customer feedback data from relevant sources, in sufficient quantity, and properly formatted.
Potential data sources include:
- Customer reviews on websites, apps, or third-party review platforms
- Responses to customer satisfaction or Net Promoter Score (NPS) surveys
- Social media conversations including Twitter, Facebook, Instagram and more
- Transcripts from customer service calls and live chats
- Online community forums and discussions related to the brand
The key is to sample data from sources that align with the customer journey and offer textual feedback that indicates sentiment. Focusing on channels where customers naturally express opinions often yields authentic results.
In terms of quantity, more data leads to more accurate analysis. Thousands of data points may be required for enterprise-scale analysis to provide statistical significance. For smaller brands, a few hundred timely reviews can provide directional insights when getting started with sentiment analysis.
Proper data formatting is also critical. For example, analysis of audio call transcripts requires speech-to-text processing. Tools such as OpenAI's Whisper, Notta AI and Otter AI provide accurate speech transcription. Additionally, keep in mind that social media data may need cleansing to remove extraneous tags and identifiers that can confuse the algorithms.
Christy Pyrz, chief marketing officer at Paradigm Peptides, told CMSWire that her business takes sentiment analysis a step further by applying it to customer segments. "We divide our target audience into different segments based on demographics, purchasing habits, browsing behavior, location, etc.," said Pyrz. "From there, we analyze sentiment within each segment. This allows us to find specific insights that we can then apply to our personalized marketing campaigns.” Pyrz explained that it’s not enough to just know how your target audience feels as a whole toward your brand — it’s best if you know how each individual segment feels, in order to leverage that data most effectively.
Related Article: 3 Ways Sentiment Analysis Can Improve the Customer Experience
Choosing the Right Sentiment Analysis Platforms
Choosing the right sentiment analysis platforms for analyzing the voice of customer data hinges on several key considerations. Brands need to have a clear understanding of their goals and objectives. Are they looking to monitor brand reputation, enhance customer service, or gauge product reception? This clarity will influence the kind of features they should seek in a sentiment analysis platform.
Sources of data collection also play a pivotal role. Brands must ensure that the sentiment analysis platform they choose can seamlessly integrate with all the martech platforms where they're gathering customer feedback, whether it's from social media, forums, customer support interactions, or surveys.
Another critical factor is the platform's capacity to handle data volume and scalability. Brands with a large customer base will need a platform that can process vast amounts of data efficiently. As the brand expands, the platform should be able to easily scale to accommodate the increasing data influx.
Language comprehension is another essential feature. The chosen platform should be proficient in understanding various languages, nuances, and even slang or emojis. This is especially relevant for brands with a global reach. Additionally, the platform's ability to analyze sentiments in real-time can be a game-changer, especially when brands need swift responses, such as during crisis management.
Data visualization capabilities are also significantly important. Business leaders will find it easier to interpret and act on insights if the tool they are using provides them with user-friendly dashboards, graphs and charts. The accuracy of the platform, enhanced by ML, is indispensable. Over time, the platform should refine its analysis, learning from feedback or corrections.
Integration capabilities add yet another layer of convenience. If the sentiment analysis platform can integrate with a brand's existing CRM, marketing platforms, or analytics suites, it streamlines the process further. Customization, based on specific industry jargon or unique brand terminology, can render the analysis more pertinent.
While evaluating platforms, the associated costs and the expected ROI should be considered. Sometimes, a higher-priced tool might offer invaluable features that can drive substantial business growth. The reputation of the vendor, along with the support they provide, can often be as critical as the features of the platform. Brands benefit from vendors who offer robust support, training and respond quickly to queries.
Related Article: 5 AI Trends in Voice of the Customer Practices
Obtaining Actionable Insights
Once sentiment analysis produces results, brands must take an analytical approach to transform the data into meaningful, actionable insights. The first step is digesting the data to identify broader themes and trends. Are customers generally satisfied or dissatisfied? Which aspects or product features generate the most negative sentiment? How has sentiment trended over time? Obtaining high-level takeaways provides directional guidance.
Drilling down further reveals the most telling nuances. Look for patterns across customer segments — does sentiment vary by location, purchase history, or demographics? Mine the data for insights into specific customer needs or pain points. Sentiment analysis makes it possible to quantify subjective feedback at scale.
Young Pham, co-founder, and editorial manager at Biz Report, a news and guide resource for businesses and startups, told CMSWire that customer feedback is an invaluable resource for businesses seeking to understand consumer sentiment, preferences and trends. "We recognize the significance of staying attuned to our audience's needs and preferences," said Pham. "To achieve this, we've integrated sentiment analysis into our feedback analysis process." Pham said that for his business, there are several steps involved in the process of sentiment analysis:
- Comprehensive Data Aggregation: We collect customer feedback from various sources, including online reviews, social media mentions, customer surveys, and direct inquiries. This data is aggregated and organized to form a holistic view of customer sentiment.
- Sentiment Classification: Using advanced NLP and ML techniques, we classify customer feedback into various sentiment categories, such as positive, negative, or neutral. This classification allows us to gauge the overall sentiment of our audience.
- Trend Identification: Sentiment analysis also enables us to identify emerging trends, recurring issues, or areas where customers express heightened satisfaction. These insights inform our editorial and content strategies, helping us tailor our offerings to address specific customer needs.
- Competitive Benchmarking: We use sentiment analysis not only to gauge our performance but also to benchmark it against our competitors. By understanding how our sentiment compares to others in the industry, we can identify areas where we excel and areas where there is room for improvement.
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For sentiment analysis to be effective, brands should involve stakeholders from across the business to diagnose root causes. Sales and marketing may spot gaps in positioning or messaging, support can pinpoint recurring service issues, and product teams might identify usability improvements. Every customer touchpoint stands to gain from the insights that surface.
Lastly, once action has been taken on the insights obtained from sentiment analysis, brands should inform customers about the improvements that have been based on their input.
Final Thoughts
Customer experience is always an emotional endeavor, and sentiment analysis can shine a spotlight on those emotions by extracting insights from unstructured feedback. Sentiment analysis turns customer feedback into actionable insights that inform customer-centric strategies that enhance customer satisfaction and improve the overall customer experience.
