Editorial
The Gist
- Understanding customer sentiment. Customer sentiment is crucial for driving loyalty, repeat purchases and advocacy.
- Real-time sentiment data. Real-time sentiment data allows businesses to respond quickly to negative feedback, which improves customer satisfaction and retention.
- Using sentiment analysis. Customer sentiment analysis can be gathered through surveys, social media and support data to provide valuable insights for improving CX.
Customer sentiment is the emotional tone or attitude expressed by customers toward a brand, product or service. Understanding this can give companies a significant advantage in shaping business outcomes. Let’s explore why customer sentiment analysis is critical, how it can influence business success and how to track and analyze sentiment effectively.
Why Understanding Customer Sentiment Is Crucial
Customer sentiment plays a key role in determining the future engagement of your audience. Positive emotions resulting from positive experiences often lead to loyalty, repeat purchases and advocacy, while negative emotions from negative experiences may drive customers away.
Higher Lifetime Value (LTV)
Satisfied customers are more likely to become repeat buyers, which significantly increases their customer lifetime value (CLV). Studies consistently show that customers who have a positive experience with a company not only return but also spend more money on future purchases. This increase in spending is due to several factors:
- Trust: When customers trust a brand, they feel more secure in their purchasing decisions, which increases the likelihood for future transactions.
- Perceived value: Favorable experiences enhance the perceived value of a product or service, which leads to higher satisfaction and willingness to spend more.
- Reduced likelihood of switching: Satisfied customers are less inclined to explore competitors, particularly if they feel emotionally connected to the brand.
Word-of-Mouth Referrals
Customer sentiment also impacts word-of-mouth referrals. Satisfied customers often refer others to a business, which leads to new customer acquisition at little to no cost. Word-of-mouth marketing is one of the most powerful drivers of growth because it's based on trust and personal recommendation.
Net promoter score (NPS) is a widely used metric for gauging customer sentiment and the likelihood of people recommending a brand to others. NPS scores correlate with business growth. As higher scores indicate that customers are likely to refer the business to friends or colleagues, this drives more revenue and expands the customer base without the company having to invest heavily in acquisition.
Higher Spending Through Upselling and Cross-Selling
Satisfied customers tend to be more open to upselling and cross-selling opportunities. Trust in the brand makes them more receptive to exploring additional or higher-priced products and services. For example, a customer who has had a positive experience with a basic subscription may be willing to upgrade to a premium version, which increases the average transaction value.
Moreover, loyal customers often have a deeper understanding of the brand’s offerings, which makes them more likely to explore complementary products and services that improve their overall experience. This openness to additional purchases is reinforced by the familiarity and positive sentiment they already hold. For instance, a user who has benefited from a software tool may be more inclined to purchase add-on features or support services.
Reduced Price Sensitivity
Satisfied customers are often less price-sensitive. This is because they perceive higher value in the products or services they receive and are willing to pay a premium. For brands, this means less pressure to compete on price alone, meaning that a brand that supports positive sentiment can maintain or even raise prices without risking a significant loss in customers. This, in turn, allows brands to invest more in quality and innovation, which differentiates their offerings from competitors.
Reduced Churn and Increased Retention
Delighted customers are more likely to stay with a company, leading to reduced churn. Retaining existing customers is less costly than acquiring new ones, and a satisfied customer base translates to higher profitability in the long term.
Various publications state that increasing customer retention by 5% can lead to profit increases ranging to up to 95%. When sentiment is tracked effectively, businesses can address concerns before they escalate, which reduces the risk of churn.
Related Article: Emotion Is the New Metric: The Rise of Sentiment Analysis in Retail
The Mechanics of Sentiment Analysis
Traditionally, customer sentiment has been ascertained by people in corporate functions like sales, support and customer success along those journey phases that have touchpoints with buyers as part of standard customer relationship management. Sentiment analysis is the process of identifying the emotional state of customers computationally. This is achieved through either one or a combination of the following processes:
Text-Based Sentiment Analysis
Text-based sentiment analysis typically involves using machine learning models to analyze written content such as reviews, social media posts or chat transcripts. These models classify text as positive, negative or neutral based on the language used. This is accomplished by not only examining adjectives and adverbs in the text, but also nouns and verbs that add context. The models also recognize phrases and idioms that convey nuanced feelings. Additionally, punctuation, repetition and intensifiers can emphasize sentiment.
Voice-Based Sentiment Analysis
Voice-based sentiment analysis takes it a step further by analyzing the actual tone of voice in customer interactions. Machine learning models analyze pitch, tone and pacing to detect emotions like frustration, calmness or happiness. This type of analysis can be used in customer service calls between a customer and an AI agent to identify real-time sentiment and adjust conversation strategies accordingly.
Image-Based Sentiment Analysis
Image-based sentiment analysis uses facial recognition technology to assess emotional states. For example, it can analyze a customer’s facial expressions during video interactions to detect dissatisfaction or enthusiasm. While this method can be highly accurate, it’s usually reserved for specialized applications due to complexity and privacy concerns.
The Importance of Real-Time Data
The more real-time data you have about customer sentiment, the faster and more effectively you can react before a customer’s negative impression solidifies beyond repair. Imagine a live chat between a customer chatbot and a frustrated customer. Being able to detect the irritation in real time with a message like, “I realize you’re upset. Let’s resolve this,” can dramatically improve the interaction.
Imagine a bad online review. Since it’s public, one unfavorable review can negatively influence many existing or potential customers. Here, a prompt reaction is important but not enough. It also must showcase the appropriate level of effective customer care. After all, the response is public, too.
Real-time sentiment data helps improve not just the individual experience but also broader outcomes like customer satisfaction scores and retention rates. By addressing negative sentiment promptly, businesses can turn potential churn situations into positive, loyalty-building experiences.
Methods for Gathering Sentiment Data
Collecting customer sentiment data can be done through a variety of methods.
Interviews
Interviewing customers can provide deep insights into their sentiment. The qualitative data acquired helps to understand the emotional tone customers use when describing their experiences. Qualitative data is invaluable in understanding nuanced issues.
How real-time can this be? Having a live conversation between a customer and a company representative can be triggered by a customer transaction or at certain pre-defined intervals with a customer sample group. Either way, interviews are rarely conducted in real-time relative to the customer's emotional experience, as scheduling and qualitative analysis take time.
Surveys
Surveys are one of the most common ways to gather sentiment data, often using structured questions that gauge satisfaction levels. Surveys can be sent to customers immediately after a purchase or support interaction, or periodically to gauge overall sentiment.
How real-time can this be? Near-real-time sentiment analysis is achievable, especially if the survey responses are collected digitally. When a customer has finished a transaction or a key interaction on an ecommerce website, follow-up surveys can be shown on the same page. When the customer submits the answers, closed-ended responses can be processed directly. Open-ended responses can be automatically analyzed for sentiment and potential issues. Negative sentiment can then be automatically flagged for prompt follow-up.
Social Media and Online Reviews
An advantage here is that customers often share their sentiment without you having to ask. The disadvantage is that they share their opinion about you to a potentially very large number of people that are easily influenced. This is great when the sentiment is positive, but dangerous when it’s negative. Constant monitoring is essential.
How real-time can this be? Like surveys, incoming data can be automatically processed for sentiment and marked for a timely reaction.
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Customer Support Data
What’s special about customer support data is that it typically reflects more negative than positive sentiment, as customers usually reach out when they're dissatisfied. Therefore, the focus here is to identify the level of dissatisfaction, understand the cause and quickly resolve the problem. For a human support agent, identifying customers’ sentiment on the phone or in an email exchange is simple. For bots, it’s not as easy but still feasible. Also, analyzing chat transcripts, email interactions or call recordings after the fact is a powerful way to track sentiment over time.
How real-time can this be? It depends on how the support request is received. In a phone call or live chat, near-real-time sentiment analysis is increasingly feasible with current technology. This lets AI agents identify frustration or dissatisfaction as it arises, which allows for immediate, tailored responses to improve customer experience.
Related Article: The Untapped Potential of Real-Time Voice of Customer Insights
Key Insights from Customer Sentiment Analysis
Customer sentiment analysis is a powerful tool for understanding how your people feel at each stage of the customer journey. By understanding their sentiment, you can improve individual customer experiences, enhance loyalty, increase spending, reduce churn and drive long-term growth. Whether through text, voice or visual cues, sentiment analysis provides actionable insights that allow you to respond quickly and effectively, which helps you build a stronger relationship with your audience.
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