The majority of organizations put enormous effort into addressing negative reviews, but the actual issue frequently emerges when consumers silently leave due to being dissatisfied with the products they received. The importance of measuring customer sentiment – how people feel about the brand – has been rising, as customer retention and loyalty depend on the company’s ability to detect and address the sentiments.
Measuring customer sentiment lets businesses learn whether people are satisfied with their products, frustrated, irritated, annoyed, or indifferent. Such information helps companies improve their products, customer experience, and relations with clients.
Many organizations lack proper sentiment analysis techniques. Although 87% of enterprises believe in the competitive advantage offered by a data-driven customer experience strategy, less than one-third of them actually apply analytics to improve products. Such organizations are missing important opportunities for resolving clients’ pain points and making them happier.
Currently, businesses increasingly use digital conversations – social media messages, online reviews, surveys, customer support communications, and others – to get insights into the customers’ emotions.
What is customer sentiment?
Customer sentiment is people’s opinions and emotions about the organization’s products, services, brand, or reputation. Customers often express such feelings through digital communication channels.
The data on how customers feel can be gathered when customers post reviews online, message in social networks, write to customer support teams, and fill out surveys.
Depending on how people feel, there can be different outcomes:
- Praise or recommendations for the company
- Criticism or complaints
- Decreased customer engagement
Overall, there are three main categories of customer sentiment:
- Positive sentiment
- Negative sentiment
- Neutral sentiment
However, modern AI technologies can also detect subtle nuances of customers’ feelings, such as excitement, frustration, indifference, disappointment, and others.
Understanding customers’ emotions is crucial for businesses as it provides an opportunity to take appropriate actions that will increase the company’s performance and revenue.
What is customer sentiment analysis?
Customer sentiment analysis is a technique used to determine the emotional tone behind customers’ text data. With sentiment analysis, organizations can easily learn whether customer comments are positive, negative, or neutral.
Sentiment analysis allows businesses to learn customers’ opinions at scale by processing huge amounts of digital communication automatically.
Conventionally, customer sentiment analysis was based on the rules manually developed by humans. For instance:
- “Great” or “excellent” was seen as positive sentiment.
- “Slow,” “terrible,” or “frustrated” was considered negative sentiment.
Despite providing valuable insight, the rule-based approach had some limitations related to sarcasm, humor, and other subtleties that could not be detected.
For example:
“Great, another app crash right before the checkout.”
In this case, despite the presence of the word that means positive sentiment, the whole sentence reflects customers’ dissatisfaction with the application.
These difficulties prompted businesses to use more sophisticated approaches.
How organizations apply sentiment analysis in the course of their work
Improving Customer Support
Sentiment analysis helps customer support specialists prioritize customer requests and calls.
For example:
- Customers expressing anger need immediate attention.
- Potential churning customers can be reached out before it happens.
- Customer service managers can learn about recurring problems with their services.
Helpdesk software and contact center platforms often integrate AI-powered sentiment analysis.
Product Improvement
Sentiment analysis is applied to help organizations learn about customer pains and what customers want in order to develop better products.
For example:
- Products can be designed in accordance with customers’ needs.
- Bug fixes can be prioritized according to customers’ comments.
- New features may be implemented.
- Some customer pains can be solved.
Instead of sending surveys, businesses can continuously track customer sentiment online.
Marketing Activities
With the help of sentiment analysis, organizations can learn about audiences’ reactions to marketing campaigns and ads.
For example:
- Audiences’ perception of the campaign can be analyzed.
- Reception of ads and messages from a business can be evaluated.
- The results can be used to improve marketing messaging.
Customer Churn Prevention
Another useful application of customer sentiment analysis is the prevention of churn.
Sentiment analysis can detect negative patterns that precede customer churn:
- Increasingly negative comments
- Decreasing engagement rate
- Poor survey results
- Frustrated customers during interactions with the customer support team
The businesses that use sentiment analysis can reach out to customers in advance.
How to make customer sentiment positive
To make sure that customers feel positively about an organization, businesses need to detect any negative tendencies regularly, learn why they occur, and take appropriate actions. Improving customer experience implies that businesses respond to clients’ expectations and do not merely gather information about their behavior.
Identify and Analyze Customer Pain Points
The first step in improving customer sentiment is finding customers’ pain points and learning about issues they repeatedly face.
To detect pain points, it is necessary to collect customers’ opinions by:
- Surveys
- Reviews
- Social media conversations
- Interviews with customers
- Interactions with the customer support team
Then, such feedback can be analyzed to detect recurring issues that should be addressed first.
Organizations working with social media posting and managing software can collect and analyze customer opinion much easier.
Develop a Plan to Address Recurring Problems
Based on the results, companies need to implement the plan aimed at solving the detected problems.
It might require collaboration between various organizational units, such as:
- Customer support teams
- Product development team
- Marketing department
- Information technology department
- Operations
For instance, it might be necessary to work on the following aspects:
- Simpler website checkouts
- More prompt customer support responses
- Improved onboarding processes
- Development of new product features
Organizations should provide relevant feedback to the corresponding units within the business to solve customers’ pain points.
Provide Discounts and Other Incentives for Loyal Customers
It is also important to reward loyal customers for their long-term cooperation and commitment to the business.
For example, a business might offer the following options for loyal customers:
- Exclusive discounts
- Special access to products
- Rewards
- Special offers
- Referral programs
Loyal customers are more likely to feel better about the business.
Continuous Monitoring of Customer Feedback
Sentiment analysis is a process that should be carried out continuously since opinions change.
Organizations can continuously collect customers’ opinions from the following sources:
- Online reviews
- Social media conversations
- Surveys
- Interactions with customer support
Continuous monitoring supported by social listening helps businesses identify changes in customer perception before negative experiences escalate.
Monitoring customer sentiment on a regular basis will allow companies to spot issues before their customers.
Optimization
Based on the gathered information and taken actions, it is necessary to measure customer satisfaction regularly to improve performance and further optimize actions.
For example:
- Customer Satisfaction Score
- Net Promoter Score
- Customer retention rate
- Customer lifetime value
- Churn rate
- First response time
Measurement will provide valuable information that will help businesses improve customer experience.
Conclusion
Customer sentiment plays an essential role in ensuring customer loyalty and satisfaction. However, many organizations miss important signals since silent dissatisfaction does harm to business. By applying sentiment analysis techniques, businesses can detect customers’ opinions and emotions that can help them improve their products and performance. Modern large language models have made it possible to process natural language in ways that were previously unfeasible.