Understanding and leveraging customer sentiment

Companies strive for positive customer sentiment. As a PM, you want to live in a world where customers write glowing reviews and recommend your product to everyone they know. However, sometimes poor interactions can lead to neutral or negative feelings about a brand. And ultimately, these perceptions can affect a company’s bottom line.

Understanding customer sentiment is crucial to knowing how customers feel about your company. There’s a plethora of data from social media posts, reviews, and other digital channels. Analyzing this data can make a significant impact on how you drive forward positive customer experiences.

In this article, you’ll explore the importance of customer sentiment and the different types of sentiment models. We’ll also discuss strategies to collect, measure, and act on customer sentiment.

What is customer sentiment?

Customer sentiment reflects how customers perceive a company and its products. It’s more than just a metric of a customer’s attitude towards a brand. Customer sentiment is about gaining a deeper understanding of the customer. It delves into why customers behave the way they do and what matters to them.

Customer sentiment evaluates a company’s marketing campaigns, product development, and customer support. If any of them are found to be unsatisfactory, it can impact the customer experience and your bottom line.

What is the difference between customer sentiment and customer satisfaction?

Customer sentiment and customer satisfaction seem similar, but they have different goals. Customer satisfaction often focuses on one interaction or feature. Then it determines if a customer finds it acceptable or not.

Meanwhile, customer sentiment focuses on the overall experience of customers and their opinions of a brand. It’s more holistic and evaluates the underlying feelings of customers.

What are the different types of customer sentiments?

There are multiple types of customer sentiment models. Here are the most common types of customer sentiment analysis models:

• Standard sentiment analysis — The most common type of sentiment analysis is classifying data as positive, neutral, or negative
• Fine-grained sentiment analysis — Fine-grained is similar to standard sentiment analysis, but it provides more choices for a more detailed overlook. For example, it may also be a five-star rating system. Or adding additional categories like very positive, positive, neutral, negative, very negative
• Emotion detection — This model is often used by machine learning algorithms to analyze text for an underlying emotion. For example, scanning social media posts could help determine if customers are feeling excited or not about a new feature
• Aspect sentiment analysis — Like emotion detection, aspect analysis can monitor texts for underlying emotions. However, it’s more advanced and can identify sentiments related to specific aspects. For example, a customer writes “I like the pricing, but the quality is poor.” Aspect analysis can determine there was a positive sentiment for price, but a negative sentiment for quality
• Intent detection — This type of model analyzes the intent behind a customer’s action. If a customer contacts chat support, then intent detection can help determine their needs. For example, if they need assistance, are making a complaint, or suggesting improvements

How to use customer sentiment to improve your business

Customer sentiment provides valuable insights into how customers feel about your brand. Without this knowledge, managers will struggle to connect with customers. They also won’t be able to provide services that meet customer needs.

There are multiple ways to use customer sentiment to improve your business. Some of these benefits include:

Improve customer experience

Customer sentiment can provide real-time insights into how customers are experiencing your product. Companies can easily identify issues and work on rectifying them. A quick resolution to problems means that customers are more likely to be satisfied with their experience.

Enhance products and services

Analyzing feedback for customer sentiment is crucial to optimizing products and services. Managers can use this data to improve or add new features that meet customer needs.

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Monitor brand reputation

Customer sentiment analysis can often review social media posts and reviews to determine how people view your brand. It’s also useful to track down people with negative experiences and help fix the situation. These actions can help improve your brand reputation online.

Optimize marketing strategies

Customer sentiment can also be used to measure the success of a marketing campaign. Reviewing how customers feel about your marketing campaigns can help you fine-tune your approach. This helps tailor your messaging and ensure it resonates with your audience.

Gain competitive insights

Customer sentiment can also be used as part of a competitor analysis. Understanding how your competitors are missing the mark can help inform your own product or marketing strategy.

Improve customer service

Chatbots are frequently used for customer service, but there comes a time when a situation escalates and a real person needs to help. Machine learning and AI can detect the intent of chat conversations and know when to call in customer support. This can help ensure your customers are having satisfactory support experiences.

Customer sentiment can also help inform re-training for customer support teams. If customers report dissatisfying experiences with customer support, it may be time to retrain the team.

How to measure customer sentiment

Measuring customer sentiment can be done through several different methods and tools. Some of the most common ways to collect and analyze customer sentiment include:

• Sentiment analysis software — There are tools to help you analyze sentiment using natural language processing (NLP) and machine learning
• Customer surveys — Surveys are often the most frequent way to collect feedback from customers. It’s quick to implement and you can gather data from thousands of customers. You can ask open-ended or closed-ended questions or a star-rating system. A net promoter score (NPS) is one example of a customer survey
• Social media monitoring — Sometimes customers complain on social media without tagging you. That’s why it’s important to monitor social media posts to find positive or negative feedback
• Reviews — Don’t forget to evaluate third-party reviews and ratings. This is another way to effectively measure customer sentiment with your product
• Interviews or focus groups — One-on-one interviews or focus groups can provide beneficial insights into customer sentiment
• Customer service interactions — Analyzing the tone from customer service teams can provide valuable data on customer attitude

Most of the time you need to elicit feedback directly from customers. Surveys can get direct feedback, but evaluating customer support interactions can also be useful. Combining different methods helps you build a full picture of customer sentiment.

A proactive approach to evaluating sentiment helps you improve the customer experience. Don’t forget to track customer sentiment over time, so you can identify trends.

How to use customer sentiment in product strategy

Once you’ve gathered the feedback and completed a sentiment analysis, the next step is to implement what you’ve learned. Otherwise, all that work is pretty useless. You need to build an actionable plan to improve or maintain positive customer sentiment.

The action plan can inform marketing strategies, product development, or customer service improvements. Here are a few aspects to consider:

• Prioritize significant issues — There may be multiple issues, but you should focus on the tasks that are most reported or are causing a significant problem. Outliers can be worked on during less urgent times
• Sort data by demographics — Organizing data by demographics can also help prioritize your tasks. For example, maybe new customers love your new product features but long-term clients don’t like it. This can help shape your strategy to resolve negative customer sentiment
• Examine data over time — Evaluating trends is also useful for your action plan. It can help you determine when positive or negative sentiment occurred and what may have happened that caused it
• Determine pain points for customers — You’ll need to determine what features, services, or interactions caused issues with customers. Finding pain points and creating an action plan to resolve them are crucial for improving customer sentiment
• Evaluate positive interactions — While a lot of emphasis is placed on finding and fixing negative sentiment, don’t forget to analyze the positive sentiment. It can help you discover what makes you unique from your competitors and what you can do to continue to create positive sentiment
• Train customer support — The customer support team is in the best position to turn a negative experience into a positive one. Analyzing conversations between customers and the support team can help you find areas of improvement. For example, expressing more empathy or writing in a clear style. Training customer support can help foster more positive interactions

You may also want to make an action plan to turn neutral sentiment into positive sentiment. How can you help turn neutral customers into positive customers? Maybe you need to add a new feature or modernize your product design. Thinking of these ideas can help improve your positive sentiment score.

Key takeaways

Sentiment is the driving force behind a customer-centric business model. You can’t create positive interactions with customers if you can’t understand their sentiment. It’s also more difficult to manage issues that customers are facing if you don’t know them.

Proactively surveying customers and monitoring reviews is a part of measuring customer sentiment. It provides you with the data you need to make informed decisions. Managing customer feedback ensures you are building long-term relationships with loyal customers. Those relationships are often rewarded with less churn and more revenue.

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