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5 Real-World Sentiment Analysis Use Cases

Sentiment analysis
Sentiment analysis is a way of attaching a metric to a snippet of data that conveys how positive or negative its connotation is.

It’s broader and deeper than the traditional customer survey that asks a participant to assign a value to a statement, such as “strongly agree,” “strongly disagree” or something in between. In fact, you can apply sentiment analysis to unstructured data, such as the text of an email, a Facebook comment or a Tweet. Through machine learning algorithms, sentiment analysis becomes more exact as the models consumer and analyze more data.

Sentiment analysis

Businesses of every type can use sentiment analysis to gain deep insight into how their customers think and feel. Here are five use cases for sentiment analysis:

1. Track Customer Sentiment vs. Time

If your current data gathering indicates people are happy with your products and services, that’s terrific; but it can lead to complacency. Sentiment analysis can prevent complacency by showing how customer sentiment tracks over time. You could, for example, see an uptick in consumer sentiment and trace that data back to its most likely cause, such as the introduction of a newer product model.

2. Determine Which Customer Segments Have the Strongest Opinions

Sentiment analysis can tell you which customers feel negatively about your brand for whatever reason, providing you an opportunity to address their concerns. You could find out, say, that the most negative sentiment is coming from people who don’t like your returns process. Likewise, sentiment analysis can help you identify which customers are your strongest brand ambassadors — customers you definitely don’t want to neglect.

3. Plan Product Improvements

Sentiment analysis
How do they feel about the new control layout? What specific product improvements put a smile on customers’ faces?

Sentiment analysis can use unstructured data to help you learn what people loved or didn’t love about your latest product release, for example. It could tell you that people love the product’s appearance, but find it isn’t as easy to use as earlier models. And sentiment analysis can tell you over time if complaints about “ease of use” are related to a new product learning curve, or something more significant.

4. Determine the Most Effective Communication Channels

While consistency is important across communication channels, you may find that people respond more positively on some channels than others. Maybe the percentage of positive tweets about your brand is higher than the percentage of positive Facebook comments. Why might that be? Such information can help you re-evaluate how you interact with customers on various platforms, and make changes as necessary.

5. Prioritize Customer Service Issues

Customer support tickets tend to be answered in order of submission, but is that the best approach? Companies can use sentiment analysis to put top priority customer service tickets at the head of the queue. Done strategically, this can help companies quickly address negative feedback.

Sentiment analysis is an essential tool for improving online reputation, because it takes largely unstructured data that would be a nightmare to analyze traditionally, and gives you actionable facts and figures that you can tactically and strategically address. knows how to help customers find, choose and recommend your business, and we would be delighted to show you how. We invite you to download our free Online Reputation Management Guide.