The amazing thing about the 21st century is the fact that there is track-able data in everything we do. However, most businesses don’t know what to do with their data or how it can be manipulated to gain insights on areas outside the normal sales and financial reports. There are many ways you can use analytics to drive better business decisions. One way is by leveraging Power BI text analytics and sentiment analysis.
What is Power BI Text Analytics?
Text analytics is the process of finding meaning through written communication. In business terms, this is mostly related to customer experience. Written customer communication can come in a variety of formats such as customer reviews, social media comments, survey results, and much more.
In the past, companies had to read each communication/response and make insights based on what they read. This was very time consuming and used many resources. Now, using text analytics software, you can gain insights into thousands of reviews in a matter of seconds without having to read a single entry.
Why is Text Analytics Important?
Customer satisfaction and the customer experience are crucial aspects of every business. Therefore, it is important to listen and understand customer feedback. This feedback will help you understand trends in your business and help you gain insights into what your customers are saying about your industry and your competitors.
Manually collecting and analyzing data can be extremely time consuming and inaccurate due to subjectivity. Power BI text analytics helps to eliminate these issues by streamlining this process through the automation of data collection and analyzation. You can obtain this data in a matter of seconds, allowing you to gain insights into your customer satisfaction in real time.
Performing a Sentiment Analysis
There are a wide variety of ways to apply text analytics to your business. However, performing a sentiment analysis in Power BI is one that is extremely useful when evaluating customer reviews. A sentiment score evaluates each review based on specific language used, which allows you to analyze and interpret large amounts of text. The most common way to use a sentiment score is by its polarity, meaning to rank each comment by its positivity or negativity. This will enable you to get an overall average polarity score of your responses.
Emotion-based Sentiment Analysis
If you want to gain an in-depth understanding of how your customers feel, you can use emotion-based sentiment scores. Emotional based sentiment evaluates each response with emotions such as; joy, disgust, fear, anger, trust, sadness, anticipation, and surprise. This score can be beneficial not only to understand how your customers feel, but also to see if certain products or advertisements invoke a specific emotional response.
After you understand how your customers feel about your product, it is important to understand why they feel the way they do. Aspect-based sentiment gives you more detail about customer responses by extracting commonly used words, allowing you to understand the main topics discussed in reviews. This is particularly useful in today’s customer-centric world because it allows you, as a business, to read between the lines of your customer reviews and to gain better understanding.
Give it a Try
To demonstrate the functionality of text analytics and the different types of sentiment, I have linked a Power BI report to this post. In this report, you can see how each of the types of sentiment can help Aces, an imaginary card game app, understand what their customers are saying about their product. Feel free to interact with the report by using slicers and clicking on different visuals. You’ll be surprised how easy it is to gain insights using these features within Power BI.
By using these text analytic features, you create a seamless customer experience as well as increase your overall customer retention. For help with your analytics or for more information on Power BI, contact our business intelligence specialists.