Business problems solved with AI Builder Sentiment analysis
AI Builder Sentiment analysis provides insight into the sentiment of the text that it analyzes. Being fully integrated with Microsoft Power Platform allows you to automate a wide range of scenarios that you might be otherwise performing manually.
Sentiment Analysis has many possible applications for your business, including:
Analyze sentiment of your brand by using data from social media and trigger an alert for negative customer entries.
Send an automatic response to negative customer feedback from surveys.
Analyze trends on user sentiment for each product, geography, customer representative, and so on.
Check the sentiment of your email before sending it to an important customer.
Analyze social media perception
With AI Builder Sentiment analysis, you can analyze social media perception of your brand or product.
Your customers use social media such as Twitter, Facebook, and others to voice their opinions about your brand, product, issues, and so on. Considering that new customers check such opinions before deciding to engage with your company, it's important for all companies to maintain positive brand perception.
Example:
A bank customer tweets, using a hashtag of the bank that they are upset with, about not being able to access their account and having no notification about possible site maintenance.
Sentiment analysis of a tweet as such is analyzed by the prebuilt AI Builder Sentiment analysis model, and a flow is triggered to send a notification to a customer service representative.
The customer service representative immediately responds to the negative tweet with an explanation, stating that the weekly site maintenance lasts between the hours of 12:00 AM to 2:00 AM and that the account information will be available soon. The desired result is that the quick response on social media will help improve the customer's experience with the bank.
Analyze email sentiment
Frequently, when communicating with customers, you want to maintain a positive or neutral voice in emails. Sentiment analysis could confirm if your email has been written in the sentiment that you intended.
Example:
A product manager wants to engage through email with customers outside their company and is unsure if their email seems appropriate.
After the manager runs the email text through the prebuilt AI Builder Sentiment analysis model, the results show that the email contains negative sentiment.
The product manager adjusts a few sentences to appear more neutral. Rerunning the email through the prebuilt AI Builder Sentiment analysis model shows the email's sentiment as positive and therefore ready to be sent.
Analyze employee morale and workplace health
Employee morale and workplace health are important signals about the wellbeing of a company. Sentiment analysis can be used to identify employee satisfaction by analyzing employees' feedback.
Example:
An HR manager wants to better understand the employee sentiment about the new HR medical policy. The manager analyzes the sentiment of the employees' comments that are entered in the survey. Noticing an overall neutral sentiment, the manager extracts all the negative sentiment comments.
When going through the comments, the manager realizes that many employees don't understand the benefits of the policy. As a result, the manager proactively sets up brown bag sessions to go over the policy in detail to address the concern.
You now have insight of some of the many business scenarios that could be enhanced with the help of the prebuilt AI Builder Sentiment analysis model.