Uncovering What Matters

In recent years, the use of AI for topical analysis has become an increasingly popular approach for companies seeking to understand the topics and themes within large sets of written text. Topical analysis is the process of automatically identifying the main themes or topics present in a text or set of texts. This technology can be particularly useful for companies looking to gain insights into employee survey data, identify trending topics in employee communications, and understand customer sentiment in interactions with customer service teams.

Topical analysis has recently been improved by combining it with other AI tools such as sentiment analysis and emotional analysis. By combining the identification of a topic, with understanding the positive vs negative sentiment people have for that topic, and then also doing an emotional analysis to understand if people are feeling joy, anger, fear, sadness or surprise about the topic, companies can get a very detailed understanding of what people are thinking about.

How Does It Work?

The process typically involves several steps. First, the text data is pre-processed to remove irrelevant information and converted into a machine-readable format. This can include tasks such as tokenization, stemming, and stop word removal.

Next, machine learning algorithms such as clustering, classification, or topic modeling are applied to identify the underlying topics in the text. These algorithms use statistical methods to group together similar pieces of text and identify patterns in the data.

Finally, the results of the analysis are visualized in a way that makes it easy to understand and interpret. This can include trending topics lists, word clouds, topic models, or other types of graphical representations.

Topics in Surveys

One of the key areas where AI-powered topical analysis can be used is in analyzing employee survey data. Employee surveys are a critical tool for organizations seeking to understand the experiences and perspectives of their employees. However, with the amount of data generated from employee surveys, it can be difficult to identify the most important issues and themes within the feedback. This is where AI-powered topical analysis can be particularly useful.

By using machine learning algorithms, topical analysis can automatically identify the most common topics and themes within employee survey data. While multiple-choice surveys are easy to understand by looking at percentages and ratings, surveys where employees can add free-form comments are much more difficult to summarize. In the past, this required humans to read through large amounts of text to find patterns in opinions. This led to limited reliability because of subjective biases and mistakes of omission.

By using AI to analyze these texts for the main topics, organizations can quickly and easily identify the issues and concerns that are most important to their employees. For example, a company might use topical analysis to identify themes such as employee engagement, work-life balance, and career development. By understanding the key issues facing their employees, organizations can take targeted actions to improve the employee experience and foster a more positive workplace culture.

Topics in Communications

Another area where topical analysis can be used is in understanding the trending topics in employee communications. With the rise of digital communication tools such as email, instant messaging, and collaboration platforms, employees are generating vast amounts of written text on a daily basis. By using topical analysis tools like TruPulse software, companies can automatically identify the most common themes and topics within this data, and gain insights into the issues and concerns that are top-of-mind for their employees. TruPulse not only identifies these trending topics, but it also combines this with the sentiment and emotions about the topic.

Why is this so important to have a deeper look into topics? If the topic of layoffs is trending, are people feeling positive that the company is not laying off people or negative that the company is laying off and handing it badly. Maybe employees are feeling anxious because the company is not communicating well on the subject and the rumor mill has started. Only by understanding what the topic is, what the sentiment around the topic is and what the emotions of the topic are can HR teams really understand the employee experience.

This can help organizations to be more responsive to the needs and concerns of their employees. For example, if a company identifies that the topic of work-life balance is trending among its employees, it can take targeted actions such as offering more flexible work arrangements or promoting initiatives to reduce workplace stress. By using AI-powered topical analysis to identify the most pressing issues and concerns facing their employees, organizations can foster a more positive workplace culture and improve the overall employee experience.

When analyzing company communications for topics, it is critically important to respect the individual privacy of employees. Care should be taken to ensure that communications are de-identified before analyzing so employees can be confident that they can freely use the corporate communications platforms to do their jobs without a concern that their privacy is being encroached on.

Topics in Customer Interactions

Finally, AI-powered topical analysis can also be used to understand customer sentiment in interactions with customer service teams. Customer service interactions generate a vast amount of written text in the form of customer feedback, complaints, and requests. By using topical analysis to automatically identify the most common themes and topics within this data, organizations can gain insights into the issues and concerns that are most important to their customers.

For example, a company might use topical analysis to identify themes such as product quality, delivery times, and customer service responsiveness. By understanding the key issues facing their customers, organizations can take targeted actions to improve customer satisfaction and build more positive relationships with their customers. This can help to improve the company's reputation and foster a culture of customer-centricity.

Topical Analysis is Here to Stay

One of the key benefits of using AI-powered topical analysis is that it allows companies to hear the "voice of the employee" or the "voice of the customer" in a more systematic and data-driven way. By analyzing large sets of written text, companies can gain insights into the issues and concerns that are most important to their employees and customers and deeper insights about how they feel about them. This can help organizations to take targeted actions to improve the employee experience, employee engagement, cultural health, and customer satisfaction. 

Provided companies use topical analysis while respecting employee and customer privacy expectations, the technology will be an important part of uncovering insights in the workforce and customer base for the foreseeable future. Since the technology can be used in an automatic, real time and continuous way, it can save companies from the laborious task of identifying trending topics be reading large amounts of texts and subjectively analyzing them.