These 2023 text analytics trends will take businesses to the next level
We’ve been seeing the signs while working with clients, and we’ve developed both Zurvey.io and Neticle Media Intelligence to support the needs associated.
One of our main goals in our day-to-day work is showing the power of text analysis to as many businesses as we can, so that their work becomes not only more time-effective but also richer in insight. As the text analysis industry reaches new milestones, important trends emerge, and we’ve found three that will certainly define the landscape in 2023.
The democratization of data
Large organizations are always richer in data, but due to their complex arrangements, many departments and higher number of employees, it is also more difficult for them to synchronize work and provide access to data to everyone who could benefit.
This can often become a major setback in effectiveness, because data-driven insights are key to understanding customers, developing strategies, enhancing products and rationalizing internal operations.
The power to act on these insights needs to become available to frontline and shop floor staff, as well as non-client facing functions such as finance and marketing, and even non-technical employees. Thankfully, more and more businesses understand the importance of this, and so it looks like 2023 will be the year when self-service access to analytical data becomes a major defining trend.
An important step that a company committed to the democratization of data needs to take is to put a business intelligence software into use, and choose a data analytics tool that is compatible with it, so that analysis results can easily flow between them, and reach their destinations. An excellent example of this is how Budapest Bank is using Zurvey.io and Microsoft Power BI to create an insight pool for all interested parties.
Bigger emphasis on sentiment analysis
Since the pandemic began in March 2020, the online space has become an even more important platform for expressing various emotions, and this trend has only increased by 2023. As almost every aspect of our lives has found an online alternative, it’s extremely important to be able to connect to others and show our feelings, whether it be in forums, reviews, groups or social media comments.
Therefore, one of the basic requirements of NLP models now is to understand the emotional or sentimental aspect of text data, along with its context. Applying such a model leads to better customer experiences, which in turn improves customer loyalty. So, if you want to better understand and please your customer base this year, it’s a great time to upgrade to a text analysis tool that provides high level sentiment analysis.
Both of Neticle’s major products, Zurvey.io and Neticle Media Intelligence, are equipped with an NLP engine that can not only recognize the positive, neutral or negative sentiment of a given text, but it can also detect the eight basic emotions (enjoyment, astonishment, disgust, fear, anger, longing, pleasure and sadness). What is more, it can do that in over 30 languages!
Structuring data to answer specific questions
All social media platforms give brands some insight on what is happening around their content, but this provides them little advantage in 2023, as everybody has learned to do this by now. For surfacing deep and valuable consumer insights, this just doesn’t cut it anymore. Brands or companies who want to truly understand what is going on in their stores, or with their reputation and competitors, are now using tools that are capable of advanced data structuring.
Once raw data is structured in the right way, such as with custom labels and with visualization methods, analysts can manipulate it to get a much deeper analysis. Structured data helps you understand the correlations with complex topics, lets you see the changes and ratios, and most importantly, it will lead to pinpointing specific issues and turning points in processes.
Analysts in 2023 will not shy away from answering complex questions based on textual data, like who the best influencer for a certain market was in any given month, or what the biggest trigger warnings are in a brand’s customer feedback.
Do you already have a text analytics CX project in mind?