5 criteria for choosing your Semantic Analysis Software

How to choose a Semantic Analysis Software? What are the points of vigilance to watch? This blog post is based on an article “What to Look For In a Text Analytics Platform”, written by analysts at Bain & company, an international strategy consulting firm.

The analysis of textual data offers many possibilities for understanding the Customer Experience, and for identifying the root causes of problems encountered in interactions between customers and businesses.

More and more customer-centric companies are therefore equipping themselves with Semantic Analysis Software, in order to finally take advantage of all this untapped data: emails, satisfaction surveys, messages on social networks …

But you still have to choose the right tool! Which is not easy, because there are many players, and the market is constantly evolving.

Analysts at Bain & Company, an international strategy consulting firm, identified 5 criteria they believe are the most useful to consider when choosing your Semantic Analysis Software. We were inspired to write this blog post.

1- Semantic analysis methods and technologies

According to Bain & Company, the most advanced solutions use a combination of methods to analyze text, including:

  • Machine learning (supervised or unsupervised);
  • Natural language processing techniques (NLP);
  • But also statistical and business rules that must be able to adapt to the customer.

Platforms based primarily on machine learning use complex algorithms. They are not very customizable, and they suffer from the “black box” syndrome: it is sometimes difficult, if not impossible, to justify and explain the results of the analyzes. Users have to trust the machine, which is not always easy when faced with a result that you do not understand.

These platforms offer high performance, with quickly accessible results. The corollary is that they only work with large volumes of data.

Conversely, platforms that rely primarily on natural language processing technologies (NLP) and business dictionaries tend to specialize in a particular use case or industry. This approach works quite well, and allows you to launch a Semantic Analysis project quickly. But be careful, if the data to be analyzed goes beyond the framework already managed by these platforms, the results may be disappointing.

When it comes to analyzing the tone or polarity of speech, also called “Sentiment Analysis”, most platforms allow a verbatim to be identified as positive, negative or neutral. Some tools go further, by noting the tone of a verbatim on a scale of 1 to 10. But the degree of precision is very variable and depends on the quality of the data. According to Bain & Company, there are few solutions capable of obtaining a good reliability rate on this aspect of the analysis, so the chosen tool must allow you to assess the reliability of the analysis and measure the rate of “false positives”.

2- Integration of metadata associated with textual data

We are talking here about the platform’s ability to integrate data that enriches textual data. According to Bain & Company, many Semantic Analysis Platforms do not offer this possibility.

In the case of customer verbatims, this can be customer segmentation data: age, country, city, type of customer, relationship history, etc. One of the most interesting metadata is the satisfaction score or the NPS score associated with a customer comment. Being able to compare written customer feedback and the quantified score provides very useful insight into the root causes of satisfaction or dissatisfaction, and the drivers of customer loyalty.

3- Customizable dashboards and reporting

Bain & Company experts have noticed that many Semantic Analysis Software offers fairly basic graphical reporting.

This is why they recommend focusing on solutions that offer the possibility of customizing dashboards and creating tailor-made reporting.

They also stress the need to be able to easily export data and statistics, in various formats.

4- Easy and fast navigation in the data

The fourth choice criterion highlighted by Bain & Company’s analyzes is the ergonomics of the Semantic Analysis Software, and the ease of use. Use of the tool should not be restricted to experts. For users to use it easily, it is imperative to check the performance of the tool: navigation must be smooth and fast.

5- Semantic Analysis in several languages

The final criterion to consider when choosing your Semantic Analysis Software is its ability to analyze data in multiple languages. The best tools natively support multiple languages, without having to always translate the original data into a “hub language”.

And Proxem Studio in all of this?

The Proxem Studio Software checks all the boxes identified by Bain & Company:

  • A Semantic Analysis engine that combines Natural Language Processing, Machine Learning, and User-Personalized Business Rules;
  • Possibility of associating external data such as your customer segments or the NPS;
  • Varied and customizable dashboards and graphics;
  • Ergonomics and speed of loading results;
  • Native support for 27 languages.