Text analytics is the gateway for getting in touch with your customers’ thoughts and feelings in a more intimate manner. With this type of qualitative analysis, you’re not only assessing small-scale issues here and there, but also the entire online customer journey as a whole.
How, you ask? Well, text analytics can reveal a plethora of information:
- It identifies the root of the problem or source of satisfaction. With open-ended questions, customers are given the chance to identify what is or isn’t to their satisfaction and why.
- It also enables the emerging trends to surface that many surveys often limit or restrict entirely.
- Issues can be prioritised quickly and efficiently. For example, by identifying the most frequently used words or using word pairing.
- Customers’ ideas and suggestions materialise and are elaborated upon, which can lead to an enhanced digital experience.
Implementing text analytics gives you the power to hear your customer’s voice in all its forms.
To find this in the Mopinion software please follow these step:
Go to 'Reporting' at the top of the page and click on 'Text analytics'. Now you will see a screen with different words. These are the most frequently used by customers by giving their feedback. This is a very handy and quick overview to see how positive and negative customers are in general. The words are shown in different colours. Green (positive), Orange (neutral), Red (negative).
The text analytics techniques:
Frequency of words (word count)
This is text analytics in its simplest form, whereby the topics are counted and brought to the top based on the frequency with which they are mentioned.
Word grouping (or word pairing)
Often times a group of words can provide you with more insight than just one word alone. For example, the words “costs”, “expensive” and “monthly” are grouped together. With this information, it’s pretty safe to conclude that there are many customers that think the monthly costs for one of your products or services are too expensive. But to take a closer look you can always open the individual comments.
At this point, you know which words occur the most often and with which other words they are paired, but is the feedback positive, negative or neutral? More often than not your customers are going to provide you with feedback on topics they feel strongly about. Text analytics can gauge the severity of the feedback based on positive, negative and neutral word usage as well as the sentiment associated with commonly used words.
Categorising feedback comments
Using machine/automated learning, comments can be grouped based on the issues they pertain to. To refine this process, the user can advise the system based on whether or not the comments were categorised in the right manner. Over time, the automated categorisation improves based on this user feedback.