You may have heard of the terms natural language processing, text mining or simply, text analytics but how does it work and as a business, how do you take advantage of text analytics to improve and take your customer experience to the next level?
The Importance and Value of Text Analytics
Until now, businesses have only had scored survey questions and verbatim written or spoken comments to use, to uncover the drivers of their customer experience success. This has forced them to focus on the Customer Experience survey scores such as NPS and ignore the hugely valuable verbatim comments – as they take too long to read through. Now, machine learning, artificial intelligence or text analytics can be used to automatically and instantly uncover what your customers feel and what matters the most.
Using Natural Language Understanding to uncover the sentiment, topics discussed and categories of subjects mentioned, you can find the “who”, “when”, “what”, “why” and “where” in customer experience surveys, and turn unstructured data into structured data, guiding you to better ‘listen’ to what customer conversations revolve around and what is driving your scores.
A typical use for text analytics is in the case of issue management. Since an issue might occur very quickly such as the COVID-19 epidemic, new issues and potentially disastrous workload demands can be spotted and alerted quickly, allowing you to spot the trends before they are trending.
Another example might be problems with your website and again, new topics, falling sentiment and the sudden change in certain web-related categories can be spotted early on with real-time dashboards allowing you to make smart decisions and resolve quickly.
Unless that business is actively listening and deciphering the intelligence your customers are giving you, their comments are wasted – and will soon feel they are being ignored.
To put it simply, text analytics is the most effective tool for giving you a clear understanding of what motivates a customer, as well as their likes and dislikes. Text analytics is fundamental to improve the customer experience. Not too long ago, Facebook introduced ‘Topic Data’, which makes use of text analytics to reveal what users are saying on FB about brands, events, activities, subjects, etc. Marketers are taking advantage of this information to devise product roadmaps more effectively and make better informed decisions around future activities.
The benefits of text analytics are numerous and include the power to:
Uncover the true reason behind your Net Promoter scores and comments
Shorten the length of future surveys
Identify emerging trends quickly
Improved analysis of the customer experience journey
More effective resource allocation
Do the hard word for you, automatically, so you can concentrate on what matters most.
How Does Machine Learning Topic Analysis Improve CXperience?
Making complete sense of the underlying meaning in user feedback surveys is essential for any business. However, the main challenge lies in ‘extracting’ this meaning at scale.
Historically speaking, businesses have managed a variety of customer interactions by placing them in categories. Unfortunately, manual categorisation has become quite cumbersome at scale for several reasons:
Not all data types can be effectively labelled manually by a customer service representative
Pre-determined categories or buckets of customer experience surveys are unlikely to pick up emerging trends or service issues
People don’t categorise and label the same way machines do – any discrepancies in survey categorisation will render customer experience feedback useless
AI-based (artificial intelligence), machine learning topic or text analytics, category and sentiment analysis can detect common patterns in unstructured CX survey comments and computationally categorise opinions, subjects and the sentiment, helping businesses by:
Pinpointing certain products/services that are gaining/losing popularity
Determining customer sentiment around specific topics and issues
Indicating changing and emerging trends in customer feedback