How is text analysis AI looking in 2023, how it generally works, and how you can use it to take your customers, employees, and brand to the next level – all this and more in our in-depth article.
Did you know that text-based feedback is the closest we’ll ever get to having a 1:1 conversation with each customer, citizen, and employee? In free text, people tell us what they really care about and why they do, because they are not constrained by the questions we might ask them. This is important for businesses to understand as this is where your customers get to decide what matters to them the most.
Now, here’s a challenge: attempting to internalise tens of thousands of customer feedback pieces means putting together a novel and then trying to categorise every single sentence. Not ideal, is it? It’s going to be laborious, time-consuming, back-breaking, and you’ll have a tough time trying to make the text actionable to help fuel business decisions.
So, how do we understand open text feedback at scale? This is where text analytics API and text analysis tools come in – to help bring to light the most important pieces of feedback you gather from your customers' text.
Let’s go through the basics of what an AI text analysis tool can do for you and what text analysis generally is.
Text analysis is a process where information is extracted automatically and then classified from the text data your customers leave you. If we specifically focus on the field of customer experience management, for example, the text could be in the shape of support tickets, survey responses, social media posts, product reviews, call centre notes, or any other piece of feedback relayed to you by your customers through text.
Text analytics or text analysis is an important subject for businesses to understand as it enables you to extract some very interesting insights from essentially what are unstructured forms of data.
From the outset, an AI text analysis tool or AI text analyzer will help you answer two very important questions:
How am I performing in terms of topics I already know about, such as service reliability, wait time, and cost?
What crucial bits of data am I missing which I typically don’t even bother looking for, such as an employee onboarding process which may be ineffective, general flaws in my product, or bugs in the software/interface my customers and employees are using?
A powerful text analysis API or text analytics API (both terms are correct and can be used interchangeably) can answer the above questions at scale and while helping you tap into your customer’s voice (aka. VoC), along with the next ‘optimal actions’ to take.
At present, the two most commonly used approaches in AI-powered text analytics or text analysis AI are:
Sentiment analysis – This helps to identify the main customer sentiment (whether it’s positive, negative or neutral) of text responses;
Topic detection (aka. topic categorisation) – This is where similar themes relevant to your business and the industry you’re in (such as ‘staff performance’, ‘product availability’ or ‘food quality’) are grouped or bucketed.
Both these approaches are typically used concurrently, giving you a view of the topics people are talking about and whether they are talking about those topics negatively or positively.
These two broad methods or approaches encompass all other ways of identifying emotions and intent – however, some pieces of software and systems claim that they are capable of emotion analysis simply from text – where they usually make use of several combinations of words in the text to arrive at a specific emotion.
But there’s a problem with this. If a customer says: “My flight was delayed”, they could have said that either in a state of despair, joy, anger or excitement. So, the software or system cannot determine the tonality or expression behind those words, thus, making it hard to understand whether the customer’s response was neutral or negative.
Therefore, using both topics and sentiment from words or text is currently the only way to determine emotion and intent, rather than attempting to use a ‘catch all’ kind of algorithm.
To gain a more thorough and meaningful understanding of text analysis tools (free and paid) and what AI text analyzers actually do to boost a business’s bottom line, we must bring text mining and NLP text analysis into the picture, where the ‘NLP’ refers to natural language processing – a term you might have likely heard being thrown around a lot when we talk about text analysis, data structuring, and data science in general.
We should get this out of the way early on, however: people tend to talk bout text analysis while using terms like text mining and text analysis nterchangeably. In fact, some often create confusion between the two. oth the terms mean different things, as a matter of fact, so to explain hings further, let’s take a look at their specific applications, and ot definitions:
Text mining may be thought of as a technical concept or process where statistical techniques are employed to acquire quantifiable data from unstructured text. This can then be used for specific applications, such as fraud detection, job application screening, MIS reporting, regulatory non-compliance, and more. This quantitative text analysis (text mining) is important to every business, although it’s not capable of pulling actual sentiment from customer feedback text.
Text analysis, conversely, is strictly a business-focused concept or process where techniques similar to text mining are used but in an enhanced way, thus, helping to identify patterns, sentiment, insights, and trends for either customer experience programs or employee experience programs. Therefore, text analysis, or specifically, text analysis AI tools focus on discovering actionable insights within specialised fields, like customer experience management, for example.
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NLP text analysis, as the name suggests, is a part of text analysis, and also sometimes referred to as NLU (natural language understanding). It’s a kind of sentiment analysis which helps your business technology software, systems and tools understand or “read” text from actual human language. NLP algorithms used in conjunction with ML (machine learning) algorithms can help you understand and evaluate highly valuable customer data, and that too without any bias whatsoever. In fact, this combination can be so effective and sophisticated that it can understand the underlying context of text data, even when ambiguities and complicated concepts are present within that text.
To bluntly answer that question: no.
AI tools for text analysis, AI text analyzers and text analysis AI in general have become a critical aspect of most business intelligence processes – especially as part of customer experience management programs since they actively look for ways to improve the business’s experiences related to brand, product, employee and customer.
When AI text analytics had not gone mainstream, the vast majority of businesses were using quantitative survey data only to identify areas where they could improve the experience related to either one (or all) of the above.
A lot has changed since then – businesses are waking up to how a text analysis AI tool can help them dig out new insights which are highly actionable – as opposed to quantitative data on its own which, even though useful, is severely limited as it provides predetermined sets of answers only.
For example, a business dealing in, say, fibre optic connections, may ask a typical question related to CSAT (customer satisfaction) score, following a support call: “How satisfied are you with [business name]’s services?”
Post-service follow-up questions or customer surveys might try to understand the different reasons behind either a good CSAT score or a negative one, with options to consider like:
Quality of service
Supervisor or product manager attitude
Waiting time for issue resolution
Speed and effectiveness of resolution
And so on
As you can probably tell, the above predetermined responses or answers are limited and, therefore, restrict the analysis which can be potentially done on the CSAT scores. For instance, if your customer’s reason is not listed in the above response set, then you are not going to capture valuable insight – which can otherwise be used to tweak a variety of aspects within your business.
Frankly speaking, it would be nearly impossible to list every single reason in your customer survey, which is why having open text feedback instead helps to dig much, much deeper into the customer experience – to get the answers you need.
And, this brings us back to AI text analysis tools. You see, text analysis is absolutely crucial in identifying the unknown unknowns – the themes your business does not know (yet) but the same themes which could be responsible for driving customer satisfaction down.
A better way to approach things would be asking an open-ended question to understand why your customer gave a specific score, for example: “Why did you choose that score?”
Survey text analysis techniques on an open-ended response will then enable your business to understand the specific topics and themes your customers mention every time they are dissatisfied with a particular aspect of your service. More importantly, this approach helps to identify extremely negative topics and themes versus the not-so-negative ones – providing you with some very valuable insights which would likely have been missed otherwise.
When you ask customers to use their own words to explain why they were or weren’t satisfied with a specific service or experience, you are in a better position to extract insights. So, as it turns out, AI text analytics help you to get a lot more specific with the actions you must take to improve your customers' experience.
Plus, being able to identify and drive direct correlations between structured and unstructured data offers some extremely powerful information – in other words, highly actionable insights which you can use to get to the bottom of each customer’s response. Even if you have thousands to millions of customers responding to a survey, you can use an AI tool for text analysis to gain actionable insights from every single one of the responses. There – your work just got easier!
While you try to drive correlations between structured and unstructured data, you might make some very interesting discoveries too. For example, it might turn out that there’s a very strong correlation between people who consistently give you a high CSAT score and your staff giving a clear explanation of the benefits of a certain product or service to them. Similarly, there could be a strong correlation between those customers who commend your staff on having exceptional product knowledge and a high CSAT score. See where this is going?
With the right AI-powered text analytics or analysis tools, all this data can be conveniently organised and then fed straight into your experience management program – just like you would have done with your quantitate data to gain deeper insights into what actually drives the experience around your customers, brand, employee, or product.
Once you know what your customers are talking about in their own words (as opposed to set responses only) in regards to a specific experience, you can perform sentiment and topics analysis in real time to identify the improvements you must make. This is something which would have definitely gone unnoticed if you had been relying on qualitative data only.
Great, you made it till this point. Time to grab another cuppa as it’s about to get really interesting.
We now know that text analytics refer to examining and acquiring deep, insightful conclusions from unstructured data, which involves methods like NLP text analysis, sentiment analysis and topic modelling.
Here are some spine-tingling and exciting real-world applications of text analysis:
Ever noticed how an email provider’s search engine automatically recognises context, intent, and spelling variations to label an email message as “spam”? Or, how mainstream search engines can penalise a business website for ‘cheating’ which was using keyword stuffing and other black hat SEO techniques to improve its search ranking? Those are text analytics APIs at work which can also be used to enhance and power your own website’s search engine.
This effectively places the power in your hand to semantically search for every single document across your entire company or enterprise – such as training materials, white papers, webinar videos, interview transcripts, etc. By recognising pertinent subjects, subject matters, and underlying themes, for example, you can programme a text analysis API to help you search for specific words, logos, images, and text overlays throughout your complete video library, for instance – that is, if you are searching for video documents or evidence only to make business decisions.
Similarly, your AI text analyzer can also be programmed to recognise specific subject matters and/or themes to dig up white papers from years or even decades back. Imagine if you could instantly dig up a specific document within seconds to arm a customer with the required information or to present it as evidence in court.
We’re living in the cancel culture, be it in society or the workplace, which is all the more reason for your public image to be absolutely flawless. One of the things a text analysis AI tool can do is analyse everything from tweets and comments to news stories and other kinds of mentions or feedback via text mining – enabling businesses to interpret data extracted through social listening and VoC (voice of the customer) initiatives.
Of course, investors, employees, executives of the corporation, political parties, partners, or any other groups/organisations the business supports are going to be included in this. By taking action before a reputational catastrophe can potentially occur, businesses can boost their reputation in real time.
This is possibly the one application that has us excited the most! While they say prevention is far better than treatment, this certainly rings true in the world of crime. Can you imagine how empowered law enforcement agencies will feel when they know where or what time of the day a crime might occur and what to do to prevent it altogether?
This seems like a scene right out of the Tom Cruise film, Minority Report, no? Well, they say life imitates art but we beg to differ: it can work the other way around too. The vast majority of criminals plot, plan, and communicate through a variety of online channels because, well, the internet has the lowest footprint, and it allows criminals to coordinate anonymously.
Now, it’s understandable that millions to trillions of people around the globe utilise online platforms to communicate, which can indeed make it very challenging to pinpoint messages which qualify as ‘threats’. However, advanced AI text analysis tools (free and paid) can make the above task for investigators and law enforcement agencies simple by scanning online communication sources in real time and sending different levels of alerts or threat warning, as they come across specific kinds of content.
In fact, these technologies are already being used by some law enforcement personnel around the world to deal with potential terrorist attacks, foil criminal activity or locate criminal sleeper cells, for instance.
It turns out Minority Report wasn’t too far off from reality, after all – specialised text analysis APIs could make the world a safer place.
Fraud cases are continually rising in the insurance sector, unfortunately, but text analysis AI has been successfully implemented to analyse gargantuan case files databases to determine if an insurance claim could be fraudulent.
Specialised software with AI-empowered text analytics and NLP text analysis at the core would automatically ‘red flag’ cases where a high risk of fraud was imminent, thus, reduces the back-breaking work of insurance company officials.
Software programs like these aren’t exactly foolproof (not yet, anyway) ut they do serve as a filter so that human intervention can occur only n situations that require it. In order to fully benefit from the text ining technological advancements working ‘under the hood’ of such oftware, insurance companies are combining their own research and indings to create structured data via this technology, as they form artnerships with industry experts to prevent fraud and also process laims a lot quicker.
One of the best ways to pay closer attention to what your customers are saying is to listen and listen actively. This is where social listening comes in.
Even though businesses conduct surveys to gain insights, highly valuable data can be gained by looking at your customers' comments on social media. As spontaneous and informal as they may be, there is a tonne of information that can be unearthed, which polls or surveys cannot possibly cover.
Furthermore, social media has made it very favourable for businesses to speak directly with their customers, resulting in more personalised relationships and even entertaining online exchanges! Several businesses, in fact, including Microsoft, have engaged in some really witty, amusing, and humorous conversations with their audiences – all the while using a text analysis tool to enhance their service delivery and main user interface.
Text analytics can drastically improve how businesses assess data and use it to gain a competitive advantage as well as improve the customer experience, among other things.
Fast Data Science’s text analysis tool powered by highly sophisticated AI, ML and DL algorithms can be a game-changer for businesses, bringing them closer to discovering new customer insight and pain points, which can help to serve their customers and stakeholders a lot better.
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