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A general Sentiment Analysis definition is that it is a part of Text Analytics that involves detecting, categorizing, and quantifying attitudes and customer sentiment within pieces of text, such as customer feedback, online reviews and public social media posts.
In the context of Customer Experience (CX), this definition of Sentiment Analysis can be expanded to refer to the process of analyzing unstructured feedback to understand how customers feel about:
1. A brand in general, its products, and its services
2. Experiences with a brand across the customer journey
Unstructured feedback, also known as open-ended feedback, gives brands a window into the minds of their customers, and provides invaluable insights brands can then use to improve CX.
However, brands can’t quantify or trend customer sentiment from unstructured feedback without first reviewing, gauging, tagging, and assigning sentiment to it. Manually performing this task is time-intensive, particularly for larger brands who regularly collect large amounts of feedback.
Sentiment Analysis automates this task, and allows brands to more easily stay on top of how customers feel about them and their experiences at a specific point in time, or continuously over time.
We are now in the age of the Customer Experience. A time in which:
Technology has empowered customers more than ever before. With a world of information at their fingertips, customer expectations are growing to the point where brands are struggling to keep up.
Technology has also made it easier for people to share feedback about their experiences, and to larger audiences. At the same time, companies have access to more feedback than ever before.
The problem they face? Going through all this feedback and finding actionable insights about how their customers feel.
Good news spreads quickly online, but bad news even more so. The need to offer great CX has never been more critical. Being able to pinpoint shifts in customer sentiment is now an essential component of any successful CX program, not to mention effective public relations.
Sentiment Analysis helps gauge customers’ feelings towards a brand, their offerings and their experiences with a brand across the customer journey, so they can:
Gartner reports that about 80% of the world’s data is unstructured4. Brands now have access to several sources of customer feedback, via both private and public channels.
Due to the sheer amount of unstructured data available to brands today, staying on top of customer sentiments can require a lot of time and resources. Unfortunately, time is often a luxury many brands don’t have.
Sentiment Analysis provides relief and helps filter out the noise and extract insights, more quickly.
“67% of customers are willing to pay more if it means getting a better experience.”
Sentiment Analysis is an automated process that involves teaching a system how to automatically identify and extract customer sentiment from unstructured feedback it is asked to analyze.
During this process, the system assigns a sentiment score to the feedback based on the nature it detects within it. Based on this score, the system categorizes the feedback as being either Positive, Negative or Neutral. Although, the nuance of these categories can vary based on the system performing the Sentiment Analysis.
Sentiment Analysis can be performed via Manual or AI-based configurations.
Traditional sentiment analytics leverages manual configuration. Also known as Rule-Based Text Classification, manual configuration involves leveraging keyword dictionaries and lexicons to teach a system how it should assign sentiment and topics to text.
A pitfall of this approach is that keyword dictionaries and lexicons must be continuously updated manually to ensure the system correctly categorizes any future texts it analyzes.
Advancements in Artificial Intelligence (AI) have made it possible to further automate, simplify, and expedite Sentiment Analysis.
AI-based configuration involves a system using Natural Language Processing to review and understand human languages (in this case, unstructured data). Then, it leverages Machine Learning and Deep Learning to continually learn and adapt how it assigns sentiments to text based on previous texts it has analyzed.
Sarcasm presents an interesting challenge for Sentiment Analysis.
When speaking with someone either face-to-face or over the phone, it is usually easy to detect if they are being sarcastic. Their tone, wordings, and gestures can give this away. However, detecting sarcasm can present an important challenge for systems when dealing with just text.
"I just love it when the airline loses my luggage. It's not like I needed it anyways!"
(No one loves that...)
"It’s funny how customer service puts me on hold for 15 minutes every time I call"
(Not that funny...)
Not being able to detect sarcasm can often lead to misunderstandings about the nature of the feedback in question. Unless proper measures are put in place, this can impact the level of accuracy in the Sentiment Analysis.
With AI-based configurations, algorithms can be programmed into the system to help it identify the presence of sarcasm in text, and the system can continually adjust itself over time as it analyzes more pieces of text.
According to Gartner, 81 percent of brands expect to compete mostly or completely based on Customer Experience5. As such, in today's experience-driven landscape, brands must place the customer at the heart of every key CX decision to stand out.
That means brands must monitor how customers feel about and perceive their brand, their offerings, and the experiences they deliver. These insights can help:
1. Identify issues that require immediate attention
2. Find opportunities to make good experiences even better
3. Guide long-term Customer Experience strategies
There are several practical use cases for brands to use Sentiment Analysis to help manage the Customer Experience. Here are just some of them:
Customer feedback is a critical source of insights to any successful CX program.
Brands can use Voice of the Customer (VoC) surveys to collect customer feedback on all of their touchpoints to learn about their customers. That includes understanding their needs, their expectations, their preferences, and how they evaluate their experiences with the brand, wherever they are in their journey.
VoC surveys are a valuable resource for both structured and unstructured feedback that helps them better understand their customers. However, extracting insights about customer sentiment from all of these surveys, especially ones that collect large amounts of responses daily, can be very time-consuming.
Sentiment Analysis helps brands sift through and categorize all of this feedback based on customer sentiment towards a certain topic, allowing CX professionals to uncover issues that require immediate action and even find opportunities to expand their VoC research.
Brands must stay on top of what people are saying about them online. Otherwise, their bottom line can be impacted in more ways than one.
Customers have a plethora of ways to publicly share feedback about your brand and their experiences with you, including (but not limited to) social media networks and customer reviews. Sentiment Analysis helps find emerging trends in how people speak about you and your offerings online, so you can get ahead of any negative experiences and turn them around.
According to Gartner, the majority of brands now use CX as the central platform to stand out from the competition. As a result, brands must not only stay on top of their own CX but also gauge how people feel about their competition.
Sentiment Analysis can be performed with any public source of unstructured feedback, like online reviews and social media. As a result, brands do not need to limit their Sentiment Analysis efforts to just their own customer base.
Sentiment Analysis can help brands compare how people view their brand versus their competitors and find opportunities to tweak their CX program to outshine their competition.
At iperceptions, we believe in the power that unstructured data provides in helping you understand customers’ needs, expectations, perceptions and emotions across the customer journey.
ipertext, iperceptions AI-powered Text Analytics solution, leverages advanced natural language processing and machine learning technology to perform Sentiment Analysis on all your customer feedback. This includes feedback from your iperceptions surveys, customer reviews, and even chat and call center transcripts. Plus, it brings all of this feedback in one place, helping brands get a more complete view of their customers and their experiences across the customer journey.
ipertext goes beyond detecting positive and negative sentiments in your feedback. It also detects whether the feedback includes suggestions for improvement, and even if the comment contains a question that requires your attention.
Plus, it is the only tool that automatically adapts itself to any business context, without needing to manually configure or customize anything for it to work. No more dealing with keyword dictionaries.
Our quick and simple setup will have ipertext working for you in no time, meaning that we offer you the fastest time-to-insight with minimal effort on your part.