Using Sentiment Analysis, businesses can gauge customer sentiment more accurately and win deals, every time! In 2021, businesses have become adept at acquiring customer engagement data. However, an over-reliance on these data points often leads to the same businesses designating customer feedback as a mere metric -- a very one-dimensional way of listening to the voice of the customer. The voice of the customer cannot be badged and translated into just a number. It must be read, distilled, and most importantly, understood. The truth is, brands need to actively listen to what their customers are saying on every channel they engage with them on - be it calls, emails, or live chat. Monitoring and analyzing the sentiment behind customer feedback should be every brand’s priority, but brands have long struggled to process this data and turn it into actionable insight. With Sentiment Analysis, that’s no longer the case. Here is what we’re covering in this article:
- What Is Sentiment Analysis?
- Benefits Of Sentiment Analysis
- Different Types Of Sentiment Analysis
- How Does Sentiment Analysis Work?
- Applications of AI Sentiment Analysis In Business
- Challenges In Sentiment Analysis
Sentiment analysis--also known as conversation mining-- is a technique that lets you analyze opinions, sentiments, and perceptions. In a business context, Sentiment analysis enables organizations to understand their customers better, earn more revenue, and improve their products and services based on customer feedback. The difference between a software system capable of analyzing customer sentiment and a salesperson/customer support rep trying to deduce it is the former’s sheer ability to derive objective results from raw text -- This is primarily done through natural language processing (NLP), and Machine learning techniques.
Here are the important benefits of sentiment analysis you can’t overlook.
- Gives your ear-to-the ground user feedback to improve your product
- Enables better prospecting and an increase in sales revenue
- Improves upselling opportunities among your product’s champions
- Enables proactive customer service
- Help understand your brand’s perception with your audience.
Typically SA models focus on polarity (positive, negative, neutral) as a go-to metric to gauge sentiment. But sentiments can also be tailored to fit specific use-cases and needs of a business, For example, a customer service rep would glean customer satisfaction insights from the tone of an email, or the mood of the customer on a call, whereas a sales rep might need to know the engagement levels of a prospect (engaged or disengaged) from a particular meeting they had, so they can plan their next follow-up and close the deal.
Here are some of the most popular types of Sentiment Analysis methods:
1. Standard Sentiment Analysis:
This is one of the most common types of Sentiment Analysis since it recognizes the overall tone of a written text and classifies it as positive, negative, or neutral. Some common examples of text and how categorization happens:
-'I love how Facebook connects people around the world ' → Positive -“I still need to see if the Facebook messenger is actually useful to me or not' → Neutral -“Facebook is so confusing to me, it is not intuitive at all' → Negative
2. Fine-Grained Sentiment Analysis:
This method employs a more elaborate polarity range and can be used if businesses want to get a more precise understanding of customer sentiment/feedback. The response gathered is categorized into the sentiment that ranges from 5-stars to a 1-star.
- Very positive - 5 stars
- Positive - 4 stars
- Neutral - 3 stars
- Negative - 2 stars
- Very negative - 1 star
3. Aspect-Based Sentiment Analysis:
While fine-grained analysis helps you determine the overall polarity of your customer reviews, aspect-based analysis delves deeper. It helps you determine the particular aspects (attributes or components) of a product people are talking about. Imagine you’re a salesperson selling a project management solution, you had a meeting with a prospect and are convinced that the meeting went well, but running the recording of the meeting through a Sentiment Analysis tool can reveal the actual subtext behind certain statements.
With aspect-based analysis, you can determine that the prospect has said something “negative” about your “API infrastructure scaling well in the long-term”, you can quickly act on this by setting up a meeting with your solution engineers and making sure queries around your product are addressed. Also Read: Best Revenue Forecasting Models
There is a myriad of techniques and methods that are used to analyze sentiment, this differs from one organization to another based on their needs. Sentiment analysis works by using Machine learning and its constituent Deep learning algorithms to create SA models. These models are trained by feeding it millions of pieces of text to detect if a message is positive, negative, or neutral. Sentiment analysis works by breaking a message down into topic chunks and then assigning a sentiment score to each topic. For example, take the following piece of text: “I tried Dispo, a social sharing app. I was really impressed with it. The ‘Rolls’ feature was a little disappointing, but the user experience was amazing” A sentiment analysis tool would break this into topic chunks and then assign a sentiment score to each topic, based on a set scale:
- Dispo social sharing app, I was really impressed = +4
- The ‘Rolls’ feature was a little disappointing = -2
- The user experience was amazing = +3
The system would then sum up the scores or use each score individually to evaluate components of the statement. In this case, there was an overall positive sentiment of +5, but a negative sentiment towards the ‘Rolls feature’.
In essence, Sentiment analysis equips you with an understanding of how your customers perceive your brand. Apart from the CS tickets, live chats, and user feedback your business gets on the daily, the internet itself can be an opinion minefield for your audience. Being able to not just access these opinions, but process them at scale, and get an overall understanding of your market presence, is a key advantage for any business looking to improve their products, selling process, and brand presence. [caption id="attachment_11421" align="aligncenter" width="481"]Applying Sentiment Analysis To BusinessApplying Sentiment Analysis To Business[/caption] Here are some important business functions that could use Sentiment Analysis in their operations:
- Sales:
Every savvy salesperson has their own theories, instincts, and experiences that they rely on to engage prospects and close deals. Relying on these traits leaves a lot to gut instinct and luck. AI sentiment analysis can help change this, and enable the salesperson to take the guesswork out of their prospecting calls. Sentiment Analysis tools can parse through meeting transcripts and give you the overall tone, and sentiment (positive, negative, or neutral) of the entire call. Moreover, SA tools can help pinpoint keywords, competitor mentions, pricing references, and so much more -- information that could be the differentiator between a salesperson winning or losing a deal.
- Customer Service:
According to a study, 32% of customers stop using a product or brand after one bad customer service experience. With the increase in the number of channels customers can reach brands on, the risk of one bad experience becomes multifold. The ability to track brand sentiment across the web can keep CX teams in the know about what’s coming their way. For example: if an aspect of your product has been criticized on an online forum, your customer support team can even use those opinions to put together an emergency plan to tackle them.
- Brand Outlook Monitoring:
Besides social media, people talk about brands in blogs, news sites, forums, and product reviews. And while it’s alright to track brand mentions (in terms of quantity), it’s even more important to analyze how they are talking about you. Sentiment analysis can add valuable context to quantitative metrics and help you understand the nuances of customer opinions. You can analyze brand sentiment over time and notice any sudden changes in them. You can also track public sentiment to assess the impact of a PR crisis on your brand and evaluate whether your efforts to handle the situation were successful.
- Market Research:
Sentiment analysis can be very useful to analyze your competitors, spot market trends, and conduct market research. You can analyze sentiment in product review sites, social media posts, and community forums about your competition to learn about their strengths and weaknesses. Where do they excel that you may need to work on? Are your competitors not doing something that you can execute to gain more market share? - are some of the questions Sentiment Analysis helps deduce and answer. Also, you can follow specific keywords or hashtags and monitor sentiment around topics that are relevant to your industry. This can help you detect market trends or measure interest around certain topics to gain a competitive advantage.
- Human Resources:
It is as important to listen to the voice of your employees, as you do from your customers. Employee productivity directly ties to your business’s revenue. Hence it is critical that you actively source feedback from your employees about the product, company culture, and processes in place. Businesses can use Sentiment analysis tools to analyze internal surveys to weed out organizational and operational issues that are impacting the workforce. This allows your employees to be heard, which is crucial for any company.
One of the most important challenges around Sentiment analysis is that companies usually struggle with is the accuracy with which they are able to gauge customer and prospect sentiment. Moreover, there are complementary factors like bias and context that make SA a challenge to implement at scale. Sentiment analysis can be difficult simply because machines have to be trained to analyze and understand emotions like humans do. Here are some of the major pitfalls that a business might face while trying to analyze sentiment:
- Irony and sarcasm
In a sarcastic text, people express their negative sentiments using positive words. Here is an example of a tweet from an irate customer: “I love how robust their tool is, I probably experience only 5 hrs of downtime a day “ Expressing negative sentiment using compliments can make it tough for SA tools to detect sarcasm and irony, especially without having a good understanding of the context of the situation. The solution for this problem is in training a SA model dataset that not only has to be precise but also needs to be exhaustive.
- Tonality of text:
Tone can be difficult to interpret verbally, and even more difficult to figure out in a text. The most important challenge for businesses and brands is to differentiate between subjective sentiment (i.e: I personally don’t like pizza) and objective sentiment (i.e That car is red in color). Brands predominantly look for subjective reviews and opinions on the web, so they can understand how they are perceived among their audience. For example: “I love the tool, but my budget does not” is a text that can be interpreted as “The cost of the tool makes me consider it less”. This is a subjective response that a SA tool can struggle to categorize, and when a large volume of data that contains tonal responses like these are analyzed, things can get tricky.
- Multipolarity
Context is critical. Even if you're speaking to a person, you'd have trouble continuing the conversation if you didn't have context. One of the problems that can arise due to lack of context is changes in polarity. A question like “What do you like the most in our tool” can elicit responses like “Everything” and “Nothing”, whereas the same responses can fit into a question like “What do you dislike the most in our tool” -- the negative in the question changes the context and by extension, the sentiment altogether. In addition to these challenges above, there are some limitations in understanding negation in text, comparative sentences, defining neutral, etc. The good news is, as data and machine learning continue to evolve, sentiment analysis tools are becoming well suited to tackle these issues better.
To Summarize, Sentiment analysis is a great way to understand the opinion or feeling of a customer. It has its own set of challenges and limitations but is currently improving at a rapid pace. Nevertheless, sentiment analysis is an excellent way to obtain unbiased opinions from customers and can help improve your business across verticals like sales, marketing, and customer service.
- Detect Emotions from Data
- Customer Sentiment Analysis
- Ukraine Russia War Twitter Sentiment Analysis
- Sentiment Analysis using Lexicon and Machine-Learning Based Methods
Let me know if you have any questions. We can connect on LinkedIn