Itamar Rogel

Why does customer sentiment analysis matter?

The simple cycle of a customer buying products and services, and then commenting about them on one of the many online platforms for companies to analyze his voice, continues all the time.


Data tracked from a customer experience, or the many customer interactions, turns into an analysis of customer sentiment. So customer sentiment tells a company how customers generally feel about the brand, specific products, and services or about the business’s social media presence.


A customer sentiment analysis shows in the brand sentiment score, which is calculated as the percentage of positive comments, at a defined time period, out of all positive and negative comments. Such analysis takes place via an AI and machine learning operation to evaluate real-time millions of customer conversations all over the open web as well as internal sources such as ticketing systems or chats.


Why does customer sentiment analysis matter?

It is important to compare such scores in order to evaluate the level of customer satisfaction and also to understand whether the business is improving. However, businesses must also take into account that this is just one out of several sentiment analysis tools.


By analyzing the customer experience and customer sentiment, through customer opinion mining and via a machine learning process, every company can gain insights on how to improve a product or service for better customer satisfaction.


As customer data, tracked and monitored over time, shows emotions from customers regarding such things as customer service, brand reputation, customer loyalty, and more, a company aggregates data that enables it to plan how to further improve the customer experiences with its services, products, and brand.


Businesses then use sentiment analysis for a variety of future planning regarding their product, marketing, customer support, and brands. Use cases and a service experience are analyzed, product launches can be planned as better product designs are being drawn, marketing strategies and marketing campaigns are formed, etc, and all are the result of the machine learning automated process of sentiment analysis work.


With today’s technology, real-time data insights can be drawn, and it is all thanks to a keyword analysis of what customers say. Natural language processing (NLP), the ability of computer software to figure out the human language, provides an accurate picture of customer sentiment.


As every online mention is tracked and monitored for inclusion in the customer sentiment analysis, be it positive or negative as well as neutral, customers form a collective opinion which brands can evaluate for their true customer needs framework. Their emotions wants, needs, and thoughts all figure out in what they comment about online, so examining such customer sentiment and customer feedback can serve businesses with valuable data for taking actionable insights.


How the AI determines sentiment based on keywords


Customer sentiment analysis helps better understand customer emotions about a business.


The sentiment analysis begins by tracking millions of customer comments from all over the open web, be it on app reviews’ boards, special forums, or social media. Each comment and conversation is then divided into three groups, namely positive, negative, or neutral.

That division helps to present an in-minutes analysis of what customers are saying about a product or service, or in other words, and it helps form the brand sentiment. A consumer’s voice is then formed, all based on keywords collected from what customers mention all over the open web.


The customer sentiment analysis evolves from the tracking of positive, negative, or neutral customer comments and then comparing and drawing conclusions from them. Customer feedback data taken from reviews, social media posts, customer tickets, and other online mentions, is digested and goes through a keywords analysis.


The customer sentiment, as described by their words, shows in each of these three groups:


1. Positive sentiment

When describing their positive experience of a product, a company’s customer service experience, or brand appreciation, they tend to use sentiment keywords such as “user-friendly”, “great service”, “good job” or “appreciate”.


Each positive customer experience word adds up in this category to indicate the collective positive sentiment for the brand. Expressions of such satisfaction customers feel are then figured out in the overall customer sentiment analysis for organizations to evaluate.


2. Negative sentiment

Negative experiences, as described by customers in their online feedback, often relate to a brand, product, or service and use a variety of keywords.


“Can’t close accounts”, “bad service”, “glitches”, “bugs”, “can’t understand”, “poor quality” or “expensive prices” represent the downside in the customer sentiment analysis. When customers go through negative experiences and then express them in brand mentions and business reviews, companies that wish to improve customer satisfaction must locate their specific problem areas and aim to fix them quickly.


Such negative sentiment must lead to taking proper actions in order to improve customer negative emotions and improve the product or service but also such things as customer support, customer loyalty, and overall customer sentiment.


3. Neutral sentiment

The third and final part of the word sentiment analysis figures out times and places where a customer expresses his opinion on a brand or product that is neutral.


If the customer experience with products and services is neither good nor bad, it falls under the “neutral sentiment” category. Sometimes customers are not voicing their opinion but are asking a question or giving advice, so the AI and machine learning count them as neutral.


For accurate data analysis, it is also key to also filter out bots and spam. Sentiments from customers must be based on pure conversations which represent the true consumer’s voice.

Customer sentiment analysis is then determined by tracking and evaluating the entire consumer’s voice, based on the above three categories. Customer satisfaction, or lack of, can be figured out and compared from time to time, to see whether the sentiment analysis shows an improvement of the customer’s voice, a status quo, or a change for the worse.


And as the customer sentiment analysis shifts up and down over time, businesses can better figure out their current situation and take action from their data with regard to improving their products and services.


The advantages of and the lessons to learn from analyzing your customer sentiment

The advantages of and the lessons to learn from analyzing your customer sentiment

Customer sentiment analysis is all about understanding what your customers feel about your brands, products, and services. Businesses must use customer sentiment to stay in tune with their customer experience and they have a lot to benefit from such customer data analysis.


The advantages of performing a customer sentiment analysis are many, as they provide businesses with a true picture of their customer experiences. Sentiment analysis helps in the following areas.

  • Real-time customer feedback analysis

Instead of conducting periodical surveys and focus groups, taking the time to analyze the data, and then taking decisions, the time frames dictate a much speedier approach.


With the AI-powered and machine learning technological help, companies are now able to conduct a giant poll of their customer opinions and analyze the real-time aggregate data of many people. This action enables management to learn quickly what their customer sentiment is and use such sentiment analysis to take actionable insights. There’s less of a need to rely on time-consuming data collection and analysis methods.


Such sentiment analysis support helps organizations form their future business model for their brands. But since all processes must move quicker now, the reception of customer data in real-time enables every company to act fast and respond to their customer sentiment and industry trends in a much faster manner.

  • Understanding customer narratives through their keywords analysis

Instead of asking customers survey questions, companies can now easily follow market trends as well as their customer sentiment via the use of keyword analysis.


Such an analysis enables companies to follow and track opinions and feedback as it stems from what their customers are thinking. Their narratives, be it positive or negative, regarding brands, add up all over the open web and can signal to every company how happy (or not) its customers are.


Sentiments may change rapidly, so constant tracking and monitoring of customer sentiment, through a keyword search and analysis can keep organizations closer to what their customers feel and need.

  • The ability to track feedback from all over the open web

Customers voice their opinions in different places and on a variety of platforms. The only way to track their aggregate voice about brands, products, or services, is to focus on each platform and gather their comments into one huge data pool.


Once all comments and conversations are gathered, AI and machine learning are able to structure the data and gain insights. When companies evaluate the structured customer sentiment analysis, they can improve their business model for their brands or design new ones.

  • Using keywords to measure sentiment

Customer feedback, whether positive, negative, or neutral, is voiced through various keywords.


And while each online platform is different, and its lingual style may not be similar to those on other platforms, it is the AI’s ability to conduct a keyword analysis that would place all words on an equal level.


A complaint email to the company’s website or a support ticket vs. a Twitter short message may be written and expressed differently. But when each message/comment is drawn into the analysis platform, they are processed on an even basis and with pre-determined parameters.


The end result is a customer sentiment analysis that treats all keywords similarly, from each and every platform, for the aggregate customer voice and opinion. In other words, natural language processing takes care of figuring out what customers feel and think.

  • The ability to compare periodically to see how sentiment changed over time

The customer sentiment analysis takes place all the time so that companies are able to compare their score and data from period to period. Sentiment analysis data can go up or down, but when the score changes, organizations can figure out what caused such changes and what customers had to say about each situation.

  • Unbiased information

Such customer sentiment analysis is built upon millions of customer mentions that do not respond to specific meditated questions, such as in a survey, but rather speak their minds freely.


Comments and conversations appear in social media posts or in review boards whenever customers wish to express their good or bad emotions towards brands. Sentiment analysis gathered from such comments is therefore unbiased, unfiltered, mostly free, and often relates to what a customer truly thinks.

  • Competitors comparison to better understand your market position

Performing a customer sentiment analysis is possible nowadays not only for the organization but also for its competition. It is basically the same type of customer sentiment analysis, that is what customers feel and think about brands, but regarding an organization’s rivals.


Following competitors’ brands can teach an organization a lot about what works and what doesn’t as well as about industry trends and future planning.

  • The ability to clear out spam and bots for true results

Taking out undesired data, such as spam and bots, leaves out the true consumer voice.

Opinion mining must rely on comments and conversations from real persons who truly voice their thoughts on brands, otherwise, the end results may be biased and wrong.

  • The ability to better understand mention peaks and what caused them

Sentiments often change, and they go through ups and downs. But now it is possible to figure out what caused an up peak or a down peak.


A customer sentiment analysis that follows peaks, through the feedback keyword analysis, is able to explain customer experience comments volumes. This way, organizations can better follow up on customer reactions to their own action, their rivals’ activities, or events and trends.


The disadvantages of analyzing your customer sentiment


Sentiment analysis can help organizations a lot, but they also must be aware of their disadvantages. Sentiment analysis tools are very useful, but a company’s management must also pay attention to problems that can’t be solved by such systems.

  • It’s only a number, so it isn’t enough

A sentiment analysis tool such as a brand reputation score can only offer one general indication of what customers think about products. But since it is only a general number, a business must dig deeper to figure out the entire customer sentiment analysis situation.

Companies need a broader brand analysis, which consists of many insights from customers. The list of insights may cover items such as the product, service experience, marketing strategies, marketing campaigns, how good the organization’s customer service agents or how to improve customer service in general. And customers opine and comment on all of these items, so tracking and analyzing their voices may give direct answers to these items.

  • Is it a true representation of your consumer’s voice?

There are those who claim that sentiment analysis is not based on representative demographics such as done in a poll and therefore does not show the actual customer sentiment. They also claim that in order to use customer sentiment, it is not advisable to rely only on those active customers who gave their feedback on social media or via customer support tickets.


However, performing a customer sentiment analysis does give organizations an exact description of actual customers in general and since such analysis covers countless mentions and comments, it still represents a wide and important opinion range.

  • Neutral comments do not count

Customer sentiment analysis helps organizations track and analyze the positive and negative conversations, but there are also those who comment but do not take sides. That chunk of comments does not support the organizations’ efforts to figure out what their customers think and feel, and is therefore viewed by some as wasted data.


Critics of the neutral comments collection claim that lots of AI and natural language processing (NLP) efforts become useless since such neutral comments do not contribute to the customer sentiment analysis process.


However, the focus is on positive vs. negative anyway, as the same takes place in any other poll. There are always those who stay neutral for whatever reason. There are those who are undecided, others are confused or simply not articulate enough. The collection of comments is always made up also from those who stay neutral.


Affogata’s tips for benefitting from customer sentiment analysis


Affogata supports customer-obsessed organizations which are looking to track and analyze their consumer feedback. Providing a sentiment analysis tool among its many other platform features, Affogata collects millions of customer reactions and conversations from all over the open web and then analyzes them within minutes so that organizations’ management can take data-driven actions regarding its brand, services, and products.


The customer experience and sentiments, as described on a variety of digital platforms such as review boards and social media, serve as the aggregate customer feedback and features in the overall customer sentiment analysis model.


There are several tips for how organizations can benefit from using Affogata for their customer sentiment analysis:

  • The customer sentiment score is just a temperature check of your brand reputation. Make sure to also deep-dive into the qualitative intel for the complete picture.

  • Compare sentiment over time to notice/predict brand and product changes.

  • Be alert to sentiment changes for quick action, especially in crisis-prone situations.

  • Use this analysis to increase retention and reduce churn.

  • Learn what customers are looking for by using keywords (topics, trends).