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Understanding what customers have to say about your brand and what they feel will remain the most important factor for any business.
Businesses are joining the digital world. Customers as well are shifting towards using apps or websites to interact with the companies. Especially now when businesses have little or no interaction with their customers, customer feedback is a curious business across all industries.
However, with all these different communication channels available to the customers it is difficult to sort through the data overload and understand customer sentiment. This brings in Sentiment Analysis, the most effective method to analyze customer sentiment.
Sentiment Analysis utilizes NLP and Machine Learning algorithms to detect emotion behind text feedback across the internet. There are primarily three techniques of how sentiment analysis works. Each of the three techniques depends on the amount of data you want to analyze.
A rule-based approach involves human-crafted rules to determine the polarity, subjectivity, or sentiment of the text conversation.
For example:
However, it does not consider how words are arranged or combined to form a sequence. If you add new rules to support vast vocabulary or expression it may make the system very complex. It may also affect the previous result.
Developing data, uploading it to the system, and maintaining the system need to be done manually. Thus, it may be insufficient when data has to be changed or complex data needs to be analyzed.
The Automatic System follows the same process as Rule-Based Approach. However, in the case of an Automatic System, there is no need for manual work.
The system works independently as Machine Learning classifies the text, analyzes it, and identifies the sentiment category (positive, negative, or neutral).
The common types of Machine Learning Algorithm used for Automatic Systems are
It is a combination of both the systems mentioned above. A Hybrid System is considered more efficient and accurate because of the balance in automation and manual process.
Sentiment Analysis is used across different industries namely, healthcare, banking, call centers, government sectors, etc. There is an endless no. of ways Sentiment Analysis can be used to enjoy the benefits in all these sectors of industry.
Social Media can either be a blessing for your brand or it could become a nightmare as well. Positive and negative news spread across all sorts of social media platforms like wildfire. Thus, it makes complete sense to monitor and track any conversation regarding your brand on social media.
For customers, social media is the platform that empowers them to share their opinion about their experience with your brand. Sentiment Analysis can help you to analyze how customers feel about a certain situation and prevent it from spiraling out of control. This prevents any PR crisis for your marketing team to handle.
Monitoring social media can help your brand:
Brand Mentions includes reaching news articles, blogs, product reviews, forums, press releases, and many more channels to gather information. Sentiment Analysis observes and gathers brand mention across the web and delivers them to designated teams.
It helps you understand the emotion behind the customer’s opinion. Tracking sentiment in regard to brand mention can help you to identify the shift in customer sentiment along with the rise and fall in satisfaction about the brand, products, or services.
Sentiment Analysis automatically tracks and analyzes all the interactions around your brand across the web. It helps you respond to any potential crisis while you can still prevent it.
Sentiment Analysis works in textual data. You can make use of it by analyzing the responses on the survey as well as interactions on customer support.
Companies collect customer feedback via email surveys, NPS® , phone interviews, user reviews, etc. However, all such feedback is unstructured data. Sentiment Analysis can be used to categorize the data and identify customer sentiment.
Response to open-ended questions can be difficult to analyze. With the use of Sentiment Analysis, you can classify customer feedback into positive, neutral, and negative sentiment. This will help you gain insight into what makes customers satisfied or unsatisfied with your product/service.
You can also use Sentiment Analysis to further investigate NPS® surveys. NPS® helps you identify promoters, detractors, and passives in your customer base. Although it provides you quantitative data, you can ask respondents to explain their reason for the score. You can then use Sentiment Analysis to gain qualitative insight.
The ultimate goal of observing customer sentiment is to identify unsatisfied customers and elevate them into satisfied customers. It can separate positive and negative responses. As a result, you can prioritize customer feedback and focus on resolving any potential issue.
Sentiment Analysis can help a brand with exploring market trends and also in competitive analysis. It sifts through the web to provide you market reports, news, or product reviews that are relevant to your business.
Customer feedback regarding the products and services you offer are treasures that you can take advantage of. The reviews and comments about the products and services help detect the performance of specific aspects.
It helps you uncover which aspect of the product or service leads to a negative review from customers. Analyzing customer reviews can help you identify the gaps in customer expectations and what you deliver.
The insight you gather from all over the web is especially important at initial stages of marketing campaigns when you need to have those products tested by users. Sentiment analysis at this point enables a company to make improvements in their products and services to align with customer demand and expectations.
Sentiment Analysis can also be used to analyze your competitors. You can monitor social media to spy on how your competitors are performing in the market. Using sentiment analysis to analyze your competition can help you see how things are seen by customers in general.
You can track your competitor’s customers to examine their strengths and weaknesses. You can identify what customers dislike about your competitors and build excellent strategies to counter those weaknesses and improve customer experience.
Sentiment Analysis can identify positive and negative emotions in textual context. Now let’s see how you can benefit from them.
With Sentiment Analysis, you can identify what campaigns, conversation or message triggers their emotional response and how. Customers base their decision on the emotions they develop toward your brand.
For example, the use of “emoji” and “appreciative message” triggers positive emotion and changes the customer’s mood to satisfaction.
If you can identify these emotional triggers you can customize the message or conversation that will help you give better service and create a positive experience for the customer.
As mentioned above, positive emotion can help create a better experience for your customers. You can use Sentiment Analysis to identify your happy customers. You can also recognize how they perceive your products or services, which aspect they like, and who are more likely to spend more.
This helps you recognize the opportunity to offer recommendations to your customers based on products and services they liked. You can use Sentiment Analysis to recognize what makes them happy and personalize recommendations, instead of upsetting them with recommendations at random.
It may be difficult for your customer service agent to figure out the best approach for every customer. The customer’s mood may change mid interaction which can make it difficult for agents to adapt to the change without any clear indication.
Sentiment Analysis can give live insight to your agents during the interaction. They can see the shift in customer’s moods in real-time which can help the agent know how the interaction is going. The agent, as a result, can adapt to the mood of the customer and provide an empathetic service.
Monitoring customer’s opinions on social media and other platforms across the web can help in identifying any potential issue. Sentiment Analysis comes in handy to identify any negative feedback towards your brand so that your brand can prevent any escalation.
Timely intervention during those situations can help prevent it from spreading all over the internet. Sentiment Analysis enables a brand to stay updated on any negative thread online that may become a potential issue for the brand.
Sentiment Analysis can give you the insight to understand the needs, expectations, and demands of the customer base. It gives you a glimpse of specific comments and opinions regarding the services and products that your brand offers. It offers you insight into the problems that customers face. This way you can offer an effective solution that can help elevate customer sentiment.
Sentiment Analysis can fall victim to some roadblocks. Machines are programmed to analyze human emotions but they may still have difficulty in understanding a few intricacies of human language. Here are some of these challenges in Sentiment Analysis.
Sentiment Analysis identifies the extreme words such as “like” and “hate”. It can recognize words from sentences that are either positive or negative. However, there are many in-between terms used by users in their comments such as “not so bad” or “kind of good”. These terms imply average emotion, i.e., mid-polarity. Oftentimes Sentiment Analysis fails to pick up on these emotions.
Internet users often use irony or sarcasm in their comments. For example, “The phone was so good that it broke down after a month of using it.” People use backhand compliments to express negative emotions.
Sentiment Analysis, however, will take note of the word “good” and give you a positive score on analysis. It is difficult for the tool to detect the real context behind the sentence. And, you may receive a higher volume of “positive” sentiment score.
Emojis are another hurdle for Sentiment Analysis. It is trained to understand and analyze human language. It can extract text from some form of an image. However, in the case of emojis they are a language in itself, for example, 🙂 means happy or satisfied while 🙁 means sad or unsatisfied.
Most sentiment analysis tools cannot detect these emojis and as a result, you may not obtain holistic insight.
These can be difficult to detect by Sentiment Analysis. These sentences may not outwardly show any sentiment but the comments rather state an opinion on the subject.
For example, “This house is bigger than the others we have seen.” This sentence cannot be classified as negative, positive, or neutral. But when the context is taken into consideration a human can tell that “this house” is considered in positive emotion.
So, for Sentiment Analysis, it will be difficult to categorize comparative statements.
We know double negative turns the sentence into positive. But the Sentiment Analysis tool may not know it. For example “I don’t want no pasta”, this comment implies that the person would like to eat pasta. But, Sentiment Analysis may not pick up on the context and may not regard the comment.
The usefulness of Sentiment Analysis depends on whether you know how to use it to its full capabilities or not. You need to be well aware of where you can apply it and how you can benefit from it.
Sentiment Analysis can help you identify negative emotions toward your brand, angry customers and prevent any PR nightmare. But it can also help you identify happy customers who are more likely to be a promoter or brand ambassador. You need to know what you are looking for and how to work with it.
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