As per a study by Deloitte, customers are likely to spend 140% more once they have a positive experience with your brand. Also, an unhappy customer will tell averagely 16 people about a negative experience.

Customer sentiment is inherent for every business success. Your customer’s have certain feelings for your company which they might not express explicitly. When customers review the company online, it is only a part of the sentiment that comes out. Sentiment tracking is necessary to improve customer experience. The best way to do that is by listening, tracking, and learning from customers. Imagine having customer feedback which you can’t understand or explain why. This is why customer sentiment analysis is important.

Having access to such information about customers can help you spot critical issues, boost customer satisfaction, and be a game changer. Sentiment analysis is when text is analysed to understand the sentiment behind it.

What is Customer Sentiment Analysis?

Customer sentiment analysis is an automated process of discovering customer emotions when they interact with your service, product, or brand. It is when the algorithms can detect whether the customer is happy, sad, or neutral. It helps businesses get effective insights that can be used to improve targeting.

Through scientific models like Natural Language Processing (NLP) and some specific algorithms, customer sentiment analysis is possible. Simply put, you can read and tag data to deal with customer tickets in the best manner possible. Here are the parameters for analysing customer communications:

• Magnitude: This algorithm shows how strong customer emotions are
• Polarity: Another algorithm that indicates whether the customer emotion is positive or negative

Why is Customer Sentiment Analysis Important?

Customer sentiment analysis is used by multiple businesses to improve their profits and grow. Some of the benefits include-

Better Customer Service

One in three customers will leave your brand after a single bad experience. You can track customer feedback with customer sentiment analysis. Customer feedback analysis will provide insights that can help understand factors that lead to negative experiences.

Increased ROI

Since you have access to some high-level data, you can narrow down your products as per customer sentiments and finetune it accordingly. This will increase your ROI and help gain better results from any campaign.

Improved Offerings (Products/Services)

Sentiment analysis can help predict industry trends and understand how to improve product and service offerings. You can upgrade products, improve some features, reduce glitches, and release new products with this data.

Brand Management

You can manage your brand reputation with customer sentiment analysis. Brand mention on product reviews, social media can mean a lot to the online reputation. Having to deal with negative PR can be bad.

Better Marketing Strategies

Powerful insights can create powerful strategies. Marketers can use these insights to identify new issues, address them and reduce them. You can also segment customers based on this and target ones who are likely to churn with attractive propositions.

How to Conduct a Customer Sentiment Analysis?

What are the best practices of a customer sentiment analysis? That will be sorted in this section and here are the examples. The importance of customer sentiment analysis is known and here are few ways of analysing customer sentiment.

Analysis of customer sentiment can be done in the following ways.

• Gather customer feedback through direct means. Just ask customers what they feel and what can be improved. Figure out how you can assign a score to customers who are not faring well.
• Use social media to analyse customer sentiment. You can collect the data through their respective API.
• Online surveys are a great tool to understand customer sentiments. Many reputed survey companies give you authentic details of what customers feel. Keep the surveys short and crisp.
• Product reviews from online platforms like G2 crowd, Capterra, Amazon etc.
• Ratings sites and crowd forums can help gather customer sentiment feedback. Google reviews is another transparent place where customer reviews are mentioned.
• Customer support tickets can also help analyse brand sentiment. CSMs and CS teams know best the sentiment that surrounds the product from a specific customer. You can use a customer success software like SmartKarrot to stay ahead and intelligent in the space.
• Deploy a chatbot to collect customer data. These are natural humanized bots that will collect customer data while helping them with queries and issues.
• Opinion mining via Google Analytics will help capture customer sentiments

5 Ways Customer Sentiment Analysis Improves Customer Experience Strategy

Analysing customer sentiment can be helpful in improving customer experience strategy.

1. Target customers who feel negatively and improve their service. Basically diffuse an explosive situation to make customer experience a priority.
2. Track customer sentiment or NPS over time effectively. NPS score change can affect customer service when measured.
3. Measure and understand how small changes can affect your customer sentiment. A marketing campaign or a press release can have multiple, varied repercussions. These can be measured to improve customer experience.
4. Understand demographic and customer segment-based data quantitatively and qualitatively. Are customers in a certain region facing usage issues or return problems? Are customers feeling negative about a certain cultural facet that was not considered?
5. Find out with data science which customers trust the business more than others. You can track detractors and promoters and influence NPS by rebuilding your CX strategy.

It is important to note that all feedback must be considered as good feedback. Regardless of the customer sentiment, one must value customer feedback. Steps must be taken to make the product or service better for customers.

Types of Customer Sentiment Analysis

Types of customer sentiment analysis is necessary to keep a close look on negative aspects, comments, issues, or potential crises.

Standard Sentiment Analysis

The popular type of sentiment analysis is that is notices and studies the tone and expression of opinions and ideas. If a customer expresses a certain point, that is a sentiment that needs to be considered. This analysis can help categorize subjective details and bring out customer sentiment.

For example statements like these convey the following emotions.

• I love how this works- Positive
• I think it works okay for now-  Partly positive
• I need time to understand how this works and might be helpful – Neutral
• The product is confusing and tough to use- negative

Fine-grained Sentiment Analysis

This is a type of customer sentiment analysis that focuses on noticing people’s opinion but more closely. It means calculating precisely what the sentiment is. The feedback is classifying into rankings that is-

• Very positive
• Positive
• Neutral
• Negative
• Very negative

Emotion Detection

This type of sentiment is often found in customer’s feelings, tests, or responses. The feedback observed is associated with frustration, anger, happiness, and more. The only part is confusion can be represented without recognising its various branches. For example: ‘This service is unbelievable!’ contrasted with ‘this service is unbelievable’. Here, the word unbelievable has different connotations.  

Aspect-based Sentiment Analysis

Product reviews are a great way to understand customer thoughts. Sometimes customers may feel deeply about a certain aspect of the product or service. This can be improved to further improve the product and enhance its quality.

Intent Analysis

Intent analysis is understanding that action underlying the opinions or reviews by customers. This sentiment analysis is providing chances or opportunities to resolve customer issues or complaints. For example: If a customer says- ‘help me with this’, CS team can detect the source and intent of the customer problem and analyse accordingly.

Challenges in Sentiment Analysis

Sentiment analysis is a hard task for natural language processing as it is tough to analyse accurately. Here are some of the challenges of sentiment analysis.

Subjectivity

Subjective texts are the ones where it is tough to analyse the sentiment properly. It is highly tough to ascertain correctly what the exact meaning is. This is what makes it difficult to ascertain and analyse. For example- the package is nice is vague. This subjectivity can be tough to classify and make sense of.

Context and Polarity

Everything can mean differently with context. Making sense of the sentiment can be tough without context. It is tough to analyse then. What machines face a problem with is understanding the context. This will create a problem. Processing this data will be tough for any company or natural language processor.

Emojis

Emojis are tough to understand by machines. Emojis play an important role in understanding the sentiment. Social media content, particularly, focuses a lot on emojis. Also, emojis are different in different regions. This is what makes them difficult to analyse for sentiment analysis performance.

Final Thoughts

Analysing customer sentiment helps understand customers better, empathise with them, and improve product experience. As you listen to your customers, you understand their feelings and the rationale behind their rating or sentiment. This will help understand and analyse customer relationships better. Assigning sentiment scores is helpful to boost customer satisfaction. AI technology and data science will help classify customer opinion reducing a lot of effort and time. You can build customised strategies to address the sentiment issues thereby boosting customer happiness.

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