The Complete Guide to Customer Sentiment
Analysing customer sentiments will help improve their satisfaction. With customer sentiment analysis, you can connect with customers better.
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.
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:
Customer sentiment analysis is used by multiple businesses to improve their profits and grow. Some of the benefits include-
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.
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.
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.
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.
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.
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.
Analysing customer sentiment can be helpful in improving customer experience 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 is necessary to keep a close look on negative aspects, comments, issues, or potential crises.
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.
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-
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.
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 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.
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.
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.
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 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.
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.
Originally Published April 21st, 2021, Updated May 3rd, 2021
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