BusinessTech

What Is Customer Sentiment Analysis – Why Do You Need It?

Emotions affect consumer behaviors that can benefit or hurt your sales targets. Therefore, your company requires customer sentiment analysis to discover what prompts your clients to purchase from you. It also helps you assess brand awareness and customer loyalty. This post will describe the scope and benefits of customer sentiment analysis. 

What Is Customer Sentiment Analysis? 

Customer sentiment analysis identifies the emotional state of customers by interpreting their reviews, survey feedback, and social media interactions using data analytics solutions. It also elaborates on what they think about your brand, products, services, pricing, and customer support systems. 

Positive emotions increase the probability of a customer returning to purchase more products or services from you. Also, they might like to suggest new product development ideas and engage more with your brand profiles on social media. 

Likewise, negative emotions cause customer attrition and poor reviews or ratings on online forums. Therefore, corporations use data analytics solutions to monitor these customer sentiment metrics. 

Importance of Customer Sentiment Analysis 

Companies want to leverage customer sentiment analysis to improve customer relations and resolve issues efficiently. After all, negative customer sentiment implies your products, services, or values have upset some consumers. 

If you ignore this crucial area of data analytics solutions, new customers might experience the same problems since you have yet to fix them. So, your high conversion rate in marketing and sales means nothing if customers stop using your products and services due to those unresolved issues. 

Customer service platforms utilize automated sentiment analysis to reduce the time and effort required to assess how the clients appreciate or regret using your commercial offerings. Therefore, advanced customer sentiment analysis is important for business growth and customer satisfaction metrics across all industries. 

Components of Customer Sentiment Analysis 

1| Sentiment Polarity 

Polarity in customer sentiment analysis encompasses the spectrum of positive, neutral, and negative  feelings. Positive sentiments encourage more beneficial word-of-mouth marketing due to high customer satisfaction. Negative sentiment polarity can result in a potential increase in cart abandonment, rumors, and bad reviews. 

You can use data analytics solutions to understand sentiment polarity by finding patterns in consumer ratings and social media engagement. 

2| Sentiment Magnitude 

Data analytics solutions can measure the intensity of customer sentiment to guide you in developing a more efficient customer service strategy

Meanwhile, reinforcing the low-intensity positive sentiments to increase brand loyalty might demand more marketing and relations efforts, unlike strongly favorable emotions. 

Also, solving the problem of strong sentiments with a negative polarity is the most challenging operation. If some customers consistently associate unwanted values with your brand, consider stopping your persuasion or retargeting tactics since these activities might be ineffective. 

3| Dictionary Creation for Customer Sentiment Analysis 

You often hear about negative keywords to avoid in your marketing and ad bidding policies. Similarly, marketing and media teams maintain a list of blocked words to facilitate automated spam detection and online community management. 

You can leverage an identical approach of creating a single repository of words to highlight whether the language used by customers suggests a positive or negative attitude toward your products, services, brands, and business announcements. 

4| Machine Learning Models for Categorization 

ML (machine learning) is a technological advancement that enables computers and other electronic devices to recreate human-like intelligence development capabilities. So, an ML model can learn from every input dataset. 

Categorization services are advanced data analytics solutions that identify similarities in data objects to sort them into groups automatically. It is a rule-based data labeling approach, and customer sentiment analysis uses it to identify new dictionary words. 

Businesses require categorization because you cannot extend the sentiment analytics dictionary manually. Customers often find new ways to imply they enjoy or criticize your products and services. Besides, customer feedback can contain sarcasm or ironic content that word-intent association algorithms cannot attribute correctly. ML-powered categorization solves this problem. 

5| NLP (Natural Language Processing) for Customer Review Analysis 

Qualitative market research surveys acquire descriptive customer feedback, and studying it is more complicated than processing numerical quantifiers like a five-star or ten-point rating scale. 

Recently researched and modified data analytics solutions integrate natural language processing (NLP) in their evaluations and reports involving customer sentiment metrics. 

NLP allows business owners to infer conclusions from descriptive customer responses collected via social listening and primary market research surveys. Also, you can train your NLP models to process multilingual reviews. 

This feature helps international organizations use customer sentiment analysis across different regional languages, increasing their understanding of a more extensive consumer base. 

Why Do You Need Customer Sentiment Analysis? 

High-quality machine learning models and natural language processing require sufficient investments to acquire reliable insights via sentiment analytics. However, you want to know the benefits and use cases of customer sentiment analysis to assess its strategic advantages. 

Let us understand how customer sentiment analysis and related data analytics solutions help corporate leadership.

1| Customer Service Enhancements via Sentiment Analysis 

Refund requests and order replacements affect your sales and delivery management systems. Therefore, companies want to understand why customers demand a refund or replacement. Your customer service and tech support helpdesks must handle such demands with empathy and data-driven standard protocols. 

The insights acquired through customer sentiment analysis can guide you in creating and maintaining a standardized customer service and dispute resolution framework.

Likewise, you can use sentiment data to evaluate whether customers perceive their issues from a logical perspective or through an emotional lens. So, the helpdesk and service portal managers can select from predetermined customer relations strategies suitable for the situation. 

2| Strategic Social Media Management 

The trend of mentioning brands in tweets, YouTube comments, or LinkedIn posts has become more popular among consumers. It also benefits your company and revenue targets when loyal and satisfied clients freely promote, endorse, or refer to your brand with a genuinely appreciative sentiment. 

Still, if you can benefit from social media exposure, you can also encounter the disadvantages of unsatisfied consumers voicing their frustrations in front of the whole world. Your team must be careful when responding to critical or sarcastic social media posts or comments. 

Social media listening teams can process the online community feedback through customer sentiment analysis to ensure that UGC (user-generated content) metrics encourage a positive brand perception. 

Also visit: Latest Front End Development Trends to Watch

What Are Customer Sentiment Metrics? 

You can focus on the metrics used in consumer sentiment analysis to check your brand’s health. These sentiment metrics also help companies prevent bot posts, trolls, memes, or unethically biased reviews from sabotaging their corporate interests. 

1| Reaction and Likes-to-Dislike Ratio 

Social listening includes user engagement monitoring on public networking platforms like Instagram, Facebook, Twitter, Pinterest, and YouTube. Therefore, you want to integrate the following considerations into your consumer sentiment analysis. 

  1. Your team must track the number of likes and dislikes received within a specified timeframe. 
  2. You want to maintain a healthy likes-to-dislikes ratio. 
  3. If dislikes suddenly increase after a business announcement post, this event must alert you, and your team must investigate what caused the sudden hike in dislikes. 
  4. Similarly, you must report unusual changes in user likes. 

2| Brand-Related Reviews, Blogs, Vlogs, and Social Shares 

Twitter allows retweets, whereas Tumblr facilitates re-blogs. Likewise, video-sharing platforms allow remixing existing videos with additional edits or voice-overs. E.g., YouTube shorts, TikTok, and Instagram Reels. 

You cannot completely prevent these activities, and your team must refrain from censoring UGC too aggressively. Otherwise, you will upset online communities, fans, followers, and professional content creators. Still, monitoring how influencers, competitors, and standard user accounts share content related to your brand is vital. 

3| A Customer’s Net Promoter Score (NPS) in Sentiment Metrics 

User-generated social media content is one of many considerations in customer sentiment analysis. Market research surveys are also integral to developing insights into how customers feel about your branded offerings. 

So, the net promoter score (NPS) is the customer loyalty indicator that estimates the likelihood of a customer suggesting your products or services to friends, family, coworkers, and business partners. An NPS model utilizes the following questions or templates in feedback collection forms or pop-ups. 

  1. “Will you consider recommending [product name] to others?” 
  2. “What is the likelihood that you might recommend this service to your friends?” 

Consumer service representatives can improve sentiment analysis by requesting the surveyed customer to select a number between 1-5 or use a ten-point scale range to identify promoters. Later, you can create and manage online communities for your brand or products through appropriate website plugins or other community platforms. 

Conclusion 

Anger and love indicate the same psychological mechanism in consumers, i.e., passion. However, the intensity of passionate customer reviews varies, affecting your relationship with clients. Therefore, you require consumer sentiment analysis. 

Sentiment score metrics help you devise customer service strategies and protect your online presence through periodic assessments of the customer’s satisfaction level. So, you want reputable data analytics solutions to quantify these metrics, identifying hidden trends in user engagement, refund requests, and complaints. 

SG Analytics, a leader in consumer sentiment analysis, empowers organizations and brands to extract and evaluate customer relations’ emotional aspects. Contact us today.

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