At a time when customer experience (CX) is getting so much importance from brands, conducting customer sentiment analysis can help you take your brand to the top. Customer sentiment refers to the different emotions that your customers experience — positive or negative — while engaging with your brand. In this age of instant communication, how can brands conduct customer sentiment analysis? Let’s find out.
What is Customer Sentiment Analysis?
Emotions are the foundation of strong and enduring relationships. Be it with friends, family, or customers; emotions have the power to bring people together. Consumers undergo a variety of emotions when dealing with brands. They could be delight, excitement, happiness, joy, sadness, frustration, and anger.
Sentiment analysis is the research of words written or said by a person to determine the emotions they’re most likely feeling at the time. Analyzing people’s gestures and expressions is also an accepted method for conducting sentiment analysis.
Sentiment analysis helps brand managers and entrepreneurs understand how consumers feel and what they want from your organization. It is an excellent indicator to find out what your target customers think about your company and its products or services.
Consumer sentiment analysis will be the driver for customer-centric brands and will set new standards in customer service.
Why customer sentiment analysis is imperative for businesses
Contextual conversations and timely solutions to problems improve customer emotions and have a transformational impact on CX. However, it is not practically possible to always meet your client's expectations. Sometimes prospective clients may not be impressed with the products and services they are offered or pitched.
Customers expect fast service that works for them on demand at any place, time, or channel. Most customers are willing to use self-service, but they often don’t expect it to work. No wonder it is high time for businesses to get self-service right.
Sales and service teams know too well that situations that make customers unhappy can be resolved without delay, resulting in positive outcomes. However, if immediate attention is not given to miffed customers, the situation can swiftly deteriorate. This results in strained ties between the client and the brand, which can further lead to a decline in sales and renewed purchases, low customer satisfaction (c-sat) scores, and, ultimately, churn.
The brand’s reputation takes a hit. The brand’s PR, advertising, and marketing teams will go on an overdrive to revive the image — incurring huge financial costs to the brand. Sadly, no one can guarantee to restore the organization’s past glory.
Between stress from the pandemic and the economy, rude customers, and changing technology, many customer-facing employees are burnt out. Mental health is crucial to everyone’s well-being, especially frontline workers and contact center agents who face challenges and difficult conversations every day.
Sentiment analysis to the rescue
Now, imagine if your customer-facing teams have a way of knowing what the miffed client or prospect is thinking! That would be a game-changer for sure. That is why conducting a sentiment analysis of customers or prospects can help brands identify where they went wrong in their service or customer outreach program and make amends immediately.
AI and machine learning (ML) can help brands analyze people’s speech, their expressions and track behavioral patterns. Businesses can use the data from sentiment analysis to drive revenue and guide marketing efforts.
Let us understand how sentiment analysis can help brands with a scenario.
Jean purchases a multimedia player from a leading electronics retailer. Unfortunately for her, the device stops working within a week after purchase. Upset, Jean decides to get in touch with the brand’s contact center. She knows that the manufacturer’s warranty on her product will take care of the problem. Unfortunately, all support agents are busy when Jean dials the customer care number, and now she has to wait for a long while to be able to speak to a support agent.
Before making the call, Jean wanted to get her multimedia player repaired; however, the long hold time irritates her, and she disconnects the call. Frustrated with how everything turned out so far — Jean vents her anger on social media and tags the electronics brand in her posts.
Within an hour of her posts on social media, she receives a call from a support agent. The agent tells Jean that her complaint on social media has been acknowledged, and steps are being taken to resolve her complaint.
The agent on the call apologizes on behalf of the company to Jean and tells her that the non-functional multimedia player will be replaced with a new model at no additional cost to her.
Jean was thrilled by the support agent’s behavior and the brand’s commitment to professionalism and decided henceforth she would make all her electronic purchases from this brand and recommend it to family and friends.
The outcome of this scenario is beneficial for both the customer and the brand. This positive outcome is the result of a successful customer sentiment analysis.
The electronics brand is serious about listening to VoC and is alert across multiple channels of communication. A sentiment analysis of Jean’s social media posts identified her anger and unhappiness. That’s why the brand prioritized her issue and offered her a solution that satisfied her.
How to conduct customer sentiment analysis
Sentiment analysis is important for improving working conditions across industries, improving the quality of products/services, shortening turnaround times, and helping brands uphold service level agreements (SLAs). Let us look at the various ways one can conduct sentiment analysis on customers.
- Collect Data: Gather customer feedback through social media, surveys, online reviews, emails, and other VoC channels.
- Pre-process Data: Remove irrelevant information, such as spam or promotional content, and clean up the data.
- Classify Text: Use natural language processing techniques to classify text as positive, negative, or neutral.
- Analyze Results: Identify frequent keywords and topics mentioned by customers, the overall customer sentiment (positive, negative, or neutral), and the reasons behind such sentiments.
- Visualize Data: Create charts, graphs, or other visualizations to help communicate results to stakeholders.
- Take Action: Use customer sentiment analysis to inform business decisions, such as product improvements, marketing strategies, and customer service initiatives.
Are there any tools to conduct sentiment analysis?
A sentiment analysis tool is AI software that automatically analyzes text data to help you quickly understand how customers feel about your brand, product, or service.
The sentiment analysis tool performs semantic analysis to understand and interpret written or printed matter. Semantic analysis is performed on customer reviews that are received or are available across various channels like online forums, comments on product/service pages, opinion pieces, and social media to understand whether the feedback is positive, negative, or neutral.
If you are wondering what semantic analysis is, it is a subfield of natural language processing (NLP) that attempts to understand all forms of content that are written or printed.
Insightful information such as context, emotions, and sentiments is extracted from within the data available. These insights can give brands a clear picture of what their customers are thinking.
Companies leverage audience sentiment analysis to better understand their consumers, enhance decision-making, and grow their businesses. Truth be told, every organization, irrespective of size or industry, should make customer sentiment analysis imperative.
It enables you to enhance your business strategy, CX, and brand perception, besides helping you to better understand and classify prospects and customers and make informed decisions.
ThinkOwl — the AI-powered helpdesk software helps you keep a finger on your customer’s pulse. The helpdesk’s analytics and reporting feature uses AI to keep you updated on your customers’ moods and satisfaction.
It helps you understand what’s important for your clients and your brand through semantic topic analysis, custom KPI analytics, and dedicated reports that provide you with comprehensive data on customer satisfaction and team efficiency. It keeps you up to date about your customer’s requirements with graphics/visualizations that are easily understood.
With ThinkOwl, you can recognize where problems arise in the customer journey and react immediately with appropriate measures. Meet client expectations, ensure there is value in what you have to offer, and amaze your customers with stellar CX.
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