In the current era of digitization, in order to deliver the best experience possible to customers, the business has to understand the sentiments of its customers. The numbers are their testimony: it has been studied that a customer would spend 140% more once they have a positive experience. On the other hand, an unhappy customer might complain to an average of 16 people about their unpleasant experience, thereby creating a bad image of the company. This will be hard to balance out because happy customers are likely to tell just nine people about their good experiences!
Businesses can employ AI-reinforced customer sentiment analysis to keep a finger on the pulse of customer satisfaction levels. Such customer sentiment analysis uses data from customer feedback, e.g., online reviews and surveys, to understand what customers think about a business. This data will also help businesses identify satisfied as well as dissatisfied customers so that they might be served better and even better products. Therefore, analysis through AI can effectively improve customer experience while identifying how customers feel about one specific product or service in a particular market. So keep the focus on product reviews uk.
Let’s dive deeper into this version of AI-powered customer sentiment analysis.
What is AI-powered customer sentiment analysis?
Customer sentiment analysis utilizes natural language understanding and processing, text analysis, and algorithms of machine learning to analyze customer feedback and opinions from which subjective information can be identified and extracted in order to determine the overall sentiment that a product, brand, or service has received. The ultimate goal of analyzing sentiment is to know exactly how customers feel about an issue and to apply it to making business decisions accordingly.
Some of the use cases for sentiment analysis across the various industries and applications include:
Marketing:
Companies track and analyze customer opinions and feedback about their product, services, and brands with the aim of improving customer satisfaction and making data-driven decisions on marketing.
Customer Service:
It can be used by the support teams to partially or fully automate the process of customer service, assign priorities route support requests, and understand customer sentiment towards their support interactions.
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Social Media Monitoring:
It can be employed to monitor and analyze what people think about their brand, its product line, and its competitors on social media.
Financial Services:
The sent of the market can be monitored in the financial sectors by using the concept for the investment decisions. It would monitor the customer’s feelings toward a specific stock or any financial product.
Health Care:
Sentiment analysis has a wide application in the healthcare industry to monitor and examine patient opinions regarding their encounter with healthcare providers, treatment options, and healthcare insurance.
Types of Customer Sentiment Analysis
Mood, urgency, and intention are identified as the key elements of sentiment analysis models. You can further tailor your sentiment analysis based on the result you want to achieve. The majority of the automated methods depend on advanced dictionary words, which are commonly considered as thoughts or feelings. For example, when customers comment on Facebook about their “orders” as “complicated,” the sentiment analysis software automatically takes into account the dimensions of the “orders” and the feelings associated with it. For example, in some cases, ‘complicated’ would be considered as something negative or annoying.
Fine-grained analysis, emotion detection, and aspect-based sentiment analysis are all further varieties of customer sentiment analysis that give a deeper and richer understanding of customer sentiment.
A fine-grained analysis measures the polarity score of words in a text. It allows for a much more precise assessment of customer sentiment, ranging from very positive to very negative.
Emotion detection goes a step further. It identifies and analyzes particular emotions and moods in the text. It gives a deeper understanding of how the customers feel.
Aspect-based sentiment analysis breaks the text into sub-parts and measures the aspects or features of a product or service to which it relates, thus providing a more in-depth analysis of customer opinion.
Companies can therefore bring together these advanced analyses to derive a more comprehensive and fine-grained understanding of what their customers feel about their brand.
Manual Customer Sentiment Analysis
Customer sentiment analysis can be done manually. All this requires is the reading and analysis of customer feedback and opinions – perhaps online as presented through customer reviews, opinion expressions through surveys, and customer service interactions.
How to Do Manual Sentiment Analysis
- Gather customer feedback and opinions from various sources, including customer reviews, opinion expressions through customer service, and customer surveys.
- Categorize this as positive, negative, or neutral. This can be done through reading the text and deciding on the tone and language being used in the piece of feedback.
- Record sentiment for every piece of feedback in a spreadsheet or database. Further analysis can now be carried out, pulling out insights from this analysis.
- This helps analyze recorded sentiment for patterns or trends in customer feedback and proceed to calculate the percentage of positive, negative, or neutral feedback or even find common themes for the feedback.
- Determine key insights from the customer feedback analysis, including improvements required or best practices in customers’ interactions.
How AI Enhances Customer Sentiment Analysis
As mentioned in the discussion above, traditional methods of customer sentiment analysis involve a lot of manual analysis and are time-consuming and complex. AI is poised to complement customer sentiment analysis, as it will now enable the automatic processing of thousands of customer feedback pieces within real-time, and the business will be furnished with valuable insights and actionable information at scale. Secondly, AI algorithms are many times faster than humans and are less prone to mistakes in the same instance.
NLP is a subset of artificial intelligence (AI).
This improves this method of getting a computer to comprehend and interpret meaning within human language. For mass amounts of customer feedback using data like that found with social media or service reviews; with NLP techniques through the analysis tools that go under the purview of methods used under sentiment analysis, text classification, as well as nameable entities, all point the way for classifying every one of these comment and people’s emotions use in such judgments.
Conclusion
Keep collecting actual business data because it reflects the customer/user sentiment. This way, you can validate which exact impact that type of sentiment has on your business. Use simple models to get familiar with the process. Validate the process once again and then jump to more complex models. Many customers never leave a positive or negative comment. This becomes a blindspot.
