In this article, we list some of the most important statistics regarding customer sentiments, sentiment analysis methods, and future predictions to help companies make better and informed decisions.
- In 2020, 54% of companies stated that they had adopted the technologies analyzing customers’ sentiment from their reviews or social media, which is expected to exceed 80% in 2023. (Bain&Company)
Figure 1. The percentage of companies currently use tools to understand customers’ sentiment vs. the expected usage in 2023.
- Using tools that help companies to sense their customers’ feelings and understand their opinions (e.g., sentiment analysis, online behavior tracker, or face/voice recognition) shows the strongest correlation with company revenue growth, among other customer insights tools. (Bain&Company)
- Artificial neural networks achieve 85% accuracy in identifying the sentiment, the highest accuracy level among other methods.
Customer sentiment statistics
- 70% of customers feel frustrated when they do not receive a personalized experience.
- A recent report shows a discrepancy between companies’ opinions on customer satisfaction and reality. While more than 50% of businesses believe their customers are satisfied with the service and have a positive sentiment toward their brand, only 15% of customers agree.
- While random recommendation tools achieve 65% customer satisfaction, providing customers with some suggestions based on their sentiments in their previous experiences increases customer satisfaction by 90%.
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Statistics by region
- In the US, there is a drop in consumers’ positive sentiment and confidence towards the companies in 2022 from 44% to 38%, and the most substantial decline is in high-income consumers.
- In Europe, almost 60% of customers feel negative about their country’s economic state, increasing to 69% in the UK. (McKinsey)
- North America has the highest revenue share in the global emotion detection market. (Fortune Business Insights)
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Statistics by sector
- Chatbot services: Customers’ satisfaction with customer service chatbots reaches only 30%, indicating an inability to satisfy customer needs.
Check our article on chatbot sentiment analysis to learn more.
- Social Media: Sentiment analysis can help detect fake reviews by implementing machine learning algorithms that can achieve 85% accuracy.
If you want to learn how to benefit from sentiment analysis in social media, feel free to check our article on social media sentiment analysis.
- Service sector: Adding one star to Yelp reviews increases the restaurant’s revenue by almost 10%.
- Finance: Stock price movements also correlate with public sentiment, and when the forecasts are made considering public sentiment, the accuracy of prediction models increases by 20%.
Check our article on sentiment analysis in the stock market and the best data sources.
- Cryptocurrency: The number of tweets sent regarding cryptocurrency by bot accounts is estimated to be almost 15%, distorting the sentiment analysis results.
Read our article on cryptocurrency sentiment analysis and learn how the change in crypto prices correlates with the sentiment of the public or the investors.
- Healthcare: The accuracy of 86 studies on sentiment analysis in the healthcare sector is around 80%.
Feel free to read our article on sentiment analysis in the healthcare industry and what are the best applications and use cases.
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