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“Brand Sentiment analysis - the best approach to trace the heartbeat of your brand.”
Brand sentiment analysis is a process to determine the feedback and attitude towards your brand, service or product.
These days media monitoring tools are the best platform where you can check sentiment analysis.
Sentiment analysis would be used when you want to know the precisely how individuals feel about your business.
Sentiment analysis should be considered as the subset of social listening. While organizations ought to monitor their mentions, sentiment analysis dives into the good, bad and neutral feelings encompassing those mentions.
Does your product provide clients with a warm, fuzzy emotions? The quality of service you provide are been met with the customers’ expectations? You can find those answer by Sentiment analysis.
Why is brand sentiment important?
Informed decisions could be made when knowing your brand sentiment which helps your business in improving. We will showcase some aspects where sentiment analysis would be beneficial:
1. Brand customer reviews
Twitter and Facebook, the popular social media platform is filled with ratings on various brands, people, topics, products and also different opinions, reviews.
Additionally, you will get a more sensible knowledge for your brand sentiment and would be more acquainted with the customers, when you will be following mentions all around the internet and online media.
On the off chance that you see that the mentions of your brand are turning increasingly more negative over the long run, it’s an indication that something is not good and you need to discover the base of the issue and fix it. All these things would help you to avoid degrading your brand’s reputation and image and to lose your valuable customers
2. Campaign performance analysis
Your campaign is liked or not by the target audience? All the answers would be provided by sentiment analysis.
Furthermore, monitoring of the sentiments are good decision for the campaign for eliminating any future conflicts for your brand. The problems would be recognised immediately resulting in quick changes. Many customers around the would are been influenced by the social media influencers for the recommendations of your brand, so sentiment analysis would provide an excellent option to discover the best social media influencer for your brand.
3. Respond to urgent queries first
Sentiment analysis can help figure out which brand mentions are more significant. To express gratitude towards the customers for recognition or help correct an issue you’ll know when you need to reach out to them.
4. Keep away from PR Crises
Online media monitoring can detect problems right away of your brand in real time.
For example, take United Airlines. A passenger episode prompted a spike in negative online media mentions, after the organization was blamed for racial profiling. The topic spread quickly to China, where the scene turned into the top trending topic on a microblogging site.
Furthermore, this all occurred inside only hours of the incident. In circumstances like this, sentiment analysis can tell you of negative issues immediately, so you can manage them before they grow into a more serious issue.
5. Get Insights on Product Design
Discover what customers are stating just after product launches. Or then again search over long stretches of reactions you may have never observed. You can look for explicit keywords relating to another product to discover just the information you require.
Organizations like Instagram are continually delivering new features – like their in-application video managing tool. Furthermore, they have to know the public’s response immediately, or it could hurt their brand name. With the help of brand sentiment analysis, you can take advantage of precisely what you need, just after the new feature is launched.
AI based model would be implemented which would perform real-time analysis of the text data taking the required historical data into the training. With the current advancement into the Artificial Intelligence there are various algorithms and approach for NLP sentiment analysis such as BERT and LSTM based text classifiers with good results. The sentiments like frustration of the customers would be analysed from the insights gained from the dataset and this would improve the customer service experience.
