This blog contains the secret to double your revenue in a very short time. Okay, maybe not double but Sentiment Analysis can certainly help you increase your revenue by helping you understand what your customers and people on social media are really thinking about your product or service.
Sentiment Analysis is the use of Natural Language Processing (NLP), text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. There are multiple ways to analyze the sentiment of a text corpus but the most common one uses a mix of an automated system and some human input.
McKinsey & Company came out with a report that shows 70% of customers reduced their commitments because of a bad customer experience or because their emotional needs were not met. Irrespective of the experience, most of the customers tend to leave a feedback in the form of a review. In addition to that, most of the people check the ratings and the reviews before buying a product or signing up for a service and for that reason sentiment analysis has played a crucial role in planning long-term strategies for some of the world’s biggest companies and helped increase their revenue. Below are some of the many benefits that can help a business increase the revenue or increase customer satisfaction.
- Improve your marketing strategy
There are about 330 million users on a social networking site like Twitter and the number likes, comments and followers may not give you an insight on how successful a marketing campaign is. The ‘@’ mentions as well as the sentiment of the text with it could help you gauge the ROI of your marketing campaign. It helped companies like Nespresso to establish and track reception of targeted marketing campaigns.
- Make effective decisions
Sentiment analysis can help to obtain in-depth information from blogs, reviews, forums news and social media posts to identify the emotion or motive behind the positive or negative sentiment about their product or service. This can also help them to compare the overall sentiment against their competitors and different markets and drive effective strategies and decisions. IBM and Intel used responses and posts on their internal employee-facing websites to identify and resolve issues before talent starts to leave.
- Know your customers better
A couple of the most important aspects of increasing revenue is to minimize customer churn and to acquire new customers. Sentiment analysis helps in identifying the strong as well as the weak aspects of their product or service so that they can make adjustments based on the general opinion of the current customers. Moreover, it helps in identifying the reason for customer churn or attracting new customers that might be using a competitive brand by making adding features that can motivate them to switch over. Bloomberg has incorporated Twitter feed as one of the main components of their best-selling financial products to get an edge over the competitors.
- Mitigate risk
Barclay’s launched a mobile banking application called PingIt. In the days following the launch, Barclay’s made significant changes to the app as a result of real-time social media analysis. There are several other cases where sentiment analysis came in handy to help keep the campaign and launch running smoothly or to anticipate the general reaction of the audience based on references on social media. In both the cases, it can help make crucial decisions to make adjustments to the timing or the production quantity during a launch.
- Improve customer service
Regardless of the size of a company, it’s always a massive task to go through customer feedback and complaints manually without investing a lot of time, effort and resources in it. With the help of automated sentiment analysis rather than sifting through all the complaints or reviews, the ones with the most negative or positive sentiment can be easily identified and addressed instead of the neutral or slightly negative ones. This, in turn, helps in changing the sentiment of the customers from negative to positive because of the quick response time or a response coming from a person higher up in the chain. One of the very good examples is Elon Musk responding to very specific customer complaints on Twitter.
Sentiment analysis is a very powerful tool and although it’s still a few milestones away from being comparable to a human brain, it still is a much better option when you’re dealing with huge amounts of data. The accuracy is proportional to the amount of data that’s gathered with a little input from the business experts. It’s easy to implement and can prove value to the business with very little effort and time. It’s an incredibly useful tool when it comes to analyzing data from millions of customers, existing or potential.
The Contributors:
Nikhil Bhat is a Data Science Consultant at Axis Group. He got his Master’s degree in Information Science from Cornell University before joining Axis in 2014. He’s passionate about progressive rock, playing soccer and exploring different parts of NYC.