Social Media Metrics: The New Focal Group
The number of citizens that are active on a social network continues to grow rapidly as they are measured by social media metrics. Companies are utilizing these social media metrics to provide an understanding of social engagement among social media users. At the moment there are more than 1 billion Facebook users, of which around 850 million are active on a monthly basis. There are more than 100 billion connections and each day gives us 2.7 billion new likes. Twitter has 500 million users and 180 million tweets are sent every day. Approximately 100 million Twitter users are active on a monthly basis. Linkedin has 200 million users and almost 175.000 users sign-up every day. Pinterest has over 10 million users, of which 97% are women. Instagram has over 5 millions images uploaded every day and Google+ receives 5 billion +1’s every day.
These are only the social networks developed in the Western world. There are numerous social networks from Asia that you probably have never heard of, but that are as big or bigger as let’s say Twitter. Qzone, a website were users can create blogs, share photos and music and much more has 712 million users. The Chinese edition of Twitter, Tencent Weibo, has 507 million users. Sina Weibo, a hybrid version of Facebook and Twitter, has 500 million users. Wechat, a micro-messaging app similar like WhatsApp, has 300 million users. Social media is huge and drives massive amounts of data on a daily basis, worldwide.
With the rise of the social media metrics and the span of their networks, came the ability to understand what people say online, why they say it and what their opinion is about products or services. Sentiment analysis is therefore also called opinion mining and it means the application of data analytics to social network data to understand how people think about certain topics. Using social media algorithms it is possible to understand the sentiment of your customers in real-time, knowing how they think about new products, services or commercials. In addition, it can be used to find the most important influencers.
The Process of Measuring Social Media Metrics
Sentiment analysis uses natural language processing (NLP), text mining and data mining capabilities to find subjective information hidden in the data. This information refers to the attitude of the data, whether this is positive, negative or neutral. Proper sentiment analysis has to take into account the meaning of the words as well as the context when someone said something through what channel.
Using a sentiment analysis, companies can understand what their customers think of their service / product offering / latest commercial etc. Even more, all the available social data can be used to perform predictive analysis about what customers want and when they want it. Based on the feedback they post on the social networks, companies can obtain insights that would normally require expensive traditional research. This is done especially in the entertainment industry, where online sentiment analyses are a very good predictor about whether or not a movie will become a success.
Sentiment analytics is valuable for almost any company in any industry. The analysis of what is being said online will provide retailers with additional insights into what customers are really looking for and it will enable retailers to optimize their assortments to the local needs and wishes. Governments can use big data sentiment analytics to discover potential areas of civil unrest and this can help to take preventive action if required. It can help to address issues before they spread too big and can help to improve the service.
Social sentiment analytics should really be a no-brainer for all organizations that want to better understand their customers and to deliver products that better fit with the needs of those customers. It offers marketers the wisdom of the crowd and gives them a focus group of millions of customers that can be tracked in real-time what they think about your product or service. What’s not to like about that?
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