We do eat, we do travel and we do have life events but more importantly, we post in social media. Whether we actually live those in reality or not, we seldom fail to update it as a status. Likes, comments, and shares, what more and what naught? We supply our words, thoughts, and sentiments. As a data science enthusiast, I may not be interested in your post, but the “data” fascinates my mind to fickle.
Before we plunge into Social Media Analytics, I would brief the term Social Media and then explain how analytics would enable us to gain insights from social media. In a bird’s eye view, it is important to study the ecosystem of the social media. It is comparable with the ripples of a wave, wherein a post has the capacity to travel leaps and bounds from a point simultaneously in many directions. If we get to look at it as a medium of communication, this is entirely different from traditional broadcasting and forms a different league of its own called multicasting, which allows simultaneous broadcasting by many users.
“This is not an information age, but it’s an age of networked intelligence.” – Don Tapscott
How can I use data in Social Media for my business?
1. Selecting Brand Ambassador
We can narrow in few names as far as selecting a brand ambassador is concerned, but if we focus on social media marketing strategy we need someone who has high social media index. Suppose if we need to promote men t-shirts, we need to pick and choose between Virat Kholi with 35,606,178* people as his Facebook followers and Shah Rukh Khan with 23,467,399* Facebook followers. Hence we have empirical proof that choosing Virat as the brand ambassador would increase the likelihood to reach a large segment of the population. However in-depth geographical analysis of followers could reveal an insight that Virat has a fan base all over the globe while Shah Rukh Khan has his fan base clustered in India. So if I need to proceed with marketing in India, I prefer SRK while keeping other variables constant.
*Data captured as on 06/09/2017
“A “Like” is worth a considerable $1.41 in revenue on an average. A Facebook share is incredibly valuable, most likely because of how many people it reaches. A share is translated to an average $3.58 in e-commerce revenue, according to AddShoppers.”
2. Capturing Trend
Using Text mining and Natural Language Processing, we can obtain the trending words of the real world in social media. If we can capture a unique trend in an off-seasonal period like trending of a movie or an event, we can inform production department to produce goods in alignment with that trend.
Example: During January 2017 in Tamilnadu social media trended with Jallikattu. If the marketing manager of a textile firm is able to get this trend earlier, he can communicate to the production unit to produce t-shirts related to it.
3. Pre-launch Product Demand Analysis
Products demand can be sensed by carefully analyzing the number of likes, shares, and comments for that product. These metrics can effectively capture the consumer’s awareness and their interest when captured from post 1 to post n. (n > 1).
Even though gathering data in a longitudinal analysis is difficult, this kind of analysis can be a close substitute for it.
Example: Before launching some products which are technology related, like office365, we can keep track of opinion of consumers by analyzing the counts of likes/shares etc. from our post 1 to post n.
4. Supply and Demand of Products
When we are selling multiple products like e-commerce marketplace, it is important to promote products which are actually demanded by consumers. We can use data to analyze supply and demand. Using the commonality and comparison word clouds we can determine the gap in products so that we can reduce the time in promoting the products which are not preferred by consumers.
5.Replacement of Market Survey and Research
A typical market survey is bound by construct and respondents are restricted to questions asked. There is a fair chance that true opinion about the topic may not be obtained. In this era of internet, people often post their reviews about a movie, product, and an event in their own way. Using sentiment analysis (Opinion mining) we can capture the true emotions of consumers.
6. Ethical Way to Understand The Competitor
This gives us a fair and legal way to evaluate the performance of competitor and what is the response for them in the market.
7. Way to Reach Customers
By means of customer care, using Artificial Chatbot (developed using machine learning) we can reach consumers. Consumers likes to be governed in their service in many aspects, and this also gives them satisfaction. Once a customer posts in social media, chatbots are helpful to respond quickly.
8. Customer 360
Recent surge in analysis of customer profile is Customer 360 approach, in which we are trying to capture the overall behavior of customers. To understand the customer in their social relation we get in the analysis of their social data. This might give us their recent perception about technology, their strengths, and weaknesses, their area of interest etc. This is effective when we have less number of valuable customers to cater our goods/services.
Not to confine the extent of reach by social media to business needs, it can be handy in any other domain as well (can be used even in Disaster Relief Coordination). Deciphering data-network effect can yield us much more insights in many real time applications as well.
No more holding up and waiting long, get hands-on and encash data!!
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