On one front, social media is seen as another platform for companies and businesses to broadcast their brand, products, and/or services to the world. With rising world connectivity on the Internet and social media platforms, this is certainly one of the most efficient avenues for companies to meet customers. Experts, however, believe that data mining is the new frontier of social media.
With comments, ‘like’ buttons, tweets, buzz, video and picture media, status updates, etc., social media is a great and abundant source to gather important information on what customers are saying and how customer are interacting with companies. This, what I like to call a reservoir of human sentiment, is public information that can be aggregated to create customer profiles such as demographics and behavior (such as buying patterns).
Challenges of Data Mining
The biggest hurdle in mining social media data is the sheer size of its data stream—it’s huge. To give you an idea, Twitter alone produces approximately 50 million tweets per day; and Twitter is just the second largest social networking site. The largest social networking site, Facebook, has over 600 million active users; and data stream from Facebook and Twitter doesn’t take into account data streams from other popular social media platforms such as WordPress, LinkedIn, YouTube, Blogger, etc.
Measuring in petabytes (to give you an idea, 1 petabyte equals 1 million gigabytes, or 15 zeros), experts, however, believe that only 20% of the data stream is relevant. So the biggest challenge in realizing the full potential of social media data is sifting through this gargantuan amount and finding the diamond in the rough.
Integration with Master Data Management (MDM)
Master Data Management (MDM) is the processes of managing critical, non-transactional business data on such fields as customers, marketing, suppliers, employees, etc. At its most basic level, take for example a customer that went to a bank and took out a mortgage. Poor MDM in part by the bank would see its marketing department try to solicit a mortgage from this customer unknown to the fact that this customer has already taken out a mortgage. Excellent MDM provides one cohesive master data for a company or business.
Once your company or business has sifted through the most relevant and useful social media data, modern technology allows you to integrate social media data to produce the ultimate, and more accurate, MDM across a greater number of sources.
What this means is that a financial institution, for example, would know what products its customer has purchased (such as stocks and bonds), and know if this same customer was, say, also interested in golf. This financial institution could increasingly narrow marketing its products to professional golf tournaments, TV commercials for professionally golf tournaments, or social media platforms with a strong golf following. Mining social media data together with MDM would give insight to personal preferences for a more authentic one-to-one company-to-consumer relationship.
Written By: Jaszver Bauzon
