At some point in our lives we've all come across the saying, it isn't what you know, but who you know. We heard it back in our college lecture halls as professors emphasized the importance of networking in order to land our first jobs. We still hear it today as a means of job growth, promotions and even just expanding business opportunities in general.
Big data has become one of the most talked about business trends, some calling it a game changer. The rise in popularity and prevalence of cloud computing has played a major role in the emergence of big data, which promises to deliver great insights for those capable of mining the mounds and mounds of information. What's interesting is how many different ways we can use that information, evidenced by a new emerging subset of big data dubbed 'buddy profiling.'
We all prefer to interact with those we have some sort of connection to. We'll only go to a party if so-and-so is there. We're hesitant to accept a facebook request because we don't have any mutual friends. We'll only do it if they do it first. If we've never met someone, we're a lot more likely to get along if we have a common friend. What's true in our personal lives remains constant in our professional lives. We're more likely to trust and do business with those we have a connection with over someone we don't.
It's on the back of this premise that 'buddy profiling' is developing. It's the process of using big data analytics to sift through large volumes of data from people's list of connections. The idea builds on knowing how everyone is connected to each other. It also helps us understand a number of other things, like knowing who in our networks is best suited to help us meet our objectives.
Initially, the process was adopted by consumer product companies looking to better understand the networks of 'early adopters', which would help them create more targeted and effective marketing campaigns. However, the financial service industry has taken a recent interest in the concept. As stated earlier, people prefer working with those they have a connection with. Finance companies are looking to map common connections to help convert cold calls into warm leads.
Companies are already realizing there is a huge benefit and strong market for this kind of data analysis. For example, LinkedIn has already rolled out a service built on this idea. It's called Sales Navigator, and it recommends sales leads you should connect with, and helps find the right mutual connections. It also allows you to track updates and news related to specific companies. Because of LinkedIn's extensive list of professionals and organizations, it's well suited for developing this type of service.
While initially used for investment management, there is potential to have 'buddy profiling' spread to other financial services, like consulting, financial advising and investing. The hope is it can be refined to create models to predict future behavior and trends. So instead of just having an extensive network of connections, you know who the right connectors are, and who best to pair with in order to be successful or increase the likelihood of a positive outcome.
Of course, 'budding profiling' isn't restricted to consumer product or financial industries. The extent to which buddy profiling will expand is limitless, just like big data in general. Any number of industries will buy into this idea and tweak it to meet their specific needs. The entertainment industry could use it to promote films and movies through your individual connections. Sales reps for alarm or pest control companies could show up at your door, mentioning how your good friend Ruth or Uncle John are using their service. All of this is just the latest step in creating more targeted marketing. By using the right influencers, in this case not industry experts but friends or colleagues, marketers can create specific messaging through reliable channels and see greater success in their campaigns.