Digital Marketing via Network Analysis
Artificial Intelligence(AI) is gonna drive the world. In this process, network analysis is gonna play a big role.
Slowly we are moving from traditional data analytics to the most charming data science in a way that we are moving from traditional marketing to digital marketing.. As a result of it, companies become smarter in catering to the needs of their customers, in predicting their sales volume, resource needs, the next problems, the right recommendations and in automating many manual processes. Moreover, so-far-piled up data suddenly become an asset for the companies for a reason the data science is gonna find the hidden insights and patterns. In this process of data science evolution, network analysis is emerging as a very important concept.
For digital marketing professionals, it is important to know the networks of their potential customers. In layman's terms, a person is most likely to be an average of his most connected friends. We can send the right recommendation (of discount, offer, coupon, event invitation) to our customers; and as a result of it, conversion ratio ( a measurement which reflects the impact of the marketing campaign) can be dramatically improved. When we are doing influence marketing, we need to know main influencers of the brand and pattern of their mentioning the brand at regular interval.It will help us get the right strategy for the companies.
Let us have a look at key words of network analysis/digital marketing. Graphhas been taken from graph theory to emphasize that rigorous mathematical analysis will be applied as opposed to the relational representation in the social network.The social graph in the internet context is a graph that depicts personal relations of internet users. The interest graph is an online representation of the specific things in which an individual is interested. Suppose a person wants to find new movie to watch or a new place to eat or a new music to listen . While knowing what movies, authors and music his friends like, social graph or interest graph is quite useful.
Facebook has achieved online dominance through strong grasp on both the interest graph and the social graph . Facebook is growing their advertising business by leveraging the knowledge of social and interest graphs.
While the social graph and the interest graph are based on explicit people-to-people connections and people-to-object connections, the taste graph tries to understand why these connections are made. In theory, taste graph finds out why a person liked a movie based on his/her taste, then it can recommend the right movies to the person.
Let us take an example of entire social graph for an analysis of an interest group/pressure group in our society. Now imagine some of the connections between the nodes/persons suddenly start to disappear. The social graph gets fragmented into a collection of separate islands/groupings/communities. In other words, the initial group get divided into smaller groups Whole process can be mathematically analyzed and hence we can dramatically improve our recommendation engine, predictive models, clustering/segmentation process and associative mining.
