A social graph helps to illustrate and map the overall structure and interrelation of social network members.
Every observation on the demographic data represents a connection between two people and the data continues to describe the gender, year of birth and the race of the connected persons. The aim is to analyze variables that shape the relationship between these connections. The impacting variables will help understand the social graph better.
Our primary list of customer provided by Salamander focuses on people residing in Ohio area based on which a company has generated two target population:
- Random Target list, which does not specify connections.
- First-degree connections list which is a target population consisting of first degree connections of the recent customers.
After launching the online ad Campain and sending the online advertisements to the target users we get to observe two distinct activities which include whether or not the user clicked on the advertisement and whether or not the user bought a car. To know which list performed better we need to know which list prompted more clicks and purchases. Our second best scenario would be the list which prompted more click and no purchases.