Network Structure of e-Shops Profile as Factor of Its Success: Case of VK. com
Modern internet technologies open a wide range of opportunities
for enterprises: keeping accounts online, connecting with customers
from different locations, collecting and analyzing data about their target
audience and other advantages. One of the actively explored factors
related to the potential success is using the Internet tools for projects
presentation. The aim of this study is to identify the network distinctive
patterns forming the strategies for running and maintaining an online
shop’s profile on Russian social networking site vk.com. We collected
data about 706 e-shops profiles on vk.com including their descriptions,
information about the communities followers and posts on profile wall.
For each profile we built an ego graph of followers network and calculated
its centrality measures which were further used to run the k-means
clustering algorithm. As a result, we identified six distinct clusters which
we assume will approximate different strategies of maintaining an e-shop.
These clusters differed in terms of important profile features such as community’s
audience size, posting activity, followers network connectivity,
the presence of “hubs”, e-shops operating mostly on vk.com or having
an external head website. Considering the network-structure patterns as
a result of an online shop’s formed strategy, the potential success can be
estimated. Taking a monthly number of visits to a website from vk.com as
a success metrics, it turns out that the centrality’s indicators themselves
and generalized clusters have associations with a site-visiting frequency.