The Social Network Cashes In and Analytics Plays a Role


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Facebook is going public this week with an initial valuation approaching $100 Billion. That’s a lot of money – is it a fair valuation? I’m not an IPO expert so I will not debate the valuation but I do know that a site approaching a billion users per month, where the users are very engaged and interconnected with others on the site, is valuable. Furthermore, the data generated by the users is a goldmine if ‘data mined’ correctly in order to increase user relevance and continue to bring communities together. And web sites such as Facebook & LinkedIn are doing a very good job tapping into their user data through techniques such as social network analysis. Social Network

Traditional customer analytic environments can benefit from those Social Network Analysis (SNA) techniques as well but what is SNA and how can it be used within traditional customer analytic environments? SNA, specifically SNA applied to people networks, is a type of analysis which determines interrelations between individuals and groups. These analyses may be used to:

  • determine ‘influencers‘ or those individuals who show the greatest influence on the network
  • determine ‘connectors‘ or those individuals who, if they left the network, could have the greatest negative impact
  • determine groups / clicks that have the greatest influence on the full network

SNA can enhance your customer analytics environment through increased insight. Knowing who your influencers and connectors are is the first step. Metrics created during the development of social network analyses may be used directly within customer analytic environments. Those SNA metrics include –

  • Degree: the number of contacts / friends in a group.
  • Closeness: measures if an individual is an outlier or in the middle of the pack.
  • Between-ness: measures how interconnected an individual is to the full universe
  • Influencer score: Overall Influencer scores are also available

Once an SNA is complete the output may be applied to the individual users and then used to enhance many strategic customer-engagement tactics including –

  • Creating additional cross-sell strategies based on a customer’s SNA connectivity and group
  • Enhancing retention knowledge and retention strategies based on an individual’s connections and group relationships.
  • Determining group associations, and influence on other groups, provides insight into investment decisions such as web site design and marketing treatment cadence
  • Enhancing customer value models given influencers have a greater direct impact on overall customer value including current value, potential value and lifetime value.

Facebook and LinkedIn have shown that SNAs provide incremental relevance to their users, which translates into bottom line value. As these SNA techniques become well-known by more traditional customer analytic professionals, SNA will become a common analysis technique throughout the customer analytic community.

Republished with author's permission from original post.

Roman Lenzen
Roman Lenzen, Partner and Chief Data Scientist at Optumine, has delivered value added analytical processes to several industries for 20+ years. His significant analytical, technical, and business process experience provides a unique perspective on improving process efficiency and customer profitability. Roman was previously VP of Analytics at Quaero and Director of Analytics at Merkle. Roman's education includes a Bachelor of Science degree in Mathematics from Marquette University and Masters of Science in Statistics from DePaul University.


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