Social Network Analytics: Strategies for Service Providers
|出版日期||內容資訊||英文 11 Pages
|社群網路分析:服務供應商導向的策略 Social Network Analytics: Strategies for Service Providers|
|出版日期: 2012年03月29日||內容資訊: 英文 11 Pages||
Social network analytics (SNA) is becoming increasingly popular as service providers are looking for ways to have a competitive edge and better understand their customers. SNA is a type of analytical scoring system that enables providers to analyze CDR and IPDR data, and then use that data to identify influencers and patterns among social calling circles of friends and families. This information is then used by providers in both a defensive and offensive manner to help with overall customer experience management (CEM).
It is becoming increasingly popular because providers want to get a better understanding of customers' needs. SNA can help provide timely up-selling offers that come across as appropriate, rather than pushy. In today's competitive telco environment, providers must analyze messy data incoming from sources as varied as customer care, product/service/device portfolios, cost and billing, and network service quality. SNA looks at customers' social habits and usage to gain an understanding of all these customer care elements.
To effectively deal with this massive amount of data, SNA requires a next-generation business intelligence architecture to capture, filter, clean, organize, analyze and process incoming data and events as they occur, alongside massive amounts of historical CDR data. This all-embracing understanding of churn probability and the next-best-offer enables operators to do a better job in retaining customers and gaining new ones.
With any communications service provider, one of biggest challenges is trying to do deal with very large amounts of data. Advanced analytics - especially predictive analytics - takes this large amount of data and makes it useful to the provider.
Social network analytics enables service providers to better understand not only the individual customer, but the behavior of groups or circles of customers and most importantly, influencers. Effective predictive models look at both historic and real-time data. And, for large numbers of customers, using SNA to determine customers' needs, wants, likes and dislikes will help prevent churn and increase retention while being able to customize and improve promotions.
The key to SNA is being able to execute it quickly. This is the real value of SNA, and the ability to precisely target and cross-sell or up-sell offers to an appropriate influencer and social circle in the moment will be the key to a service provider's success.
‘Social Network Analytics: Strategies for Service Providers’ examines the SNA market, discussing how it works and the offensive and defensive strategies it provides to service providers. Finally, the report profiles seven leading vendors in the market.
Excerpt: Social Network Analytics Terms
|Nodes||The circles or squares represent people or individuals within the network|
|Ties||The lines (or relationships) connecting them|
|Trendsetters||Have a large network and a high number of reciprocal ties with other nodes|
|Follwers||Have a positive profile similar to trendsetters but with a slightly smaller network size|
|Outliers||Have a large network but few reciprocal ties between nodes; ties are mostly unidirectional|
|Marginals||Have a small network and few ties with other nodes|
Source: Heavy Reading Insider