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<\/li><\/ul>These statistics give insights into the digital footprint of telecom sector in India. The voice market is maturing and the new era of data has embarked. The enormous amount of data transaction happening through a telecom operators bit pipe can turn this dump pipe into a smart one.
Opportunity for Telecom players:<\/b>
The big data can be defined in 4 dimensions. Volume, Velocity, Variety and Validity but without the 5th V (Economic Value) it has no use. Operators have access to different types of data: Structured data (Call Data Records, IP data records, Network data, Operations Support System, Business Support System) and Un-structured data (Traffic data, mails, Web search, SMS, Social media, mobile application). Telecom operators can leverage on this unstructured data by converting it into structured data through data analytics, which can then be used for creating innovative services for the end users.
Compete or collaborate with the over-the-top (OTT) players: <\/b>Today the Telcos are losing their importance and revenue to OTT players. The big-data provides an opportunity to Telcos to compete and collaborate with the OTTs. Telcos and OTTs can use the real-time data such as CRM data, habits, preferences, etc. along with the demographics information for better personal user experience and targeted marketing. Customer Experience Management is all about serving the customer close to their perception: where they shop, what product they like, which brand they go for.
M2M: <\/b>According to GSMA, deployed M2M devices will grow from 2 billion in 2011 to 12 billion in 2020; out of which 2.3 billion will connect with cellular networking. These M2M devices will produce huge amount of data which will need advanced analytics for better monitoring and control of these devices.
Location Based Services (LBS): <\/b>Telcos have the exclusive information of the location of a subscriber. The location along with the time and activity can be used to provide new services to customers as well as other businesses. e.g. Starbucks uses LBS service to provide customers with new offers when they are in the vicinity of the store.
Social media: <\/b>The usage of social media apps and websites is on the rise. The huge amount of data generated by user interactions could be used to enhance customer relationships by understanding their sentiments and monitoring the company's social media presence.
Security: The big data is also important from the security perspective of the individuals and the nation. Intelligence agencies analyse the call data records and other information passing through the networks that can provide evidence of threats and risks.
Forecasting: <\/b>The big data can be used to understand the trends in the market and the changes in customer preferences in relation to various macroeconomic factors. It can predict the customer behaviour and market performance in near future. The churn rate of customers can help in predicting the demand and supply analysis.
The Telcos maintain good infrastructure to store huge amount of network and customer data. With monetization of the huge amount of daily data, the Operators can gain new revenue stream as well as new business opportunity in the near future. Moreover, in the era of cloud computing Telcos are deploying world class data centres which can help in providing big-data analytics services to enterprises. Considering the sensitivity of the information, the operators need to guarantee that the private data is managed with appropriate level of confidentiality and anonymity.
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- 互联网用户:2.43亿
- 无线用户:9.04亿
- 电子商务行业:Rs 6270亿
电信玩家的机会:
大数据可以被定义在4维空间。体积、速度、多样性和有效性但没有5 V(经济价值)它没有使用。运营商获得不同类型的数据:结构化数据(数据记录电话、IP数据记录、网络数据、业务支持系统,业务支持系统)和非结构化数据(流量数据、邮件、网络搜索、手机短信、社交媒体、移动应用程序)。电信运营商可以利用这种非结构化数据通过数据分析将它转化为结构化数据,可以用于为最终用户创造创新服务。
竞争或合作过多(OTT)球员:今天,电信公司正在失去其重要性和收入奥特的球员。大数据提供了一个机会去电信公司与奥特竞争和协作。电信公司和奥特可以使用实时数据,如CRM数据,习惯,喜好等以及更好的个人用户体验的人口统计信息和有针对性的营销。客户体验管理就是服务于客户接近他们的看法:他们店,产品他们喜欢什么,他们去哪个牌子。
M2M:根据GSMA,部署M2M设备将从20亿年的2011人增长到120亿年的2020;其中23亿将与蜂窝网络。这些M2M设备会产生大量的数据,需要先进的分析更好的监视和控制这些设备。
基于位置的服务(LBS):电信公司的独家信息用户的位置。位置随时间和活动可以用来向客户提供新服务以及其他业务。例如星巴克使用LBS服务为客户提供新的报价时附近的商店。
社交媒体:社交媒体的使用应用程序和网站正在上升。用户交互所产生的大量数据可以用来增进客户关系,了解他们的情绪和监控公司的社交媒体的存在。
安全:安全角度的大数据也很重要的个人和国家。情报机构分析调用数据记录和其他信息通过网络,可以提供证据的威胁和风险。
预测:大数据可以用来理解市场和客户偏好的变化趋势和各种宏观经济因素的关系。它可以预测客户行为和市场表现在不久的将来。客户的流失率可以帮助预测需求和供给分析。
电信公司维持良好的基础设施来存储大量的网络和客户数据。货币化的大量的每日数据,运营商可以获得新的收入来源,以及新的商业机会在不久的将来。此外,在云计算时代的电信公司正在部署世界级数据中心可以帮助提供大数据分析服务企业。考虑到信息的敏感性,运营商需要保证私有数据管理与适当级别的保密和匿名性。
