7 Models of operators ' mining large data value

Mobile Internet era, the data explosion after the increase in traffic, operators in the revenue has not been the corresponding upgrade, but also faced with the value-added of the data stream by the internet companies "suck" the challenge, facing the embarrassment of becoming a pipeline. What do operators do in the next competition for mobile internet? For telecom operators, large flow, large data to bring more severe test, but at the same time opportunities and challenges coexist, operators in the hands of the vast number of data, but also in the industry chain other links to the point. In addition, the efficient information analysis ability, will help operators in the increasingly fierce market competition, accurate decision-making, depth mining flow and data value, so as to get rid of the "pipeline" risk.

With the rise of internet, mobile Internet, IoT, cloud computing and the rapid popularization of mobile intelligent terminals, the network of operators has obtained more complete user data. For example, at the user level, in addition to the common age, brand, tariff, network channels and other basic information, the data also includes the time of the Internet, Internet location, browsing content preferences, the use of various applications, and so on, at the terminal level, including IMEI, MAC, terminal brand, terminal type, terminal pre-installed what applications, terminal operating system , terminal size, etc. In addition, there are data such as Web browsing records, sensor signals, GPS tracking, and social networking information. According to the statement in the outbreak, the eruption shows a way of thinking, not a prediction. From physics to human society in the era of large data our behavior can be predicted, we enjoy some free services, while selling their own preferences. "From these huge user data, can analyze different user's behavior habits and consumption preferences, and ultimately improve business efficiency."

Operators have been deeply aware of the importance of large data, within the enterprise has been using large data to achieve accurate marketing and refinement of operations. China Mobile, through the MOU of user data (average user call time), business income and other data analysis, provide more accurate module support, greatly facilitate the marketing staff daily marketing. If the roaming fee for more users, it is recommended roaming packages; For users who often use their mobile phones to surf the internet, they recommend traffic packs. Through the analysis of user behavior, provide IM services, such as flying a letter, fly chat. In the Operation analysis System, the deep excavation fuses the market, the group, the customer, the service, the network, the financial data, provides the complete user data analysis for the business and the policy-making department, causes the company decision from "the empirical" to "the analytic type", realizes the fine operation.

However, these are far from enough. Although operators have begun to try to provide data services to the outside world, they remain in the supply of raw data, which is a serious waste of large data. To provide high value-added data Analysis Services for massive data, to encapsulate data as service, to form the core ability of opening to the outside, to commercialize, to realize the innovation of business model, can the operators really excavate large data this gold mine. The author thinks that there are at least 7 kinds of models that operators can practice.

Mode 1: Data storage space for lease

Enterprises and individuals have a large amount of information storage needs, only the data properly stored, it is possible to further explore its potential value. Specifically, this business model can be subdivided into two categories for personal file storage and enterprise users. Mainly through Easy-to-use APIs, users can easily put a variety of data objects in the cloud, and then like the use of water, electricity, according to the amount of charge. At present, many companies have launched the corresponding services, such as Amazon, NetEase, Nokia and so on. Operators have also launched corresponding services, such as the China Mobile cloud business.

To enhance the competitive power of differentiation, operators should make efforts in data analysis. For personal text

Part storage should improve the relationship chain management, improve the efficiency of the individual, but in enterprise Services, from simple file storage, the gradual expansion of the data aggregation platform, the future of the profit model will be unlimited.

Mode 2: Customer Relationship Management

Customer management applications are based on customer attributes (including natural attributes and behavioral attributes), from different angles of in-depth analysis of customers, understand customers, so as to increase new customers, improve customer loyalty, reduce customer churn rate, improve customer consumption.

For small and medium-sized customers, specialized CRM is obviously large and expensive. Many small and medium-sized businesses will fly letter as a primary CRM to use. For example, the old customers added to the letter group, in the group of friends to release new product announcements, special sales notice, the completion of pre-sales after-sales service. On this basis, China Mobile may the introduction of the customer relationship management platform based on data analysis, according to industry classification, for different customers to take different promotional activities and services, to provide more targeted services, and then provide online payment channels through to form a closed loop, to create a practical customer relationship management system.

Mode 3: Guidance of business decision-making

Operators can use the user data to use the mature operation analysis technology, effectively enhance the enterprise's data resource utilization ability, make the enterprise's decision-making more accurate, thus enhances the overall operation efficiency. In short, the operator's internal data analysis technology is commercialized to provide decision basis for the enterprise. To cite a simple example, a shop selling milk, through data analysis, know that in our shop to buy milk customers will often go to another shop to buy dumplings, the number of many, then the store can consider to cooperate with the dumplings, or directly in the store to sell dumplings.

(Responsible editor: The good of the Legacy)