In the era of mobile Internet, the explosive growth of data brought huge traffic. Operators did not receive corresponding improvements in revenue, and they also faced the challenge of being “sucked away†by the added value of data streams, and faced the challenge of becoming a pipeline. awkward. In the next mobile Internet competition, what should operators do? For telecom operators, large traffic and big data bring more severe challenges, but at the same time opportunities and challenges coexist, and the vast amount of data that operators hold is also far behind the other links in the industry chain. In addition, an efficient information analysis capability will help operators to make accurate decisions in the increasingly fierce market competition, and to tap into the value of traffic and data in depth so as to get rid of “pipelined†risks.
With the rise of the Internet, mobile Internet, Internet of Things, and cloud computing, and the rapid spread of mobile smart terminals, operators' networks have obtained more complete user data. For example, at the user level, in addition to the basic information such as common age, brand, tariff, and network access, the data includes the time spent on the Internet, the location of the Internet site, the preferences of browsing content, and the duration of use of various applications. At the terminal level, the IMEI and MAC are included. , terminal brands, terminal types, which applications are pre-installed on the terminal, the operating system of the terminal, and the size of the terminal. In addition, there are data such as web browsing records, sensor signals, GPS tracking, and social networking information. According to the statement in the book “Outbreakâ€, “The outbreak is a way of thinking, not a forecasting method. From the physics to the era of human social big data, our behavior can be predicted, and we enjoy some free services at the same time. , Selling your own preferences.†From these large user data, you can analyze the behavior of different users and consumer preferences, and ultimately improve operating efficiency.
Operators have been deeply aware of the importance of big data. Within the company, big data has been used to achieve accurate marketing and refined operations. China Mobile provides more accurate module support after analyzing data such as the MOU (average user call duration) and service revenue of user data, which greatly facilitates the daily marketing of marketing personnel. For users with more roaming charges, roaming packages are recommended; for users who frequently use mobile phones to access the Internet, they are recommended to use traffic packets. Through the analysis of user behavior, provide IM services, such as Fetion, flying chat and so on. In the business analysis system, the company digs deeply into the integration of market, group, customer, customer service, network, and financial data to provide more complete user data analysis for the business and decision-making departments, so that the company’s decision-making will change from “experienced†to “analyticâ€. Realized refined operations.
However, these are not enough. Although operators began to try to provide data services to the outside world, they stayed at the level of providing raw data. This is a serious waste of big data. Providing high value-added data analysis services for massive data, encapsulating data as services, forming core capabilities that can be opened to the outside world, and being commercialized, and innovating business models to enable operators to truly tap into the big data gold mine. . The author believes that there are at least the following 7 modes that operators can practice.
Mode 1: Data storage space for rent
Companies and individuals have the need for massive information storage. Only by properly storing data can they further tap into their potential value. Specifically, this business model can be further subdivided into two categories: personal file storage and enterprise users. Primarily through an easy-to-use API, users can conveniently place various data objects in the cloud and then use the same amount of water and electricity as their usage. At present, several companies have launched corresponding services, such as Amazon, NetEase and Nokia. Operators have also launched corresponding services, such as China Mobile's cloud business.
To improve differentiated competitiveness, operators should work hard on data analysis. As for the storage of personal files, it is necessary to improve the relationship management and promote personal efficiency. In enterprise services, it is gradually expanded from simple file storage and sub-items to data aggregation platforms, and the future profit model will have infinite possibilities.
Mode 2: Customer Relationship Management
The purpose of the customer management application is to deeply analyze customers and understand customers from different perspectives according to their attributes (including natural attributes and behavioral attributes), thereby increasing new customers, increasing customer loyalty, reducing customer churn, and improving customers. Consumption and so on.
For small and medium-sized customers, specialized CRM is obviously large and expensive. Many small and medium businesses use Fetion as primary CRM. For example, the old customers are added to the Fetion Group, and new product announcements, special sales notices, and pre-sales services are completed in the group of friends. On the basis of this, China Mobile may launch a customer relationship management platform based on data analysis. According to industry classification, different promotion activities and service modes may be adopted for different customers to provide more targeted services, and then online payment will be provided. The channel opened up to form a closed loop, creating a practical customer relationship management system.
Mode 3: Business Decision Making Guidance
Operators can use user data to apply mature operational analysis technologies to effectively improve the company's data resource utilization capabilities and make the company's decision making more accurate, thereby improving overall operational efficiency. In short, the commercialization of the internal data analysis technology of the operator provides the decision-making basis for the enterprise. To give a simple example, a store sells milk, through data analysis, knows that customers who bought milk in the store often go to another store to buy buns afterwards, and the number is still quite large. Then the store can consider cooperation with buns stores. , Or directly in the shop to sell buns.
Mode 4: Personalized precision recommendation
Within the operator, it is common to recommend various types of services or applications according to user preferences, such as application software recommendation, IPTV video program recommendation, etc., and after the intelligent analysis algorithms such as association algorithm, text digest extraction, and sentiment analysis, can be implemented. Extend to commercialization services, use data mining technology to help customers with precision marketing, future profitability can come from the value-added part of the customer.
Taking the daily "spam messages" as an example, information is not all "junk" because people who receive it do not need to be seen as garbage. After analyzing the user behavior data, the required information can be sent to the people who need it, so that "spam messages" become valuable information. At McDonald's in Japan, users download coupons on their mobile phones and go to restaurants to pay for their mobile wallets with the operator DoCoMo. Operators and McDonald's collect relevant consumption information, such as what burgers they often buy, which stores they want to consume, and how often they spend, and then push the coupons to users.
Pattern 5: Building a Localized Data Mart
We all know that data is a very valuable thing. Therefore, being able to download or access data platforms naturally becomes a business requirement. Operators can build data marts. Data providers can upload data to the platform for free download, or sell for a certain price, so that everyone can find the data set they need.
The full network and localization advantages that operators have will enable operators to provide platforms that can provide maximum coverage of local services, entertainment, education, and medical data. The typical application is China Mobile's "Wireless City", driven by the closed loop system of "Two-dimensional Code + Account System + LBS + Payment + Relationship Chain", which brings a diversified profit model to the localized data mart platform.
Mode 6: Data Search
Data search is not a new application. With the advent of the era of big data, the demand for real-time and full-range search becomes more and more intense. We need to be able to search various social networks, user behaviors and other data. Its commercial application value is to link real-time data processing with analysis and advertising, namely real-time advertising services and in-app mobile advertising social services.
The user's online behavioral information, which the operator possesses, makes the acquired data “have a more comprehensive dimension†and has more commercial value. Typical applications are China Mobile's "Pangu Search."
Model 7: Innovative Social Management
For operators, data analysis has great prospects in the government service market. For example, with the help of big data, what time period, which road congestion, and other issues can be learned through analysis. Through the displacement of multiple users' mobile phones on the same road, the current road conditions can be determined and accurate warnings can be made for congestion. The United States has used big data technology to analyze variables such as historical arrest patterns, paydays, sports items, rainfall weather, and holidays to optimize police deployment.
In China, operators can also make big data technologies more effective in transportation, emergency response, and stability maintenance.
Operators are in a data exchange center and have inherent advantages in mastering user behavior. As another revolution in information technology, the emergence of big data is bringing new directions to technological advancement and social development. Whoever masters this direction will be successful. For operators, in terms of data processing and analysis, it is not only technical and legal issues that need to be transformed, but also the need to change the way of thinking and thinking about big data marketing from the perspective of commercialization.
With the rise of the Internet, mobile Internet, Internet of Things, and cloud computing, and the rapid spread of mobile smart terminals, operators' networks have obtained more complete user data. For example, at the user level, in addition to the basic information such as common age, brand, tariff, and network access, the data includes the time spent on the Internet, the location of the Internet site, the preferences of browsing content, and the duration of use of various applications. At the terminal level, the IMEI and MAC are included. , terminal brands, terminal types, which applications are pre-installed on the terminal, the operating system of the terminal, and the size of the terminal. In addition, there are data such as web browsing records, sensor signals, GPS tracking, and social networking information. According to the statement in the book “Outbreakâ€, “The outbreak is a way of thinking, not a forecasting method. From the physics to the era of human social big data, our behavior can be predicted, and we enjoy some free services at the same time. , Selling your own preferences.†From these large user data, you can analyze the behavior of different users and consumer preferences, and ultimately improve operating efficiency.
Operators have been deeply aware of the importance of big data. Within the company, big data has been used to achieve accurate marketing and refined operations. China Mobile provides more accurate module support after analyzing data such as the MOU (average user call duration) and service revenue of user data, which greatly facilitates the daily marketing of marketing personnel. For users with more roaming charges, roaming packages are recommended; for users who frequently use mobile phones to access the Internet, they are recommended to use traffic packets. Through the analysis of user behavior, provide IM services, such as Fetion, flying chat and so on. In the business analysis system, the company digs deeply into the integration of market, group, customer, customer service, network, and financial data to provide more complete user data analysis for the business and decision-making departments, so that the company’s decision-making will change from “experienced†to “analyticâ€. Realized refined operations.
However, these are not enough. Although operators began to try to provide data services to the outside world, they stayed at the level of providing raw data. This is a serious waste of big data. Providing high value-added data analysis services for massive data, encapsulating data as services, forming core capabilities that can be opened to the outside world, and being commercialized, and innovating business models to enable operators to truly tap into the big data gold mine. . The author believes that there are at least the following 7 modes that operators can practice.
Mode 1: Data storage space for rent
Companies and individuals have the need for massive information storage. Only by properly storing data can they further tap into their potential value. Specifically, this business model can be further subdivided into two categories: personal file storage and enterprise users. Primarily through an easy-to-use API, users can conveniently place various data objects in the cloud and then use the same amount of water and electricity as their usage. At present, several companies have launched corresponding services, such as Amazon, NetEase and Nokia. Operators have also launched corresponding services, such as China Mobile's cloud business.
To improve differentiated competitiveness, operators should work hard on data analysis. As for the storage of personal files, it is necessary to improve the relationship management and promote personal efficiency. In enterprise services, it is gradually expanded from simple file storage and sub-items to data aggregation platforms, and the future profit model will have infinite possibilities.
Mode 2: Customer Relationship Management
The purpose of the customer management application is to deeply analyze customers and understand customers from different perspectives according to their attributes (including natural attributes and behavioral attributes), thereby increasing new customers, increasing customer loyalty, reducing customer churn, and improving customers. Consumption and so on.
For small and medium-sized customers, specialized CRM is obviously large and expensive. Many small and medium businesses use Fetion as primary CRM. For example, the old customers are added to the Fetion Group, and new product announcements, special sales notices, and pre-sales services are completed in the group of friends. On the basis of this, China Mobile may launch a customer relationship management platform based on data analysis. According to industry classification, different promotion activities and service modes may be adopted for different customers to provide more targeted services, and then online payment will be provided. The channel opened up to form a closed loop, creating a practical customer relationship management system.
Mode 3: Business Decision Making Guidance
Operators can use user data to apply mature operational analysis technologies to effectively improve the company's data resource utilization capabilities and make the company's decision making more accurate, thereby improving overall operational efficiency. In short, the commercialization of the internal data analysis technology of the operator provides the decision-making basis for the enterprise. To give a simple example, a store sells milk, through data analysis, knows that customers who bought milk in the store often go to another store to buy buns afterwards, and the number is still quite large. Then the store can consider cooperation with buns stores. , Or directly in the shop to sell buns.
Mode 4: Personalized precision recommendation
Within the operator, it is common to recommend various types of services or applications according to user preferences, such as application software recommendation, IPTV video program recommendation, etc., and after the intelligent analysis algorithms such as association algorithm, text digest extraction, and sentiment analysis, can be implemented. Extend to commercialization services, use data mining technology to help customers with precision marketing, future profitability can come from the value-added part of the customer.
Taking the daily "spam messages" as an example, information is not all "junk" because people who receive it do not need to be seen as garbage. After analyzing the user behavior data, the required information can be sent to the people who need it, so that "spam messages" become valuable information. At McDonald's in Japan, users download coupons on their mobile phones and go to restaurants to pay for their mobile wallets with the operator DoCoMo. Operators and McDonald's collect relevant consumption information, such as what burgers they often buy, which stores they want to consume, and how often they spend, and then push the coupons to users.
Pattern 5: Building a Localized Data Mart
We all know that data is a very valuable thing. Therefore, being able to download or access data platforms naturally becomes a business requirement. Operators can build data marts. Data providers can upload data to the platform for free download, or sell for a certain price, so that everyone can find the data set they need.
The full network and localization advantages that operators have will enable operators to provide platforms that can provide maximum coverage of local services, entertainment, education, and medical data. The typical application is China Mobile's "Wireless City", driven by the closed loop system of "Two-dimensional Code + Account System + LBS + Payment + Relationship Chain", which brings a diversified profit model to the localized data mart platform.
Mode 6: Data Search
Data search is not a new application. With the advent of the era of big data, the demand for real-time and full-range search becomes more and more intense. We need to be able to search various social networks, user behaviors and other data. Its commercial application value is to link real-time data processing with analysis and advertising, namely real-time advertising services and in-app mobile advertising social services.
The user's online behavioral information, which the operator possesses, makes the acquired data “have a more comprehensive dimension†and has more commercial value. Typical applications are China Mobile's "Pangu Search."
Model 7: Innovative Social Management
For operators, data analysis has great prospects in the government service market. For example, with the help of big data, what time period, which road congestion, and other issues can be learned through analysis. Through the displacement of multiple users' mobile phones on the same road, the current road conditions can be determined and accurate warnings can be made for congestion. The United States has used big data technology to analyze variables such as historical arrest patterns, paydays, sports items, rainfall weather, and holidays to optimize police deployment.
In China, operators can also make big data technologies more effective in transportation, emergency response, and stability maintenance.
Operators are in a data exchange center and have inherent advantages in mastering user behavior. As another revolution in information technology, the emergence of big data is bringing new directions to technological advancement and social development. Whoever masters this direction will be successful. For operators, in terms of data processing and analysis, it is not only technical and legal issues that need to be transformed, but also the need to change the way of thinking and thinking about big data marketing from the perspective of commercialization.
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