Big Data Strategy Case Study and Conclusion

Big Data Strategy Case Study and Conclusion

We are about to usher in an "era of big data". How far is Chinese business from this revolution, which has always been in step with change? And how fast will it take to catch up with the leaders?

Conclusions

■The essence of big data marketing is a question of influencing consumers' pre-purchase mental paths, which was difficult to do before the era of big data.

■For traditional enterprises, big data is indispensable to connect online and offline marketing and realize new business models, such as O2O.

■ Although the application of big data is often focused on big data marketing, but for some enterprises, the application of big data has long exceeded the scope of marketing, a full range of enterprise supply chain, production, logistics, inventory, website and in-store operations and other aspects.

■■For most companies, due to the different perspectives and thinking directions between data analysts and business people, there is a disconnect between big data analytics and operations, which is the biggest resistance to big data not being able to be used for business operations

■■For most Internet companies, the amount of big data and the amount of big users is a cyclical process that promotes each other, and the strongest is the stronger.

■For large Internet platforms, big data has become the blood in their ecological cycle, and for these companies, the most important

is not how to use big data to improve their own operations, but to use big data to better prosper the platform ecology.

■ For platform companies, their big data strategy is gradually changing from big data operation to big data operation, and the difference between the former and

the latter is that the former is just a driving force for operational improvement, while the latter becomes a core resource for companies to realize their future strategies.

We've all been told over and over again that we're about to embark on an "era of big data".

Big data applications, like cloud computing and 3D printing, will upend the rules and become the key to success for early adopters.

How far are Chinese companies, which have always been in tune with the changes, from this revolution? And how fast will it take to catch up with the leaders?

Data from the Internet, mobile Internet, Internet of Things (IoT) sensors, and video capture systems are growing in huge volumes, converging into an ocean of big data, accompanied by breakthroughs in massive data storage and analysis technologies, all of which bring unlimited possibilities for enterprise applications.

The China Entrepreneurship Research Institute (CERI) has summarized and categorized the current status of big data application in Chinese enterprises to help them understand the difficulties and dilemmas in the practical application of big data, and to provide typical cases of leading enterprises for reference.

Table 1

Table 2

Big data operation - the driving force for enterprises to improve efficiency

For most enterprises, the application of operation field is the most core application of big data, and before that, enterprises mainly use all kinds of report data from production and operation, but with the advent of the era of big data, the huge amount of data coming from the Internet, But with the advent of the big data era, a huge amount of data from the Internet, the Internet of Things, and a variety of sensors have come to us. As a result, some enterprises have begun to mine and utilize these data to promote the improvement of operational efficiency. The application of big data in operations is divided into three categories: for external marketing, for internal operations, and for leadership decision-making.

I. Big Data Marketing

The essence of big data marketing is to influence the psychological path of the target consumer before shopping, and it is mainly applied in three aspects: 1, big data channel optimization, 2, precise marketing information push, 3, the connection between online and offline marketing. Before consumers shop, through various ways, directly intervene in their information collection and decision-making process. This intervention is based on the analysis of online and offline massive user data. Compared with the traditional marketing of bombardment or waiting for customers to come to the door, big data marketing has a very big advantage in terms of initiative and precision. It is currently the main big data application field.

Big data marketing is not only using big data to find out the target customers and release promotional information to them, it can also do:

Enabling channel optimization. According to the user's Internet traces of channel marketing effect optimization, that is, according to the Internet customer's behavioral trajectory to find out which marketing channel has the most sources of customers, which source of customers actually buy the most, whether it is the target customer and so on, so as to adjust the investment of marketing resources in various channels. For example, Dongfeng Nissan utilizes the tracking of customer sources to improve the placement of marketing resources on various online channels such as portals, search and microblogging.

Precise marketing message push. Precision is built on the basis of behavioral analysis of a huge number of consumers. Consumers' online browsing and searching behaviors are left behind by the Internet, and offline purchases and viewing can be recorded by POS machines and video surveillance in stores, together with the identity information they leave behind in the purchasing and registration process, in front of the merchants, an ocean of consumer information is gradually being presented.

Some companies are collecting massive amounts of consumer information and then using big data modeling technology to tap into target consumers by consumer attributes (such as location and gender) and dimensions such as interest and purchasing behaviors, and then classifying them, and then based on those, pushing marketing messages to individual consumers. For example, the maternity clothing brand October Mamma through the big data analysis of their own microblogging fan comments, to find out the comments have "favorite" related keywords of fans, and then tagged, to their marketing information push. Li Xi, deputy general manager of Jingdong Mall, said, "Using big data to identify different segments of customer demand groups and then marketing accordingly is what Jingdong is currently doing." Xiaoya Cosmetics uses its own website as a radar to collect consumer information and recommend appropriate skin solutions for different consumers. Founder Xiao Shangliu hopes that in the future, big data marketing will replace the role of the website and truly become a front-end for customers.

Bridging online and offline marketing. Some companies will be on the Internet to the sea volume of consumer behavior traces of data and offline purchase data through, to achieve the synergy of online and offline marketing. For example, Dongfeng Nissan, online and offline synergistic marketing approach is: its portal site brings order leads, and through these leads, service personnel to make a return phone call, thus promoting the customer offline transactions. In this process, Dongfeng Nissan records the data of consumers' access, browsing, clicking, registering, calling back and purchasing, realizing a closed-loop marketing path that spans online and offline, supported by big data analysis, and optimizing the marketing effect continuously. Gridsum Technology measures the effectiveness of offline promotions in a certain region by looking at the number of searches on the Internet for promotional content from that region. Some companies, by encouraging offline customers to use WeChat and Wi-Fi and other devices that can track consumer behavior and preferences, to open up the online and offline data flow, Yintai Department Store plans to lay Wi-Fi, encourage customers to use it in the shopping malls, and then according to the Wi-Fi account, to find out the customer, and then through the cooperation with other big data mining companies, to find out the customer's historical traces on the Internet by means of big data. history traces to understand the type of needs of this customer.

Two, big data for internal operations

Compared with big data marketing, the application of big data in internal operations is more in-depth, and requires a higher level of informatization, as well as data collection and analysis capabilities within the enterprise. Essentially, it is to link the massive consumer data outside the enterprise with the massive operation data inside the enterprise, to get new insights in the analysis and to improve the operation efficiency. (For details, see Table 5 of P96: Application of Big Data in Internal Operations)

Table 5

Three, Big Data for Decision Making

In the era of Big Data, enterprises are faced with many new data sources and massive data, can they make decisions based on insights from these data and turn them into a source of competitive advantage for an enterprise? Compared with big data marketing and big data internal operations, decision making using big data is the most difficult because it requires a habit of mind that relies on data.

A few companies are already trying. For example, when some domestic financial institutions launch a financial product, they will extensively analyze the application and effect of the financial product, data on the target customer group, various transaction data and pricing data, etc., before deciding whether or not to launch a financial product.

But the China Entrepreneurship Research Institute (CERI) has found that the use of big data for decision-making is very rare among Chinese companies, and many business leaders are still accustomed to relying on historical experience and intuition when making decisions.

Big data products -- a new source of profit growth

Big data, in addition to being used for operations, can also be combined with enterprise products to become the core support for the competitiveness of the products behind the enterprise products or directly become products. Enterprises that provide big data products are divided into two categories, those that provide big data products directly and those that use big data as the core support for their products and services. The former are mainly participants in the big data industry chain that provide data services, including data owners, storage companies, mining companies, analyzing companies, etc. The latter are mainly those companies that use big data as the core support for their products, most of which are Internet companies, whose products and services inherently have big data genes, which include search engines, online antivirus, Internet advertising trading platforms, and many other products rooted in the mobile Internet, which provide users with the most competitive products and services. mobile Internet, providing users with life and information services such as APP.

Table 3

Table 4

I. Big data as core product support

They mainly use big data in the following aspects:

1. Providing information services. Many Internet companies provide information services for individuals and enterprises through the integration and analysis of massive Internet information and offline information, typically such as Baidu, where to go, Yitao, Gaode Maps, Chunyu doctors and so on. In the United States, some Internet companies even based on big data to provide more in-depth predictive information services, the United States science and technology innovation company farecast, by analyzing the price of air tickets on specific routes, to help consumers predict the price trend of air tickets.

2. Analyzing the personalized needs of users to provide personalized products and services, or to achieve more accurate advertising. Typical of these are mobile social tools such as Stranger, Baidu, Tencent, advertising trading platform PinYou Interactive, and some Internet gamers. Such applications often start by collecting massive amounts of data on users' Internet behavior, classifying users, and offering personalized products or providing personalized promotional information based on different types of users. For example, portals such as NetEase have introduced a subscription model that allows users to easily customize and integrate information from different sources according to their personal preferences.

3. Enhancing product features. For many Internet products, such as antivirus software, search engines, and so on, the processing of massive amounts of data can make the product smarter and more powerful, if there is no big data, the function of the product is greatly weakened. For example, Qihoo 360's 360 antivirus software, with the massive amount of daily antivirus processing, the establishment of a huge virus database, which enables it to find viruses faster, while some small antivirus software companies are unable to do so.

4. Control credit status and provide credit services. Alibaba brings together a large number of small and medium-sized daily funds and goods transactions, through the aggregation and analysis of these transactions data, Alibaba can find the flow of funds and income of individual enterprises, analyze their credit, identify anomalies and possible fraud, and control credit risk.

5, realize intelligent matching. Marriage websites, trading platforms, etc., can utilize big data to provide accurate and efficient matching services. Netflix will dig into user behavioral data, such as clicking on which pages of the opposite sex, what kind of comments, to establish a user interest model, so as to dig into the type of the user's expectations of the other half, and then take the initiative to recommend the other side of the match with the higher degree of the candidate. 2010, Alibaba experimental launch of the "Light Cavalry" service, by the In 2010, Alibaba experimented with the launch of the "Light Cavalry" service, by which Alibaba quickly matches suppliers in China's industrial clusters with the personalized purchasing needs of overseas buyers, relying on the consolidation and mining of the suppliers' massive transactional data information.

Two, big data directly as products

For some enterprises, big data directly become products, these products include massive data, analysis, storage and mining services, etc. At present, the big data industry chain is in the process of formation, the emergence of a number of companies and institutions that open, sell, and authorize big data and provide big data analysis and mining, and the former is mainly a number of companies that have massive data The former are mainly some companies that own huge amounts of data and take data services as a new source of profit. Such as large-scale Internet platforms, civil aviation, telecom operators, some government agencies with big data, etc. The latter mainly includes some companies that can store massive data or combine massive data with business scenarios to analyze and mine, or provide related products, such as IBM, SAP, Tuozhi, and Tianrui. They provide services such as massive data storage, data mining, image and video, intelligent analysis, and related system products for big data applicants.

Big Data Platform - Nourishing Agent for Prosperity of Enterprise Clusters

And Baidu has built five major data system platforms including Baidu Index, Sinan, Wind and Cloud List, Data Research Center, and Baidu Statistics, which help enterprises on its marketing platform to understand the behavior of consumers, changes in their interests, as well as the development of the industry, market dynamics and trends, and competitors' needs. status, market dynamics and trends, competitor movements and other information.

To address these issues, various platforms are actively working on them. For example, Alibaba has established a data committee, sparing no effort to try to unify data format standards, ensure the quality of data from the source, collect and process refined data, and ensure that it can meet the application scenarios of platform enterprises. Especially in terms of big data refinement, Alibaba is the focus of its big data strategy. In this regard, Tencent is also currently accelerating its pace. For example, the new version of QQ.com has a "one-click login" tip, users can subscribe to their own concerns through some segmentation labels. In fact, this is also an effective means for Tencent to collect more refined user interest data.

Tips

Big data handbook

When applying big data to internal operations, companies will encounter some common problems

1How do companies acquire and analyze data?

The Internet is a major source of big data, which is difficult for some offline traditional enterprises to obtain. But they can:

a Partner with platforms, businesses, and government agencies that own or can grab massive amounts of data. For example, e-commerce companies on Taobao buy the portion of the massive data collected by Taobao that is relevant to their own operations and use it for their own business. Another example is Kraft, through cooperation with IBM, captured 479,000 discussion messages about its own products in blogs, forums and discussion boards, and analyzed consumers' favoritism and consumption patterns of Kraft foods through big data.

b Establishing its own platforms on the Internet, such as Chaoyang Joy City's use of its own WeChat and Weibo platforms to collect consumer review data.

cMany traditional enterprises do not have the ability to analyze huge amounts of data, at this time they can cooperate with big data analysis and mining companies, at present there are already a number of companies in the market that provide big data analysis and mining services such as Tianrui, IBM, Percentage, Huasheng, etc., which are the forces that can be relied upon by the traditional enterprises to carry out big data analysis.

2How to avoid departmental segmentation when applying big data?

For many enterprises, their information flow is divided by the departments each other, data is difficult to interoperate, for this case, big data **** enjoyment and pooling is just a bubble, it is more difficult to realize the depth of the application of big data.

To break through the situation of information division between departments, the first step is to establish a unified, centralized data system. As Wang Yonghong, director of information and knowledge at LIBRE, said, "To really use big data, companies need to adopt a large centralized information system." From a more in-depth point of view to talk about, enterprise information flow of departmental division, more in the division between enterprise departments, for example, there are some enterprises marketing according to the channel segmentation, resulting in for the customer's big data collection and analysis of the effect of a big discount.

Yang Xuqing, director of IBM Intelligent Business Technology, believes that "very often due to organizational structure issues, big data analysis effectiveness is greatly reduced." This requires organizational and process level redesign, in this regard, Alibaba's department head rotation system, for breaking departmental barriers is undoubtedly a good medicine. And some companies in order to break the departmental division, the establishment of a matrix-type organizational structure, to strengthen the horizontal cooperation between departments, these are undoubtedly for the pooling of big data, **** enjoyment and application of the creation of good conditions.

3How to make business people pay attention to the application of big data?

To solve this problem, on the one hand, lies in a hand on the whole enterprise data culture advocate, such as 1 store chairman Yu Gang asked business personnel whether in the meeting, or reporting work, all speak with data, and Ma Yun is big data to a strategic height.

On the other hand, it also lies in the drive of the data department, Alibaba data committee head Che Pinjue shared the experience, "Because it is difficult for business people in the operation department to see the potential of big data, you can first start from some of the data projects that have a quick and significant effect on the business, and mobilize each other's motivation through the success of one or two projects, and then gradually lead one by one."

4Why are big data efforts disconnected from operational needs?

This is often due to the different perspectives and expertise of data people versus operational people. Big data people make a lot of efforts, but business people see them as irrelevant. How can this be resolved?

Some companies have made an effort in organizational design to bring big data under the management of the business analytics department, harnessing data with business. For Chaoyang Joy City, the department primarily responsible for strategy and business analysis manages big data work, with the big data analysts therein acting as support staff. In the opinion of the person in charge, Zhang Yan, big data should be guided by the laws of business, and the key is to find the point of business demand, which is then realized by data analysis and mining personnel. In the specific operation, Joy City on WeChat data mining, mining what kind of keywords, determined by the business analysts, while the specific mining by the data department to do; some companies start from the process design, to promote the communication between the business sector and the data department personnel, the establishment of data personnel work and the results of the assessment mechanism linked.

For example, Alibaba assesses data miners based on the effectiveness of data mining (such as the increase in the conversion rate of commodities), and assesses data analysts based on whether their analysis results can appear in the report of the person in charge of operations. From the data department's own point of view, it is necessary to reduce the barriers and thresholds for the operation department to use data, for example, the data personnel of LIBERTY Group will make efforts to try to provide the operation department with a more understandable and vivid graphical data analysis interface. This allows the boss to have a clear picture of the sales situation of the national distributors in the same month. Then Alibaba developed wireless Bi, so that operators can also see the results of big data analysis on the phone, to take the words of Che Pinjue, "the oxygen of the data surrounded by operators."