1. Amazon’s “Information Company”
Amazon has to deal with massive amounts of data, and the direct value of transaction data is huge. As an "information company", Amazon obtains information from each user's purchase behavior, records the user's behavior on the website, the time spent on the page, the user's review comments, search keywords, browse products, etc. Amazon's sensitivity and emphasis on data value and its mining ability make it far beyond traditional operation methods.
2. Google "intent"
The technology company that has accurately defined the concept of " big data " is Google. In just one month, Google processed 12.2 billion search terms, according to data from search research institutes. Google's size and scale give it more access than most other businesses.
Google not only stores the network connections that appear in the search results, but also stores the user's behavior of searching for keywords. It can accurately record the time, content and method of people's search behavior. A large amount of machine data generated by the network. This data allows Google to optimize ad ranking and convert search traffic into a monetization model. People's actions leave traces and paths on the Internet, and Google can predict intent. This kind of capturing, storing and analyzing massive human-machine data and then making predictions is a data-driven product.
3. eBay Analytics Platform
eBay established a big data analysis platform in 2006 . To accurately analyze user shopping behavior, eBay defines more than 500 types of data. On the platform, combine structured and unstructured data to drive business innovation and profit growth through analytics. In the investment of Internet advertising, eBay will introduce potential customers to the website by purchasing web search keywords.
4. Target’s “Data Association Mining”
Use advanced statistical methods to build models through the analysis of users' purchase history, predict future purchase behavior, and then design promotional activities and personalized services to avoid user loss. Example: Target can "guess" who are pregnant by analyzing the purchase records of all female customers. It found that female customers were buying unscented lotions in bulk around the fourth month of pregnancy. From this, 25 items that are highly related to pregnancy were excavated to make a "pregnancy prediction" index. Once the due date is calculated, it can be one step ahead and send discount coupons such as maternity clothes and cribs to customers. Target has also created a model of how women's buying behavior changes during pregnancy, and not only that, but if users buy baby products from their store, they'll regularly give them over the next few years based on the baby's growth cycle. These customers push relevant products, so that customers form long-term loyalty.
5. China Mobile's digital operations
Through big data analysis , targeted monitoring, early warning and tracking of the entire business of enterprise operations. The big data system can automatically capture market changes in the first time, and then push it to the designated person in charge in the fastest way, so that he can know the market situation in the shortest time. For operators, data analytics holds great promise in the government services market. Operators make big data technology play a greater role in transportation, response to sudden disasters, and stability maintenance. Operators are in the position of a data exchange center and have inherent advantages in grasping user behavior. As another change in information technology, the emergence of big data is bringing a new direction to technological progress and social development. 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 way of thinking to think about big data marketing from a commercial perspective.
6. Twitter interest and sentiment
By filtering user attribution, tweet location and related keywords, Twitter builds a series of customized customer data streams. For example, it is possible to know which movies are the most popular in cities such as Los Angeles, New York and London by filtering by movie title, location and sentiment tags. According to the personal behavior descriptions posted by users, even Japanese tourists who are skiing in Canada can be searched. From this perspective, Twitter's interest graph is more efficient than Facebook's social graph. The real value of these social networking sites may lie in the data itself. Twitter doesn't operate every data product itself, but it licenses the data to data services companies like DataSift, many of which have used Twitter social data to make all kinds of amazing applications, from social monitoring to medical applications , and even to track flu outbreaks. When precise data is combined with social media data, future predictions can be very accurate.
7. Precise targeting of Tesco
Tesco, the world's second most profitable retailer (after Walmart), has benefited enormously from user behavior analysis. From the user's purchase history of its membership card, Tesco can learn what "category" of guests a user is, such as fast eaters, singles, families with school-going children, and more. Such classifications can provide great market returns. For example, personalized promotions can be sent to users by mail or letter, and in-store staff can also promote products in a targeted manner according to the preferences of the surrounding people and consumption periods.
8. Facebook friend recommendation
Facebook is a social networking giant, and it's worth mentioning friend recommendations. Facebook uses big data to track the behavior of users on its network. By identifying your friends on its network, it can recommend new friends. The more friends a user has, the higher the stickiness between them and Facebook. . More friends means users share more photos, post more status updates, and play more games.
9. The headhunting value of LinkedIn
The LinkedIn website uses big data to make connections between job applicants and job openings. With LinkedIn, headhunters no longer have to make unfamiliar phone calls to potential hires to try their luck, but can find potential hires and contact them with a simple search. Similarly, job seekers can naturally market themselves to potential employers by connecting with others on the site.
10. Walmart’s data gene
In 1969, Wal-Mart began to use computers to track inventory, and in 1974, its distribution centers and various stores used computers for inventory control. In 1983, Walmart started using barcode scanning systems. At the same time, the installation of the satellite system within the company has been completed, enabling real-time two-way data and voice transmission between the headquarters, the distribution center and the shopping mall.
Now Wal-Mart has the world's largest data warehouse, which stores detailed records of every sale of Wal-Mart's thousands of chain stores in 65 weeks, which allows business personnel to understand customers by analyzing purchasing behavior. In April 2012, Walmart acquired Kosmix, a company that researches network social genes, and added social genes to the data genes.