Big data cases 11-20: Big data for recommended after marriage

11 Ali Xiaoyu and Jushi Tower

Although Ali's balance is in full swing, in fact, Ali Xiaolu really reflects the value of big data. As early as 2010, Ali has established “Taobao Small Loan”. Through the comprehensive evaluation of downstream orders, upstream suppliers and operating credits of loan customers, it is possible to lend money to customers without meeting. This is of course Mining big data on the Ali platform. The data comes from the “Juishi Tower” – a large data sharing platform that creates business value by sharing data resources from various subsidiaries of Alibaba. This product is the result of the big data team integrating the data of all aspects of the Taobao transaction process, and then classifying, storing and analyzing the information based on business understanding and connecting with decision-making behavior.

12 Sears' data integration

In the past, the US retail giant Sears Holdings needed eight weeks to develop a personalized sales plan, but when it was often made, it was no longer the best solution. After painstakingly, I decided to integrate the three brands that I specialize in – Sears, Craftsman, Lands'End customers, products and sales data, use clusters to collect data from different brands, and analyze the data directly on the cluster, instead of the previous Save it in the data warehouse first, avoiding wasting time - first combine the data from all over and then analyze it. This kind of adjustment makes the company's marketing plan faster and more accurate, and can extract value from massive information, but the value is huge and the difficulty is huge: the data needs to be analyzed on a large scale and dispersed in different brands of databases and data warehouses, not only The number is huge and fragmented.

Sears's predicament is very common in traditional enterprises. These entrepreneurs have been unable to understand. Since Internet retailer Amazon can recommend reading bibliographies, recommending movies, recommending products for purchase, why their companies can't do similar things. Things. Phil Shelley, chief technology officer of Sears, said: If a series of complex recommendations are to be of higher quality and require more timely, detailed and personalized data, the traditional enterprise IT architecture cannot To complete these tasks, you need to make up your mind to complete the transformation.

Middle article Light company data entrepreneurial carnival

In this "data feast", is there only a big company's carnival? Not so, light companies engaged in the big data industry will be everywhere. Emerging startups are more focused on providing a single solution by selling data and services, and commercializing and commercializing big data is a model that deserves our attention. This will bring a new wave of entrepreneurship and industrial revolution after portals, search engines and social media, and will have a strong impact on traditional consulting companies.

13 PredPol's Crime Forecast

PredPol works with police in Los Angeles and Santa Cruz and a group of researchers to predict the probability of a crime based on variants and crime data from earthquake prediction algorithms, accurate to 500 square feet. In areas where the algorithm was used in Los Angeles, the distribution of theft and violent crimes fell by 33% and 21%.

14 Tipp24 AG's gambling behavior prediction

Tipp24 AG's betting and forecasting platform for the European gaming industry. The company uses KXEN software to analyze billions of transactions and customer characteristics, and then use the predictive model to conduct dynamic marketing campaigns for specific users. This initiative reduced the construction time of 90% of forecast models. SAP is trying to acquire KXEN, "SAP wants to reverse its long-standing weakness in predictive analytics through this acquisition."

15 Inrix's traffic jam prediction

The variety of participants in transportation is the most valuable area of big data. Traffic flow data company Inrix relies on analytics history and real-time traffic data to provide timely traffic reports to help drivers avoid road jams and help them plan their trips in advance. Inrix's traffic reports are needed for automakers, mobile app developers, transportation companies, and various Internet companies. Giants such as Audi, Ford, Nissan, and Microsoft are all Inrix customers.

16 Panjiva's fashion forecast

Consumers pursue the lifestyle of opinion leaders. Panjiva is using data analysis to predict trends, and even based on global trade. For example, they tracked the badges and socks of Twilight in 41 times, analyzed the influence of the protagonist's costumes on the trend in this movie, and informed the users of the results, suggesting that they should Make the right adjustments.

17 Pandora's Music Recommendations

Pandora, the American online music site, hired some music experts to spend an average of 20 minutes on each of them analyzing a song and giving each song 400 different attributes. If you say you like a song, the program will automatically look for the same song as the song "gene", guess you will like it and recommend it to you using recommendation engine technology. With this human tactic, the Pandora website has analyzed 740,000 songs.

18 Futrix Health's medical plan

Futrix Health is a company that specializes in developing medical solutions for patients with data, from personal health applications installed on smartphones to electronic health recorders used by doctors in clinics and hospitals, and even revolutionary digital genomic data. , both connected to the backend data warehouse. Thereby developing the best hospital choices and medical choices for patients. How to collect a large amount of operational information from a healthcare organization, analyze the patient's condition or treatment effect, and implement any high-efficiency measures to make it more meaningful—the opportunity provided by the big data era is no longer simply collecting the data. It is how to use the data to better understand the world.

19 Retention Science user stickiness

In the retail space, the startup Retention Science has released a platform for e-commerce companies to enhance user stickiness in data analysis and marketing strategy design. Its user modeling engine has self-learning capabilities to design and optimize users by using algorithms and statistical models. Sticky strategy. The user data analysis of the platform is performed in real time to ensure that user behavior predictions are always consistent with actual user behavior updates; at the same time, dynamic promotion of some promotion strategies based on these behavior predictions. RS has currently received $1.3 million in investments from Baroda Ventures, Mohr Davidow Ventures, Double M Partners and a number of prominent angel investors.

20 Recommended after marriage

Jiangsu Zhongyu United Data Technology Co., Ltd. has built such a big data platform – targeting newcomers who are ready to marry and adding businesses related to marriage and shopping. A couple of newcomers took a wedding photo to the Weiwei Bridal Wedding Photo Studio. After registering their own information in real name, they will be uploaded to the big data platform. The big data platform can roughly analyze and judge the follow-up consumer demand of newcomers according to the consumption situation and preference style of the newcomer in the wedding photo studio, and immediately send rewards and promotional text messages. For example, invite them to buy furniture from Red Star Macalline, buy bedding from Red Bean Home Textiles, buy home appliances from Gome, and dine at Hilton Hotel. If newcomers buy Chinese furniture at Red Star Macalline, they prefer China. Traditional culture, they are recommended to buy Chinese household items of red bean home textiles.