How do business giants play big data?

In the dark rush of big data-driven business revolutions, either learn to use the leverage of big data to create commercial value, or to be eliminated by a new generation of big data-driven business patterns.

The first story about big data took place at Target, the second-largest supermarket in the United States. Pregnant women are a very high customer base for retailers. But they usually go to specialized maternity stores instead of buying pregnancy supplies at Target. When people mention target, they tend to think of daily necessities such as cleaning supplies, socks and toilet paper, but ignore everything the target has for pregnant women. So what can target do to keep this segment of customers out of the hands of the maternity products store?

To this end, target marketers have turned to Andrew Pole, senior manager of Target's customer data Analysis Department (Analytical Services), asking him to build a model Confirm them in the 2nd trimester of pregnancy. The birth record in the United States is open, and when the baby is born, the mother of the newborn is surrounded by a deluge of product offers, and Target is late for action and must be in the 2nd trimester of pregnancy. If Target is able to find out which customer is pregnant before all the retailers, the marketing department can send them a tailored maternity offer early on, outlining valuable customer resources.

But pregnancy is a very private message, how can you accurately determine which customer is pregnant? Andrew Pole thought of a registration form for the baby shower at target. Andrew pole began modeling and analyzing the consumer data of the customers in these registration forms, and soon discovered many useful data patterns. For example, the model found that many pregnant women at the beginning of the 2nd pregnancy will buy many large packaged fragrance-free hand cream, in the first 20 weeks of pregnancy to buy a large number of supplements calcium, magnesium, zinc, such as good health products. Finally, Andrew Pole selected 25 typical consumer data to build a "pregnancy prediction Index", through which target can predict the client's pregnancy in a very small margin of error, so target can send a pregnant woman's offer to the customer early.

Would it be scary for customers to receive such an ad? Target was smart enough to avoid the situation by mixing the promotional ads for maternity supplies with a host of other ads that are not related to pregnancy, so customers don't know that target knows she is pregnant. In secret, Target's offer of this kind of preferential advertising indirectly caused an ignorant father to accidentally find his high school daughter pregnant, the matter was even reported by the New York Times, the result of target data of the great power of the United States.

According to Andrew Pole's big data model, Target has developed a new advertising marketing program, resulting in explosive growth in Target's pregnancy supplies sales. Andrew Pole's large data analysis technology has been extended from the customer segment of the pregnant woman to a variety of other customer segments, from 2002 to 2010, when Andrew Pole joined target, target sales rose from $44 billion to $67 billion.

What we can imagine is that many pregnant women, unaware of the situation, have become the target's perennial loyal pump, and many maternity products stores are bankrupt without knowing it. Unaware of the background, the big data is driving a strong business revolution in the dark, businesses will sooner or later have to face a problem is: in the unaware of the rise, or in ignorance of the demise.

Who's the big data?

Big data is hot, but few people can say what big data is. To really figure out what big data is, we first have to see how target collects large data.

Whenever possible, Target's large data system will give each customer an ID number. You swipe your credit card, use coupons, fill out questionnaires, mail return orders, call Customer service calls, open AD mail, visit the website, all of which will be recorded in your ID number.

And this ID number will also be taken with your demographic information: age, whether you are married, have children, live in a city, address from Target, salary, recent move home, credit card in your wallet, frequently visited URLs, etc. Target can also purchase your other information from other relevant agencies: race, employment history, favorite magazines, bankruptcy records, marital history, home purchase records, school records, reading habits, etc. At first glance, this data makes no sense, but in the hands of Andrew Pole and the Customer data analysis Department, these seemingly useless data burst into the powerful power of the foregoing.

In the business world, Big Data is a collection of data about consumer behavior that is collected as target. These data transcend traditional storage methods and the capabilities of database management tools, and must use large data storage, search, analysis and visualization technologies (such as cloud computing) to tap into the great business value.

Commercial value of large data

Big data so fire, so many people with the wind, say will pro data, but many people not only do not understand the big data is what the problem, also do not know where the big data can go to what areas to excavate a huge business value. Such a elephant is doomed to fail miserably, like a swarm of social networks and group buying. So where does big data dig up huge business value? Based on a summary of the results of IDC and McKinsey's large data studies, large data can be found in the following 4 major areas of business value: Segmentation of customer groups, and then tailored to each group to take a unique action; Use large data to simulate reality, explore new needs and increase return on investment To improve the sharing of large data in all relevant departments, improve the return on investment of the whole management chain and industry chain, and innovate the business models, products and services. The author referred to them as 4 commercial value levers of large data. Enterprises must make a clear analysis of the actual situation and strength of these 4 levers before they invest in big data fields.

1. Segment customer segments, and then tailor specific actions to each group. The target story at the beginning of this article is the case of leverage, targeting specific customer groups for marketing and service is the pursuit of business. Cloud storage of massive data and large data analysis technology makes real time and extreme segmentation of consumers is highly cost-efficient possible. For example, before the big Data era, to understand the large number of customers pregnant, to invest in astonishing human, material, financial resources, so that the subdivision of the business significance of the action.

2. Use large data to simulate reality, explore new needs and increase return on investment. Now more and more products are equipped with sensors, the popularity of automobiles and smartphones makes the collection of data can be explosive growth. Social networks such as blogs, Twitter, Facebook and Weibo are also generating huge amounts of data. Cloud computing and large data analysis techniques enable businesses to store and analyze data in real time, along with transactional behavior, in a cost-efficient manner. Transactional processes, product use, and human behavior can be computerized. Large data technology can integrate these data for data mining, so that in some cases, through model simulation to determine the different variables (such as different areas of different promotional programs) in the case of the highest return.

3. Improve the sharing of large data in the relevant departments, improve the overall management chain and the investment rate of the industry chain. Large data-capable departments can share large data and large data-poor departments through cloud computing, the Internet and internal search engines to help them create business value with large data. The leverage case is a story about Wal-Mart.

Wal-Mart has developed a large data tool called Retail link, which allows suppliers to know in advance how each store sells and stores, so that it can replenish itself before the Wal-Mart orders, which can greatly reduce the condition of the shipment and the overall inventory level of the supply chain. In this process, the supplier can more control of the goods in the store furnishings, through and in-store staff more contact to improve their product knowledge; Wal-Mart can reduce inventory costs, enjoy the product knowledge of employees to improve the results, reduce the store of merchandise display input. In combination, the entire supply chain can improve service quality in the context of cost reduction, and suppliers and Wal-Mart brand value are also upgraded. By sharing large data technologies across the entire supply chain, Wal-Mart has detonated the manufacturing efficiency revolution in retailing.

4. Innovation in business models, products and services. Large data technology enables companies to strengthen existing products and services, create new products and services, and even create new business models. This lever will cite Tesco as the case. Tesco has collected a large number of customer data, through the analysis of a large number of customer data, Tesco for each customer's credit level and related risks will have a very accurate assessment. On this basis, Tesco launched its own credit card, and in the future Tesco has ambitions to launch its own deposit service.

The commercial revolution of large data

With the above 4 levers, big data can produce huge business value, and no wonder McKinsey says big data will be the 5th largest factor of production after the traditional 4 major factors. Large data on market share, cost control, input rate of return and user experience will play a great role in promoting, large data advantage will become the most important comparative competitive advantage of enterprises. According to McKinsey's estimate, if the retailer can give full play to the advantages of large data, its operating profit margins will have an annual growth of 60% space, productivity will achieve an average annual growth rate of 0.5% to 1%. When the concept of big data is scrambled, it has been found that business giants like Wal-Mart, Target, Amazon and Tesco have quietly used large data technology for many years, using large data to drive marketing, drive cost control, drive product and service innovation, drive management and decision-making innovation, Drive business model innovation. Many of the business pride of the competition, but Target's puzzled mystery has finally broken.

In the dark rush of large data-driven business revolutions, it is not just a arty battle to keep pace with the times, either by learning to use the leverage of large data to create business value or by being eliminated by the large data-driven new generation of business. This is a godsend and a war of life and death. Winners will be the beikewen of China's industrial chain upgrade, the losers have only regrets.