The Commercial Value of Big Data to Retail Industry

The Commercial Value of Big Data to Retail Industry

In the undercurrent of the business revolution driven by big data, either learn to use the leverage of big data to create business value, or be eliminated by the new generation business pattern driven by big data.

The earliest story about big data happened in Target, the second largest supermarket in the United States. Pregnant women are a customer group with high gold content for retailers. But they usually go to specialized pregnant women's stores instead of buying pregnant products at Target. When people mention Target, they often think of daily necessities such as cleaning supplies, socks and toilet paper, but ignore that Target has everything a pregnant woman needs. So what can Target do to intercept these customers from pregnant women's products stores?

To this end, Target's marketers turned to Target's customer data analysis department (Guest Data &; Andrew Pole, the senior manager of analytical services, asked him to build a model to identify pregnant women who are pregnant for the second time. In America, birth records are public. As soon as the baby is born, the newborn mother will be surrounded by overwhelming product discount advertisements. It will be too late for Target to act again, so it is necessary to act when the pregnant woman is pregnant for the second time. If Target can know which customer is pregnant before all retailers, the marketing department can send them tailor-made preferential advertisements for pregnant women and identify valuable customer resources early.

However, pregnancy is very private information. How can I accurately judge which customer is pregnant? Andrew Bohr thinks the target has a registration form for the baby shower. Andrew Bohr began to model and analyze the customer consumption data in these registration forms, and soon found many very useful data patterns. For example, the model found that many pregnant women would buy a lot of odorless hand cream in large packages at the beginning of the second pregnancy; 20 weeks before pregnancy, buy a large number of high-quality tablets and other health care products that supplement calcium, magnesium and zinc. Finally, Andrew Bohr selected the consumption data of 25 typical commodities and constructed the "pregnancy prediction index". Through this indicator, Target can predict the pregnancy of customers within a small error range, so Target can send preferential advertisements for pregnant women to customers in advance.

So, will customers be afraid of receiving such advertisements? Target cleverly avoided this situation. It mixes the preferential advertisements of pregnant women's products with a large number of preferential advertisements of other products unrelated to pregnancy, so that customers don't know that Target knows that she is pregnant. Target's preferential advertisement indirectly made a father in the dark accidentally discover that his high school student's daughter was pregnant, which was a secret and even reported by The New York Times. As a result, the great power of the target big data sensationalized the whole country.

According to Andrew Pole's big data model, Target made a brand-new advertising marketing plan, and as a result, Target's sales during pregnancy showed explosive growth. Andrew Bohr's big data analysis technology has been extended from pregnant women to other sub-customers. From Andrew Pole joining Target in 2002 to 20 10, Target's sales increased from 44 billion dollars to 67 billion dollars.

It is conceivable that many pregnant women have become Target's loyal pumps all the year round without knowing it, and many pregnant women's stores have gone bankrupt without knowing it. In the forgotten context, big data is driving a powerful business revolution. Sooner or later, businessmen have to face a question: whether to rise unconsciously or die unconsciously.

Who is big data?

Big data is hot, but not many people can say clearly what big data is. To truly understand what big data is, we must first look at how Target collects big data.

Whenever possible, Target's big data system will give each customer an ID number. You swipe your credit card, use coupons, fill out a questionnaire, mail a return form, call customer service, open an advertising mailbox, and visit official website, all of which will be recorded in your ID number.

Moreover, this ID number will also record your demographic information: age, whether you are married, whether you have children, city where you live, the distance from your address to your destination, salary, whether you have moved recently, credit card in your wallet, websites you frequently visit, and so on. Target can also buy other information about you from other relevant institutions: race, employment history, favorite magazines, bankruptcy records, marriage history, house purchase records, school records, reading habits and so on. At first glance, you will think that these data are meaningless, but in the hands of Andrew Pole and the customer data analysis department, these seemingly useless data have exploded into the aforementioned power.

In the business field, big data is a huge amount of relevant data about consumer behavior collected like Target. These data are beyond the functions of traditional storage methods and database management tools, and need to use big data storage, search, analysis and visualization technologies (such as cloud computing) to dig out great commercial value.

The Commercial Value of Big Data

Big data is so hot, so many people follow suit and call it big data, but many people not only don't understand what big data is, but also don't know where big data can tap huge commercial value. The blind man's follow-up is doomed to a fiasco, just like rushing to chase social networks and group purchases. So where can big data dig out huge business value? According to the summary of IDC and McKinsey's big data research results, big data can tap huge commercial value in the following four aspects: subdividing customer groups, and then taking unique actions for each group; Use big data to simulate the real situation, explore new needs and improve the return on investment; Improve the sharing of big data achievements in relevant departments and improve the return on investment of the entire management chain and industrial chain; Innovation of business model, products and services. The author calls them four commercial value levers of big data. Before investing heavily in big data, enterprises must analyze the actual situation and strength of these four levers.

1, subdivide customer groups, and then take unique actions for each group. At the beginning of this article, Target's story is a case of this lever. Marketing and service for specific customer groups has always been the pursuit of businesses. The massive data of cloud storage and the analysis technology of big data make it possible to segment consumers in real time and with high cost performance. For example, before the era of big data, to find out the pregnancy situation of a large number of customers, it needs to invest amazing manpower, material resources and financial resources, making this subdivision meaningless.

2. Use big data to simulate the real situation, explore new demands and improve the return on investment. Nowadays, more and more products are equipped with sensors, and the popularity of cars and smart phones makes the data that can be collected explode. Social networks such as blogs, Twitter, Facebook and Weibo are also generating huge amounts of data. Cloud computing and big data analysis technology enable enterprises to store and analyze these data and transaction behavior data in real time, which is very cost-effective. Trading process, product use and human behavior can all be digitized. Big data technology can integrate these data for data mining, so in some cases, we can judge which scheme has the highest return on investment under different variables (such as different promotion schemes in different regions) through model simulation.

3. Improve the sharing of big data achievements in relevant departments and improve the return on investment of the entire management chain and industrial chain. Departments with strong big data capabilities can share big data achievements with departments with weak big data capabilities through cloud computing, the Internet and internal search engines to help them create business value by using big data. This leverage case is a story about Wal-Mart.

Wal-Mart has developed a big data tool called Retail Link. Through this tool, suppliers can know the sales and inventory situation of each store in advance, so that they can replenish their own goods before Wal-Mart gives instructions, which can greatly reduce the shortage situation and the overall inventory level of the supply chain. In this process, suppliers can control the display of goods in the store more and improve their product knowledge through more contact with the staff in the store; Wal-Mart can reduce inventory costs, enjoy the fruits of employees' product knowledge improvement, and reduce the investment in in-store product display. Taken together, the whole supply chain can improve the service quality with lower cost, and the brand value of suppliers and Wal-Mart can also be improved. By sharing big data technology throughout the supply chain, Wal-Mart has completely changed the productivity of the retail industry.

4. Innovate business models, products and services. Big 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 a case. Tesco has collected a huge amount of customer data. By analyzing the massive data of each customer, Tesco will make an extremely accurate assessment of the credit degree and related risks of each customer. On this basis, Tesco launched its own credit card, and in the future, Tesco has the ambition to launch its own deposit service.

The Business Revolution of Big Data

Through the above four levers, big data can generate huge business value. No wonder McKinsey said that big data will be the fifth largest factor of production after the traditional four factors of production. Big data will greatly promote market share, cost control, return on investment and user experience, and the advantages of big data will become the most worthy comparative competitive advantage for enterprises. According to McKinsey's estimation, if retailers can give full play to the advantages of big data, their operating profit margin will have an average annual growth space of 60%, and their production efficiency will achieve an average annual growth rate of 0.5%- 1%. At the moment when the concept of big data is hot, people find that business giants like Wal-Mart, Target, Amazon and Tesco have quietly used big data technology for many years, using big data to drive marketing, drive cost control, drive product and service innovation, drive management and decision-making innovation, and drive business model innovation. Many business people lament the fierce competition, but the mystery of Target has finally been solved.

In the undercurrent of the big data-driven business revolution, keeping pace with the times is not only an arty card war, but also learning to use the leverage of big data to create business value or being eliminated by the new generation business pattern driven by big data. This is a godsend opportunity, and it is a battle of life and death. The winner will be the protagonist of China's industrial chain upgrading, and the loser will only have regrets.

The above is what Bian Xiao shared for you about the commercial value that big data brings to the retail industry. For more information, you can pay attention to Global Ivy and share more dry goods.