The glamour of Big data

According to foreign http://www.aliyun.com/zixun/aggregation/31646.html "> media reports, IDC retail Insights recently released a new report on the retailer's large data and analytical theory. Under the background of omni-directional retailing, the new research investigates the large data of the third party platform in the Information technology field and the relevant data of the analysis pillar.

The third-party information technology platform and its four pillars of analysis, large data and analytics, liquidity and multi-frequency, social and business and cloud services, the ecosystem that it creates in the retail business and IT strategy will continue to evolve. Third-party platforms have been widely used in retailing, creating new business roles, basic competition, application procedures, and analytics.

Combined with all these factors, you can create an information environment where retailers can use the environment to earn customer loyalty. To introduce successful new products to the market, to work with business partners through the supply chain, to strengthen the pool to reduce risk and improve their brand. Of course, to achieve this effectively, retailers need to use large data and analytic theories.

Greg Girard, IDC Retail Insights Project director, said that the smart retail four was divided into customer data resources, social data resources, market data resources and supply data resources. Smart retailing can produce a steady stream of data, creating millions of of transactions and billions of interactions. Investment returns in large data and analysis environments will be monetization through traditional customer loyalty, revenue growth, cost reduction, and new business models.

Even insiders believe that, in the dark rush of big data-driven business revolutions, either learn to use the leverage of big data to create business value or be eliminated by the big data-driven new generation of business.

It is understood that the first story about large data analysis occurred in the United States at the time of the second largest supermarket target, it is simply from the pregnant women used to the special maternity shop, a large number of customers in the high gold content of the customer group-pregnant women to retain. And one of the biggest heroes is the big data analysis.

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? The Target marketing manager, Andrew Pole, thought of the company having a registration form for the baby shower, and began modeling and analyzing the consumer data of the customers in these registration forms, and soon discovered many very useful data patterns. For example, many pregnant women at the beginning of the 2nd pregnancy will buy a lot of 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 storage tablets and other health care 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.

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.

It is hard to imagine that many pregnant women, unaware of the situation, have become the target perennial loyal pump, many maternity products stores are unaware of the bankruptcy. Unaware of the background, big data is driving a strong business revolution in the dark.

According to a survey by Angel Knowledge Network, a North American retail manager from May 2012 to June, only 17% of retail managers did not know the "big data" concept. The rest of the respondents had varying degrees of familiarity with "big data", with 10% saying they understood the idea of "big data", but were unsure how the concept would affect retail sales.

Retailers believe they have benefited the most from "Big Data" in E-commerce and multi-channel sourcing, with 62% per cent of respondents mentioning these areas. The second is marketing (60%), Commodity (44%) and supply Chain (29%) fields.

Retailers have been working on a large amount of data for a long time, and for years barcode and inventory management tasks require information analysis. But "Big data" has challenged retailers who think they have good data analysis capabilities. Of the respondents, 46% per cent thought that dealing with large amounts of data was the biggest challenge, while 34% said that only a large number of data types took up a lot of their attention, and 20% thought that data was too frequent and was a problem for them.

More than half of the retailers have developed "big data" strategies or are developing such strategies. But only about three-tenths of retailers have implemented or are about to implement such strategies. As the potential returns from data analysis are substantial, a significant number of retailers set aside budget space for "big data" strategies, and 44% per cent said they had budgeted for "big data" strategies or planned to make a budget in the next two years.

What are the factors that prevent retailers from putting more resources into the "Big Data" field? Its potential earnings and return on investment remain uncertain, with nearly half of respondents saying these are their biggest challenges and their most important concerns about "big data".

(Responsible editor: The good of the Legacy)