Big data: New arms for electric dealers

Although businesses are well aware that every customer has its own needs, each customer is a market segment. However, for each customer to subdivide, provide personalized service, for most businesses is not realistic, personalized service is only a few high-end customers exclusive. However, the emergence of a new generation of data analysis technologies, represented by large data, "one customer, one market" is becoming a reality on e-commerce websites. Some of these advanced technology E-commerce Web site is through this personalized service, providing including competitive product recommendations, intimate shopping experience, enhance the site's customer conversion rate, for its fierce competition in the market to win the opportunity.

Website Product Accurate Recommendation

Online buyers of e-commerce sites are not unfamiliar with the product recommendation service, in the net buyer will buy a product after the shopping cart, the site will immediately recommend a number of related products, for example, the Internet buyers have just chosen a notebook computer, the bottom of the page or sidebar will recommend some computer bags, wireless routers, memory, mouse and other commodities.

A good recommendation system can greatly enhance the site browsing conversion rate, to bring new opportunities for the site, not only to improve E-commerce website cross-selling ability, but also improve customer loyalty to e-commerce sites. All E-commerce sites know this, so most e-commerce sites or their own development or outsourcing will provide product recommendations. However, most of the products recommended effect is not ideal, browsing conversion rate is not high. One of the important reasons is that because of the limitations of technology and financial strength, E-commerce Web site does not use enough of its accumulated large number of visitors data, and the emergence of large data to bring new technical means, thereby giving birth to the next generation of commodity recommendation system.

Last month, Jingcheng Group's Big data brand ETU the intention to release a specifically for the electrical design of the Precision Recommendation system ETU recommender for E-commerce sites to provide product referral services. Etu Recommende is a soft and hard integrated system based on large data technology, it is based on data mining and analysis, collects the click information of the visitors of the website, and combines the consumer's similar group behavior, simulates the sales staff of the traditional store to offer the product recommendation to the customer, on the one hand helps the visitor to find At the same time can also through the recommendation system to improve the site's click-through and user loyalty.

Of course, the ETU Precision recommendation system is not the only system in the market that uses large data technology to implement a product recommendation, and Beijing percentile Information Technology Limited (hereinafter referred to as the percentage) also uses large data technology to provide product referral services, which are only different from Etu's knowledge of the intended use of prefabricated solutions, Percent of the product is recommended entirely based on the form of cloud service delivery.

However, both prefabricated solutions with large data technology and cloud services are effective in practice. According to ETU, the director of the Courio, Lanmiu underwear using ETU known intent of the commodity recommendation system, sales conversion rate increased 15%~30%, and the recommendation of the percentage point of the bowser website its email push service open rate more than 70%, sales conversion rate reached more than 14%.

Big Data technology to help

The core technology of the new generation product Recommendation system is the big data, which is also a very hot topic nowadays. The so-called large data is a kind of data different from the traditional structured data, it generally has three typical characteristics, namely the data quantity is big, the data type is diverse, the production speed is fast, the data of the E-commerce website is the typical big data.

For example, the commodity recommendation of an E-commerce website involves the historical transaction data of the visitor, the browsing data of the visitor on the website, the browsing information on the other partner's website, and the user's comment behavior, which has a large amount of data and many types. Obviously, the greater the amount of data, the more the correlation between the data and the higher the demand for IT systems. To meet processing requirements, traditional commodity referral systems either invest large sums of money to build their own proprietary BI systems (such as data warehouses) or compromise their recommendations based solely on the data of the visitors ' historical transactions.

The advent of large data-related technologies such as Hadoop has made a difference. It drastically reduces the threshold for data analysis, making it possible for ordinary e-commerce sites to enjoy the benefits of data analysis. According to Courio, compared with the traditional business intelligence system based on data Warehouse, large data technology has obvious advantages.

First of all, large data can shorten the time of data analysis and improve the efficiency of analysis. Usually the construction of data Warehouse must first preprocess the data, that is, ETL (data extraction, transformation and upload), then modeling, then the data in the Data warehouse can be analyzed, usually the analysis should be in days. The large data analysis platform such as Hadoop does not have such a process, all the data collected, whether structural or unstructured data can be directly imported into Hadoop, which can greatly improve the recommendation system refresh rate, and ultimately improve the purchase conversion rate of the electricity quotient. For example, Lanmiu underwear website's product recommendation system can be done every two hours, so as to give the latest recommendation results;

Secondly, based on the Data Warehouse Business intelligence analysis, its construction costs and technical requirements are very high, the financial and technical strength of enterprises is not a small challenge. And Hadoop is open source software, its use of the server is also a general-purpose x86 server, the cost is lower.

Third, large data scalability is very good, as soon as necessary, can add nodes, this is very important to the market for fast-changing electric dealers. From the point of view of the electric quotient, all want the website traffic and the user quantity continues to grow, however, the input of IT system or data platform cannot be linear, because this will "eat" the electricity Shangben to be not much profit.

"Hadoop is a good advantage is that its several times the input can be a few times the performance, closely follow the flow and user growth, its input-output efficiency is very easy to estimate." "It is on this basis that the ETU recommendation system is built on its large data machine, which is the All-in-one machine for Hadoop," Jiang said.

Rewrite marketing rules

In fact, marketing based on large data is becoming a new trend, in the past, based on intuitive or extensive marketing decisions are being replaced by more scientific and accurate large data marketing. Industry insiders predict that the future of enterprise marketing in addition to some of the brand, most of the delivery is under the guidance of large data, the enterprise's consumer groups where the distribution, the company's potential users where? Find them with big data, and then use creative forms to make them "fans" of the business and form sales.

The product recommendation system is also just a kind of application of big data marketing, more and more big data marketing successful cases are emerging, these cases show that who can use the big Data technology in the marketing, who can win the market opportunity. Among them, the simplest and most likely to have large data landing is the breakdown of customers as described above and the implementation of the appropriate customer segmentation of the sales strategy.

Of course, as a starting point, enterprises can also carry out in-depth analysis of customer data. For example, you can integrate consumers in a number of areas (such as shopping, micro-blog, friends, entertainment, etc.) data, through the integration of data to build the overall profile of consumers to accurately predict the new needs of consumers, so as to provide consumers with personalized solutions.

(Responsible editor: The good of the Legacy)