Zhangbei Intelligent POS VP Chen Tengfei participated in the 2015 China Mobile Internet Finance Annual Conference and the establishment of the Internet Finance Committee of the Guangdong Internet Society, and delivered a keynote speech "Big Data Finance under Mobile Payments". For the C-side big data financial application scenarios, such as staging, such as white bars, is it only the platform can do it, is it only Jingdong, Tmall can provide this service to their users. Zhangbei Intelligent POS VP Chen Tengfei participated in the 2015 China Mobile Internet Finance Annual Conference and the establishment of the Internet Finance Committee of the Guangdong Internet Society, and delivered a keynote speech "Big Data Finance under Mobile Payments". The following is a speech record: Hello everyone! My name is Chen Tengfei and I am from PDA POS VP. We are a data platform for store payment and e-marketing. There will be a lot of data for offline merchants. I will talk about some of the problems seen in our perspective. Mobile payment is based on mobile phone's WeChat payment, Alipay payment, or various payment data generated by SMS on the mobile phone. Mobile payment is a variety of data generated between consumers and e-commerce platforms, O2O platforms, and physical store stores. The data we see is generally the customer unit price, the average spending power of customers and consumers. When they accumulate, they form the flow of each store and the operation of each store. This is a summary of the individual categories of the store, as well as each consumer's preference and history for the category, as well as frequency and geographic location. The geographical location is where the consumer initiates payment, whether at home or in a store, from offline or online. The fifth is time preference, the time period in which these payments occur, including the habits of each individual consumer, as well as the peak seasons of each store operation. After classifying the previous data, we classify it into two types, one is called B-side data, and the other is called C-side data. The B end is based on the merchant side, with the data of the merchant attribute, we can see the customer unit price, how the store's consumption level. Whether we enter this store is one hundred yuan or ten thousand yuan, it is an ordinary store or a luxury store. Followed by category, frequency, privilege promotion. The original mobile payment is to pay for one thing, leaving the data to be paid at most. We have added privilege promotion to add all the marketing activities of the store to the mobile payment link. For example, when I use mobile phones to pay, I use the electronic coupons of this store. Or I use an e-coupon card every time I go to this store. The previous coupon card can't get through the payment data of the store. New data is now formed through mobile payments. What the C side sees is the consumer's spending power, preferences, and his privileges. We saw a consumer. We didn’t know that Chen Tengfei’s person was outside so many stores. How many stores did he have? Now that we have added a new label, we can know that Chen Tengfei’s 20 stores in Guangzhou have a total of 1,000 yuan, and the history has already used 5,000 yuan. Based on the data classification mentioned above, what we did was mobile payment +. The first one is the + e-coupon, which is affixed to the consumer's head and on the head of the store. The second is to add orders. The original order of the consumer is to contact the merchant through some platforms, such as group buying platform. Now through our product, it is possible to connect directly to the merchant, and the data we see on the backstage is more accurate. Each consumer's order is of concern to each merchant, and we can see these orders. The third is to introduce the flow, we are not just doing payment, we are doing marketing, so that we can bring customers to the store. Behind all, we can see some of the data we are generating now. This data is the data generated by our products more than a year ago. It may not be particularly large, but it has already shown us the prototype of big data. The first is that we can now make 40 million transactions a year. Merchants use our products to make mobile payments in their stores with customers. Order delivery, we are now about 20 million. The privilege promotion includes the distribution of the members to collect usage data, and we have already achieved more than 100 million data strips. In the three years of development of the entire industry, we predict that in the next three years, order data can be more than 200 million, and privileged data can be more than 30 billion. Based on this data, what we want to do, how to help these stores, use this data to contact financial institutions, or it uses this data to serve its consumers. We have two directions. The first one is a financial company for the B-end. Our data has a merchant attribute, and we can see its flow. For example, it is a food and beverage store. It is in the food and beverage industry of all my palms. What is its status, how many members and how many customers? We use this data to make a comprehensive evaluation of the business, and then to connect with the third party. Financial institutions, to provide loans to them. It is very difficult for you to objectively evaluate these small and micro enterprises. We just want to use its stores because it can't run, and it is an objective evaluation of the data that happens every day to help it find more. The right source of funding. For the C-side big data financial application scenarios, such as staging, such as white bars, is it only the platform can do it, is it only Jingdong, Tmall can provide this service to their users. There is no white strip in the store, at most, credit card, installment payment. Here I also have store membership properties, including the transactions of members in it, and the transactions in other stores. The loyalty of its members in it, including the historical data generated by this member in all palm-street businesses, we can also help our business to objectively evaluate whether this customer has the ability to spend and have the ability to repay. Can we Give this user some relevant financial services and provide instalment loans. We also want to use our big data, combined with third-party platforms to help merchants provide financial services to customers. From the perspective of a physical store, we are serving physical stores, how to use the perspective of mobile payment plus privileges, plus unprecedented data related to stores and individuals, to form an evaluable and explicit data. To dock the financial industry and realize the further extension of the service scope of the physical store. The multi-channel mobile payment data of the store under the collection line is tagged to each consumer's head. Based on the merchant's mobile payment data, we will work together with financial institutions to provide financial services to B-side and C-end customers.
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On November 17th, organized by the GMCS Committee, hosted by the Guangdong Internet Industry Association, and the “2015 Global Mobile Internet CEO Summit and the 6th China Mobile Application Developers Conference†hosted by Ai Media Consulting Group on November 17-18 It was grandly opened at Halls 5 and 6 on the third floor of Guangzhou Pazhou Poly Exhibition Hall. The theme of the conference is "Internet +, Subversion or Subversion". There are 1 main forum and 9 sub-forums. Internet + transformation, mobile health care, internet finance, digital marketing, big data applications, new smart hardware, 90 Hot topics such as entrepreneurship and new three-board venture capital investment will be staged.