Why Automatic Vending Machines Are the New Retail Landmark (Part II)







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From orders to target customers: offline consumption data are disconnected with consumers before the popularity of mobile payment. It is very difficult to deeply operate users and improve the conversion rate because they do not know who are the users and what are their consumption preferences. With the popularity of mobile payment, offline consumption transforms from orders to target customers. Businesses obtain user ID and consumption information through offline mobile payment. After the online and offline consumption records of future consumers are fully recorded in Big Data, the user profile of a single user will be clear enough. Through the big data analysis, the online and offline consumption conversion rate of users can be greatly improved, and the value of future data is huge.



The features of offline traffic entrance, high-frequency transaction, fixed demand, big data and being closer to users are exactly what the e-commerce needs most.



The integration of online and offline business channels will produce greater value in the future, so we see a grand scene of e-commerce giants marching into the automatic vending machine market.



What can be done to improve the integration of the e-commerce and automatic vending machines?



Offline mobile payment can record each user's ID and consumption record. E-commerce can analyze the user's past consumption data through the background transaction records, and supply the goods frequently purchased online by the users to the offline vending machines in advance, so as to promote the transformation of consumers to products. In this vein, e-commerce has obtained more offline distribution channels, while automatic vending machines have also featured higher conversion rate. These could not only realize the growth of sales volume, but users can purchase their favorite goods via more channels online and offline.



This is an in-depth and user-centered reconstruction of online and offline retails based on the big data of user transactions between e-commerce and automatic vending machines.




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