Transactions are caused by consumption behaviors in different scenarios, and it is more meaningful to distinguish the types of trading platforms.
The trading platform itself is also an information matching platform, but the matching information is strongly related to the transaction.
The premise of the transaction is that the platform first b2c email list achieves the effective classification and display of transaction information.
Then how to match or distribute the information is the core. The premise of matching is standardization, which is divided into supply and demand side standardization.
So I tend to classify trading platforms according to matching logic:
①Only provide information to match, and leave the rest to supply and demand to discuss, such as blind date and recruitment.
You can let the demand side choose actively (such as Taobao), or let the supply side choose (such as Didi), or you can give rights to both parties (such as the second-hand market).
I classify such platforms as information matching trading platforms.
②Provide information b2c email list matching, and also provide certain matching logic.
Supply can be matched according to demand, such as Douyin. It is also possible to match demand according to supply, such as cargo pulls.
The depth of matching can be distinguished, providing information screening in a shallow way, and giving the right to actively choose to supply or demand, such as Didi’s order grabbing model and the official account subscription model.
Deeply achieves platform distribution directly, such as Didi's order dispatch mode and Douyin's algorithm recommendation mode.
I classify such platforms as information distribution trading platforms.
Transaction matching rules
The core of improving the transaction rate is the matching rules, that is, how different orders are matched to different users.
Taking the content-based product Douyin as an example, algorithm recommendation can significantly improve the efficiency of information distribution.
The Douyin recommendation strategy is based on two dimensions: clustering of things (if you like A, you may like B), and people b2c email list clustering (users similar to you like A, you may also like A).
So for the trading platform, how to effectively distribute orders?
There are 5 kinds of logic.