I classify such platforms as information distribution trading platforms. 4. Transaction matching rules The core of improving the transaction rate is the matching rules, that executive list 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 executive list distribution. The Douyin recommendation strategy is based on two dimensions: things are clustered together (you may like B if you like A), and people are clustered (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. 1. Subscription model Here is an example of dripping. If Didi drivers executive list often go from point A to point B, then the driver can actively subscribe to this route and let the platform often recommend orders from point A to point B. For short intra-city hauls, the subscription executive list model may not be reliable, as drivers don’t need a stable line to support themselves. For long-distance logistics, the subscription model is a good model because the mileage is long, time-consuming, expensive.

The number of orders that drivers run each month is limited (basically no more than 10 orders). In theory, the starting point of each order is the same as executive list the end point of the previous order. The driver has the highest transportation efficiency and the lowest cost, but it is often impossible in reality. So what long-distance drivers care about: stable order volume & executive list order lines that can be linked together. Thinking a little more deeply, what drivers need is income and the order volume to stabilize demand. A stable order volume requires a stable owner and a stable supply, and a stable supply leads to a stable line.