Overview
Here I use the word lottery rather than red packet, because only about 1% of the users get the final rewards.
After the last round of lottery, most of the users started to question whether it's a deception.
Here comes a very interesting question, how many percents of people should Alipay choose to win the lottery will be satisfied ? Does it really a magic number or can be calculated and proved to be the most satisfied number?
Here are two models to simulate the group of people.
Granovettor model
Granovetter has done research on a model of how fads are created. Consider a hypothetical mob assuming that each person's decision whether to riot or not is dependent on what everyone else is doing. Instigators will begin rioting even if no one else is, while others need to see a critical number of trouble makers before they riot, too. This threshold is assumed to be distributed to some probability distribution. The outcomes may diverge largely although the initial condition of threshold may only differ very slightly. This threshold model of social behavior was proposed previously by Thomas Schelling and later popularized by Malcolm Gladwell's book The Tipping Point. via wiki
It's a threshold model that is to say there is a threshold to decide if Alipay lottery will be rejected by most of the users.
Stand up and clap hands model
Sorry I forget the real name of this model. In this model, Q is quality of a drama, E is bias, and T is threshold of satisfaction. If Q + E > T, people will stand up and clap hands for a drama. And if x percents of people stand and clap hands, everyone will stand and clap hands.x is called The Tipping Point.
Solution
In this event, two of the most importent values are Q and x. if Q is too small, for example, everyone got 0.1 CNY, it will make this event meaningless and be forgotten. And if x is too small, like now, most of people won't stand up and give a applause.
It needs data to simulate how to give a balance between Q ans x.
Summary
Alibaba has enough resource and data to calculat The Tipping Point with their proud big data tech. But they did not use and made the whole event a failure. Sigh.
