我有一个来自客户级别的数字服务的数据集,并希望对使用量随时间的减少进行建模。我想解释一下客户在服务中执行的活动(例如,与其他用户的消息传递)使用率的下降情况。
我正在寻找对这种情况进行建模的最佳方法,同时还要考虑长期影响(我拥有每位用户50至300天的数据)。
这是数据集的外观:
da <- data.frame(custid = c(1,1,1,1,2,2,2,2), dayid = c(1,2,3,4,1,2,3,4), active = c(1,1,0,0,0,1,0,1), secondsused = c(200,100,0,0,0,600,0,60), n_messages = c(6,1,0,0,0,12,0,7))
da
custid dayid active secondsused n_messages
1 1 1 1 200 6
2 1 2 1 100 1
3 1 3 0 0 0
4 1 4 0 0 0
5 2 1 0 0 0
6 2 2 1 600 12
7 2 3 0 0 0
8 2 4 1 60 7
custid = customer identifier
dayid = days since first usage
active = Was the user active yes (1) vs. no (0)
secondsused = Number of seconds the user has used the service on a day
n_messages = Number of messages the user has sent to other customers
我已经考虑过隐藏的马尔可夫模型。你觉得呢?