In this example, we will use the TraMineR package to analyze data from a club membership. The clients can choose from three membership levels/tiers—L1 (cheap membership), L2 (general membership), or L3 (VIP membership), and they can obviously cancel their membership. We want to characterize these levels further by age and sex tiers. We have four cohorts defined by the intersection between two age groups (18-25 and 26-45) and sex (F or M).

This club offers a discount after 12 weeks, in order to entice clients to jump into either the L2 or L3 membership (which are obviously more premium, and thus more expensive). We should expect to see the majority of customers on the L1 membership initially, and then observe them jumping to L2 or L3.

1. Import the library:

library(TraMineR)

2. Load the data and set the labels and the short labels for the plots:

datax       <- read.csv("./data__model.csv",stringsAsFactors = FALSE)
mvad.labels <- c("CLOSED","L1", "L2", "L3")
mvad.scode  <- c("CLD","L1", "L2", "L3")
mvad.seq    <- seqdef(datax, 3:22, states = mvad.scode,labels = mvad.labels)
group__     <- paste0(datax$Sex,"-",datax$Age)

3. The seqfplot function can be used to plot the most frequent sequences:

seqfplot(mvad.seq, with.legend = T, border = NA, title = "Sequence frequency plot")

This results in the following output: