I'd like to model the evolution of the sales of a store.
Here are the data I have :
Customers are aggregated into monthly cohort depending on the date of the first purchase eg : customers who did their 1st purchase in Jan 2015 are in the cohort 1, customers who did their 1st purchase in Fev 2015 are in the cohort 2.
Sales : aggregated for each Month * Cohort (cohort 4 is not visible because there is no line for only 1 record)
Question: How to predict the spending of the next 2 months, ie :
- the spending of the cohort 1 in its aging 5 and 6
- the spending of the cohort 2 in its aging 4 and 5
- the spending of the cohort 3 in its aging 3 and 4
- the spending of the cohort 4 in its aging 2 and 3
But also for the cohort who don't exist yet :
- the spending of the cohort 5 in its aging 1 and 2
- the spending of the cohort 6 in its aging 1
I have 2 methodologies in mind, but don't know if they're good:
- Polynomial regression by using 2 parameters:
- aging: because there is a clear evolution over the time, up then down.
- cohort number: because more recent cohort seems to spend less.
- Time series : I didn't perform time series recently I'm a bit rust so I would prefer to use something else but I think it could fit with this problem.
Indeed it seems obvious that yt-1 t in yt-2... are good predictors of yt but how to predict spending of future cohort with no history ?