We have a longitudinal panel of X users with their online spending patterns and are trying to measure certain metrics within the panel. We have time series information about the users such as their total online spending, browsing habits, spending per online merchant etc. We also have cross sectional fixed data about the user like their geolocation, some demographic info etc. We are trying to look at a certain time series metric across the population of say X users.
Examples of what we are trying to measure
- Total growth rate of spending month over month
- Spend per transaction month over month
- Spend per merchant per month
- Other monthly (or period of choice) metrics
If all X users were reporting in the panel the entire time the exercise is easy, we simply calculate metric we need.
However only a small percent (15%) of the panel is reporting the entire length of the panel. Most users come into the panel late or drop out early. For each individual in the panel, the exact lifespan in the panel is fairly random. Moreover, some month, their usage is not complete, its partial and thus should not count, but this is a secondary concern.
The primary challenge is how to accurately calculate the metrics in question given this setup. One solution would be to construct a panel of users who are only present in the panel the entire length of the panel (lets call it Full Life User Panel). This would be a small % of users and assume that the users that are not in that panel behave in the same way as the users who are.
We are not aiming to measure the effect of one set of parameters on another. I.e. we are not trying to predict the spending at a specific merchant given information about the users’ other spending patterns and fixed attributed.
We can try to cluster the users using longitudinal clustering or some kind of latent growth curve analysis to “impute” the missing data. I haven’t found any landmark canonical material on this topic and would appreciate any help in addressing the question or references.