Seeking guidance on approach.
I have some marketing spend data along with historic target variable revenue.
If management would like to estimate online revenue over the next 3 months given a media plan (A document showing which dollars will be spent on which channels), would you use a regression or e.g. decision tree model, or would you go the time series route?
Some data:
eg_data <- data.frame(
revenue = round(rnorm(100, 10000, 100),0),
youtube_spend = round(rnorm(100, 100, 10),0),
facebook_spend = round(rnorm(100, 200, 20),0),
tv_spend = round(rnorm(100, 5000, 1000),0)
)
# create a date range and combine to data frame
yesterday <- Sys.Date() - 1
yesterday_99 <- yesterday - 99
date_range <- seq(yesterday_99,yesterday, by = '1 day')
eg_data <- cbind(eg_data, date_range)
head(eg_data)
revenue youtube_spend facebook_spend tv_spend date_range
1 9938 97 227 4781 2017-10-24
2 9882 96 172 4363 2017-10-25
3 9990 101 184 6514 2017-10-26
4 9970 93 156 4140 2017-10-27
5 9955 99 215 4377 2017-10-28
6 9951 104 250 6716 2017-10-29
I could build a linear model where revenue is a function of youtube, facebook and tv spend.
However, if I have a date field, should I use it? What is the conventional approach here if management want to predict sales over the next month?
If I do go the time series route can I include the spend variables?
Putting the question another way, is it possible (or even advisable), to combine a regression model with a time series in some way?
