Compress Customer-by-Sufficient-Statistic Matrix
Combines all customers with the same combination of recency, frequency and length of calibration period in the customer-by-sufficient-statistic matrix, and adds a fourth column labelled "custs" (with the number of customers belonging in each row).
bgnbd.compress.cbs(cbs, rounding = 3)
calibration period CBS (customer by sufficient statistic). It must contain columns for frequency ("x"), recency ("t.x"), and total time observed ("T.cal"). Note that recency must be the time between the start of the calibration period and the customer's last transaction, not the time between the customer's last transaction and the end of the calibration period.
the function tries to ensure that there are
similar customers by rounding the customer-by-sufficient-statistic
matrix first. This parameter determines how many decimal places
are left in the data. Negative numbers are allowed; see the
This function was made for compatibility and consistency with the Pareto/NBD function, but will not provide speed gains for the BG/NBD model.
This function only takes columns "x", "t.x", and "T.cal" into account. All other columns will be added together - for example, if you have a spend column, the output's spend column will contain the total amount spent by all customers with an identical recency, frequency, and time observed.
A customer-by-sufficient-statistic matrix with an additional column "custs", which contains the number of customers with each combination of recency, frequency and length of calibration period.
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# Create a sample customer-by-sufficient-statistic matrix: set.seed(7) x <- sample(1:4, 10, replace = TRUE) t.x <- sample(1:4, 10, replace = TRUE) T.cal <- rep(4, 10) ave.spend <- sample(10:20, 10, replace = TRUE) cbs <- cbind(x, t.x, T.cal, ave.spend) cbs # If cbs is printed, you would note that the following # sets of rows have the same x, t.x and T.cal: # (1, 6, 8); (3, 9) bgnbd.compress.cbs(cbs, 0) # No rounding necessary # Note that all additional columns (in this case, ave.spend) # are aggregated by sum.
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