Some problem with the calculation of monetary value

Hey, I’m confused about the calculation of monetary value. It seems that it is defined as average transaction value = Total Monetary value/ transaction frequency. I don’t know whether this frequency means the number of distinct purchase date (if I use daily data) or purchase times. For example, I test the function calibration_and_holdout_data() with the following dataset.

	user_id	purchase_date	net_gmv
3	2	2017-01-01	110
4	2	2017-01-05	120
0	1	2017-11-19	100
1	1	2017-11-19	150
2	1	2017-12-19	200
7	1	2017-12-19	300
6	1	2017-12-20	250
5	3	2018-01-01	150
8	1	2018-01-01	500
9	1	2018-01-01	700
10	1	2018-02-01	50
11	2	2018-09-01	125
12	2	2018-09-02	100

And I run the function like this:

summary_cal_holdout = calibration_and_holdout_data(
    data, 
    'user_id', 
    'purchase_date',
    monetary_value_col='net_gmv',
    calibration_period_end='2017-12-31',
    observation_period_end='2018-09-25'
)  

The result I got is like this:

user_id	frequency_cal recency_cal	T_cal monetary_value_cal frequency_holdout  monetary_value_holdout	duration_holdout							
1	2.0	31.0	42.0	375.0	2	416.666667	268
2	1.0	4.0	364.0	120.0	2	112.500000	268

The frequency cal is 2 and the total repeat purchase value for user id =1 in calibration time is: 200+300+250=750. However, there are 3 transactions. But the monetary value calculated by this function is 375.5 which equals to 750/2. For the calibration period, the monetary value = total purchase value/frequency.

However, when we look at the holdout period. For user_id = 1, the frequency = 2 and the total purchase time=3, total_purchase_value=150+500+700=1350. The monetary value = 416.67 which equals to total purchase value/total purchase times instead of frequency. Are we supposed to use a different equation for the calculation of monetary value in calibration and holdout period???

With this confusion, I could not understand what exactly are these two functions predicting:

1. bgf.conditional_expected_number_of_purchases_up_to_time()

   • Should this be frequency or purchase times. If one day one user purchased two times, should this be counted as 2 or 1?

2. ggf.conditional_expected_average_profit()

   • For the average profit here, should it be total purchase value/total transaction times or total purchase value/frequency?

Thanks a lot for the explanation!!!

[aggie13]

This doesn’t seem right. seems like a bug!

[Trollgeir]

This is because summary_data_from_transaction_data() aggregates the rows with the same days.

0 1 2017-11-19 100 1 1 2017-11-19 150 - > 2017-11-19 250

2 1 2017-12-19 200 7 1 2017-12-19 300 - > 2017-12-19 500

6 1 2017-12-20 250 - > 2017-12-20 250

The first transaction is always ignored for the RFM calculations (Only used for T if it’s the only transaction).

Hence you’re only left with averaging 500 and 250, with the frequency of 2. Hence 750/2 = 375.5