To calculate monetary variable, we can use analytic function sum (amount) over(). Thus, we can combine the quintiles for recency, frequency and monetary to create a RFM code for each customer. This can be done using the following query. RFM code 555 is the best group of customers.
USER_ID CAT STATE_ID CAMPAIGN_DATE CAMPAIGN_ID RESPONSE AMOUNT
---------- -- -------- --------- -------- -------- ----------
1001 A MA 29-MAY-12 A N
1001 A MA 22-MAY-12 A N
1001 A MA 15-MAY-12 A Y 50
1001 A MA 08-MAY-12 A N
1001 A MA 01-MAY-12 A N
1001 B CT 06-JUN-12 A N
1001 A MA 06-JUN-12 A N
1002 B CT 08-MAY-12 A N
1002 B CT 15-MAY-12 A Y 50
1002 B CT 22-MAY-12 A N
1002 B CT 29-MAY-12 A Y 50
1002 B CT 01-MAY-12 A N
with temp_tbl as
(
select
user_id,
min(sysdate-campaign_date) as recency,
count(1) frequency,
sum(amount) as sum_amount
from TBL_DIRECT_MAILING a where RESPONSE='Y'
group by user_id
),
temp_tbl2 as
(
select
a.user_id,
recency,
frequency,
sum_amount from
(select distinct user_id from TBL_DIRECT_MAILING) a,
temp_tbl b
where a.user_id=b.user_id(+)
),
temp_tbl3 as
(
select user_id,
ceil(cume_dist() over(order by recency desc nulls first)*5) recency_quintile,
ceil(cume_dist() over(order by frequency nulls first)*5) frequency_quintile,
ceil(cume_dist() over(order by sum_amount nulls first)*5) amt_quintile
from temp_tbl2)
select user_id, recency_quintile*100+frequency_quintile*10+amt_quintile as RFM_Code from
temp_tbl3 order by user_id;
The result may look like the following.
USER_ID RFM_CODE
---------- ----------
1001 333
1002 555
1003 233
This blog is mostly focused on the technical aspect of data mining.
3 comments:
I have try your code on Microsoft SQL Server 2012 Express version.
It seem have some problem. Below are my code, anyone help to justify what problem appear?
with temp_tbl as
(
select [CreditCardNumber],
min(SYSDATETIME-[TranscationDate]) as recency,
count(1) frequency,
sum([Amount]) as sum_amount
from [dbo].[Transcation] a
group by [CreditCardNumber]
),
temp_tbl2 as
(
select
a.[CreditCardNumber]
recency,
frequency,
sum_amount from
(select distinct CreditCardNumber from Transcation) a,
temp_tbl b
where a.CreditCardNumber=b.CreditCardNumber
)
temp_tbl3 as
(
select[CreditCardNumber] ,
ceiling(cume_dist() over(order by recency desc nulls first)*5) recency_quintile,
ceiling(cume_dist() over(order by frequency nulls first)*5)frequency_quintile,
ceiling(cume_dist() over(order by sum_amount nulls first)*5) amt_quintile
from temp_tbl2),
select CreditCardNumber, recency_quintile*100+frequency_quintile*10+amt_quintile as RFM_Code
from temp_tbl3 Order by CreditCardNumber;
They are Oracle SQL scripts. SQL server may have different syntax.You may need to do some "translation". Good luck!
The SQL function NTILE is designed to support n-tile analysis (quartile, quintile, percentile, etc.) The following example shows how to create RFM cell codes of equal size as recommended by Arthur Hughes. Classic RFM that just concatenates R, F and M quintiles can place a large percentage of the population in an individual cell. This approach divides the customer population into 125 equally sized RFM cells.
SELECT
MEMBERSHIP_NBR,
MAX_TRANSACTION_DATE,
VISIT_COUNT,
AVG_VISIT_AMT,
10*RF +
NTILE(5) OVER (PARTITION BY RF ORDER BY AVG_VISIT_AMT) RFM
INTO ES78700.dbo.MEMBERSHIP_RFM_CELL
FROM
(
SELECT
MEMBERSHIP_NBR,
MAX_TRANSACTION_DATE,
VISIT_COUNT,
AVG_VISIT_AMT,
10*R +
NTILE(5) OVER (PARTITION BY R ORDER BY VISIT_COUNT) RF
FROM
(
SELECT
MEMBERSHIP_NBR,
MAX_TRANSACTION_DATE,
VISIT_COUNT,
AVG_VISIT_AMT,
NTILE(5) OVER (ORDER BY MAX_TRANSACTION_DATE) AS R
FROM
(
SELECT
MEMBERSHIP_NBR,
MAX(TRANSACTION_DATE) MAX_TRANSACTION_DATE,
COUNT(*) VISIT_COUNT,
AVG(TOTAL_VISIT_AMT) AVG_VISIT_AMT
FROM STORE_VISITS S
GROUP BY MEMBERSHIP_NBR
) AS MEMBERSHIP_RFM
) AS MEMBERSHIP_R
) AS MEMBERSHIP_RF
ORDER BY RFM
;
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