new vs old customers sql - sql

Question

I am trying to get new customer's vs returning customers and for this I have to create multiple tables. Is there a better way to aggregate the data shown like below:
my SQL code looks like below:
---- ALL INDIVIDUALS WHO PURCHASED IN CURRENT WEEK---------
CREATE TABLE PURCHASES_FEB_WK2 AS (Select DISTINCT INDIVIDUAL_ID
from DM_OWNER.TRANSACTION_DETAIL_MV
WHERE BRAND_ORG_CODE = 'BRAND'
and is_merch = 1
and currency_code = 'USD'
AND LINE_ITEM_AMT_TYPE_CD = 'S'
AND TRUNC(TXN_DATE) BETWEEN '10-FEB-19' AND '16-FEB-19')
----------MINIMUM PURCHASE DATE OF ALL CUSTOMERS------------
Create table feb_wk2_min as
Select distinct Individual_ID, MIN(TRANSACTION_DATE) as FIRST_TRANSACTION
from dm_owner.transaction_mv
WHERE BRAND_ORG_CODE = 'BRAND'
and transaction_type_code in ('PR','EP')
group by individual_ID;
------- NEW CUSTOMERS FOR THE WEEK---------
Select Count(distinct B.INDIVIDUAL_ID)
from PURCHASES_FEB_WK2 A
JOIN FEB_WK2_MIN B ON A.INDIVIDUAL_ID = B.INDIVIDUAL_ID
where FIRST_TRANSACTION between '10-FEB-19' and '16-FEB-19'
---- ALL RETURNING CUSTOMERS
SELECT COUNT (DISTINCT INDIVIDUAL_ID)
FROM PURCHASES_FEB_WK2
WHERE INDIVIDUAL_ID IN (SELECT INDIVIDUAL_ID FROM DM_OWNER.TRANSACTION_DETAIL_MV WHERE TRUNC(TXN_DATE) < '10-FEB-19' AND BRAND_ORG_CODE = 'BRAND' AND IS_MERCH = 1 AND line_item_amt_type_cd = 'S' AND STATUS = 'A')
-------NEW CUSTOMERS DOLLAR_VALUE_US------
SELECT SUM(DOLLAR_VALUE_US) FROM DM_OWNER.TRANSACTION_DETAIL_MV
WHERE INDIVIDUAL_ID IN (Select distinct B.INDIVIDUAL_ID
from PURCHASES_FEB_WK2 A
JOIN FEB_WK2_MIN B ON A.INDIVIDUAL_ID = B.INDIVIDUAL_ID
where FIRST_TRANSACTION between '10-FEB-19' and '16-FEB-19')
AND BRAND_ORG_CODE = 'BRAND'
and is_merch = 1
and currency_code = 'USD'
AND LINE_ITEM_AMT_TYPE_CD = 'S'
AND TRUNC(TXN_DATE) BETWEEN '10-FEB-19' AND '16-FEB-19'
-------RETURNING CUSTOMERS DOLLAR_VALUE_US------
SELECT SUM(DOLLAR_VALUE_US) FROM DM_OWNER.TRANSACTION_DETAIL_MV
WHERE INDIVIDUAL_ID IN (SELECT DISTINCT INDIVIDUAL_ID
FROM PURCHASES_FEB_WK2
WHERE INDIVIDUAL_ID IN (SELECT INDIVIDUAL_ID FROM DM_OWNER.TRANSACTION_DETAIL_MV WHERE TRUNC(TXN_DATE) < '10-FEB-19' AND BRAND_ORG_CODE = 'BRAND' AND IS_MERCH = 1 AND line_item_amt_type_cd = 'S' AND STATUS = 'A'))
AND BRAND_ORG_CODE = 'BRAND'
and is_merch = 1
and currency_code = 'USD'
AND LINE_ITEM_AMT_TYPE_CD = 'S'
AND TRUNC(TXN_DATE) BETWEEN '10-FEB-19' AND '16-FEB-19'
To get the quantity and the count of order, I am replacing the sum (dollar_value_us) with count of distinct orders and sum of quantity. Is there an easy way to pivot and combine this code so that I can just copy paste the data in the format (picture attached) I have provided.

Answer 1

Based on the comments, I understand that you want to split the customers into two groups : customers that had their first transactions during the period should be separated from thoses who had transactions before. For each group, you want to count the number of customers and sum the value of the transactions.
NB : your sql code does not show hot to compute qty and count_of_orders, so I left it apart (but this will likely follow the same logic).
Given this sample data:
INDIVIDUAL_ID | DOLLAR_VALUE_US | TXN_DATE | RAND_ORG_CODE | IS_MERCH | CURRENCY_CODE | LINE_ITEM_AMT_TYPE_CD
------------: | --------------: | :-------- | :------------ | -------: | :------------ | :--------------------
1 | 10 | 01-FEB-19 | BRAND | 1 | USD | S
1 | 10 | 10-FEB-19 | BRAND | 1 | USD | S
1 | 10 | 15-FEB-19 | BRAND | 1 | USD | S
1 | 10 | 28-FEB-19 | BRAND | 1 | USD | S
2 | 11 | 11-FEB-19 | BRAND | 1 | USD | S
2 | 11 | 12-FEB-19 | BRAND | 1 | USD | S
3 | 11 | 12-FEB-19 | BRAND | 1 | USD | S
Considering week range from February 10th to 16th included, customer 1 is a returning customer with 2 transactions in the window, and customers 2 and 3 are new customers with respectively 2 and 1 transactions. You would expect the following output:
TYPE_OF_CUSTOMER | COUNT_OF_CUSTOMERS | SUM_DOLLAR_VALUE_US
:------------------ | -----------------: | ------------------:
New Customers | 2 | 33
Returning Customers | 1 | 20
To solve this, you need to set up several levels of aggregation. First, use window function MIN() OVER() to recover the date of the first transaction of each customer. Then, filter on the anlaysis period, split customers into new/returning groups, and aggregate the money spent. Finally, aggregate all results together.
Query:
SELECT
DECODE(is_new, 1, 'New Customers', 'Returning Customers') type_of_customer,
COUNT(individual_id) count_of_customers,
SUM(dollar_value_us) sum_dollar_value_us
FROM (
SELECT
individual_id,
SUM(dollar_value_us) dollar_value_us,
CASE WHEN MIN(txn_date) = min_txn_date THEN 1 ELSE 0 END is_new
FROM (
SELECT
individual_id,
dollar_value_us,
txn_date,
MIN(txn_date) OVER(PARTITION BY individual_id) min_txn_date
FROM transaction_detail_mv
WHERE
rand_org_code = 'BRAND'
AND is_merch = 1
AND currency_code = 'USD'
AND line_item_amt_type_cd = 'S'
) t
WHERE
txn_date >= TO_DATE('10-02-2019', 'DD-MM-YYYY')
AND txn_date < TO_DATE('17-02-2019', 'DD-MM-YYYY')
GROUP BY
individual_id,
min_txn_date
) x GROUP BY is_new
This demo on DB Fiddle demonstrates each step of the computation.