Re: how to effectively SELECT new "customers" - Mailing list pgsql-sql

From Jan Ostrochovsky
Subject Re: how to effectively SELECT new "customers"
Date
Msg-id 966514640.595308.1393587852428.JavaMail.root@mobiletech.sk
Whole thread Raw
In response to Re: how to effectively SELECT new "customers"  (David Johnston <polobo@yahoo.com>)
List pgsql-sql
From: "David Johnston" <polobo@yahoo.com>
To: pgsql-sql@postgresql.org
Sent: Thursday, February 27, 2014 4:09:34 PM
Subject: Re: [SQL] how to effectively SELECT new "customers"

Jan Ostrochovsky wrote
> Hello, I am solving following task and it seems hard to me to find
> effective solution. Maybe somebody knows how to help me: We have table
> "purchases" and each one record is identified by "customer_id". We want to
> know not only how many different customers did at least one purchase per
> time period, grouped by time periods (easy task: "COUNT(DISTINCT
> customer_id)" with "GROUP BY period"), but also to know how many NEW
> customers there were. We define new customer as customer_id, which had
> first record in table "purchases" after 12 month of inactivity (no record
> in table "purchases" previous 12 months). I have found one solution, but
> it is very slow and ugly. I tried several other concepts, but without
> success. Any hint could be helpful for me. Thanks in advance! Jano

Without incorporating additional meta-data about the purchases onto the
customer table the most basic solution would be:

SELECT DISTINCT customer_id FROM products WHERE date > (now() - '12
months'::interval)
EXCEPT
SELECT DISTINCT customer_id FROM products WHERE date <= (now() - '12
months'::interval)

---

Another solution:
WHERE ... >12 AND NOT EXISTS (SELECT ... WHERE <= 12)

---

Depending on the frequency that you need to run this query it may be
worthwhile to create a materialized view that captures the necessary data
and then whenever a new sale is generated you simply update that view by
changing the attributes of that single customer.  At any point you can
quickly determine, using the view, which customers were active at the time
of last purchases and which ones were dormant or non-existent.

David J.

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