Sunday, November 25, 2007

Adding value to sales data: willingness to use service again

Among other things this blog is about saving advantageous solutions for future use. After writing the previous posting Viewing sales data by using XY charts, it came into my mind to compose a more general solution how to add value to your sales data.

Let's start with the following sales table (same as before, products A-Z, etc):

CREATE TABLE salesdata (
product VARCHAR(1),
customerno INTEGER,
market_area INTEGER,
day DATE,
quantity INTEGER);

By using the salesdata table, we create a new table salesdata_prod_cust_pair that summarizes sales for each product-customer pair within a season.

CREATE TABLE salesdata_prod_cust_pair (
product VARCHAR(1),
customerno INTEGER,
market_area INTEGER,
first_day_overall DATE,
first_day_season DATE,
next_day DATE,
count_season INTEGER,
quantity_season INTEGER,
new_customer_flag INTEGER,
use_again_flag INTEGER,
use_again_days_between INTEGER);

The following SQL populates the table:

/* Explore season 2007-Q1 */
INSERT INTO salesdata_prod_cust_pair

SELECT s.product, s.customerno, s.market_area,
MIN(overall.min_day) overall_first_day,
MIN( seasonal_first_day,
MIN(seasonal_next.next_day) next_day,
COUNT(*) count_season,
SUM(s.quantity) quantity_season,
1-SIGN(DATEDIFF(MIN(,MIN(overall.min_day))) new_customer_flag,
SIGN(DATEDIFF(MIN(seasonal_next.next_day),MIN( use_again_flag,
DATEDIFF(MIN(seasonal_next.next_day),MIN( use_again_days_between
FROM salesdata s

SELECT nxt.product, nxt.customerno, MIN( next_day
FROM salesdata nxt,
(SELECT product, customerno, MIN(day) min_day
FROM salesdata
WHERE day >= '2007-01-01'
GROUP BY product, customerno) q
WHERE nxt.customerno = q.customerno
AND nxt.product = q.product
AND > q.min_day
GROUP BY nxt.product, nxt.customerno) seasonal_next
ON s.product = seasonal_next.product
AND s.customerno = seasonal_next.customerno,

(SELECT product, customerno, MIN(day) min_day
FROM salesdata
GROUP BY product, customerno) overall

WHERE s.product = overall.product
AND s.customerno = overall.customerno
AND BETWEEN '2007-01-01' AND '2007-03-31'
GROUP BY s.product, s.customerno, s.market_area;

Please note that you must change red texts according to your database. DATEDIFF() returns days between two dates. 2007-01-01 and 2007-03-31 specify the season to explore; in this case 2007-Q1.

Here's some data:

As you can see:
  • Product A was sold to Customer 4 first time in May 2006, so C-4 is not a new customer during season 2007-Q1, therefore new_customer_flag = 0.
  • Product A was sold to Customer 13 first time on January 5th, 2007, so C-13 is a new customer during season 2007-Q1, thus new_customer_flag = 1. This customer used the product/service again on March 8th, so use_again_flag = 1 and use_again_days_between = 62.

OK. This was only the first part. By using the new salesdata_prod_cust_pair table, you can write in SQL:

SELECT product,
SUM(quantity_season) "Total quantity of items sold",
100*SUM(new_customer_flag)/COUNT(*) "% new customers in this season",
100*SUM(use_again_flag*new_customer_flag)/SUM(new_customer_flag) "% willingness to use product/service again",
AVG(use_again_days_between/new_customer_flag) "Average days before using
product/service again"
FROM salesdata_prod_cust_pair
GROUP BY product;

.. and get the following results:

See the last two columns! For example the willingness to use product/service again is an excellent indicator to monitor how much your customers trust your products.

By using an XY chart you can visualize the data:

Let's analyze:

  • There must be some problem in products U and B. Marketing might be ok (42% new customers), but once used the first time, only 10 % of customers are willing to use the service again.
  • The product C is in the other extreme. When customers use it, they probably love to use it again. Perhaps the teams responsible for products U and B can learn something from the team C?

I think I'm done with the sales data for now. Next time, something else.

1 comment:

Tom said...


i only want to thank you for that good article. It helped me to implement such a analysis into my work.