Questions:
We look at weighted average because we want to see how retention is changing over
time. This will show if our company is truly creating stickier users or not that are
retained and increasingly relying on our service in this case (door to door delivery
of products—currently mostly food). Basic inisights show if the company is
becoming more and more useful to a user. We can also see if the company’s
activation of new users increases. If it does then the value of our product to a new
user increases relative to old users and that means a higher investment of time,
energy, and resources from our users.
Here is a graph to show that:
As you can see the month 2 retention to month 3 retention to month 4 retention and
so on increases over time month over month. Each subsequent line from bottom to
top shows how the second and third and so forth’s month has evolved.
From a business perspective this shows us how different company priorities
correspond to our retention. Does the dip in June suggest that the product had
significant outages or is June a low month for people to come back and want to
order (maybe it summertime). Linking our actions to our data give us a better
picture of product-market fit. This paints the picture that our product is more
valuable to customers over time.
Exercise
I found it much neater if the exercise allowed usage of GitHub code.
1. Use https://github.com/ankane/groupdate.sql
SELECT GD_MONTH (actual_delivery_time, SF) AS MONTH,
COUNT *
FROM consumer_delivery
GROUP BY MONTH;
CONSUMER_DELIVERY
Month
2014-11-01 00:00:00
2014-12-01 00:00:00
2015-01-01 00:00:00
2014-02-01 00:00:00
Count
W
X
Y
Z
2. Assuming date format is 4-digit-year/2-digit-month/2-digit-day
SELECT GD_MONTH (actual_delivery_time, San Francisco) AS month,
COUNT (consumer_delivery.id) AS monthly_total_deliveries,
FROM consumer_delivery
JOIN consumer_consumer
ON consumer_delivery.id = consumer_delivery.id
WHERE DATE_FORMAT(consumer_delivery.actual_delivery_time, ‘%Y/%m’)=
‘2014/11’
GROUP BY restaurant_restaurant.market_id, Month
Month
2014/11
2014/12
2015/01
2015/02
Market
1
2
4
5
monthly_total_deliveries
3
22
55
88
3.Create a new table with join function with all fields and the columns “Month” and
“monthly_total_deliveries” we produced in #1 and #2 called Retention and name the
columns the same name for simplicity:
SELECT
Retention.months
Consumer_delivery.*,
SUM(monthly_total_deliveries) FROM consumer_delivery AS total_deliveries,
Retention.monthly_total_deliveries,
consumer_consumer.id,
Consumer_delivery.monthly_total_deliveries AS total_deliveries,
consumer_delivery.actual_delivery_time,
Retention.monthly_total_deliveries/retention.total_deliveries*100 AS
Retention.Percentage
FROM
SELECT (
DATE_FORMAT(consumer_delivery.actual_delivery_time, ‘%Y/%m’)
AS Cohort,
COUNT (DISTINCT consumer_consumer.id) AS total_users,
FROM consumer_consumer
JOIN consumer_delivery ON consumer_consumer.id=
consumer_delivery.id
GROUP BY cohort, months) AS Retention.cohort
JOIN
( SELECT DATE_FORMAT(date, ‘%Y/%m’) AS Cohort
Count(id) AS Total
FROM consumer_consumer
GROUP BY cohort ) AS retention.Total ON
retention.cohort=consumer_delivery.monthly_total_deliveries
WHERE Retention.Retention_Percentage BETWEEN 0 AND 100
AND Retention.Month< Date_FORMAT(NOW(), ‘%Y/%m’)
4. See above. We will cohort our users by the # of month they are in using a
DATEDIFF function, subtract the most recent event firing date from the date from
consumer_ordercart.is_first_order_cart, divide by 30.4 (30.4 is the average days in a
month) inserted below as indicated:
SELECT ( INSERT DATEDIFF FUNCTION HERE
DATE_FORMAT(consumer_delivery.actual_delivery_time, ‘%Y/%m’)
AS Cohort,
COUNT (DISTINCT consumer_consumer.id) AS total_users,
FROM consumer_consumer
JOIN consumer_delivery ON consumer_consumer.id=
consumer_delivery.id
GROUP BY cohort, months) AS Retention.cohort
5.
If you want to see by a particular market:
See #3 but this time add
AND restaurant_restaurant.market_id=1
If you want to group by market:
See #3 but this time add
GROUP BY restaurant_restaurant.market_id, Month