Project A hast helped more than 15 of its portfolio companies with setting up Data Warehouses and BI infrastructure in general. Doing so, we developed quite some understanding of the technical and organisational aspects of providing an organisation with data. However, we oftentimes struggled with defining the right KPIs that were relevant for different operational teams and for other use cases such as recommendation, segmentation and prediction. In this talk, I will share some our of the best practices for metrics and attributes of customers, transactions and marketing touchpoints that we developed over the last 5 years. In addition to that, I will talk about the technical aspect of consistency and correctness and about http://commerce-reporting.com, an initiative to share knowledge about reporting best practices.

1. 1
KPIs for e-commerce startups
Dr. Martin Loetzsch, Project A
@martin_loetzsch

2. 2

3. http://commerce-reporting.com
3

4. KPIs?
4

5. KPIs = Aggregations over individual entities
5
▪
▪
▪ All time orders?
▪ Revenue October 1st?
▪ Sales by product?
▪ Allowed query operations
▪ Aggregations (count, distinct-count, sum, avg)
▪ Filtering
▪ Grouping
item
id
order
id
has
voucher
price day product
1 1 20 09-30 Cat
2 1 10 09-30 Dog
3 2 2 20 09-30 Cat
4 3 30 09-30 Cow
5 4 4 10 10-01 Dog
6 4 4 30 10-01 Cow
# Sold items: count(item_id)
# Orders: distinct-count(order_id)
# Orders with vouchers: distinct-count(has_voucher)
Revenue: sum(price)
Avg basket value [Revenue] / [# Orders]
SELECT count(distinct order_id) FROM order_item;
SELECT sum(price) FROM order_item WHERE day = ’10-01';
SELECT count(item_id) FROM order_item GROUP BY product;

6. Dimensional modelling
6
▪ Move redundant categorial data to “dimension” tables
order_item
item_id
order_id
has_voucher
price
day_fk
product_fk
day
day_id
day_name
month_id
month_name
product
product_id
product_name
item
id
order
id
has
voucher
price
day produc
t
1 1 20 930 1
2 1 10 930 2
3 2 2 20 930 1
4 3 30 930 3
5 4 4 10 1001 2
6 4 4 30 1001 3
day
id
day
name
month
id
month
name
930 09-30 9 Sep
1001 10-01 10 Oct
product
id
product
name
1 Cat
2 Dog
3 Cow

7. Analysis entities
7
orders
users
products
price
histories
emails
clicks
…
…
operation
events

8. Customers
8

9. Customer metrics & attributes
9
▪ Customer
▪ Name, phone, email, gender, age, location, company
▪ Take data protection seriously!
▪ Categorization
▪ Customer type/ group / segment / cluster (B2B vs B2C,
frequent buyer, etc)
▪ Profession, Industry, Company size
▪ Newsletter subscription status, marketing opt-in
▪ Time
▪ Registration date, First touchpoint date, First order date,
Last order date, Last touchpoint date
▪ Time since last order, time since X, time from X to Y
▪ Purchase behavior
▪ # Orders lifetime, Revenue lifetime
▪ # Orders first 6 months, Revenue first 6 months
▪ # Orders last 6 months, Revenue last 6 months
▪ # Orders predicted, Revenue predicted
▪ Revenue group
▪ First product, Last product, Most bought product, 2nd most
bought product, 3rd most bought product
▪ First category, Last category, Most bought category, 2nd
most bought category, 3rd most bought category
▪ Order frequency & recency
▪ # Paused subscriptions, last NPS

10. Customer metrics & attributes II
10
▪ Marketing / On-site behavior:
▪ # Visits, Avg. number of page views per visit
▪ First touchpoint channel / partner / campaign etc.
▪ First page, Last page, First product, Last product
▪ First viewed product / category, Last viewed product /
category, Most frequently viewed product / category
▪ # Visits since last order
▪ Sales
▪ Lifecycle status, Funnel status
▪ Sales agent, account manager
▪ # Sales contacts
▪ Time X to Y
▪ Operations
▪ # Cancelled orders, # Returned orders, % Cancelled orders,
etc
▪ # Unpaid orders, Open balance
▪ Fraud status, scores
▪ Average NPS, last complaint

11. 11

12. 12

13. Orders (order items)
13

14. Order item metrics & attributes
14
▪ Time
▪ Order date, Payment date, Accounting date, Customer
registration date
▪ Time since X
▪ Who
▪ All customer attributes
▪ Order rank
▪ What
▪ All product attributes
▪ Vouchers
▪ Subscription information
▪ Operations
▪ Payment method & provider
▪ Status (payment & logistics), Cancellation reason, return
reason.
▪ Actual delivery time, promised delivery time,
▪ NPS
▪ Counting
▪ # Orders, # Items, # Unique products
▪ # First orders, # Second orders, # Second or subsequent
orders
▪ # B2B orders
▪ # Items returned, # Orders completely returned, # Orders
only partially returned

15. 15

16. 16

17. 17

18. Margin structure a.k.a. Margensalat
18

19. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++

20. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++
VAT
gross
revenue
+
+

21. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++
VAT
gross
revenue
+
+
net discount
amount
net revenue
after discounts
+
-

22. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++
VAT
gross
revenue
+
+
net discount
amount
net revenue
after discounts
+
-
CM 1
+
purchase
cost
..
product
cost
+ +
-

23. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++
VAT
gross
revenue
+
+
net discount
amount
net revenue
after discounts
+
-
CM 1
+
purchase
cost
..
product
cost
+ +
-
payment
cost
fulfillment
cost
cost
of sales
CM 2a
+
+ +
-

24. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++
VAT
gross
revenue
+
+
net discount
amount
net revenue
after discounts
+
-
CM 1
+
purchase
cost
..
product
cost
+ +
-
payment
cost
fulfillment
cost
cost
of sales
CM 2a
+
+ +
-
return
cost
CM 2b+ -

25. Margin structure a.k.a. Margensalat
18
net shipping
revenue
net product
revenue
net
revenue
++
VAT
gross
revenue
+
+
net discount
amount
net revenue
after discounts
+
-
CM 1
+
purchase
cost
..
product
cost
+ +
-
payment
cost
fulfillment
cost
cost
of sales
CM 2a
+
+ +
-
return
cost
CM 2b+ -
marketing
cost
CM3+ -

26. Touchpoints, Operations, Products, Sales
19

27. Consistency
20

28. Consistency: Conceptual challenge
21
▪ Similar sounding things in different domains
▪ If they are the same, call them the same
▪ If a non-trivial transformation happened between
cubes, then clearly mark it
▪ Find the right domain for a concept and then transfer
orders
users
clicks
# orders
# orders
# orders

29. Consistency: Technical challenge
22
▪ Single source of truth ▪ Avoid duplicating business logic
application
databases
json files
csv files
apis
reporting
crm
marketing
…
search
pricing
DWH
SELECT sum(total_price) AS revenue
FROM os_data.order
WHERE status IN ('pending', 'accepted', 'completed',
'proposal_for_change');
SELECT CASE WHEN (o.status <> 'started'
AND o.payment_status = 'authorised'
AND o.order_type <> 'backend')
THEN o.order_id END AS processed_order_fk
FROM os_data.order;
SELECT (last_status = 'pending') :: INTEGER
AS is_unprocessed
FROM os_data.order;

30. Consistent dashboards
23
▪ Contribution margin 3a ▪ Use schemas between reporting and database
▪ Mondrian
▪ LookerML
▪ your own
▪ Or: Pre-compute metrics in database
SELECT
order_item_id,
((((((COALESCE(item_net_price, 0) :: REAL
+ COALESCE(net_shipping_revenue, 0) :: REAL)
- ((COALESCE(item_net_purchase_price, 0) :: REAL
+ COALESCE(alcohol_tax, 0) :: REAL)
+ COALESCE(import_tax, 0) :: REAL))
- (COALESCE(net_fulfillment_costs, 0) :: REAL
+ COALESCE(net_payment_costs, 0) :: REAL))
- COALESCE(net_return_costs, 0) :: REAL)
- ((COALESCE(item_net_price, 0) :: REAL
+ COALESCE(net_shipping_revenue, 0) :: REAL)
- ((((COALESCE(item_net_price, 0) :: REAL
+ COALESCE(item_tax_amount, 0) :: REAL)
+ COALESCE(gross_shipping_revenue, 0) :: REAL)
- COALESCE(voucher_gross_amount, 0) :: REAL)
* (1 - ((COALESCE(item_tax_amount, 0) :: REAL
+ (COALESCE(gross_shipping_revenue, 0) :: REAL
- COALESCE(net_shipping_revenue, 0) :: REAL))
/ NULLIF(((COALESCE(item_net_price, 0) :: REAL
+ COALESCE(item_tax_amount, 0) :: REAL)
+ COALESCE(gross_shipping_revenue, 0) :: REAL), 0))))))
- COALESCE(goodie_cost_per_item, 0) :: REAL) :: DOUBLE PRECISION
AS "Contribution margin 3a"
FROM dim.sales_fact;

31. Correctness
24
▪ Do tests
SELECT util.assert_equal(
'The number of orders in the data table should equal the number of orders in the dim table',
'SELECT COUNT(DISTINCT order_id)
FROM os_data.sold_item
WHERE order_item_price > 0;',
'SELECT COUNT(order_id)
FROM os_dim.order;');
SELECT util.assert_almost_equal(
'The sum of prices in the dim table should equal the sum of prices in the data table',
'SELECT sum(order_item_price * order_item_quantity)
FROM os_data.sold_item
WHERE order_item_price > 0
AND coalesce( spryker_order_id, '''') NOT LIKE ''CH%'';',
'SELECT sum(net_product_value + tax_amount_product)
FROM os_dim.order
JOIN os_dim.sales_country ON sales_country_id = sales_country_fk
WHERE sales_country_name <> ''CH'';', 1);
SELECT util.assert_equal(
'The number of orders in the data table should equal the number of orders in the dim table',

32. http://commerce-reporting.com
25

33. Work with us
26
▪ https://www.project-a.com/en/career/jobs
▪ Director of Business Intelligence
▪ Produkt Manager Business Intelligence
▪ Data Engineer / Data Scientist

34. 27
Thank you
@martin_loetzsch