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![KPIs = Aggregations over individual entities
5
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▪ 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;](https://image.slidesharecdn.com/2017-04-27kpisforecommerce-170509212632/85/KPIs-for-e-commerce-startups-5-320.jpg)
Uploaded byMartin Loetzsch
KPIs for e-commerce startups
The document discusses key performance indicators (KPIs) essential for e-commerce startups, focusing on various metrics such as orders, revenue, customer behavior, and marketing effectiveness. It outlines methods for aggregating and analyzing data using dimensional modeling and emphasizes the importance of categorizing and protecting customer data. Additionally, it addresses consistency and correctness in data reporting and the significance of a single source of truth for accurate business insights.
KPIs for e-commerce startups
- 1. 1 KPIs for e-commercestartups Dr. Martin Loetzsch, Project A @martin_loetzsch
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- 5. KPIs = Aggregationsover 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 ▪ Moveredundant 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
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- 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
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- 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
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- 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+ -
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- 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 ▪ Contributionmargin 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 SELECTutil.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',
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- 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
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