Ensure the business is operating smoothly.
Which products have the highest rate of return?
Returns can be costly. Find out which products hurt your margins.
Build the Analysis
DATASET
ALL Purchased Product
FIRST AFTER Returned Product (where purchased product activity_id = returned product activity id)
ANALYZE
Decrease Conversion Rate to Returned Product After (%)
by analyzing the influence of Purchased Product Sku
by month and ☑️ filter data for people who can still convert
Do weekend orders take longer to ship?
It could be time to re-think weekend staffing in the warehouse.
Build the Analysis
DATASET
ALL Completed Order
FIRST IN BETWEEN Shipped Order
Add column for Hours to Shipped Order
Add computed column for Date Part: Day of Week of Completed Order
ANALYZE
Decrease Average Hours to Shipped Order (#)
by analyzing the influence of Day of Week of Completed Order
by month
Which cities have the worst fulfillment times?
Long fulfillment times lead to unhappy customers.
Build the Analysis
DATASET
ALL Completed Order
FIRST IN BETWEEN Received Shipment Add column for customer city
ANALYZE
Decrease Average Days to Received Shipment (#)
by analyzing the influence of Customer City
by month
Do certain product types take longer to fulfill?
Identify the bottlenecks in your fulfillment process.
Build the Analysis
DATASET
ALL Purchased Product
FIRST AFTER Received Product (where purchased product activity_id = received product activity id)
ANALYZE
Decrease Average Days to Received Product (#)
by analyzing the influence of Purchased Product Sku
by month and ☑️ filter data for people who can still convert
Do the number of products in an order influence the fulfillment times?
Find out if your process breaks down when high volume orders are placed.
Build the Analysis
DATASET
ALL Completed Order
AGG IN BETWEEN Purchased Product
LAST IN BETWEEN Received Shipment
ANALYZE
Decrease Average Days to Received Shipment (#)
by analyzing the influence of Count of Purchased Product
by month
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