Fulfillment
Use Cases

Problematic Order Notifications

Transaction data can be messy. Cross referencing thousands of orders with customer info and shipping data is hard enough, so spotting issues in the data can seem impossible. Cascade workflows allow you to easily join datasets and pull out problematic orders to dig into further.

We'll start with multiple different datasets containing transactions, customer details and shipping information, and in just a few steps we can automate problematic order notifications.

  • Join all 3 datasets to have all of our data in one table
  • Create a new metric calculating shipping cost compared to unit cost
  • Publish the list of problematic orders with high shipping costs to email so notifications are automatically sent at the cadence the workflow is scheduled to run on
  • Maybe take the analysis even further and dive into which types of orders are more likely to be problematic, take product containers, for example

Outputs

As mentioned above, our main output will be the notifications sent in the form of an email to necessary teammates with the order and details of problematic orders.

(Charts and tables are live embeds of assets produced in Cascade)

We can also easily pivot and group our data to calculate % of trips with high shipping costs. For example, when looking at product container size, small & large boxes seem to have a much higher percentage of problematic orders than container sizes.

(Charts and tables are live embeds of assets produced in Cascade)