How Shopify Built An In-Context Analytics Experience

Summary

Shopify’s Analytics section gave merchants access to business data, but research and quantitative analysis found that they often moved between it and admin pages while making decisions. The team therefore designed an in-context experience for the Orders page, selecting metrics through workflow analysis, segmenting merchants by factors such as order volume and fulfillment process, and examining each metric’s usefulness, variability, and time horizon. Data scientists prototyped visualizations with real, segmented data and created a specification sheet so Web, Android, and iOS used consistent queries, data sources, fields, and aggregations; the project also tested batch models before streaming them. The launched experience surfaces order and fulfillment metrics for daily, seven-day, and thirty-day periods, with comparison indicators, and links to reports covering orders, returns, products, and fulfillment, shipping, and delivery times.

Takeaways

1. 1

   Metrics were shown only where the underlying data was applicable; metrics that stayed constant or required longer time periods were excluded from the Orders page or reserved for reports.

2. 2

   Prototyping with real merchant data changed the planned visualizations, including the addition of a histogram to show the distribution of fulfillment, delivery, and in-transit event durations.

3. 3

   The launched Orders page surfaces total orders, ordered items, returns, fulfilled and delivered orders, and median fulfillment time for daily, seven-day, and thirty-day periods, with comparison indicators.