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Track returns created, approved, and processed to understand team productivity accurately

Updated over 3 weeks ago

Summary:

Track how many returns each team member creates, approves, and processes—whether the return is fully completed or still in progress. Complete and incomplete processing days are counted separately, giving you a more detailed view of team activity and workload.

Table metrics:

  1. Created

    1. The amount of returns created by an individual on your team.
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  2. Approved

    • The amount of total approved returns.
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  3. Processed (Complete)

    • Counts the number of returns processed per employee, with each processing day counted separately if a return spans multiple days.

Examples:

  1. A return that has 3 items separately processed on Day 1, Day 2, and Day 3 would increment this column value by 3.

  2. A return with 3 items separately processed on Day 1, Day 2, and Day 5 would count as 3 on the column value.

    1. If any of these days were looked at individually, the value would be 1.

Note: Processed (Complete) includes all fully processed returns & fully rejected returns.

4. Processed (Incomplete)

  • This tracks the number of returns that an employee worked on that are still in progress and not yet fully completed. A return that was processed over multiple days will have each processing day counted separately in this column until all items are completed. Once a return is fully processed, all days are counted as "complete" and moved to the “Processed (Complete)” column.

Example:

  1. If you filter by Day 1 after the return is completed, Day 1 will appear under "Processed (Complete)" instead of "Incomplete."

Note: If multiple employees work on a single return, Processed (Complete) and Processed (Incomplete) will give credit to each employee according to their level of involvement.

How this can enhance your business:

These insights help you measure team productivity more accurately, especially in shared or staggered workflows. By breaking out incomplete and complete processing days, you can identify where returns get delayed and which employees contribute most to throughput. This data can also support performance reviews, workforce planning, and more effective shift scheduling.

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