How to improve your returns processing and reporting
The full story of a return is rarely told in a single system. To understand the journey of an order, teams usually need to bring together data from Shopify, return platforms like Loop and Two Boxes, and CX tools like Zendesk and Kustomer. Without blending data across key systems, it’s nearly impossible to understand return trends and root causes — let alone efficiently process returns from an operational standpoint.
With Parabola, you can unify returns data across systems and build granular reports to understand returns by SKU, customer type, and/or 3PL. Leverage AI to deepen your understanding of return reasons and build custom workflows to help your team fully process returns without manually updating records across siloed systems.
Video overview
Why Parabola
With Parabola, we connect to the tools that our data team is actually using. This expands the problems we can solve with Parabola and contributes directly to millions in revenue and profit growth.
Returns management is the process of tracking, analyzing, and optimizing product returns. This includes understanding why products are returned, which customers return the most, and how returns impact profitability. Effective returns management helps brands minimize costs, improve customer experience, and refine policies to balance customer satisfaction with business efficiency.
- Pull returns data from systems like Loop, AfterShip, Narvar, Two Boxes, and Shopify using steps like Pull from API, Pull from inbound email, and Pull from CSV file.
- Pull support ticket data from platforms like Zendesk to identify customer-reported return issues with steps like Pull from API.
- Standardize and clean return data using steps like Edit Columns and Standardize with AI to ensure consistency across sources.
- Join returns data with product and customer records using the Combine tables step, joining on identifiers like order ID.
- If applicable, use the Categorize with AI step to classify email notes from customers into root cause categories.
- Identify returns that fall outside of your policies using logic steps like Add if/else column and set up automated alerts when out-of-policy returns occur.
- Build a reporting dashboard using the Visualize step or alert your team of timely returns using the Email a file attachment or Send to Slack steps.
- If you're using Narvar, you can automatically send data from Narvar to Parabola by setting up a subscription — automatically pushing a CSV report from Narvar to a Parabola Flow on a recurring basis. Use the Pull from inbound email step to access an email address unique to your Parabola Flow.
- Use automation to flag potential fraud scenarios, such as excessive returns from a single customer or high-return products.
- Use the Categorize with AI step to gain a more nuanced, granular understanding of return reasons.
- Blog: Why ecommerce brands need to get loud about order and returns fraud — and how to prevent it
- To start building your own returns management Flow, check out Parabola University.
