How to automate order consolidation with Parabola
This reporting Flow is especially useful for pre-ERP teams and for sales channels that aren't entirely or reliably integrated with your ERP. Several $100M+ brands use Parabola to ingest CSVs from niche sales channels such as wedding registry platforms like Zola and The Knot before using data transformation steps to standardize that data to match the format required by their ERP.
- Import order data from all sales channels, such as Shopify, Amazon, and email attachments using steps like Pull from Shopify and Pull from inbound email.
- Standardize all data by ensuring SKUs map to internal formats and date formats are consistent. Use steps like Standardize with AI, Edit columns, and Format dates to clean your data.
- Once formatting is consistent across sales channels, consolidate the datasets using the Stack tables step to create one unified dataset.
- Action on your consolidated dataset by creating dashboards with the Visualize step, setting up email alerts with the Email a file attachment step, or generating a CSV file to upload to Netsuite with the Generate a CSV file step.
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Why Parabola
Parabola has given our team an intuitive yet robust platform that simplifies the day-to-day process of data automation, transformation and storage. Parabola has added to team productivity exponentially.
Order consolidation involves ingesting and combining order data across siloed sales channels. This process is especially relevant for omnichannel brands with sales data spread across siloed systems (e.g., Shopify, Amazon Seller Central, SPS Commerce, Zola, and The Knot) as a tool to centralize order data into a unified view. This process is designed to ensure data consistency and enables timely decision-making to provide a seamless customer experience.
With Parabola, you can consolidate your order data across various sources in a single place, mapping sales channel data to your internal formats. Whether data is shared via email attachments, wholesaler portals, or API, automatically ingest, standardize, and aggregate data across sales channels.
- Standardize SKUs and date formats early in your flow using steps like Standardize with AI, Format dates, Edit columns, and Extract with AI.
- The Pull from inbound email step is one of the most useful tools for importing CSV files shared daily — offering a lightweight, reliable integration point without needing to worry about any API connections.
- Consider using a Parabola Table or static CSV file if your workflow involves consistently pulling large amounts of historical data.
- To start building your own order consolidation Flow, use the Template above and check out Parabola University.
- Learn about the full suite of order management use cases Parabola can automate.
Best practices for order consolidation include ingesting orders from all sales channels on a consistent schedule, normalizing fields like SKUs and order dates early, and aggregating data into a central dataset for visibility. Consistent use of automation reduces manual errors and improves fulfillment efficiency. For real-world impact, check out our Great Jones case study.
Teams often combine ERP integrations, CSV imports, and spreadsheets to gather order data across channels. That process is prone to errors and inefficiencies. Parabola simplifies this by linking directly to marketplaces, email pipelines, and CSV feeds to standardize and consolidate orders without code.
When orders flow in from platforms like Shopify, Amazon, or niche marketplaces like Zola, they often arrive in inconsistent formats. Manual consolidation is slow and risky. Automating this process ensures clean, unified data that powers accurate downstream workflows—in both operations and financial reporting. For more context on guarding against order-related risk, see our blog on preventing order and returns fraud.
In setups without a robust ERP, teams often rely on disparate CSVs, email attachments, or channel dashboards—making data consolidation a full-time task. Automation stitches these inputs together, transforming messy files into structured datasets ready for dashboards or fulfillment systems, enabling operators to bypass manual ingestion and focus on strategy.
