How to Automate Email-to-Order Processing

Despite the rise of B2B portals and EDI, email remains the dominant channel for purchase orders in wholesale, distribution, and professional services. A typical operations team spends 30-45 minutes per email order: opening the attachment, reading the PDF, cross-referencing SKUs, and manually entering every line item into their ERP. When you process 20-40 of these per day, that is an entire full-time employee dedicated to data entry.

Email-to-order automation eliminates this bottleneck by converting unstructured email content and PDF attachments into structured order data that flows directly into your systems. Here is the complete implementation guide.

Step 1: Set Up Email Ingestion

The first step is creating a dedicated email endpoint that captures all incoming orders. You have two primary approaches:

• Dedicated mailbox: Create a new email address (e.g., orders@yourcompany.com) and instruct customers to send POs there. Set up forwarding rules from your existing order inbox to this address.
• Email monitoring with filters: Use Make.com's Gmail or Outlook module to watch your existing inbox with filters. Configure it to trigger only on emails containing keywords like "purchase order," "PO," or from specific vendor domains.

For the dedicated mailbox approach, services like Parseur provide a unique parsing email address. Any email forwarded to this address is automatically queued for data extraction. This is the recommended approach because it cleanly separates order emails from general correspondence.

Set up an auto-reply on your orders mailbox confirming receipt: "Your order has been received and is being processed. You will receive a confirmation within 2 hours." This buys you processing time and sets customer expectations.

Figure 1: Complete email-to-order processing pipeline showing data flow and extraction points

Step 2: Configure Document Parsing Templates

This is the technical core of the system. You need to train your parser to extract structured data from unstructured PDF documents. Using Parseur as the primary tool:

• Upload sample POs: Start with 10-15 POs from your highest-volume customers. Each unique PO layout requires its own parsing template.
• Define extraction zones: Highlight and label each data field: PO number, date, customer name, bill-to address, ship-to address, and the line item table (SKU, description, quantity, unit price).
• Configure table extraction: Line item tables are the trickiest part. Define the column headers and tell the parser where the table starts and ends. Use Parseur's table detection feature to handle variable-length item lists.
• Test accuracy: Run 20-30 test POs through each template and verify extraction accuracy. You need 95%+ accuracy before going live. Tweak extraction zones for any fields with errors.

For email body orders (no PDF attachment), configure text-pattern extraction. Many B2B customers send orders as plain text in the email body. Use regex patterns to identify SKUs, quantities, and pricing from the text content. See our in-depth PDF order processing guide for advanced parsing techniques.

Step 3: Build the Transformation Layer in Make.com

Parsed data from Parseur arrives as raw JSON. Before it reaches your ERP, it needs transformation and enrichment:

• Customer matching: Look up the customer name or email domain in your ERP. If the customer exists, retrieve their internal ID. If not, flag for manual review or auto-create with default terms.
• SKU translation: Customers often use their own part numbers. Maintain a mapping table (Airtable works well for this) that translates customer SKUs to your internal SKUs. Flag any unmatched SKUs.
• Price validation: Compare extracted prices against your current price list. If the customer has a contracted price and the PO price differs, flag the discrepancy but still process the order at the contracted rate.
• Address standardization: Use a USPS or Google Maps API call to validate and standardize shipping addresses before order creation.

Step 4: Create Orders and Trigger Fulfillment

With clean, validated data, create the order in your destination system:

• Use Make.com's QuickBooks module to create a Sales Order with all mapped fields, including customer ID, line items, quantities, rates, shipping address, and PO reference number.
• Send a PO acknowledgment email back to the customer with order details and expected ship date.
• Push the order to ShipStation for shipping label automation.
• Update inventory levels to reflect the committed stock.

Step 5: Handle Exceptions Gracefully

No parser is perfect. Build an exception handling workflow for orders that cannot be fully automated:

• Low-confidence parsing: If the parser's confidence score is below 90% for any field, route the order to a human review queue in Slack or email with the parsed data pre-filled for correction.
• Unknown customers: New customers should trigger a Slack notification to your sales team for verification before the order is processed.
• New PO formats: When an email arrives from a customer without a matching template, queue it for manual processing and flag it for template creation.
• Missing data: If critical fields (ship-to address, payment terms) are missing from the PO, auto-send a clarification request email to the customer.

Start by automating your top 5 customers by volume. They likely represent 40-60% of your email POs and have the most standardized formats. Once those templates are running reliably, expand to the next tier.

Measuring Success

Track these metrics after implementation to validate your automation:

• Straight-through processing rate: Percentage of orders processed without human intervention. Target: 70-80% within the first month, 85-90% after template refinement.
• Processing time: Average time from email receipt to order creation. Target: under 5 minutes vs. the previous 30-45 minutes.
• Error rate: Percentage of orders requiring correction after automation. Target: under 2%.
• Hours saved per week: Use our cost calculator to quantify the time and dollar savings.