DoorDash Reconciliation

DoorDash reconciliation helps restaurant finance teams compare internal order and sales records against DoorDash settlement and payout reports. The goal is to identify fee deductions, commissions, refunds, cancellations, payout timing differences, and missing remittances before they affect the books or month-end close.

Why DoorDash reconciliation is difficult

DoorDash-related transactions are often spread across multiple reports and reporting periods. Finance teams usually need to compare operational order data with settlement and payout files, then account for deductions that do not appear in the same format as the original order.

Common challenges include:

• High transaction volume across breakfast, lunch, and dinner peaks
• Multiple fee types such as commission, delivery, processing, and promotional deductions
• Refunds, cancellations, and adjustments that change the final payable amount
• Payout timing differences between order date, delivery date, and settlement date
• Partial matches where the order exists but the amount does not fully agree
• Missing or late files that delay close and create open items

When this work is done in spreadsheets, the logic is hard to repeat, difficult to audit, and easy to break when report formats change.

How Cointab structures DoorDash reconciliation

Cointab uses a Side A and Side B reconciliation model.

• Side A is your internal record, such as the restaurant sales report, POS export, order ledger, or revenue file
• Side B is the external DoorDash record, such as a settlement report, payout report, fee file, or adjustment report

Users upload the required files, map the key fields once, and run reconciliation in a structured workflow. The system supports CSV, XLS, and XLSX files.

Typical fields mapped for DoorDash reconciliation include:

• Order date or transaction date
• Gross amount and net payout amount
• Order ID, transaction ID, payout reference, or settlement ID
• Refund or cancellation reference
• Fee, commission, or adjustment columns

Typical data sources

Supporting data can also be uploaded to enrich the primary files before reconciliation. For example, a store master, tax mapping file, or order metadata file can help normalize identifiers or fill missing details.

Matching logic for DoorDash transactions

Cointab's reconciliation engine applies structured matching logic before analyzing open items with AI.

It can support:

• One-to-one matching when one order maps to one payout line
• One-to-many matching when one order is split across multiple settlement lines
• Many-to-one matching when multiple order lines roll up to one payout line
• Net-to-net matching when fees and deductions need to be offset against gross sales
• Partial matching when identifiers match but amounts differ
• Contra-style matching for offsetting entries or reversals

This is useful when DoorDash data includes grouped adjustments, batched payouts, or different reference formats across reports.

What finance teams review after the run

Once reconciliation is complete, users can review a report dashboard that separates transactions into clear buckets:

• Fully matched records where the expected order or payout agrees with the external report
• Partially matched records where the reference matches but the amount needs review
• Unmatched records that appear on one side but not the other
• Skipped records that were excluded because of missing data, invalid values, duplicates, or file issues

The report also supports filters and transaction-level review, so finance teams can focus on exceptions instead of checking every line manually.

If a record could not be matched automatically, users can apply a manual match when the business context is clear and the totals tally.

Reusable workflows for recurring DoorDash reconciliation

DoorDash reconciliation is rarely a one-time task. Most restaurant finance teams need the same workflow every day, week, or month.

Cointab is built to make that setup reusable. Once the reconciliation is configured, the same logic can be used for future periods without rebuilding the process from scratch.

Recurring workflows can also be automated through:

• Email-based data intake
• SFTP-based file delivery
• API-based data transfer
• Scheduled reconciliation runs

That means the team can set up the workflow once, receive or pull files on a schedule, run reconciliation automatically, and review the output when it is ready.

Where DoorDash reconciliation adds value

A structured DoorDash reconciliation process helps restaurant finance teams handle the operational details that often create leakage or confusion.

Common use cases include:

• Verifying sales against DoorDash payouts
• Reviewing commission and delivery fee deductions
• Checking refunds, cancellations, and reversals
• Identifying payout delays or missing settlements
• Supporting month-end close with cleaner exception tracking
• Creating audit-ready reports for internal review

Because matched, partially matched, unmatched, and skipped items are separated clearly, teams can spend less time searching through spreadsheets and more time resolving actual exceptions.

Handling exceptions and missing files

Real-world reconciliation often involves late files or incomplete data. If a file was missed, users can upload it under the same reconciliation and refresh the report.

This is especially useful when settlement files, refund details, or payout summaries arrive later than expected. Instead of rebuilding the workflow, the reconciliation can be updated and reviewed again in the same dashboard.

Why restaurants use a structured reconciliation platform

Restaurant finance teams usually care about four things: accuracy, speed, control, and auditability. DoorDash reconciliation touches all four.

A structured platform helps teams:

• Apply the same matching logic every period
• Reduce manual spreadsheet work
• Keep a visible audit trail of what matched and what did not
• Review exceptions in a consistent way
• Reuse the setup across periods and locations

That makes the reconciliation process easier to manage as transaction volume grows or as reporting formats become more complex.