Amazon Marketplace Reconciliation Using OMS

Amazon marketplace reconciliation using OMS helps finance teams compare internal order data with Amazon's external reports so they can confirm what was sold, paid, settled, refunded, deducted, or left open. For businesses that sell on Amazon, this workflow reduces manual spreadsheet work and gives the finance team a clearer view of discrepancies across orders, settlements, and disbursements.

Cointab supports this process as a reusable reconciliation workflow. Teams can upload Amazon reports and OMS exports, map the required fields once, run reconciliation, and review matched and unmatched records in an audit-ready format.

Why reconcile Amazon marketplace data with OMS

Amazon marketplace data rarely sits in one clean report. Finance teams often need to compare multiple files, such as order data, MTR reports, settlement or disbursement reports, return reports, reimbursement reports, and supporting reference files like SKU masters or bank statements.

The OMS acts as the internal source of truth for the business. Amazon acts as the external record. Reconciliation helps teams identify:

• Orders recorded in the OMS but not reflected in Amazon reports
• Amazon transactions that are missing in the OMS
• Amount differences between order values and received settlement values
• Partial payments, deductions, or short receipts
• Returns, refunds, or reimbursements that need follow-up
• Rows that should be skipped because the file is incomplete or unusable

This is especially useful for marketplace finance teams that need a repeatable process at month end, quarter end, or during audit review.

How Cointab handles this reconciliation

Cointab follows a structured Side A and Side B model:

The workflow is designed to be transparent and reusable:

1. Create a new Amazon marketplace reconciliation.
2. Upload Side A and Side B files, or configure automated data input.
3. Map fields such as date, amount, and order or transaction identifiers.
4. Upload supporting data if needed, such as SKU master or other lookup files.
5. Create derived columns when the raw file needs cleaning, calculation, or normalization.
6. Run reconciliation manually or on a schedule.
7. Review the reconciliation report and exception buckets.
8. Download the Excel report for internal review or audit.

Typical reports used in an Amazon OMS reconciliation

The exact mix of reports depends on the business process, but a common setup may include:

• Amazon order reports
• Amazon MTR or invoice reports
• Amazon settlement or disbursement reports
• Amazon return reports
• Order-level reimbursement reports
• OMS exports from the internal order management system
• SKU master or product master files
• Bank statements when settlement needs to be tied back to receipts

Supporting data is optional, but it can make the reconciliation more complete. For example, a SKU master can help normalize product references, while a lookup file can help enrich order data before matching.

What happens during matching

Cointab's reconciliation engine uses structured rules to compare records across the two sides. It can handle common marketplace scenarios such as:

• One-to-one matching
• One-to-many matching
• Many-to-one matching
• Many-to-many matching
• Net-to-net matching
• Contra matching
• Partial matching

For Amazon reconciliation using OMS, this is useful when one Amazon record corresponds to multiple OMS rows, or when Amazon and the OMS store identifiers in different formats.

The system can also compare identifiers using different methods such as equals, contains, or subset-based matching. This helps when a transaction ID, order reference, or settlement reference is present but not identical across files.

If a team needs to normalize data first, they can create derived columns. For example, a finance user can use AI to build a formula that cleans an order ID, derives a net amount, or converts a text field into a matching reference.

How exception buckets help finance teams

After the reconciliation run, Cointab separates records into clear buckets so users can focus on exceptions instead of reviewing every row manually.

Fully matched

These are records where the relevant identifiers and amounts match according to the configured rules.

Partially matched

These are records where the identifiers match, but the amounts differ. In Amazon workflows, this may point to fees, deductions, short payments, returns, or other settlement differences that need review.

Unmatched

These are records present on one side but not found on the other. For example, an order may exist in the OMS but not in Amazon's report, or a payment may appear in Amazon data without a corresponding OMS record.

Skipped

Skipped records are rows that were not used in the reconciliation because of missing data, invalid values, duplicates, or file issues. Visibility into skipped rows helps teams understand what was excluded and why.

Where AI helps in the workflow

Cointab uses AI in a conservative, reviewable way. In an Amazon marketplace reconciliation using OMS, AI can help finance teams in three main ways:

• Build Excel-style formulas for derived columns using natural language
• Analyze open transactions after structured rules have been applied
• Suggest likely reasons or next actions for unresolved items

This is useful when Amazon references are inconsistent, descriptions are unstructured, or a missing identifier makes deterministic matching difficult.

Manual review and missed files

Not every exception should be forced into an automatic match. If the system cannot confidently match a transaction, finance users can review the open items manually and match records when the totals and business context are correct.

If a report arrives late or a file was missed during setup, users can upload the missing file under the same reconciliation and refresh the report. That matters in marketplace operations, where settlement, returns, or partner files may arrive after the first reconciliation run.

Why this workflow is reusable

Once the Amazon reconciliation is configured, the same setup can be reused for future periods. Teams do not need to rebuild the logic every month.

That makes it easier to:

• Reconcile monthly, quarterly, yearly, or custom periods
• Standardize review across team members
• Keep reporting consistent over time
• Reduce repeated spreadsheet setup
• Automate recurring reconciliation runs through email, SFTP, or API-based input
• Push reconciliation output back to internal systems when needed

What finance teams gain from the report

The final report gives finance teams a clear view of the reconciliation outcome, including:

• Total summary
• Matched records
• Partially matched records
• Unmatched records
• Skipped records
• Transaction-level detail
• Filters for deeper analysis
• Excel export for audit and partner follow-up

For Amazon marketplace teams, this makes it easier to investigate settlement gaps, identify missing orders or refunds, and keep internal OMS data aligned with external marketplace records.

FAQ

What is the main purpose of Amazon marketplace reconciliation using OMS?

It helps finance teams compare Amazon's external reports with internal OMS data so they can identify matched transactions, amount differences, missing records, and open exceptions.

Which Amazon reports can be used in this workflow?

Common inputs include order reports, MTR or invoice reports, settlement or disbursement reports, return reports, reimbursement reports, and supporting reference files such as SKU masters or bank statements.

Can the reconciliation handle amount differences and partial matches?

Yes. Cointab separates fully matched, partially matched, unmatched, and skipped records so teams can focus on exceptions that need review.

Can this reconciliation be reused for future periods?

Yes. Once configured, the same Amazon OMS reconciliation setup can be reused for future runs and scheduled automation.

What if a required file arrives late?

Users can upload the missed file under the same reconciliation and refresh the report so the latest records are included in the review.