Amazon Marketplace Reconciliation Using ERP

Amazon marketplace reconciliation using ERP helps finance teams compare Amazon sales, settlements, fees, returns, reimbursements, and bank activity against internal ERP records. The goal is to confirm what was recorded correctly, isolate open items, and produce an audit-ready reconciliation report without relying on repetitive spreadsheet checks.

Cointab provides a structured reconciliation workflow for Amazon marketplace data. Users upload the required files, map the key fields once, run reconciliation, and review matched and unmatched transactions in a single dashboard. The same setup can be reused for future periods, which makes recurring Amazon reconciliation easier to manage.

Why Amazon marketplace reconciliation becomes difficult

Amazon sellers often work with multiple reports for orders, settlements, disbursements, returns, reimbursements, and deductions. Internal ERP records may also be spread across sales entries, invoices, receivables, and bank postings.

This creates common reconciliation challenges:

• Order-level and settlement-level data do not always line up one-to-one.
• Fees, refunds, returns, and reimbursements can change the final amount.
• Different reference fields may appear in different reports.
• Timing differences can make records look unmatched in one period and matched in another.
• Large files become difficult to compare manually.
• Spreadsheet formulas and VLOOKUP-style checks are hard to audit and reuse.

A finance team needs a workflow that is transparent, repeatable, and easy to review at month end.

Side A and Side B in an Amazon ERP reconciliation

Cointab uses a Side A and Side B model so finance teams can clearly define what they expect to reconcile.

This structure helps teams understand which source is considered internal truth and which source is being compared against it.

Common Amazon and ERP files used in the workflow

A typical Amazon marketplace reconciliation using ERP may use the following files:

• Amazon all order report
• Amazon MTR or transaction report
• Amazon disbursement report
• Amazon settlement report
• Amazon FBA return report
• Amazon reimbursement report
• SKU master
• Bank statement
• ERP exports from systems such as SAP, Tally, or other accounting platforms

Supporting data can also be uploaded when it helps prepare the primary reconciliation. For example, a SKU master, mapping file, or product reference file can be used to enrich records before matching.

How the reconciliation setup works

Cointab is designed to keep the setup simple while still supporting detailed finance checks.

1. Upload the reports

Users upload CSV, XLS, or XLSX files for both sides of the reconciliation. Multiple files can be uploaded under the same configured report if they follow the same format.

2. Map the required fields

For each report, users map key fields such as:

• Date
• Amount
• Order ID
• Transaction ID
• Invoice number
• Settlement ID
• UTR
• SKU
• Other business identifiers used by the team

3. Add supporting data if needed

Supporting data can be used to enrich, merge, or prepare the primary files before reconciliation. This is useful when a team needs to look up missing fields, combine reports, or normalize partner-specific identifiers.

4. Create derived columns when needed

Users can create derived columns on both sides of the reconciliation. These are calculated fields built from existing data and can help with tasks such as:

• Cleaning identifiers
• Calculating net amounts
• Normalizing references
• Preparing fields for matching
• Building lookup fields for exception review

AI can help finance users generate Excel-style formulas for these derived columns using simple natural language.

5. Run the reconciliation

Once the files are ready, users run reconciliation manually or schedule it to run automatically. The engine applies structured matching logic first, then analyzes remaining open items with AI where a deterministic rule is not enough.

What Cointab identifies in Amazon ERP reconciliation

Cointab separates transactions into clear outcome groups so finance teams can focus on exceptions rather than reviewing every row.

Fully matched

These are records where identifiers and amounts match according to the configured reconciliation logic.

Partially matched

These are records where the identifier matches, but the amount does not fully match. This is useful when an order is related but the final Amazon amount differs because of fees, deductions, refunds, or timing differences.

Unmatched

These are records present on one side but not found on the other side. For Amazon reconciliation, this may mean a record appears in ERP but not in Amazon, or appears in Amazon but not in ERP.

Skipped

These are records that were not included in reconciliation because they were excluded by rule, incomplete, invalid, or unusable. Skipped items remain visible so teams understand what was ignored and why.

Matching logic for Amazon marketplace data

Amazon and ERP data do not always match through a simple one-to-one comparison. Cointab supports more flexible structured reconciliation patterns 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

This is useful when a single Amazon settlement relates to multiple orders, when multiple internal entries roll up into a single Amazon report line, or when fees and deductions need to be netted before comparison.

The platform can compare identifiers using equality, subset, contains, and similar matching logic, depending on the reconciliation design.

Handling exceptions and open items

Once structured matching is complete, the remaining open transactions can be reviewed in detail.

Finance teams can use the report to identify:

• Missing Amazon records
• Missing ERP entries
• Under-recorded or over-recorded amounts
• Refunds and returns that explain differences
• Fees, deductions, or adjustments that affect the final amount
• Records that need manual review because the identifiers are incomplete

If the system and AI cannot confidently match an item, the transaction remains open rather than forcing a weak match. Users can then perform a manual match when the business context supports it.

Reconciliation reports that finance teams can review

After the run is complete, Cointab presents the results in a dashboard designed for analysis and follow-up.

Typical report views include:

• Total summary
• Fully matched summary
• Partially matched summary
• Unmatched summary
• Skipped summary
• Transaction-level tables
• Filters for deeper analysis
• Detailed matched transaction views
• Excel report download

This makes it easier for controllers, reconciliation managers, and accounting teams to review exceptions, share results internally, and support audit preparation.

Reuse and automation for recurring Amazon periods

Amazon marketplace reconciliation is usually a recurring finance process, not a one-time project. Cointab is built so teams can reuse the same setup for future periods instead of rebuilding it every month.

Users can reconcile:

• Monthly periods
• Quarterly periods
• Yearly periods
• Lifetime or all-time periods
• Custom settlement periods

Once configured, the workflow can also be automated through email, SFTP, or API-based data flow. This allows finance teams to receive files, validate them, run reconciliation, and export output on a scheduled basis.

Cointab can also push reconciliation output back to internal systems through email, SFTP, or API so finance, reporting, and analytics teams can work from current data.

Why finance teams use this workflow for Amazon data

A structured Amazon marketplace reconciliation using ERP gives finance teams a clearer view of what is matched, what is open, and what needs follow-up.

It helps teams:

• Reduce repetitive spreadsheet work
• Apply matching logic consistently
• Review exceptions faster
• Keep a visible audit trail
• Support month-end and period-end close
• Reuse reconciliation setups across periods
• Work together in a shared team workspace

For finance teams handling Amazon data at scale, the main advantage is not just faster matching. It is having a repeatable workflow that shows exactly what was reconciled, what was skipped, and what still needs review.