How to Prepare Amazon Ads Export Data for Power BI or Looker Studio

Preparing Amazon Ads export data for Power BI or Looker Studio involves cleaning, unifying, and formatting reports to ensure accurate and insightful analysis. This process enables effective visualization of performance metrics, facilitates trend comparison across campaigns, and supports better decision-making for Amazon advertisers and agencies.

Step-by-step guide for organizing Amazon Ads data exports

To gain actionable insights from your Amazon Ads, you need to properly prepare exported data files before importing them into BI tools like Power BI or Looker Studio. The steps below focus on cleaning, consolidating, and structuring Amazon Ads datasets for reliable visual analysis and reporting.

What You'll Need

Amazon Negative Keyword Tool by Todoza – Tool designed for analyzing Amazon Ads Search Terms data to support informed decision-making on negative keywords and bid strategies.
Amazon Ads Report Analyzer – Automates import and reporting of Amazon Sponsored Products search-term reports.
Amz Ad Waste Detector - Amazon Ads Analytics Module – Analyzes Amazon Ads data to identify inefficiencies and improve campaign performance.
Amazon Ads Dashboard by Todoza – Consolidates Amazon Ads export data into a single view for performance and trend tracking.
Basic spreadsheet software (Excel or Google Sheets)
Access to Amazon Ads campaign export files (CSV, XLSX, or TSV)

Organize Your Amazon Ads Exports for BI

For the smoothest BI workflow, collect all required Amazon Ads report types before you start cleaning and consolidation. Use clear folder structures and file naming conventions to separate different accounts, brands, or date ranges so you can easily navigate your data when importing to Power BI or Looker Studio.

Step 1: Export Data from Amazon Ads

Begin by downloading the relevant raw data from your Amazon Ads account. Choose the report types most pertinent to your analysis, such as Search Term, Campaign, or Performance reports. Export these files in CSV or XLSX format for compatibility with most tools. Always review the date ranges and selected columns to ensure they match your intended reporting scope. Always keep a copy of your original export files in a safe location in case you need to revert changes.

Step 2: Clean and Standardize Your Export Files

Open your exported files in a spreadsheet tool. Remove duplicate rows and blank lines that may cause errors in Power BI or Looker Studio. Audit your columns for inconsistent naming, missing data, or mismatched values. Standardize column headings, date formats, and metric naming for seamless BI integration.

If working with large campaigns or multiple marketplaces, consider consolidating all exports into a single master spreadsheet using the same column order. This will ensure your BI tool can recognize and relate data correctly during import.

Step 3: Filter and Aggregate Relevant Data

Next, determine which campaigns, SKUs, or search terms are most relevant to your analysis. Filter to focus only on relevant campaigns, ad groups, or marketplaces. Aggregate daily, weekly, or monthly data as needed. If you manage multiple brands or clients, include account identifiers to differentiate records.

The Amazon Ads Dashboard by Todoza can help by consolidating reports and tracking KPIs across accounts and periods efficiently.

Step 4: Address Common Data Quality Issues

Prior to importing your data, scan for common problems—such as missing headers, broken rows, or inconsistent date formats—that might prevent your BI tool from reading the data correctly. Check for and correct common formatting issues before importing to your BI tool. Convert all dates to the same regional format and ensure metrics like spend, sales, and ROAS are numerical.

Some tools like the Amz Ad Waste Detector - Amazon Ads Analytics Module can automate additional validations and highlight problematic areas.

Step 5: Import into Power BI or Looker Studio

Upload your prepared sheets to Power BI or Looker Studio. Map fields appropriately—linking date, campaign, and performance columns to the right visualizations. In BI tools, you may want to create calculated fields for values like ACoS, CPC, or conversion rate if they are not present in your raw data.

Leverage tools that help automate data processing and maintain consistency. For recurring reporting, consider using solutions like Amazon Ads Report Analyzer to schedule imports and generate repeatable, client-ready outputs.

Tips and Troubleshooting for Amazon Ads Data Preparation

Save a backup of each transformation stage, so you can quickly identify where an issue may have occurred. Confirm that your BI dashboards reflect up-to-date data by refreshing regularly. Document any custom calculations or filters applied, especially when sharing reports with teams or clients. If you encounter persistent formatting or integration issues, tools like the Amazon Negative Keyword Tool by Todoza can help automate complex data classification and export processes.

Unifying Amazon Ads Data Across Multiple Brands

If you manage several Amazon brands or campaigns, combining their exports into a single normalized sheet or dashboard saves time. Leverage tools that aggregate exports from different ad accounts, enforce consistent column formats, and standardize key metrics—this is essential for comparing performance and trends in your BI dashboards.