Optimizing Marketplace Data Management with Looker & Power BI

Enhancing Amazon Operations, Advertising, and Competitive Insights

Looker and Power BI play a crucial role in marketplace data management, particularly for platforms like Amazon Seller Central, Amazon Vendor Central, Campaign Management, and Amazon Marketing Services (AMS). data analytics companies Here’s how they help:

1. Centralized Data Management & Integration

Looker: Uses LookML to create a unified data model, allowing real-time data analysis across different Amazon data sources.
Power BI: Connects directly to Amazon APIs, databases, and third-party tools, consolidating data for better reporting and insights.

2. Dashboards & Reporting for Business Insights

Both Looker & Power BI create custom dashboards that help top management in areas like:

Logistics: Track shipments, inventory levels, and fulfillment efficiency.
Localization: Identify regional sales trends and adapt marketing strategies.
International Marketing Strategies: Analyze demand by country, optimizing pricing and promotions.
Competitor Analysis: Monitor pricing, advertising spend, and customer sentiment to stay ahead.

3. Operations Performance Tracking

Key metrics tracked in Looker/Power BI include:

TAT (Turnaround Time): Measures order processing & delivery efficiency.
DRR (Defect Return Rate): Helps identify product quality or logistics issues.

4. Campaign Performance Management (AMS & Amazon Ads)

Track Ad Spend, ROI, CTR, CPC, ACOS, and optimize ad campaigns.
Identify high-performing keywords, demographics, and placements.
Adjust bidding strategies in real-time for better ad performance.

Amazon data reporting with looker

5. Cost Optimization & Process Efficiency

Looker’s predictive analytics and Power BI’s AI-driven insights help identify cost-saving opportunities.
Detect bottlenecks in operations, reducing inefficiencies.
Balance customer experience vs. cost control by making data-driven decisions.

Using Looker & Power BI allows Amazon sellers and vendors to make data-backed decisions, leading to better inventory management, smarter ad spending, improved logistics, and an overall increase in profitability. It’s all about turning raw data into actionable insights for business growth. 🚀

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Integrating Amazon data reporting into Looker, steps:

Amazon Data Reporting with Looker

1. Data Sources & Integration

Amazon provides multiple data sources. Choose the right one for your needs:

Amazon Seller Central – For sales, orders, and performance reports.
Amazon Advertising (DSP, Sponsored Ads, etc.) – For ad performance.
Amazon Vendor Central – For first-party (1P) sales data.

To bring this data into Looker, consider these options:

Direct API Connection
- Use Amazon APIs (Selling Partner API for Seller Central, Amazon Advertising API, etc.).
- Requires development effort to fetch and store data in a database.
Third-Party ETL Tools(Easier Approach)
- Use connectors like Supermetrics, Funnel.io, Hevo, Stitch, or Dataddo to extract Amazon data into a data warehouse (BigQuery, Snowflake, etc.).
- These tools automate data syncing and transformation.

2. Data Storage & Modeling in Looker

Since Looker does not connect directly to Amazon, you need a data warehouse:

Popular choices: BigQuery, Snowflake, Redshift.
Load Amazon data into your warehouse via API or ETL tool.

Key Tables to Create in Your Warehouse:

Sales Data: Orders, revenue, refunds, marketplace breakdown.
Advertising Data: Impressions, clicks, CPC, ROAS, ACoS.
Inventory Data: Stock levels, replenishment needs.
Customer Insights: Repeat buyers, demographics, regions.

3. Building Looker Models & Explores

In Looker, you define models and views to structure the data.

Steps to Set Up Looker:

Create a LookML Project
- In Looker, define your data connections (BigQuery, Snowflake, etc.).
Define Views (.view Files)
- Each Amazon table (sales, ads, inventory) gets its own Looker view.
- Example for an amazon_sales.view file: view: amazon_sales { sql_table_name: my_database.amazon_sales; dimension: order_id { type: string sql: ${TABLE}.order_id ;; } measure: total_revenue { type: sum sql: ${TABLE}.revenue ;; value_format: "$#,##0.00" } }
Define Explores (.explore Files)
- Create an explore to connect different tables.
explore: amazon_performance { join: amazon_ads { relationship: one_to_one sql_on: ${amazon_performance.order_id} = ${amazon_ads.order_id} } }
Build Dashboards
- Use Looker’s UI to create Amazon performance dashboards.
- Key Metrics: Sales trends, Ad Spend vs. Sales, Inventory alerts.

4. Summary

✅ Extract Amazon data using APIs or ETL tools.
✅ Store data in a warehouse (BigQuery, Snowflake, etc.).
✅ Model data in Looker with LookML.
✅ Visualize key metrics (sales, ads, inventory) in Looker dashboards.

Amazon Seller Dashboard Reporting with Power BI

Amazon seller dashboard Power BI Power BI Solution

1. Introduction to Power BI for Amazon Seller Central

Power BI is a powerful tool for visualizing and analyzing data from Amazon Seller Central. With Power BI Amazon Dashboard creation, sellers can gain real-time insights into sales performance, inventory, customer behavior, and advertising metrics.

Why Use Power BI for Amazon Seller Reporting?

Automated reporting from Amazon Seller Dashboard
Interactive visualizations for key business metrics
Integration with multiple data sources like Amazon Ads, MWS, and SP-API
Customizable reports for B2B and B2C sales analysis

2. Steps for Power BI Amazon Dashboard Creation

Step 1: Connect Power BI to Amazon Seller Central

Use Amazon MWS API (Marketplace Web Services) or Selling Partner API (SP-API)
Alternatively, export reports from Amazon Seller Dashboard (CSV/Excel) and import them into Power BI
Connect third-party tools like Supermetrics, Data Hawk, or Power Automate

Step 2: Data Extraction & Transformation (Power Query)

Import raw sales, inventory, and ad performance data
Clean & structure data using Power Query Editor
Perform transformations such as:
- Removing duplicates
- Formatting dates
- Merging multiple datasets

Step 3: Creating Data Models in Power BI

Establish relationships between different datasets (e.g., Sales, Inventory, Orders)
Create calculated columns and measures using DAX (Data Analysis Expressions)

Step 4: Building Amazon Seller Dashboard in Power BI

Design interactive visualizations & KPI cards
Include charts for sales trends, top-performing products, and stock levels
Add filters & slicers to segment data by region, product category, or timeframe

Step 5: Automating & Publishing Reports

Schedule automatic data refresh for Amazon Seller Dashboard reporting with Power BI
Publish reports to Power BI Service for real-time access
Share insights with your team using Power BI apps

3. Key Metrics to Include in Power BI Amazon Seller Dashboard

Sales Performance (Total revenue, Orders, Units sold)
Customer Insights (Repeat purchases, Refunds, Average Order Value)
Inventory Analysis (Stock levels, Reorder alerts, Sell-through rate)
Amazon PPC & Advertising Performance (ACOS, ROAS, Impressions, Clicks)
Product Performance (Best-selling SKUs, Profit margins, Ratings & Reviews)

4. Summary & Best Practices

Use Power BI Amazon Seller Dashboard for data-driven decision-making
Automate reporting to save time and reduce manual errors
Optimize data connections with Amazon API, Power Query, and DAX
Ensure data security & compliance when handling customer and financial data

By leveraging Amazon Seller Dashboard reporting with Power BI, sellers can enhance profitability, track trends, and improve operational efficiency. 🚀

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