Amazon Data for Analytics Teams

A practical guide for analytics teams working with Amazon seller data. From SP-API complexity to warehouse-ready datasets, here's how to get Amazon data into your analytics stack.

Your analytics team knows SQL, dbt, and Tableau. They've built dashboards for Salesforce, Shopify, and internal systems. But Amazon data is different. The complexity catches even experienced teams off guard.

This guide is written for analytics professionals tasked with getting Amazon seller data into existing data infrastructure. We'll cover what makes Amazon data uniquely challenging, how to evaluate solutions, and practical patterns for integrating with your analytics stack.

Why Amazon Data Is Different

Before diving into solutions, understand what makes Amazon data challenging. This isn't standard e-commerce data.

According to Amazon's SP-API documentation, the API includes over 20 different report types, each with unique schemas and retention periods.

What Analytics Teams Actually Need

Most analytics teams don't need raw SP-API access. They need clean, modeled data in their warehouse. Here's the typical requirements list:

The Data Model Analytics Teams Want

Here's what a well-structured Amazon data model looks like:

-- Unified Order Fact Table
orders (
  order_id,
  order_date,
  marketplace_id,
  sku,
  asin,
  units_sold,
  gross_revenue,
  discounts,
  net_revenue,
  currency_code,
  currency_rate_to_usd
)

-- Fee Attribution Table
order_fees (
  order_id,
  fee_type,           -- Normalized from 200+ types
  fee_category,       -- FBA, Referral, Advertising, etc.
  Fee_amount,
  fee_amount_usd
)

-- Advertising Performance Table
ad_performance (
  date,
  campaign_id,
  ad_group_id,
  sku,
  impressions,
  clicks,
  spend,
  sales_attributed,
  acos,
  roas
)

-- Product Profitability View
product_profitability (
  sku,
  period,
  gross_revenue,
  total_fees,
  cogs,
  ad_spend,
  contribution_margin,
  tacos
)

For detailed schema examples, see our normalized Amazon data guide.

Solution Options for Analytics Teams

Three paths to getting Amazon data into your warehouse:

Option 1: Build Custom Pipeline

Option 2: General ETL Tools

ETL tools deliver raw data quickly but require significant transformation work. See our ETL comparison for details.

Option 3: Amazon-Specialized Providers

Evaluation Criteria for Analytics Teams

When evaluating solutions, prioritize these factors:

1. Data Completeness

2. Data Quality

3. Integration Compatibility

Integration Patterns

Pattern 1: Direct Warehouse Sync

The simplest pattern for analytics teams. Data lands directly in your warehouse.

-- Example: Nova data in Snowflake
-- Data arrives in schema: nova_amazon

SELECT 
  o.order_date,
  o.sku,
  o.net_revenue,
  f.total_fees,
  a.ad_spend,
  o.net_revenue - f.total_fees - a.ad_spend as contribution_margin
FROM nova_amazon.orders o
LEFT JOIN nova_amazon.fees_by_order f ON o.order_id = f.order_id
LEFT JOIN nova_amazon.ad_performance a ON o.sku = a.sku AND o.order_date = a.date
WHERE o.order_date >= DATEADD(day, -30, CURRENT_DATE)

Pattern 2: dbt Transformation Layer

For teams using dbt, add a transformation layer on top of provider data:

-- dbt model: models/amazon/fct_amazon_profitability.sql

WITH orders AS (
  SELECT * FROM {{ source('nova_amazon', 'orders') }}
),

fees AS (
  SELECT * FROM {{ source('nova_amazon', 'fees_by_order') }}
),

ads AS (
  SELECT * FROM {{ source('nova_amazon', 'ad_performance') }}
),

cogs AS (
  SELECT * FROM {{ ref('dim_product_costs') }}  -- Your internal COGS data
)

SELECT
  o.order_id,
  o.sku,
  o.order_date,
  o.net_revenue,
  COALESCE(f.total_fees, 0) as amazon_fees,
  COALESCE(a.ad_spend, 0) as ad_spend,
  COALESCE(c.unit_cost * o.units, 0) as cogs,
  o.net_revenue - COALESCE(f.total_fees, 0) - COALESCE(a.ad_spend, 0) - COALESCE(c.unit_cost * o.units, 0) as net_profit
FROM orders o
LEFT JOIN fees f ON o.order_id = f.order_id
LEFT JOIN ads a ON o.sku = a.sku AND o.order_date = a.date
LEFT JOIN cogs c ON o.sku = c.sku

Pattern 3: Multi-Source Unification

Combine Amazon data with other channels:

-- Unified e-commerce view across channels

WITH amazon_orders AS (
  SELECT 
    'amazon' as channel,
    order_id,
    order_date,
    sku,
    revenue,
    fees,
    ad_spend
  FROM {{ ref('fct_amazon_profitability') }}
),

shopify_orders AS (
  SELECT 
    'shopify' as channel,
    order_id,
    order_date,
    sku,
    revenue,
    fees,
    ad_spend
  FROM {{ ref('fct_shopify_profitability') }}
),

combined AS (
  SELECT * FROM amazon_orders
  UNION ALL
  SELECT * FROM shopify_orders
)

SELECT
  channel,
  DATE_TRUNC('month', order_date) as month,
  COUNT(DISTINCT order_id) as orders,
  SUM(revenue) as total_revenue,
  SUM(fees + ad_spend) as total_costs,
  SUM(revenue - fees - ad_spend) as contribution_margin
FROM combined
GROUP BY 1, 2

Common Pitfalls to Avoid

Dashboard Examples for Analytics Teams

Once data is in your warehouse, build these essential views:

Executive Dashboard

SKU Performance Dashboard

For visualization best practices, see our Tableau dashboard guide or Looker Studio guide.

Advertising Performance Dashboard

Analytics Team Workflow

Recommended workflow for analytics teams taking on Amazon data:

Frequently Asked Questions

Getting Started

For most analytics teams, the fastest path to value is a specialized Amazon data provider with warehouse delivery. You skip months of pipeline development and get query-ready data in days.

The key is choosing a provider that delivers data clean enough for your existing analytics stack. Look for pre-normalized schemas, fee attribution, and multi-marketplace support.

Explore Nova's custom analytics capabilities and data delivery options Designed for analytics teams. See our guides on connecting to Snowflake and BigQuery.