Amazon Vendor Retail Analytics & Sell-in (demo dataset)

**This listing is for an anonymized example dataset**.

Stop managing Vendor Central downloads, API errors, latency, and historical restatements.

Reason Automation provides critical Amazon 1P Vendor sell-out, sell-in, and operational performance data. Our team of ex-Amazon retail data experts delivers lower-latency, higher-quality Amazon data, ready for everything from reporting to Cortex AI.

- Accounting-grade accuracy: stop asking "why doesn't this match Vendor Central?"
- Ultra-low latency: our intelligent workflow checks Amazon systems
- Modeling expertise: our data pipeline is backed by robust data dictionaries, user manuals, and ERDs
- Actual support: dedicated implementation and technical support, trained by ex-Amazonians

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**How it works**

1. **Connect** -- you authorize Reason to access your Amazon vendor, seller, brand analytics, or advertising data.
2. **Setup** -- we create an optimized Snowflake database of your data, with all available history from Amazon.
3. **Data share** -- Reason directly shares the database with your Snowflake instance.
4. **Maintain** -- We manage updates, maintenance, and schema changes from Amazon. You focus on data-driven decision-making.

原始信息汇总

Amazon Vendor Retail Analytics & Sell-in (demo dataset)

概述

• 数据集用途：为与亚马逊有批发（1P）关系的品牌提供匿名示例数据集。
• 提供商：Reason Automation
• 试用：免费试用
• 数据特点：
  • 会计级准确性
  • 超低延迟
  • 建模专业知识
  • 实际支持

业务需求

• 业务绩效回顾
• 转化分析
• 盈利能力监控
• QBR和谈判准备
• 库存管理
• 退款恢复

数据字典

• DEFECT_REPORT
• ORDERS_CONFIRMED_POS
• PRODUCT_CATALOG
• RETAIL_ANALYTICS_NET_PPM
• RETAIL_ANALYTICS_SALES
• • 4 objects

数据预览（部分列）

• ID
• Timestamp_NTZ
• _CREATED_ON
• _LAST_UPDATED_ON
• _REVISION
• _PARTNERUUID
• ISSUE_ID
• FINANCIAL_CHARGE
• QUANTITY
• CREATION_DATE
• CARTON_LABEL_TYPE
• INFRACTION_SUBTYPE_CODE
• CARTON_LABEL_VALUE
• CARRIER_TRACKING_#PRO#
• CARTON_OR_PACKAGE_RECEIVE_DATE
• VENDOR_SUBMITTED_FREIGHT_READY_DATE_PST
• ACTUAL_FREIGHT_PICKUP_TIME_PST
• FIRST_PLANNED_PICKUP_TIME_PST
• ISD_RECEIVED_TIMESTAMP
• FREIGHT_PICKUP_CARRIER_SCAC
• VCBS_VC_ATTRIBUTE_VEHICLE_RUN_ID_HEADER_CODE
• BOL_FROM_ASN
• PRO_#_IN_ASN
• BOL_INPUT_FROM_ASN
• CARRIER_PRO
• ASN_#
• BILL_OF_LADING_#
• CARTON_WEIGHT
• CARTON_WEIGHT_UNITS
• VENDOR_CODE
• ISSUE_TYPE
• DISPUTE_BY
• CHARGE_INVOICE_#
• NOTES
• STATUS
• REVERSAL_INVOICE_#
• EARLY_DAYS
• SHIP_OR_DELIVERY_WINDOW_END
• VCBS_VC_ATTRIBUTE_DAYS_BETWEEN_CONFIRMATION_AND_WINDOW_START_HE
• DAYS_LATE
• ORDER_TYPE
• PURCHASE_ORDER_#
• FREIGHT_TERMS
• SHIP_OR_DELIVERY_WINDOW_START
• ROUTING_REQUEST_CREATION_DATE
• SUB_TYPE_OF_THE_NON_COMPLIANCE
• INBOUND_SHIPMENT_DELIVERY_ISD_ID
• DEFECT_RATE
• VCBS_VC_ATTRIBUTE_PO_QUANTITY_CONFIRMED_BEFORE_CUTOFF-HEADER
• STANDARD_CARRIER_ALPHA_CODE
• ACTUAL_DATE

使用示例

每日销售快照

sql SELECT "DATE", "ASIN", "ORDERED_REVENUE", "ORDERED_UNITS", "SHIPPED_REVENUE", "SHIPPED_COGS", "SHIPPED_UNITS", "CUSTOMER_RETURNS" FROM "RETAIL_ANALYTICS_SALES" WHERE "PERIOD" = DAILY AND "DISTRIBUTOR_VIEW" = Manufacturing AND "PROGRAM" = Amazon Retail ORDER BY "DATE" DESC, "ASIN" ASC;

采购订单绩效

sql SELECT "ORDER_DATE", "ASIN", SUM("COST") AS COST, SUM("QUANTITY_ORDERED") AS QUANTITY_ORDERED, SUM("QUANTITY_CONFIRMED") AS QUANTITY_CONFIRMED, SUM("QUANTITY_RECEIVED") AS QUANTITY_RECEIVED, SUM("QUANTITY_CONFIRMED") / NULLIF(SUM("QUANTITY_ORDERED"),0) AS CONFIRMATION_RATE FROM "ORDERS_CONFIRMED_POS" GROUP BY 1, 2 ORDER BY 1 DESC

退款详情

sql SELECT "CREATION_DATE", "ISSUE_TYPE", "STATUS", "PURCHASE_ORDER_#", "ASIN", "FINANCIAL_CHARGE", "QUANTITY" FROM "DEFECT_REPORT" ORDER BY 1 DESC

类别

• Commerce
• Inventory Management

联系方式

• 销售：[e-mail]
• 支持：[e-mail]

数据更新频率

• 每日

时间覆盖范围

• 最近3年
• 按天

地理覆盖范围

• 全球
• 按国家

云区域可用性

AWS

• Asia Pacific (Osaka)
• Asia Pacific (Seoul)
• Asia Pacific (Sydney)
• Asia Pacific (Tokyo)
• 31 More

法律条款

• Standard

关于Reason Automation

• 为1P供应商品牌、3P卖家、代理商和软件合作伙伴提供企业级亚马逊财务数据集。由前亚马逊供应商经理构建。

搜集汇总

背景与挑战

背景概述

该数据集是一个匿名化的演示示例，专为Amazon供应商设计，用于替代传统的Vendor Central数据管理方式，提供高精度、低延迟的销售与运营数据。它通过集成Snowflake数据库实现数据共享，并由专业团队负责维护更新，帮助用户专注于数据驱动的决策。