Amazon Rapid Retail Analytics: New API for Real-time Metrics

Unleash the power of your data with Amazon Rapid Retail Analytics for real-time insights for Amazon Vendor Central and Seller Central.

Amazon Vendor Central and Seller Central have a new real-time API, Rapid Retail Analytics. Openbridge has been partnering with Amazon on a closed beta of the new API, and we are excited to announce it has finally been released.

Amazon’s Retail Rapid Analytics API for Vendors and Sellers offers insight into key performance benchmarks, including hourly sales, traffic, and inventory data. At the moment, Sellers only have access to Sales data. Vendors can access Sales, Traffic, and Inventory, a game changer.

Here are the three core datasets in this release;

📊 Sales

• Ordered Units: It represents the number of units Amazon customers order for a specific ASIN within a given time frame. This number can be negative if there are more cancellations than orders.
• Ordered Revenue: The total revenue generated from the aggregated ordered product sales for the specified ASIN within a given time frame. Adjustments are made to account for cancellations.

📦 Inventory:

• Highly Available Inventory Units: This metric represents the number of units available for sale on the Amazon website across different regions and apps. It is based on the units in Amazon fulfillment centers that are in a sellable condition, excluding units in customer shopping carts or allocated for other orders.

🚀 Traffic:

• Glance Views: It indicates the number of times customers have viewed the product detail page for a specific ASIN. The views are counted when a particular merchant is the featured offer for the product. For vendors, this metric is applicable when retail is the featured offer. Glance views are reported based on the Amazon Retail Analytics manufacturing view.

Examples of Rapid Retail Analytics API Data

Here are some examples of the data included in the hourly data feeds:

• Sales: Hourly sales data would show two units of ASIN “B123456789” ordered between 7:00 pm and 8:00 pm UTC on February 20th, 2023, which generated revenue of 37.98.
• Traffic: Hourly traffic data would show ASIN “B123456789” received 5,000 “glance views” between 7:00 and 8:00 pm UTC on February 20th, 2023.
• Inventory: Hourly inventory data would show that ASIN “B123456789” had 9,000 units highly available between 7:00 pm and 8:00 pm UTC on February 20th, 2023.

The “highly available” metric refers to the number of units available for sale on Amazon with the fastest shipping speed, typically Prime, based on units in a sellable condition in Amazon’s fulfillment centers. This number considers units unavailable for sale, such as those in customer shopping carts or reserved for other orders. The quantity shown reflects this metric when a customer sees a product available to buy directly from Amazon. It’s important to note that this metric differs from “Sellable Units,” which doesn’t factor in known demand when determining inventory counts.

Real-world Use Cases For Retail API Data

We have included a collection of thought-starter SQL queries for the Retail API data. These queries aim to provide a starting point for understanding how the data can be used to drive insights that inform efficient business growth.

Unleashing Business Insights With Amazon Rapid Retail Analytics

Say Goodbye to Manual Processes with Amazon’s Retail Rapid Analytics API

Traditionally, Vendors have had to rely on manual Amazon Vendor Central Retail Analytics report downloads or legacy EDI services to access their sales data from retailers. These methods were often time-consuming and prone to errors, as they required manual generation and downloading of reports. This manual process resulted in delayed insights and an inability to make timely business decisions.

Amazon’s Retail Rapid Analytics API is a significant shift for Vendors, providing new levels of speed and automation previously unavailable. By leveraging high-velocity data, Vendors can gain deeper insights into their business performance, make more informed decisions, and drive growth and profitability.

RocketBike Soars with Real-Time Rapid Retail Analytics Insights

RocketBike, one of the early beta testers, was thrilled to compare the new API against the traditional data access methods. They noted that Vendor real-time data provides unprecedented levels of automation and accelerates the velocity of understanding vendor business performance.

As Michael Swenson, the CMO at Rocketbike, puts it;

“We use Vendor real-time data to drive growth and profitability for our clients by gaining insights into sales, traffic, and inventory faster than ever before.”

Fast Access to ASIN Level Sales, Inventory, and Traffic Data

There are three new data sets available to Vendors:

• Real-time Sales: This metric supports the Ordered Units and Ordered Revenue, giving you access to sales data in real time for the US, CA, MX, BR, UK, DE, FR, IT, ES, NL, PL, SE, BE, EG, TR, SA, AE, IN, SG, AU, JP.
• Real-time Inventory: This metric supports the Quantites and Sku, providing inventory data in real time for the US, CA, MX, BR, UK, DE, FR, IT, ES, NL, PL, SE, BE, EG, TR, SA, AE, IN, SG, AU, JP.
• Real-time Traffic: This metric supports the Views, Buy Box Percentage, and Sessions, allowing you to track your traffic in real time for the US, CA, MX, BR, UK, DE, FR, IT, ES, NL, PL, SE, BE, EG, TR, SA, AE, IN, SG, AU, JP.

Check out our Amazon Rapid Retail Analytics API documentation for a deep dive.

Seamless, Code-free Rapid Retail API Automation Unleashed

Openbridge collaborated with Amazon in a closed beta to provide automated data feeds, without the need for code, to quickly deliver Vendor data directly to Amazon Redshift, Amazon Redshift Spectrum, Google BigQuery, Snowflake, Azure Data Lake, and Amazon Athena.

This fast and unified data access enabled teams to utilize their preferred analytical tools, such as Google Data Studio, Tableau, Microsoft Power BI, Looker, or Amazon Quicksight, for various purposes, including machine learning, business intelligence, data modeling, and online analytical processing.

Get Started Automating Amazon Rapid Retail Analytics API Data for Amazon Vendor Central — For Free.

Ditch the messy, manual report downloads for code-free automation access to the new Amazon’s Retail Rapid Analytics. Openbridge integration is a code-free, fully-automated API integration. By providing Vendors with access to high-velocity data, they can accelerate the speed at which the team can gain valuable insights that deliver data-driven growth and profit.

Sign up for a 30-day free trial of our Amazon Rapid Retail Analytics API code-free automation.

References

• The power of hourly insights: Introducing rapid retail analytics
• Amazon Rapid Retail Analytics Connector | Openbridge Help Center
• Report Type Values

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Unleashing Business Insights With Amazon Rapid Retail Analytics

Rapid Retail Analytics SQL Queries

Real-world SQL queries to jumpstart your retail data analysis efforts

In the ever-evolving landscape of e-commerce, harnessing the power of data is crucial for businesses striving to drive efficient growth. As an Amazon Vendor, or Seller, you can access new near real-time Amazon Rapid Retail data feeds that unlock actionable insights and enable informed data-driven decision-making.

What Can I Do With Amazon Rapid Retail Analytics Data?

Rapid Retail Analytics provides a view of ASIN-level sales, traffic, and inventory performance on the Amazon platform. This is the first time Amazon has offered this data type to Vendors, enabling new paths for data-driven decisions that improve performance and stay ahead of the competition.

Here are five areas of opportunity this new retail data will unlock. There are more areas of opportunity, but these are the obvious and immediate areas;

1. Identifying Top Selling Products: One of the fundamental aspects of driving growth is knowing which products perform exceptionally well. We can use SQL queries to uncover the top-selling ASINs (Amazon Standard Identification Number) based on sales volume and revenue. This information allows businesses to allocate resources effectively, prioritize marketing efforts, and identify potential areas for expansion.
2. Analyzing Sales Trends Over Time: Tracking sales performance over time is crucial for identifying patterns, seasonality, and overall growth trajectory. By employing SQL queries, we can generate insightful visualizations that illustrate the daily sales trends, empowering businesses to make data-driven decisions regarding inventory management, marketing campaigns, and resource allocation.
3. Monitoring Inventory Levels: Maintaining optimal inventory levels is essential for meeting customer demand and maximizing sales potential. SQL queries can monitor inventory levels in real-time, ensuring products are readily available while minimizing the risk of stockouts. By leveraging this data, businesses can strike the right balance between inventory holding costs and customer satisfaction.
4. Assessing Traffic and Customer Engagement: Understanding customer engagement and traffic patterns is vital to efficient business growth. By analyzing the provided traffic data, SQL queries can unveil which ASINs receive the highest number of glance views. With this information, businesses can fine-tune marketing strategies, improve product visibility, and enhance customer engagement, ultimately driving sales and expanding their customer base.
5. Combining Data Sources for Comprehensive Insights: By combining sales, inventory, and traffic data through SQL queries, businesses can gain a holistic understanding of their product performance. These integrated insights provide a complete picture of sales velocity, inventory turnover, and the impact of customer engagement on overall business growth. With this knowledge, businesses can optimize operations, identify areas for improvement, and capitalize on growth opportunities.

Whether you are a data scientist, analyst, or business owner, understanding how to utilize this data effectively can unlock opportunities for efficient business growth. We will explore how to query this data by leveraging new near real-time sales, inventory, and traffic information.

For a deeper dive into this data, see our doc Amazon Retail Analytics API.

Jump below for examples of how to query this data.

Getting Started Answering Key Performance Questions

We have included a collection of thought-starter SQL queries. These queries aim to provide a starting point for understanding how the data can be used to drive insights that inform efficient business growth.

Remember to customize the queries based on your specific business goals, timeframes, and any additional dimensions or filters you may require. Lastly, they may require slight modifications for your SQL environment.

Analyzing Sales Data:

Retrieve the total ordered units and revenue for each ASIN:

SELECT asin, SUM(ordered_units) AS total_units, SUM(ordered_revenue) AS total_revenue FROM sp_vendor_rt_sales GROUP BY asin;

Calculate the daily sales summary:

SELECT ob_date, SUM(ordered_units) AS total_units, SUM(ordered_revenue) AS total_revenue FROM sp_vendor_rt_sales GROUP BY ob_date;

Identify the top-selling ASINs based on units or revenue:

SELECT asin, SUM(ordered_units) AS total_units FROM sp_vendor_rt_sales GROUP BY asin ORDER BY total_units DESC LIMIT 10;

Analyzing Inventory Data

Calculate a daily inventory summary:

SELECT ob_date, SUM(highly_available_inventory) AS total_inventory FROM sp_vendor_rt_inventory GROUP BY ob_date;

Identify ASINs with low inventory levels:

SELECT asin, SUM(highly_available_inventory) AS total_inventory FROM sp_vendor_rt_inventory GROUP BY asin HAVING total_inventory < desired_threshold;

Analyzing Traffic Data

Retrieve the total glance views for each ASIN:

SELECT asin, SUM(glance_views) AS total_glance_views FROM sp_vendor_rt_traffic GROUP BY asin;

Retrieve the total glance views, by the hour, for each ASIN, over the past 30-days:

SELECT asin, DATE_TRUNC('hour', event_time) AS hour, SUM(glance_views) AS total_glance_views
FROM sp_vendor_rt_traffic
WHERE event_time >= NOW() - INTERVAL '30 days'
GROUP BY asin, hour;

Calculate the daily traffic summary:

SELECT ob_date, SUM(glance_views) AS total_glance_views FROM sp_vendor_rt_traffic GROUP BY ob_date;

Identify ASINs with high traffic

SELECT asin, SUM(glance_views) AS total_glance_views FROM sp_vendor_rt_traffic GROUP BY asin HAVING total_glance_views > desired_threshold;

Combining Data Sources:

Join the sales, inventory, and traffic data for a comprehensive analysis:

SELECT s.asin, s.total_units, i.total_inventory, t.total_glance_views FROM (SELECT asin, SUM(ordered_units) AS total_units FROM sp_vendor_rt_sales GROUP BY asin) s JOIN (SELECT asin, SUM(highly_available_inventory) AS total_inventory FROM sp_vendor_rt_inventory GROUP BY asin) i ON s.asin = i.asin JOIN (SELECT asin, SUM(glance_views) AS total_glance_views FROM sp_vendor_rt_traffic GROUP BY asin) t ON s.asin = t.asin;

Tableau, Power BI, Looker

You can use Rapid Retail Analytics data in tools like Tableau, Looker, Power BI, or Amazon QuickSight.

Below is a collection of views that can be created as overlays on the data. Views can be a very convenient way to encapsulate logic and make that available to any user on your team.

To create a set of views that can be used in tools like Tableau, Power BI, or Looker for time series performance reports based on the provided data feeds, you can define the following views:

Sales Performance View

This view provides aggregated sales performance metrics for each day.

CREATE VIEW vw_sales_performance AS
SELECT ob_date AS date,
       SUM(ordered_units) AS total_units,
       SUM(ordered_revenue) AS total_revenue
FROM sp_vendor_rt_sales
GROUP BY ob_date;

Inventory Performance View

This view provides aggregated inventory metrics for each day.

CREATE VIEW vw_inventory_performance AS
SELECT ob_date AS date,
       SUM(highly_available_inventory) AS total_inventory
FROM sp_vendor_rt_inventory
GROUP BY ob_date;

Traffic Performance View

This view provides aggregated traffic metrics for each day.

CREATE VIEW vw_traffic_performance AS
SELECT ob_date AS date,
       SUM(glance_views) AS total_glance_views
FROM sp_vendor_rt_traffic
GROUP BY ob_date;

Combined Performance View

This view combines sales, inventory, and traffic metrics for each day.

CREATE VIEW vw_combined_performance AS
SELECT s.ob_date AS date,
       s.ordered_units AS total_units,
       s.ordered_revenue AS total_revenue,
       i.highly_available_inventory AS total_inventory,
       t.glance_views AS total_glance_views
FROM sp_vendor_rt_sales s
JOIN sp_vendor_rt_inventory i ON s.asin = i.asin AND s.ob_date = i.ob_date
JOIN sp_vendor_rt_traffic t ON s.asin = t.asin AND s.ob_date = t.ob_date;

Exploring Relationships Between Sales, Traffic, and Inventory

The types of analysis you can perform with the data can get very sophisticated. Below are a collection of exploratory and conceptual areas of analysis.

Identify the correlation between daily sales revenue and glance views for a specific ASIN.

SELECT s.ob_date, s.ordered_revenue, t.glance_views FROM sp_vendor_rt_sales s JOIN sp_vendor_rt_traffic t ON s.asin = t.asin AND s.ob_date = t.ob_date WHERE s.asin = 'desired_asin' ORDER BY s.ob_date;

Determine the top-selling ASINs based on the correlation between ordered and highly available inventory units.

SELECT s.asin, SUM(s.ordered_units) AS total_units, SUM(i.highly_available_inventory) AS total_inventory FROM sp_vendor_rt_sales s JOIN sp_vendor_rt_inventory i ON s.asin = i.asin GROUP BY s.asin ORDER BY total_units DESC;

Calculate the average revenue per ordered unit for each ASIN monthly.

SELECT DATEPART(MONTH, s.start_time) AS month, s.asin, SUM(s.ordered_revenue) / SUM(s.ordered_units) AS avg_revenue_per_unit FROM sp_vendor_rt_sales s GROUP BY DATEPART(MONTH, s.start_time), s.asin;

Find the ASINs with the highest revenue growth rate between two consecutive months.

WITH monthly_revenue AS ( SELECT asin, DATEPART(MONTH, start_time) AS month, SUM(ordered_revenue) AS revenue FROM sp_vendor_rt_sales GROUP BY asin, DATEPART(MONTH, start_time) ) SELECT current.asin, (current.revenue - previous.revenue) / previous.revenue AS growth_rate FROM monthly_revenue current JOIN monthly_revenue previous ON current.asin = previous.asin AND current.month = previous.month + 1 ORDER BY growth_rate DESC;

Identify the day of the week with the highest average revenue for a specific ASIN.

SELECT DATEPART(WEEKDAY, s.start_time) AS weekday, AVG(s.ordered_revenue) AS average_revenue 
FROM sp_vendor_rt_sales s WHERE s.asin = ‘desired_asin’ 
GROUP BY DATEPART(WEEKDAY, s.start_time) 
ORDER BY average_revenue DESC;

These strategies leverage multiple tables to analyze and derive insights from the data. Remember to replace desired_asin with the specific ASIN you want to analyze.

Find the ASINs with the highest revenue growth rate between two consecutive days:

WITH daily_revenue AS (
SELECT asin, ob_date, SUM(ordered_revenue) AS revenue
FROM sp_vendor_rt_sales
GROUP BY asin, ob_date
)
SELECT current.asin, (current.revenue - previous.revenue) / previous.revenue AS growth_rate
FROM daily_revenue current
JOIN daily_revenue previous ON current.asin = previous.asin AND current.ob_date = DATEADD(DAY, 1, previous.ob_date)
ORDER BY growth_rate DESC;

Identify changes in the “highly available” metric that impact sales negatively. The SQL query calculates the negative impact on sales when the “highly available” metric decreases:

SELECT
  i.start_time,
  i.asin,
  i.highly_available_inventory - lag(i.highly_available_inventory) OVER (PARTITION BY i.asin ORDER BY i.start_time) AS inventory_change,
  s.ordered_units - lag(s.ordered_units) OVER (PARTITION BY s.asin ORDER BY s.start_time) AS sales_change
FROM
  sp_vendor_rt_inventory i
JOIN
  sp_vendor_rt_sales s ON i.asin = s.asin AND i.start_time = s.start_time
WHERE
  inventory_change < 0 AND sales_change < 0;

Openbridge Report Automation

Openbridge Rapid Retail automation is a code-free, fully-automated integration to the Rapid Retail API. Teams ensure they get up-to-date and accurate data from official, certified Amazon APIs. With data stored in a unified data warehouse like Amazon Redshift, Amazon Redshift Spectrum, Google BigQuery, Snowflake, Azure Data Lake, and Amazon Athena, you can combine different Amazon datasets to gain a more holistic view of the business.

Your team can then leverage best-in-class analytics and business tools like Google Data Studio, Tableau, Microsoft Power BI, Looker, or Amazon Quicksight.

Get Started Automating Rapid Retail Analytics — For Free

Whether it’s identifying top-selling products, analyzing sales trends, monitoring inventory levels, assessing customer engagement, or combining multiple data sources, the power lies in understanding, interpreting, and acting upon the insights derived from the data.

Embrace the data, harness its potential, and embark on your journey to be a more data-driven organization.

Sign up for a 30-day free trial of our Amazon Rapid Retail Report automation.

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Amazon Brand Metrics: Introducing New-To-Brand Analytics

Amazon Advertising Brand Metrics

Amazon Advertising now provides accessible new-to-brand metrics for Sponsored Brands and Sponsored Display campaigns, allowing for the measurement and optimization of ongoing campaigns. These metrics aim to facilitate the development of marketing strategies that can effectively increase customer acquisition and foster brand loyalty.

What is “New To Band”?

An order is considered new-to-brand if the customer has not purchased from that particular brand during that timeframe.

So how are these metrics calculated? Amazon will model the customer’s purchase history for the past 12 months to determine whether an order is new to a brand. The list below details available new-to-brand metrics for sponsored ads campaigns and keywords.

1. New-to-brand orders: The count of initial orders for products within the brand during a 1-year observation period.
2. % of orders new-to-brand: The percentage of total orders that are new-to-brand orders. This metric is calculated by dividing the number of new-to-brand orders by the total number of orders and multiplying by 100.
3. New-to-brand sales: The total sales (in local currency) generated by new-to-brand orders.
4. % of sales new-to-brand: The percentage of sales (in local currency) attributed to new-to-brand orders. This metric is calculated by dividing the total sales of new-to-brand orders by the total sales and multiplying by 100.
5. New-to-brand units: The number of units purchased through new-to-brand orders.
6. % of units new-to-brand: The percentage of total units acquired through new-to-brand orders. This metric is calculated by dividing the number of new-to-brand units by the total number of units and multiplying by 100.
7. New-to-brand order rate (for Sponsored Brands only): The ratio of new-to-brand orders to the number of clicks. This metric is calculated by dividing the number of new-to-brand orders by the number of clicks and multiplying by 100.

How Can New To Brand Metrics Be Utilized?

Leveraging new-to-brand metrics can optimize Sponsored Brands and Sponsored Display campaigns, facilitating new customer acquisition and fostering long-term brand relationships.

Below are some recommendations for optimizing customer acquisition by identifying or creating Sponsored Brands or Sponsored Display campaigns with a specific focus on this goal:

1. After the campaign has gathered a minimum of 14 days’ worth of data, closely analyze the campaign’s new-to-brand keyword metrics. Filter keywords based on acceptable ROAS (Return on Advertising Spend) or ACOS (Advertising Cost of Sales) values.
2. From the resulting set of keywords, identify those with the highest number of new-to-brand orders. These keywords represent potential targets for driving new-to-brand orders.
3. Evaluate the metrics related to new-to-brand units and sales, as they may reveal keywords that generate new-to-brand orders with higher price points and basket sizes.
4. Continuously monitor the campaign’s new-to-brand performance over time using the performance dashboard, making necessary adjustments as required.

These metrics can also apply to broader, strategic efforts to refine positioning, competitive pressures, and product;

1. Identifying Untapped Market Segments: New-to-brand metrics can provide valuable insights into customer behavior and preferences. Businesses can uncover untapped market segments by analyzing the characteristics and demographics of customers who make new-to-brand purchases. This information can guide marketing and advertising strategies to target these specific segments, tailoring messages and campaigns to attract new customers more likely to engage with the brand.
2. Assessing Competitive Positioning: New-to-brand metrics can also offer insights into a company’s competitive positioning. By comparing the percentage of new-to-brand orders and sales against competitors in the same industry, businesses can gauge their market penetration and identify areas for improvement. If the percentage of new-to-brand orders is lower than competitors, it may indicate a need to enhance brand awareness, expand marketing efforts, or improve product differentiation to attract new customers.
3. Influencing Product Development: New-to-brand metrics can inform product development decisions by highlighting customer preferences and purchase patterns. By analyzing the new-to-brand units and sales metrics for different products, businesses can identify which products are more successful in attracting new customers. This information can guide product expansion or innovation strategies, focusing on developing new offerings that align with customer preferences and have a higher potential for attracting new customers to the brand.

Where Can These Metrics Be Found?

New-to-brand data has been available for Sponsored Brands campaigns since November 1, 2018. However, if a start date before November 1, 2018, is chosen, the new-to-brand metrics will be calculated based on November 1, 2018, as the starting point.

As for Sponsored Display, new-to-brand metrics have been available for Sponsored Display since May 1, 2021. These metrics are available for seller and vendor product targeting campaigns and seller and vendor audience targeting campaigns.

Amazon Brand Metrics Report Data Automation

Take control of your Amazon business with Openbridge, the solution that automates Amazon Brand Metrics API integrations and centralizes data storage in a unified data warehouse. Say goodbye to manual report downloads and errors in merging and tracking reports.

By leveraging automation, Openbridge eliminates the need for cumbersome manual downloads, ensuring you have up-to-date and accurate data directly from Amazon APIs. With data securely stored in a unified data warehouse such as Amazon Redshift, Amazon Redshift Spectrum, Google BigQuery, Snowflake, Azure Data Lake, and Amazon Athena, combining various Amazon reports becomes a breeze, allowing you to gain a comprehensive and holistic view of your business.

Experience the benefits of an automated and streamlined process that grants you access to all your crucial data in one trusted and private location. Empower your team to utilize best-in-class analytics and business tools like Google Data Studio, Tableau, Microsoft Power BI, Looker, or Amazon Quicksight.

Take action now and optimize your Amazon Seller or Vendor operations with Openbridge. Simplify your data management and unlock the full potential of your business insights.

Sign up for a 30-day free trial of our Amazon Advertising Brand Metrics Report automation.

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