Optimizing Amazon Brand Store Performance

Using insights to fuel Amazon Brand Store best practices, growth, and scale

Amazon now offers extensive performance insights for brand stores that provide metrics on your customer experience and products and recommendations on improving visibility.

These brand store insights are now available via Amazon APIs, which allows fast, automated access to data that allows your team new pathways to analyze, visualize, and optimize your customer experience investments.

Why Amazon Brand Stores? Enhancing Brand Visibility On Amazon

Amazon Stores allow you to showcase your brand and products in a multipage, immersive shopping experience. Amazon Brand Store enhances brand visibility and differentiation in a crowded marketplace, allowing sellers and vendors to showcase their products, tell their brand story, and engage customers with rich media content such as images and videos. This helps build brand loyalty and awareness and drives sales by providing a centralized location for customers to browse and purchase products directly from the brand.

Building Brand Affinity: Amazon Brand Stores Deliver Results

No matter the size of your brand, Amazon Stores gives you an immersive place to introduce audiences to your story, mission, and products.

Per Amazon, Brand stores have a direct impact on performance:

+83% higher dwell time. Stores with 3+ pages have 83% higher shopper dwell time and 32% higher attributed sales per visitor.
+35% higher attributed sales per visitor. On average, Stores updated within the past 90 days have 21% more repeat visitors and 35% higher attributed sales per visitor.

Brand Stores offer valuable insights into customer behavior and preferences through analytics, enabling brands to optimize their marketing strategies and product offerings.

Amazon Brand Store Examples: Analytics Insights

So, what type of data is available via API? There are three primary groups of store performance metrics;

  • Brand Performance
  • Product-level ASIN Performance
  • Quality and Recommendations

Brand Performance Metrics

All the available insight metrics for evaluating Brand Store performance on Amazon:

  • VIEWS: Number of page views.
  • ORDERS: Estimated total orders placed by Store visitors within 14 days of their visit. Orders contain one or more units sold.
  • UNITS: Estimated units purchased by Store visitors within 14 days of their last visit.
  • SALES: Estimated total sales generated by Store visitors within 14 days of their last visit.
  • VISITS: Total visits to a page within a single day. Each visitor can visit more than one page and your Store from multiple traffic sources.
  • VISITORS: Total visitors to your Store within the selected date range, calculated based on daily unique users or devices.
  • SCORE_LEVEL: Store Quality rating calculated on various factors defining the quality of a store. It can be HIGH, MEDIUM, or LOW.
  • RECOMMENDATIONS: An array of objects containing two fields: recommended action (e.g., “Add a video”) and observed average well time increase (the improvement it would bring in the overall store quality).
  • CONTRIBUTORS: An array of recommendations applied by the Store Owner improves overall store quality.
  • DWELL: Average time a customer spends in the store, specifically for store quality measurement.
  • PEER_DWELL: Average time a customer spends on other similar (peer) stores.
  • DWELL_TIME: Average time a customer spends in the store, providing insights into user engagement by calculating the average duration of visits.
  • BOUNCE_RATE: Ratio of total bounce visits (customers who landed at the store and left quickly without engaging) to total landing visits, providing insights into visitor engagement.
  • NEW_TO_STORE: Total count of unique visitors new to the store, providing valuable insights into the number of first-time shoppers.

Product-level ASIN Performance Metrics

Beyond store-wide metrics, Amazon also offers ASIN-specific data, allowing brands to drill down into the performance of individual products within their store. These metrics include views, orders, units, add-to-carts, and others, providing a granular view of how products perform and interact with potential customers.

  • VIEWS: Number of times a customer viewed an ASIN. It can happen once per page visit.
  • ORDERS: Estimated total orders placed by Store visitors on the day of the ASIN view. Orders can have one or more total units.
  • UNITS: Estimated units purchased by Store visitors during attributed orders for the ASIN.
  • ADDTOCARTS: Total number of times an ASIN was added to a cart by a customer on a store page.
  • IN_STOCK_VIEWS: Total views of an ASIN on a store page while the ASIN was in stock. For ASINs with variations, the customer must have selected a variation in stock to be counted.
  • AVERAGE_IN_STOCK_PRICE: Average price in local currency the ASIN was viewed at by customers while it was in stock.
  • IN_STOCK_RATE: Rate at which customers viewed an ASIN while it was in stock.
  • AVERAGE_SALE_PRICE: Average price in local currency for which the ASIN is sold during the order.
  • CONVERSION_RATE: Rate at which customers ordered a unit of the item over how many times customers clicked the item.
  • CLICKS: Count how often a customer clicks an ASIN-related widget on the store page.
  • CLICK_RATE: Rate at which the ASIN was clicked per view. This ratio can be above one if the widget interacts with a widget with engaging features.
  • RENDERS: Number of times the ASIN is rendered on a store page. Note — this does not guarantee that the customer saw the ASIN.
  • TOTAL_VIEWS: Total number of times customers viewed ASINs on the store’s pages. A view can happen once per store page visit.
  • TOTAL_CLICKS: Total count of times a customer clicked an ASIN-related widget on the store’s pages.

Quality and Recommendations Metrics

Amazon’s quality and recommendations metrics are particularly noteworthy. They focus on your Brand Store’s average dwell time, compare your performance to peer groups, and rate your store’s quality.

High ratings indicate effective engagement strategies, and Amazon uses these ratings to suggest specific actions further to improve your store’s performance and customer dwell time.

For example, Amazon will provide a score and ranking for your store

  • SCORE_LEVEL: High
  • DWELL: 77.93

The SCORE_LEVEL is a qualitative metric that assesses your store’s quality based on various factors, categorized as HIGH, MEDIUM, or LOW. It directly reflects the overall appeal and effectiveness of your store’s design and content. DWELL measures customers' average time in your store, offering insights into engagement and interest.

Amazon will also provide a collection of other recommendations to improve the score;

  • Add best selling products or recommended products tile to a subpage can improve the score by 0.4473
  • Add a background video to reinforce your brand message or showcase a product can improve the score by 0.4011

Get Started Automating Amazon Brand Store Performance Data — For Free.

The Amazon Brand Store Insights data offers a goldmine of opportunities for data analysis, which can be leveraged by Amazon Sellers and Vendors to refine their strategies, enhance product visibility, and ultimately drive sales.

Sign up for a 30-day free trial and request access to our Amazon Brand Store Performance Data beta.


Optimizing Amazon Brand Store Performance was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



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Optimizing Amazon Brand Store Performance

Using insights to fuel Amazon Brand Store best practices, growth, and scale

Amazon now offers extensive performance insights for brand stores that provide metrics on your customer experience and products and recommendations on improving visibility.

These brand store insights are now available via Amazon APIs, which allows fast, automated access to data that allows your team new pathways to analyze, visualize, and optimize your customer experience investments.

Why Amazon Brand Stores? Enhancing Brand Visibility On Amazon

Amazon Stores allow you to showcase your brand and products in a multipage, immersive shopping experience. Amazon Brand Store enhances brand visibility and differentiation in a crowded marketplace, allowing sellers and vendors to showcase their products, tell their brand story, and engage customers with rich media content such as images and videos. This helps build brand loyalty and awareness and drives sales by providing a centralized location for customers to browse and purchase products directly from the brand.

Building Brand Affinity: Amazon Brand Stores Deliver Results

No matter the size of your brand, Amazon Stores gives you an immersive place to introduce audiences to your story, mission, and products.

Per Amazon, Brand stores have a direct impact on performance:

+83% higher dwell time. Stores with 3+ pages have 83% higher shopper dwell time and 32% higher attributed sales per visitor.
+35% higher attributed sales per visitor. On average, Stores updated within the past 90 days have 21% more repeat visitors and 35% higher attributed sales per visitor.

Brand Stores offer valuable insights into customer behavior and preferences through analytics, enabling brands to optimize their marketing strategies and product offerings.

Amazon Brand Store Examples: Analytics Insights

So, what type of data is available via API? There are three primary groups of store performance metrics;

  • Brand Performance
  • Product-level ASIN Performance
  • Quality and Recommendations

Brand Performance Metrics

All the available insight metrics for evaluating Brand Store performance on Amazon:

  • VIEWS: Number of page views.
  • ORDERS: Estimated total orders placed by Store visitors within 14 days of their visit. Orders contain one or more units sold.
  • UNITS: Estimated units purchased by Store visitors within 14 days of their last visit.
  • SALES: Estimated total sales generated by Store visitors within 14 days of their last visit.
  • VISITS: Total visits to a page within a single day. Each visitor can visit more than one page and your Store from multiple traffic sources.
  • VISITORS: Total visitors to your Store within the selected date range, calculated based on daily unique users or devices.
  • SCORE_LEVEL: Store Quality rating calculated on various factors defining the quality of a store. It can be HIGH, MEDIUM, or LOW.
  • RECOMMENDATIONS: An array of objects containing two fields: recommended action (e.g., “Add a video”) and observed average well time increase (the improvement it would bring in the overall store quality).
  • CONTRIBUTORS: An array of recommendations applied by the Store Owner improves overall store quality.
  • DWELL: Average time a customer spends in the store, specifically for store quality measurement.
  • PEER_DWELL: Average time a customer spends on other similar (peer) stores.
  • DWELL_TIME: Average time a customer spends in the store, providing insights into user engagement by calculating the average duration of visits.
  • BOUNCE_RATE: Ratio of total bounce visits (customers who landed at the store and left quickly without engaging) to total landing visits, providing insights into visitor engagement.
  • NEW_TO_STORE: Total count of unique visitors new to the store, providing valuable insights into the number of first-time shoppers.

Product-level ASIN Performance Metrics

Beyond store-wide metrics, Amazon also offers ASIN-specific data, allowing brands to drill down into the performance of individual products within their store. These metrics include views, orders, units, add-to-carts, and others, providing a granular view of how products perform and interact with potential customers.

  • VIEWS: Number of times a customer viewed an ASIN. It can happen once per page visit.
  • ORDERS: Estimated total orders placed by Store visitors on the day of the ASIN view. Orders can have one or more total units.
  • UNITS: Estimated units purchased by Store visitors during attributed orders for the ASIN.
  • ADDTOCARTS: Total number of times an ASIN was added to a cart by a customer on a store page.
  • IN_STOCK_VIEWS: Total views of an ASIN on a store page while the ASIN was in stock. For ASINs with variations, the customer must have selected a variation in stock to be counted.
  • AVERAGE_IN_STOCK_PRICE: Average price in local currency the ASIN was viewed at by customers while it was in stock.
  • IN_STOCK_RATE: Rate at which customers viewed an ASIN while it was in stock.
  • AVERAGE_SALE_PRICE: Average price in local currency for which the ASIN is sold during the order.
  • CONVERSION_RATE: Rate at which customers ordered a unit of the item over how many times customers clicked the item.
  • CLICKS: Count how often a customer clicks an ASIN-related widget on the store page.
  • CLICK_RATE: Rate at which the ASIN was clicked per view. This ratio can be above one if the widget interacts with a widget with engaging features.
  • RENDERS: Number of times the ASIN is rendered on a store page. Note — this does not guarantee that the customer saw the ASIN.
  • TOTAL_VIEWS: Total number of times customers viewed ASINs on the store’s pages. A view can happen once per store page visit.
  • TOTAL_CLICKS: Total count of times a customer clicked an ASIN-related widget on the store’s pages.

Quality and Recommendations Metrics

Amazon’s quality and recommendations metrics are particularly noteworthy. They focus on your Brand Store’s average dwell time, compare your performance to peer groups, and rate your store’s quality.

High ratings indicate effective engagement strategies, and Amazon uses these ratings to suggest specific actions further to improve your store’s performance and customer dwell time.

For example, Amazon will provide a score and ranking for your store

  • SCORE_LEVEL: High
  • DWELL: 77.93

The SCORE_LEVEL is a qualitative metric that assesses your store’s quality based on various factors, categorized as HIGH, MEDIUM, or LOW. It directly reflects the overall appeal and effectiveness of your store’s design and content. DWELL measures customers' average time in your store, offering insights into engagement and interest.

Amazon will also provide a collection of other recommendations to improve the score;

  • Add best selling products or recommended products tile to a subpage can improve the score by 0.4473
  • Add a background video to reinforce your brand message or showcase a product can improve the score by 0.4011

Get Started Automating Amazon Brand Store Performance Data — For Free.

The Amazon Brand Store Insights data offers a goldmine of opportunities for data analysis, which can be leveraged by Amazon Sellers and Vendors to refine their strategies, enhance product visibility, and ultimately drive sales.

Sign up for a 30-day free trial and request access to our Amazon Brand Store Performance Data beta.


Optimizing Amazon Brand Store Performance was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



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via Openbridge

Private Amazon Catalog Keyword Tracker

The best Amazon keyword tracker is the one you own, fueled by data from Amazon’s API

You can now access keyword-level catalog data directly from Amazon APIs delivered to a private, trusted cloud warehouse or data lake you own. Choose industry leaders like Amazon Redshift, Google BigQuery, Snowflake, Databricks, Azure Data Lake, and Amazon Athena to store your data privately.

How Does The Amazon Catalog Keyword Tracker Work?

Unlike most keyword trackers, the data is direct from Amazon’s API. This means Amazon will tell you how it aligns the keyword you supplied to its catalog. This is Amazon saying, “This is how our system maps our catalog of products to a keyword.”

As a result, the keyword-to-product catalog offers authoritative linkages directly from Amazon about how a keyword aligns with its systems.

Amazon Catalog API vs Screenscraping

When comparing data sourced directly from Amazon APIs to data obtained through paths, typically screen scraping bots, several key differences emerge, impacting trustworthiness, usability, and strategic value.

Here’s a breakdown of these differences:

  • Authoritative: Data sourced directly from Amazon’s APIs is typically more accurate and reliable because it is provided by the platform. The APIs are designed to give developers, sellers, and vendors access to the most current and precise information about products, sales, and customer interactions.
  • Documented: The data provided through APIs is structured, well-documented, and designed for integration with existing systems and applications. Sellers and vendors have clear guidelines for interpreting and using the data. There is no mystery about how the data is sourced or the context of how it can be used.

For sellers and vendors prioritizing strategic decision-making based on reliable, compliant, and actionable insights, data from Amazon’s APIs represents a superior choice. It ensures adherence to legal and platform guidelines and provides a foundation for making informed decisions that can enhance competitive positioning and operational efficiency, given you know how the data is sourced and packaged.

Types Of Amazon Catalog Keyword Data Analysis

With data in hand, here are three types of data analysis that can be particularly impactful, along with reasons why sellers and vendors should care:

1. Keyword Optimization and Product Visibility Analysis

  • Description: By examining the relationship between products and their associated keywords, especially in the summaries and sales rank files, sellers can identify which keywords drive visibility and sales. Analysis can include identifying high-performing keywords, understanding keyword search frequency, and correlating specific keywords with sales performance.
  • Why It Matters: Keywords are the bridge between customers and products. Optimizing product listings with the right keywords can significantly enhance visibility, making products more likely to be discovered by potential buyers. This analysis can reveal gaps in keyword strategies, highlight opportunities to target less competitive keywords, and refine product titles, descriptions, and backend search terms for better search alignment.

2. Competitive Analysis and Market Positioning

  • Description: Utilizing vendor details, sales ranks, and product type data, vendors can conduct a thorough competitive analysis. This involves assessing which products (and, by extension, which sellers/vendors) dominate specific keyword categories, understanding the competitive landscape for various product types, and evaluating how competitors’ products are ranked and reviewed.
  • Why It Matters: In a marketplace as crowded as Amazon, understanding your competition is key to carving out a niche or maintaining a competitive edge. This analysis can inform sellers and vendors about where they stand with their competitors, identify high-demand but low-competition niches, and help strategize pricing, promotions, and product development to capture more market share.

3. Customer Preference and Trend Analysis

  • Description: By analyzing product summaries, including average ratings, review counts, and dimensions, along with sales rank data, sellers can gauge customer preferences and emerging trends. This analysis can reveal what product features or attributes (e.g., size, type, utility) customers favor and how these preferences change over time.
  • Why It Matters: Staying ahead or quickly adapting to customer preferences and trends is crucial for long-term success. Sellers and vendors can use this analysis to make informed decisions about product assortment adjustments, design improvements, and inventory management. Understanding customer preferences can also guide marketing strategies, allowing sellers to highlight product features most appealing to their target market.

For Amazon Sellers and Vendors, leveraging Amazon Product Catalog Keyword Tracker data for these analyses can provide insights to optimize listings, understand competitive dynamics, and align products with customer preferences.

This strategic approach enhances product discoverability and sales potential and helps make informed decisions regarding product development, marketing strategies, and inventory planning.

Get Started Automating Amazon Catalog Keyword Tracker Data — For Free.

The Amazon Catalog Keyword Tracker data offers a goldmine of opportunities for data analysis, which can be leveraged by Amazon Sellers and Vendors to refine their strategies, enhance product visibility, and ultimately drive sales.

Take control and own your keyword data. Ditch the bots and screen scrapers for code-free automation access to Amazon API product keyword data. Openbridge integration is a code-free, fully automated API integration.

Sign up for a 30-day free trial of our Amazon Catalog Keyword Tracker code-free automation.

References

Amazon Catalog Keyword Tracker | Openbridge Help Center


Private Amazon Catalog Keyword Tracker was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



from Openbridge - Medium https://ift.tt/7YgNC15
via IFTTT

Private Amazon Catalog Keyword Tracker

The best Amazon keyword tracker is the one you own, fueled by data from Amazon’s API

You can now access keyword-level catalog data directly from Amazon APIs delivered to a private, trusted cloud warehouse or data lake you own. Choose industry leaders like Amazon Redshift, Google BigQuery, Snowflake, Databricks, Azure Data Lake, and Amazon Athena to store your data privately.

How Does The Amazon Catalog Keyword Tracker Work?

Unlike most keyword trackers, the data is direct from Amazon’s API. This means Amazon will tell you how it aligns the keyword you supplied to its catalog. This is Amazon saying, “This is how our system maps our catalog of products to a keyword.”

As a result, the keyword-to-product catalog offers authoritative linkages directly from Amazon about how a keyword aligns with its systems.

Amazon Catalog API vs Screenscraping

When comparing data sourced directly from Amazon APIs to data obtained through paths, typically screen scraping bots, several key differences emerge, impacting trustworthiness, usability, and strategic value.

Here’s a breakdown of these differences:

  • Authoritative: Data sourced directly from Amazon’s APIs is typically more accurate and reliable because it is provided by the platform. The APIs are designed to give developers, sellers, and vendors access to the most current and precise information about products, sales, and customer interactions.
  • Documented: The data provided through APIs is structured, well-documented, and designed for integration with existing systems and applications. Sellers and vendors have clear guidelines for interpreting and using the data. There is no mystery about how the data is sourced or the context of how it can be used.

For sellers and vendors prioritizing strategic decision-making based on reliable, compliant, and actionable insights, data from Amazon’s APIs represents a superior choice. It ensures adherence to legal and platform guidelines and provides a foundation for making informed decisions that can enhance competitive positioning and operational efficiency, given you know how the data is sourced and packaged.

Types Of Amazon Catalog Keyword Data Analysis

With data in hand, here are three types of data analysis that can be particularly impactful, along with reasons why sellers and vendors should care:

1. Keyword Optimization and Product Visibility Analysis

  • Description: By examining the relationship between products and their associated keywords, especially in the summaries and sales rank files, sellers can identify which keywords drive visibility and sales. Analysis can include identifying high-performing keywords, understanding keyword search frequency, and correlating specific keywords with sales performance.
  • Why It Matters: Keywords are the bridge between customers and products. Optimizing product listings with the right keywords can significantly enhance visibility, making products more likely to be discovered by potential buyers. This analysis can reveal gaps in keyword strategies, highlight opportunities to target less competitive keywords, and refine product titles, descriptions, and backend search terms for better search alignment.

2. Competitive Analysis and Market Positioning

  • Description: Utilizing vendor details, sales ranks, and product type data, vendors can conduct a thorough competitive analysis. This involves assessing which products (and, by extension, which sellers/vendors) dominate specific keyword categories, understanding the competitive landscape for various product types, and evaluating how competitors’ products are ranked and reviewed.
  • Why It Matters: In a marketplace as crowded as Amazon, understanding your competition is key to carving out a niche or maintaining a competitive edge. This analysis can inform sellers and vendors about where they stand with their competitors, identify high-demand but low-competition niches, and help strategize pricing, promotions, and product development to capture more market share.

3. Customer Preference and Trend Analysis

  • Description: By analyzing product summaries, including average ratings, review counts, and dimensions, along with sales rank data, sellers can gauge customer preferences and emerging trends. This analysis can reveal what product features or attributes (e.g., size, type, utility) customers favor and how these preferences change over time.
  • Why It Matters: Staying ahead or quickly adapting to customer preferences and trends is crucial for long-term success. Sellers and vendors can use this analysis to make informed decisions about product assortment adjustments, design improvements, and inventory management. Understanding customer preferences can also guide marketing strategies, allowing sellers to highlight product features most appealing to their target market.

For Amazon Sellers and Vendors, leveraging Amazon Product Catalog Keyword Tracker data for these analyses can provide insights to optimize listings, understand competitive dynamics, and align products with customer preferences.

This strategic approach enhances product discoverability and sales potential and helps make informed decisions regarding product development, marketing strategies, and inventory planning.

Get Started Automating Amazon Catalog Keyword Tracker Data — For Free.

The Amazon Catalog Keyword Tracker data offers a goldmine of opportunities for data analysis, which can be leveraged by Amazon Sellers and Vendors to refine their strategies, enhance product visibility, and ultimately drive sales.

Take control and own your keyword data. Ditch the bots and screen scrapers for code-free automation access to Amazon API product keyword data. Openbridge integration is a code-free, fully automated API integration.

Sign up for a 30-day free trial of our Amazon Catalog Keyword Tracker code-free automation.

References

Amazon Catalog Keyword Tracker | Openbridge Help Center


Private Amazon Catalog Keyword Tracker was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



from Openbridge - Medium https://ift.tt/JEIseMj
via Openbridge

Own Your Amazon Price Tracker Data

The best Amazon price tracker is the one you own

You can now directly access ASIN-level product tracker data directly from Amazon APIs. Unlike other product pricing tracking tools, Openbridge delivers product pricing data directly to a private cloud warehouse or data lake like Amazon Redshift, Google BigQuery, Snowflake, Databricks, Azure Data Lake, and Amazon Athena. You own the data.

There are no bots or screen scraping. Direct from Amazon’s API, pricing data unlocks new opportunities for your efforts with competitive intelligence, sales rank tracking, buy box insights, and more.

Direct, Authoritative Amazon Product Pricing Data

This rich, extensive ASIN-level product data comes directly from Amazon to your private cloud warehouse or data lake every hour. Here is a collection of key data elements included in the feeds:

  1. ASIN & Seller ID: Identifies the product and the seller.
  2. Lowest Prices: Includes details about the lowest prices for different conditions and fulfillment channels.
  3. Sales Rank: Provides the sales ranking of the product in specific categories.
  4. Price Details: Includes listing price, shipping cost, and points value.
  5. Item Condition: Indicates the condition of the items (e.g., New, Used, Collectible).
  6. Total Offer Count: The number of total offers available for the ASIN.
  7. Number of Offers by Condition and Fulfillment Channel: Breakdown of offers based on their condition (e.g., New, Used) and fulfillment channel.
  8. Buy Box Winner & Featured Merchant: Indicates if the seller is a Buy Box winner or a featured merchant.
  9. Buy Box Prices: Details of prices for Buy Box eligible offers.
  10. Buy Box Eligible Offers: Information about offers eligible for the Buy Box is critical as the Buy Box winner is often the default choice for customers.
  11. Ships From: Location details from where the product is shipped.
  12. Fulfillment by Amazon (FBA): Indicates whether Amazon fulfills the order.
  13. Prime Information: Whether the offer is prime eligible and its type.
  14. Seller Feedback Rating and Counts: This represents the percentage of positive feedback and the total number of feedback entries received by the seller.

Custom, Owned Amazon Product Pricing Tracker

Our code-free, automated, and unified delivery of product pricing data enables 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.

Power BI Amazon product pricing tracking chart

Here are all the different types of analysis you can undertake with direct access to product pricing data:

  1. Price Analysis Over Time: Analyze how prices of products change over time. This can be done by tracking the price history of specific ASINs (Amazon Standard Identification Numbers) and identifying patterns or trends.
  2. Competitive Analysis: Compare prices and conditions (new, used, etc.) different sellers offer for the same products. This helps in understanding Amazon's competitive landscape for various products.
  3. Seller Performance Analysis: Evaluate sellers’ performance based on metrics like number of sales, the buying box winner frequency, and fulfillment method (Fulfilled by Amazon or not). This can help identify top-performing sellers in each category.
  4. Product Condition Impact: Investigate how the condition of a product (new, used, refurbished, etc.) affects its price and saleability.
  5. Fulfillment Method Analysis: Compare the performance of products fulfilled by Amazon versus those fulfilled by sellers directly in terms of sales volume, customer preference (Prime eligibility), and pricing.
  6. Time Series Forecasting: Use historical data to forecast future price trends, sales, or demand for certain products or categories.
  7. Geographical Analysis: If location data is available, analyze geographical trends in pricing and sales. Understand which products are popular in specific regions.
  8. Customer Review Impact: If linked with customer review data, analyze how ratings and reviews impact the sales and pricing of products.
  9. Inventory Management Insights: For sellers, understanding how inventory levels relate to prices and sales can help in efficient inventory management.
  10. Market Gap Analysis: Identify potential market gaps by analyzing products with high demand but low competition or where current offerings are not meeting customer satisfaction.

Get Started Automating Amazon Product Pricing API Data— For Free.

Take control and own your data. Ditch the bots and screen scrapers for code-free automation access to Amazon API product pricing data. Openbridge integration is a code-free, fully automated API integration.

Sign up for a 30-day free trial of our Amazon Product Pricing Data code-free automation.


Own Your Amazon Price Tracker Data was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



from Openbridge - Medium https://ift.tt/KN6fXcD
via IFTTT

Own Your Amazon Price Tracker Data

The best Amazon price tracker is the one you own

You can now directly access ASIN-level product tracker data directly from Amazon APIs. Unlike other product pricing tracking tools, Openbridge delivers product pricing data directly to a private cloud warehouse or data lake like Amazon Redshift, Google BigQuery, Snowflake, Databricks, Azure Data Lake, and Amazon Athena. You own the data.

There are no bots or screen scraping. Direct from Amazon’s API, pricing data unlocks new opportunities for your efforts with competitive intelligence, sales rank tracking, buy box insights, and more.

Direct, Authoritative Amazon Product Pricing Data

This rich, extensive ASIN-level product data comes directly from Amazon to your private cloud warehouse or data lake every hour. Here is a collection of key data elements included in the feeds:

  1. ASIN & Seller ID: Identifies the product and the seller.
  2. Lowest Prices: Includes details about the lowest prices for different conditions and fulfillment channels.
  3. Sales Rank: Provides the sales ranking of the product in specific categories.
  4. Price Details: Includes listing price, shipping cost, and points value.
  5. Item Condition: Indicates the condition of the items (e.g., New, Used, Collectible).
  6. Total Offer Count: The number of total offers available for the ASIN.
  7. Number of Offers by Condition and Fulfillment Channel: Breakdown of offers based on their condition (e.g., New, Used) and fulfillment channel.
  8. Buy Box Winner & Featured Merchant: Indicates if the seller is a Buy Box winner or a featured merchant.
  9. Buy Box Prices: Details of prices for Buy Box eligible offers.
  10. Buy Box Eligible Offers: Information about offers eligible for the Buy Box is critical as the Buy Box winner is often the default choice for customers.
  11. Ships From: Location details from where the product is shipped.
  12. Fulfillment by Amazon (FBA): Indicates whether Amazon fulfills the order.
  13. Prime Information: Whether the offer is prime eligible and its type.
  14. Seller Feedback Rating and Counts: This represents the percentage of positive feedback and the total number of feedback entries received by the seller.

Custom, Owned Amazon Product Pricing Tracker

Our code-free, automated, and unified delivery of product pricing data enables 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.

Power BI Amazon product pricing tracking chart

Here are all the different types of analysis you can undertake with direct access to product pricing data:

  1. Price Analysis Over Time: Analyze how prices of products change over time. This can be done by tracking the price history of specific ASINs (Amazon Standard Identification Numbers) and identifying patterns or trends.
  2. Competitive Analysis: Compare prices and conditions (new, used, etc.) different sellers offer for the same products. This helps in understanding Amazon's competitive landscape for various products.
  3. Seller Performance Analysis: Evaluate sellers’ performance based on metrics like number of sales, the buying box winner frequency, and fulfillment method (Fulfilled by Amazon or not). This can help identify top-performing sellers in each category.
  4. Product Condition Impact: Investigate how the condition of a product (new, used, refurbished, etc.) affects its price and saleability.
  5. Fulfillment Method Analysis: Compare the performance of products fulfilled by Amazon versus those fulfilled by sellers directly in terms of sales volume, customer preference (Prime eligibility), and pricing.
  6. Time Series Forecasting: Use historical data to forecast future price trends, sales, or demand for certain products or categories.
  7. Geographical Analysis: If location data is available, analyze geographical trends in pricing and sales. Understand which products are popular in specific regions.
  8. Customer Review Impact: If linked with customer review data, analyze how ratings and reviews impact the sales and pricing of products.
  9. Inventory Management Insights: For sellers, understanding how inventory levels relate to prices and sales can help in efficient inventory management.
  10. Market Gap Analysis: Identify potential market gaps by analyzing products with high demand but low competition or where current offerings are not meeting customer satisfaction.

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Own Your Amazon Price Tracker Data was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.



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