Enhancing Amazon Business Efficiency

Overview

MarketForce is a multi-user system designed to enhance the efficiency of managing catalogue data across Amazon and non-Amazon platforms. The system facilitates parallel processing for multiple users, enabling simultaneous data fetching for various seller accounts. This includes managing product data such as inventory, sales, and costs by fetching data from Amazon and importing data from non-Amazon sources. Once the raw data is fetched and imported, comparisons are made to determine data accuracy and identify the platform responsible for sales efficiency. It is engaged not only with the Sellers but also with the Vendor data and so the data analysis of both Seller and Vendor platforms are done in a single channel.

Modules:

MarketForce comprises several modules, each with distinct functionalities:

Analytics Overview: The system displays comprehensive data on overall revenue, orders, and units ordered, based on reports fetched from Amazon. The Sales Overview graph includes a comparison of the current year's sales (revenue) with the previous year. A country map visualises revenue data by region. To facilitate accurate regional sales analysis, the data is converted using currency conversion. This data is synchronised with specific dates to provide detailed insights.

Performance: The system presents marketplace-specific data obtained from the SP API and advertising metrics. It accurately calculates revenue derived from advertisements and provides detailed monthly comparisons of key metrics such as sales, spending, impressions, and clicks. These metrics are effectively represented graphically to display both historical and current data for advertising clicks, expenditure, sales, revenue, and orders

Account Status: Displays the health status of accounts across multiple marketplaces. It highlights the Defective Orders count, Shipping Delays, Authenticity Products, Product Safety, Policy Violations, Valid Trackings, Dissatisfaction Returns, Product Safety, shows the Account Health Rating, Customer Service Performance. Show the details of Faulty Invoices and the rate of Negative Feedbacks, A-Z guarantee claims, Chargeback claims of the data that are fetched from the seller performance reports. Account Health Rating count that shows the details about the Suspected Intellectual Property Violations, Received Intellectual Property Complaints, Product Authenticity Customer Complaints, Product Condition Customer Complaints, Food and Product Safety Issues, Listing Policy Violations, Restricted Product Policy Violations, Customer Product Reviews Policy Violations, Other Policy Violations

Collection of Reports: Provides a comprehensive set of reports that can be analysed and exported. This includes various Amazon SP API reports like fba_order_report, fba_manage_inventory, catalog_api, etc.

Inventory Module: Offers real-time data on in-stock and out-of-stock quantities. It also provides inventory forecasts based on past data and current stock levels, which can further be customised by adjusting the Vendor Lead Time and the Outbound Lead Time along with the Days Of Stock and calculating the sales velocity to predict the future stock needs. It helps in knowing the Forecast Suggested quantities that need to be Replenished in order to remain always in-stock. It also includes the functionality of sending Email Alert when the quantities are going out of stock.

Catalogue Overview: Imports non-Amazon catalogue data for storage in a MySQL database. The important parameters used here are - Sales Channel, Number of Products that are Active and Catalog Health count of the products. Within which the Buy box performance is also measured with the help of the Buy box data fetched from the Item Offer API. This data is then compared with Amazon catalogue data.

Amazon Catalog Health: Fetches the data from the Amazon SP Catalog API and compares the product data with the Non-Amazon (imported) data, Compares the Child ASIN data like Category, Subcategory, EAN, Variation, Bullet points, Item Width etc.. and highlights the discrepancies to recognize sales based on the comparison.

Amazon LQS: Basically a Quality test module which helps understand the quality of the product which is listed in Amazon. And for that first It fetches product details like the Product Title, ASIN, SKU which are Active sellers in Amazon from the fba_merchant_listing report and other details like Review count, Ratings, Review Score, Buy box and Quantity and fetched from the Rainforest Live API as they scrap the current data from the Amazon which then calculates the score by adding various criterias like if the Review Score is greater then specified characters and contains required amount of weight that means counts of characters multiplies by the weight provides the internal score then decides the quality of the product.

About the Client

GROWERS, a conglomerate of specialised brands, aimed to support online sellers in enhancing their e-commerce presence. Seeking efficiency in managing Amazon businesses, GROWERS envisioned a software solution to analyse account data across multiple users and marketplaces while ensuring content quality and competitiveness.

What were the challenges faced ?

We faced 1 challenge of ensuring the accuracy and timeliness of the data fetched from the Rainforest Live API, as the scraped data from Amazon must be up-to-date and precise to correctly calculate the product quality score based on various criteria such as review count, ratings, and buy box status.

What Solution we provided?

To address the challenge of ensuring the accuracy and timeliness of the data fetched from the Rainforest Live API, we implemented several key strategies:

Scheduled Data Updates: We set up a robust. Data Integrity Checks: We developed and implemented a series of data integrity checks to validate the data as it is fetched. These checks help identify and correct any discrepancies or inaccuracies in real-time. Fallback Mechanisms: We incorporated fallback mechanisms that trigger alternative data sources or methods if the Rainforest Live API data is delayed or unavailable. Caching Strategies: We implemented intelligent caching strategies to store frequently accessed data, reducing the need for constant API calls and ensuring faster data retrieval times while maintaining accuracy. Monitoring and Alerts: We established a comprehensive monitoring system that continuously tracks the data fetching process. Alerts are generated for any anomalies or delays, allowing for immediate intervention and resolution.