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In Amazon operations, decision-making relies on a wide range of data that comes in various forms and is distributed across multiple pages. Traditionally, gathering and integrating this data into a single report requires significant time and manpower for data collection, cleaning, and analysis. However, such manual processes are often prone to errors and lack the timeliness that accurate decision-making demands. Pangolin’s Data Pilot was created to solve these issues, offering an easy-to-use tool for Amazon sellers to automate data collection, data cleaning, and cross-page report generation with just a few steps. This article will guide you through using Data Pilot to quickly and efficiently generate a customized Amazon operations report, introduce its key features and application scenarios, and provide comparisons with other advanced data collection solutions like Scrape API and Data API.

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1. The Challenges of Amazon Operations Data Collection

1. Data Dispersal Issues

Data critical to Amazon operations is scattered across various pages and includes multiple types, such as:

• Product Detail Page Data: This includes essential information like product title, description, price, stock status, etc.
• Category Ranking Data: Ranking information within specific categories helps sellers understand a product’s market performance.
• Review Data: Customer feedback and ratings directly affect conversion rates and purchasing decisions.
• Advertising Data: Data like click-through rate (CTR), ad spend, and conversion rate show the effectiveness of ad campaigns.
• BSR (Best Sellers Rank) Data: The Best Sellers Rank is essential for analyzing sales trends and competition.

These diverse data types are scattered across numerous pages and have complex structures, making manual consolidation both time-consuming and challenging.

2. Challenges of Traditional Data Collection Methods

In traditional data collection methods, both operations and technical personnel encounter significant challenges:

• Technical Challenges: Collecting data from Amazon often involves dealing with complex anti-crawling strategies, CAPTCHA recognition, proxy IP pool maintenance, and page structure parsing.
• Operational Challenges: Manually consolidating data from multiple reports and pages is time-consuming, prone to errors, and lacks the speed needed to support timely decisions.

3. The Impact of Data Quality on Decision-Making

Data quality is essential for effective decision-making, including accuracy, timeliness, and completeness. Inaccurate or outdated data can lead to misguided operational decisions. For example, if sales conversion data lags after an ad campaign, it may affect future budget allocations. Rapid and accurate data collection is crucial for Amazon sellers looking to gain a competitive edge.

2. Data Pilot Solution Overview

Data Pilot is a data collection and integration tool designed for small and medium-sized e-commerce sellers. It allows users to create customized operational reports seamlessly through intuitive configurations, greatly simplifying data collection and analysis. With its ease of use and powerful automation capabilities, Data Pilot helps streamline the data collection and reporting process.

1. Key Product Advantages

Data Pilot allows users to go through the entire process of data collection, cleaning, and generating customized operational reports in just a few steps. Its main advantages are:

• One-Stop Data Collection and Cleaning: Data Pilot covers the entire data collection and cleaning process without requiring additional tools.
• Cross-Page Data Integration: It supports data extraction from multiple pages, automatically consolidating information into a single report.
• Customized Report Generation: Users can configure headers and select data fields to meet personalized operational needs.
• WYSIWYG (What You See Is What You Get) Configuration: A simple configuration interface allows users to carry out complex data collection workflows, lowering the technical and communication barriers.

2. Functional Features

Data Pilot provides numerous practical features to meet diverse data collection needs:

• Intelligent Data Recognition: Data Pilot automatically recognizes data fields on pages, simplifying user operations.
• Flexible Header Configuration: Users can customize required fields and headers to ensure that the report aligns with their business needs.
• Multi-Source Data Integration: It supports data extraction from multiple pages and consolidates information into a single report.
• Automatic Data Update: Data Pilot can be scheduled to update data regularly without manual intervention, ensuring data timeliness.

3. Technical Innovations

Data Pilot’s robust intelligent data collection and cleaning engine achieves a high level of efficiency in the data integration process:

• Intelligent Collection Engine: Through AI technology, Data Pilot can automatically collect page data, adapting to dynamic changes in page structure.
• Automated Data Cleaning: It removes redundant data and ensures data formatting consistency, reducing the need for manual cleaning.
• Efficient Data Integration Algorithm: Supports seamless integration of different types of data from multiple pages, ensuring the report’s consistency and completeness.

3. Operation Guide: How to Generate a Customized Operations Report

1. Preparation

Before using Data Pilot to generate a report, users need to clarify the following:

• Determine Data Metrics: Define the key metrics required for the report, such as sales rank, review count, conversion rate, etc.
• Plan Report Structure: Design the layout and format of the report.
• Prepare Data Source Pages: Identify the required data source pages for Data Pilot to collect from.

2. Detailed Steps

Step 1: Configure Headers

• Select Data Fields: Choose the required fields (e.g., price, ranking) from Data Pilot’s field library.
• Set Data Format: Select appropriate formats, such as currency or percentages.
• Define Calculation Logic: Basic formulas such as “Sales = Price * Quantity” can be defined to automatically generate related values.

Step 2: Select Data

• Choose Data Source Pages: In the Data Pilot interface, users can select Amazon’s different pages (e.g., BSR, product details page).
• Mark Target Data Locations: Simply mark the desired data, and Data Pilot will automatically recognize and collect the relevant fields.
• Confirm Data Accuracy: Ensure that each field’s location is accurate to avoid collection errors.

Step 3: Generate Report

• Automatic Data Collection: Data Pilot begins collecting data from multiple pages.
• Cross-Page Data Integration: Automatically consolidates data from different pages into a single report.
• Report Format Output: Users can export reports in formats like CSV or Excel for easy analysis.

4. Application Case Study

1. Competitive Analysis Report

In a competitive analysis scenario, sellers can use Data Pilot to quickly generate a report containing competitor information, such as price, ranking, and reviews. Users only need to select the relevant data from the required pages and configure the headers to create a report containing multiple competitor metrics, allowing for more targeted marketing strategies.

• Background: Need for competitive analysis to optimize one’s product strategy.
• Steps: Define data metrics (such as price, ranking), choose data source pages, set headers, generate the report.
• Result: The report clearly displays competitors’ performance across various metrics.
• Value: Allows sellers to gain a clear understanding of the competition and adjust marketing strategies accordingly.

2. Sales Optimization Report

Data Pilot can also be used to create a sales optimization report by collecting data like conversion rates, ad click-through rates, and sales volume. This report can help sellers quickly identify areas for improvement in their sales strategy.

• Metric Selection: Define key metrics such as ad CTR and conversion rate.
• Configuration Process: Set relevant metrics fields, select data source pages, generate the report.
• Practical Application: Sellers can optimize ad strategies based on the report to improve conversion rates.
• ROI Analysis: Real-time strategy optimization improves overall ROI.

5. Other Advanced Data Collection Solutions

1. Scrape API Introduction

Scrape API is suitable for data service providers and IT teams needing large-scale data collection. By directly retrieving raw data from Amazon’s front-end pages, Scrape API achieves near real-time batch data collection, supporting a wide range of business needs.

• Application Scenarios: Ideal for businesses requiring vast amounts of data.
• Key Advantages: High processing power, precise parsing, and customizable fields.
• Technical Features: Allows custom fields to be added within a single page type, supporting user-defined data parsing.
• Example Application: Quickly generating daily keyword performance reports or monitoring real-time sales data.

2. Data API Introduction

Data API provides single-page parsing for medium and large enterprises. Users only need to provide an Amazon product ASIN or link to retrieve the required data. With customizable fields and high data parsing accuracy, Data API is ideal for medium and large sellers with API development capabilities.

• Product Positioning: Designed for medium and large sellers, IT teams.
• Main Features: Provides single-page API data calls, adaptable to multiple business scenarios.
• Technical Advantages: Directly retrieves clean data without page parsing, reducing development workload.
• Typical Users: Suitable for large e-commerce sellers with their own systems.

6. Product Selection Recommendations

1. Scenario Matching

• Small Sellers: Data Pilot is the ideal choice, allowing them to obtain required reports with minimal configuration.
• Medium Sellers: Data API is recommended, supporting flexible data calls with high customization.
• Large Sellers and Data Service Providers: Scrape API meets the needs for large-scale collection, supporting in-depth multi-dimensional data applications.

2. Cost-Benefit Analysis

• Labor Cost Savings: Reduces the workload of manual data collection.
• Time Efficiency Improvement: Automated data collection significantly reduces data retrieval time.
• Enhanced Decision Accuracy: Ensures data quality and timeliness, improving decision-making effectiveness.
• Investment Return Evaluation: Optimizes decision-making and improves sales conversion, significantly increasing ROI.

7. Conclusion and Outlook

Data Pilot greatly simplifies the data collection and report generation process needed for Amazon operations, reducing manual tasks and technical barriers for small to medium-sized sellers. Pangolin plans to further enhance Data Pilot’s data processing and automation capabilities, providing sellers with more diverse choices and efficient e-commerce data solutions.