SEO标签：Customer Says,Amazon data scraping,Review API,review data collection,Amazon API,ecommerce data analysis,product review scraping,data mining,API solutions,Pangolin API,Amazon crawler,review insights,ecommerce operations,data driven,technical solutions
配图提示词：Professional data analytics dashboard displaying Amazon Customer Says review analysis, featuring modern UI with sentiment analysis charts, keyword clouds, and customer insight metrics, sleek blue and green color palette, high-tech interface design
图片标题：Customer Says Data Collection Technical Solution Visualization Interface
图片替代文本：Professional technical interface showing Customer Says data collection and analysis, including review trend charts and sentiment analysis results
图片说明：Customer Says data collection analysis interface implemented through professional API technology
图片描述：This image showcases a modern data visualization interface that clearly presents Amazon Customer Says data collection and analysis results, including review sentiment analysis, trend changes, and key customer insights
When Amazon gradually closed its traditional Review API interface in 2023, many e-commerce professionals who relied on review data for product analysis found themselves in a predicament. However, Amazon’s introduction of the Customer Says feature provides us with richer and more structured review insight data, but how to completely collect this data has become a new technical challenge.
The core difficulty in Customer Says data collection lies in its dynamic loading mechanism and complex data structure. Unlike traditional static review pages, Customer Says employs advanced JavaScript rendering technology to intelligently categorize and analyze user reviews, generating multi-dimensional structured data including positive feedback, negative opinions, product feature evaluations, and more. While this technical architecture enhances user experience, it significantly increases the technical threshold for data collection.
Traditional web scraping techniques often fall short when dealing with Customer Says data. Simple HTTP requests cannot obtain complete data content, and even when using browser automation tools like Selenium, issues such as incomplete data loading, anti-scraping mechanism triggers, and low collection efficiency are frequently encountered. More critically, the data structure of Customer Says changes frequently, making the cost of maintaining scraping code extremely high.
In this context, professional API solutions have become the optimal choice. Taking Pangolin Scrape API as an example, it has been deeply optimized for Amazon Customer Says data collection and can stably obtain complete datasets including review keywords, sentiment tendencies, product feature evaluations, customer focus points, and more. Compared to self-built scraping systems, API solutions not only have significant advantages in data completeness and collection efficiency but, more importantly, can continuously adapt to Amazon platform’s technical changes.
From a technical implementation perspective, Customer Says data collection needs to address challenges at multiple levels. First is the data identification level, which requires accurately locating the position and loading timing of the Customer Says module on the page; second is the data parsing level, which requires understanding Amazon’s data structure and extracting key information; finally is the data integration level, which requires organizing scattered review fragments into meaningful analysis results.
The specific technical implementation can be understood through the following code example:
import requests
import json
from datetime import datetime
class CustomerSaysCollector:
def __init__(self, api_key):
self.api_key = api_key
self.base_url = "https://api.pangolinfo.com"
def get_customer_says_data(self, asin, marketplace='US'):
"""
Get complete Customer Says data
"""
endpoint = f"{self.base_url}/scrape/customer-says"
params = {
'api_key': self.api_key,
'asin': asin,
'marketplace': marketplace,
'include_sentiment': True,
'include_keywords': True,
'parse_structure': True
}
response = requests.get(endpoint, params=params)
if response.status_code == 200:
data = response.json()
return self.parse_customer_says(data)
else:
raise Exception(f"API request failed: {response.status_code}")
def parse_customer_says(self, raw_data):
"""
Parse Customer Says raw data
"""
customer_says = raw_data.get('customer_says', {})
return {
'positive_aspects': customer_says.get('positive_aspects', []),
'negative_aspects': customer_says.get('negative_aspects', []),
'mentioned_features': customer_says.get('mentioned_features', []),
'sentiment_distribution': customer_says.get('sentiment_distribution', {}),
'keyword_frequency': customer_says.get('keyword_frequency', {}),
'review_highlights': customer_says.get('review_highlights', []),
'collected_at': datetime.now().isoformat()
}
# Usage example
collector = CustomerSaysCollector('your_api_key')
asin = 'B08N5WRWNW'
customer_data = collector.get_customer_says_data(asin)
print(f"Positive aspects: {customer_data['positive_aspects']}")
print(f"Negative feedback: {customer_data['negative_aspects']}")
print(f"Mentioned features: {customer_data['mentioned_features']}")
This API-driven collection approach has obvious advantages over traditional scraping. In terms of data completeness, professional APIs can obtain over 98% of Customer Says content, while self-built scrapers often can only obtain 60-70% of the data. In terms of collection efficiency, API solutions can process tens of thousands of ASINs’ Customer Says data per hour, while traditional scrapers, limited by anti-scraping mechanisms, often have efficiency less than one-tenth of API solutions.
In terms of data quality control, Customer Says data collection also requires establishing comprehensive validation mechanisms. Due to the dynamic nature of review data, regular validation of data accuracy and completeness is necessary. Professional API services typically provide data quality reports, including collection success rates, data completeness, anomaly detection, and other metrics to help users timely discover and resolve data quality issues.
For enterprise users requiring large-scale Customer Says data collection, a distributed collection architecture is recommended. Through reasonable task allocation and load balancing, collection efficiency can be significantly improved while ensuring data quality. Additionally, establishing data caching and incremental update mechanisms can avoid repeatedly collecting the same data, further optimizing system performance.
From a business application perspective, the value of Customer Says data far exceeds traditional review data. Through deep analysis of sentiment tendencies and keyword distributions in Customer Says, core product selling points and potential issues can be quickly identified, providing data support for product optimization and marketing strategies. Particularly in competitive analysis scenarios, Customer Says data can provide more precise market insights.
It’s worth noting that Customer Says data collection must strictly comply with relevant laws, regulations, and platform rules. When conducting data collection, request frequency should be reasonably controlled to avoid placing excessive burden on the platform. Additionally, collected data should only be used for legitimate business analysis purposes and not for malicious competition or other illegal activities.
Looking to the future, with the development of artificial intelligence technology, the value of Customer Says data will be further highlighted. Combined with natural language processing and machine learning technologies, deeper consumer insights can be mined from Customer Says data, providing more intelligent decision support for e-commerce operations.
In summary, while Amazon’s closure of the traditional Review API interface brought challenges, the emergence of Customer Says data provides us with better alternatives. By choosing appropriate technical solutions and professional API services, high-quality Customer Says data collection can be fully achieved, providing strong data support for e-commerce business development.
