Discover the power of web data extraction for ecommerce and uncover the valuable insights you can enable with data extraction.
With the explosion in the number of ecommerce websites, web data extraction has become an essential ingredient for ecommerce businesses looking to differentiate themselves from the competition.
From getting insights into customer behavior to analyzing pricing trends, web data extraction can generate valuable information that empowers your ecommerce business to increase sales and maximize profits.
This ultimate guide discusses the benefits of web data extraction for your ecommerce business, how data extraction works, what types of data you can extract and much more!
Read on to get started today leveraging web data extraction to grow your ecommerce business.
Introduction to Web Data Extraction for Ecommerce
What is Web Data Extraction for Ecommerce?
Web data extraction is the process of collecting and consolidating data from websites and web pages. It typically involves collecting data from several web pages then transforming the data into a structured format such as CSV, Excel and/or loading the data into a database or data lake.
Web data extraction for ecommerce is the process of compiling data from ecommerce websites; other data sources within, related to or adjacent to the ecommerce industry; or data providers that inform decision-makers in ecommerce companies.
Web data extraction for ecommerce is a critical component of modern ecommerce businesses, as it enables the businesses to gain critical insights into their competitors' activities, optimize pricing strategies, track market trends and consumer preferences, analyze sales performance data, optimize inventory levels and shipping times, and identify promotional opportunities.
Types of Web Data Extraction: Manual vs Automated
There are two main kinds of web data extraction: manual and automated.
Manual web data extraction primarily uses human effort to collect data from websites while automated data extraction leverages tooling and software to minimize or eliminate manual effort.
Manual Web Data Extraction
When performing manual web data extraction, you go through each webpage on each source website, copying and pasting the data you require into a spreadsheet. You may sometimes semi-automate such work by leveraging the Import Data function of the spreadsheet.
Manual data extraction is typically time-consuming and labor-intensive. It is also prone to manual error. Therefore, you run the high risk of your data being inaccurate, incomplete or inconsistent, when you perform manual data extraction.
Automated Web Data Extraction
With automated web data extraction, you leverage software to codify and execute the data collection steps that you have outlined.
Therefore, automated web data extraction is faster, more efficient and produces higher quality data than does manual data extraction.
Automated web data extraction can enable businesses to quickly and easily collect large amounts of data from multiple websites with zero to minimal manual effort.
Automation makes it simple and efficient for ecommerce businesses to capture product prices, customer reviews, stock availability and much more; as structured data and in a fraction of the time that manual data collection would require.
Web Data Extraction Use Cases for Ecommerce Businesses
Let's dive into how ecommerce businesses use web data extraction.
Ecommerce companies leverage web data extraction to gain a competitive edge and drive growth through several use cases, such as:
- Monitor competitor pricing
- Track product availability
- Analyze customer reviews
- Perform social media sentiment analysis
- Identify high-demand products
- Analyze market trends
- Perform product data analytics
- Generate sales leads
- Boost search engine optimization (SEO)
- Enhance supply chain management
- Identify unauthorized sellers
#1. Monitor competitor pricing: Generate competitive intelligence
Web data extraction automates competitor price monitoring for ecommerce businesses by enabling them to get updates on the latest prices of competitors' products.
Web data extraction can provide an ecommerce business with price change data as it occurs, such as in real-time or in near-real-time; on a scheduled basis (intra-day, daily, weekly); or on-demand, when requested by the ecommerce company.
With the knowledge ecommerce companies gain from extracting competitor prices, ecommerce businesses can make informed pricing decisions for their own products and adjust their marketing strategies, in order to stay ahead of the competition.
Price monitoring competitive intelligence can be that key differentiator that enables your ecommerce business to stay profitable and thrive in a highly-competitive market.
#2. Track product availability: Efficiently manage inventory
Web data extraction enables ecommerce businesses to track product availability across multiple suppliers.
Consequently, ecommerce businesses can make timely decisions to restock products and update or remove product listings from their websites.
Inventory management enables ecommerce businesses to mitigate or avoid out-of-stock products, minimize overstocking and ultimately boost the customer experience.
Efficient inventory management is an essential activity for the success of any ecommerce business.
#3. Analyze customer reviews: Respond to customer feedback
Customer reviews are a critical input for any ecommerce company. Ecommerce companies leverage customer reviews in several ways.
Ecommerce companies use customer reviews to identify and address actual and perceived issues with their products, customer service or other aspects of the customer experience, such as shipping times, product packaging, refund durations, etc.
Ecommerce companies that actively monitor complaints can respond to issues in a timely manner and prevent costly reputational damage or even expensive legal costs that can arise from faulty products.
Customer reviews can enable ecommerce companies with critical insights on how to improve or better position product offerings, in order to maximize sales.
Customer review analytics can also enable ecommerce businesses to identify emerging customer demand trends and to monitor the performance of competitor products across several websites.
Web data extraction enables ecommerce companies to gain a wide range of insights from customer reviews and ultimately inform decisions that can boost revenue, shrink costs, increase profit margins and scale growth for the business.
#4. Perform social media sentiment analysis: Gain insights from Twitter, Instagram & Tiktok
Sentiment analysis is a natural language processing (NLP) technique that extracts and categorizes opinions expressed in text, to determine the writer's attitude or emotional state towards a particular topic or product.
Social media sentiment analysis is a powerful capability for ecommerce businesses to learn about customer perception of their brand and products, directly from social media platforms, such as Twitter, Instagram and TikTok.
Social media posts can be the most authentic source of customer feedback and perception. This is because social media users tend to feel more comfortable expressing their opinions and experiences in their own words on social media. The reason for this is that users are typically more comfortable on such platforms and can communicate in a community and style with which they are more familiar.
By leveraging web data extraction, ecommerce businesses can collect authentic user feedback data and gain critical perception insights from user-generated content (UGC) on multiple social media platforms.
Ecommerce businesses can leverage data extraction to capture valuable data from social media posts that enables informed decision-making, improves product offerings, enhances the customer experience and, ultimately, drives sales growth.
#5. Identify high-demand products: Optimize supply chain
Ecommerce companies use data extraction software to collect data from several kinds of sources, such as keyword search trends, product reviews and social media mentions and hashtags, to identify actual or potentially popular products.
By extracting and analyzing such data from multiple sources, ecommerce companies gain valuable insights into which products are in high demand, enabling companies to adjust their inventory accordingly and optimize their supply chain management for the most popular products.
#6. Analyze market trends: Get strategic demand insights
Ecommerce companies can leverage data extraction to monitor macro market trends, such as the effects of economic cycles (E.g., inflation, recession) on product demand and supply; as well as the impact of interest rates, unemployment levels, demographic changes and geopolitical events.
At the micro level, ecommerce companies can track and estimate product and consumer trends, such as popular product categories, best-selling products and seasonal trends (E.g., summer products, holiday gifts)
Web data extraction enables ecommerce companies to collect and analyze critical market trends data, to enable companies gain an insights-driven competitive edge, by adjusting their product offerings and marketing strategies to stay in tune with market cycles and meet evolving customer demand.
#7. Perform product data analytics: Make data-driven decisions
Ecommerce companies leverage data extraction to drive the inputs they require to implement various types of product analytics to gain insights into product performance and customer behavior.
For example, ecommerce companies leverage business intelligence (BI) and descriptive analytics to examine historical sales data and customer activity to identify patterns and trends, such as best-selling products, peak sales periods or customer demographics.
Additionally, ecommerce companies extract data to feed predictive analytics models that forecast future product demand and can enable optimizing inventory management and setting sales targets.
For example, an ecommerce company can implement a simple regression model in Excel or leverage a machine learning (ML) model to analyze past sales data and predict which products will be in high demand during the upcoming holiday season.
Ecommerce companies also perform diagnostic analytics to identify the root causes of issues, such as poor product performance or unexpected customer behavior; for example, high rates of cart abandonment, product returns, web page bounces or email unsubcribes. Diagnostic analytics equip a business to make the data-driven optimizations required to fix such issues.
Additional analytics use cases that web data extraction powers for ecommerce companies include the ability to perform competitor analysis that compares their product performance with that of competitors; market basket analysis to understand which products are frequently purchased together; and customer segmentation to group customers based on similar characteristics or behavioral patterns.
By extracting and utilizing internal and third-party / external data to power product data analytics, ecommerce companies make data-driven decisions that strengthen their product offerings, improve the customer experience and drive business growth.
#8. Generate sales leads: Automate lead generation at scale
Ecommerce companies use web data extraction to generate sales leads by collecting data on potential customers, such as their contact information, interests and behavior.
Ecommerce companies can then analyze such information to identify patterns and preferences that drive the creation and execution of targeted marketing campaigns and outreach strategies that will maximize lead-to-customer conversion rates.
Ecommerce companies use web data extraction for lead generation by pulling data from various sources, such as online directories, forums, social media platforms and blogs.
#9. Boost SEO: Rank high for Google and Bing searches
Ecommerce companies can use web data extraction to collect data from search engine results pages (SERPs). Examples of data that ecommerce companies extract from search results include competitor web pages, as well as top-performing keywords.
Ecommerce companies can then leverage such SEO (search engine optimization) data to expand, enhance and optimize their web page portfolio, particularly their product pages and blog posts. Companies leverage such SEO data to identify the best keywords for product titles and descriptions, as well as for blog post topics.
An ecommerce company that implements a well-informed keyword-enabled strategy drives higher search engine rankings for its website and boosts traffic volume for its product pages and blog posts.
By extracting data on keywords and competitor websites, ecommerce companies can make data-driven decisions to enhance their SEO strategy and upgrade their search engine visibility.
#10. Optimize supply chain management: Eliminate bottlenecks
Ecommerce companies can leverage web data extraction to inform data-driven decision-making that optimizes their supply chain management.
For example, an ecommerce company can extract data on product or customer sentiment, inventory levels, delivery times and supplier performance. The company can then leverage such data to forecast product demand, fine tune inventory levels, reduce order fulfillment times and ensure timely delivery of products to customers.
Ecommerce companies can analyze supply chain data to identify bottlenecks in their supply chain and take steps to improve efficiency and reduce costs.
With accurate and up-to-date data, ecommerce companies can make data-driven decisions to ensure that products are available when customers want to buy them and minimize the risk of overstocking or stockouts.
#11. Identify unauthorized sellers: Detect fraudulent sales
Ecommerce companies implement web data extraction to monitor various sources, such as marketplaces, forums and social media platforms, in order to identify illegitimate product advertisements, listings and transactions.
Web data extraction can enable ecommerce businesses to identify unauthorized sellers, including those that sell fake/counterfeit products.
Counterfeit product transactions on social media platforms are quite common, where unauthorized sellers advertise and sell fake products using social media.
For example, a seller on Instagram can claim to be selling authentic luxury handbags at a discounted price, but in reality, they are selling replica handbags. Customers may be lured in by the low price and the appearance of the product, but end up receiving a poor-quality, fake item instead of the genuine product.
Web data extraction enables ecommerce companies with the data they need to protect their sales revenue and brand reputation by taking action against fraudsters.
Extracting Product Data in Ecommerce: Types and Examples
Product information and specifications
There is a wide variety of data that ecommerce companies extract for products, so let's break them down one at a time.
Product name & description
Ecommerce companies use web data extraction to extract product names and descriptions. Such information helps buyers to correctly and efficiently identify products.
Accurate and complete product names and descriptions also enable ecommerce companies to refine their SEO efforts and optimize marketing campaigns.
Product category & subcategory
Ecommerce businesses can use web data extraction to extract and consolidate accurate and complete product category and subcategory information from multiple websites.
High-quality product category and subcategory information enables ecommerce companies to better organize their products and improve the searchability and discoverability of products on their websites.
Well-categorized data on an ecommerce website also helps with SEO, as search engines can index the site more effectively, positioning the site for increased traffic volume and higher-quality, better-converting site visitors.
Product images & videos
High-quality product images and videos are essential in helping ecommerce website visitors make informed decisions and converting such visitors into buyers.
Ecommerce businesses use web data extraction to capture optimal multimedia assets from websites for use in product pages and marketing campaigns, enhancing the customer experience on their websites and increasing marketing conversion rates.
When extracting product images and videos, it is important for ecommerce companies to ensure that they have permission to use such assets, in order to avoid legal issues or copyright infringements.
For more information on the legality of web data extraction, check out Is Web Data Extraction Legal?
Product price is a major factor in attracting visitors to and converting such visitors to buyers on an ecommerce website.
Therefore, ecommerce companies always require up-to-date price information, in order to ensure website visitors are making decisions based on correct information.
Listing incorrect prices on an ecommerce website can lead to lost sales opportunities, negative sales margins and even harm the company's reputation.
Web data extraction enables ecommerce companies to efficiently collect and compare product prices from multiple sources, empowering ecommerce businesses to optimize their pricing strategies and enhance website conversion rates.
Ecommerce companies can use web data extraction to monitor product availability on supplier and competitor sites, enabling ecommerce businesses to stay informed on when products are available or out-of-stock.
Such competitive intelligence can provide valuable insights on consumer demand and potential opportunities for ecommerce businesses to offer alternative products and/or more competitive prices.
Product ratings & reviews
Using data extraction to compile product ratings and reviews from multiple sources can equip ecommerce businesses with valuable data to gain insights on customer perception of and feedback on products, including customer experience with and customer complaints of products.
Comprehensive and up-to-date information on product ratings and reviews enables ecommerce companies to make data-driven decisions on how they can enhance and refine their product offerings, strengthen customer satisfaction and optimize marketing strategies.
Many ecommerce products come in several variations, such as sizes and colors, to name a few.
Extracting data on such product options equips ecommerce businesses with valuable data to optimize their product listings and marketing, supply chain management and customer demand fulfillment.
In this section, we'll explore the kinds of product options data that ecommerce companies collect and use to inform their decision-making.
Product dimensions, weight & color
Ecommerce businesses use data extraction to efficiently collect data on product colors, weight and dimensions from various sources. Ecommerce companies use such data to make informed decisions on inventory management and shipping costs.
Knowledge of the exact dimensions, weight, and color of a product enables ecommerce customers to make informed purchasing decisions and ensures that customers receive products that meet their needs and expectations.
Product brand & model
Data extraction enables ecommerce companies to quickly and easily identify which brands and models of products are available from multiple sources, such as from manufacturers, wholesalers/distributors and other ecommerce companies.
Ecommerce companies use such brand and model information to ensure that their product listings are comprehensive and up-to-date.
Furthermore, ecommerce customers benefit in several ways, such as access to accurate product offerings on ecommerce websites and the ability to make informed purchasing decisions based on the availability and pricing of specific product brands and models.
Product SKU, UPC, EAN & ISBN
Let's start by defining the terms SKU, UPC, EAN and ISBN.
SKU is the abbreviation for "Stock Keeping Unit". A product's SKU is a unique code used by a retailer to identify and track the variations of the product through the supply chain.
SKUs are specific to a retailer and so the retailer assigns a SKU to every variation of each product sold by that retailer. Therefore, for any given product, the retailer will assign a SKU for each size, color, length, width, height, depth, manufacturer and so on for that product.
UPC is the short form of "Universal Product Code", which is a black barcode and 12-digit number that is used to identify and track products at the point of sale.
Like SKU, ecommerce retailers use UPC to track variations of products. However, unlike SKUs, which are specific to a retailer, UPCs are maintained universally by standards companies, such as GS1.
Similar to SKU and UPC, retailers use other kinds of product codes, such as EAN and ISBN.
EAN stands for "European Article Number". EAN is used "to identify a specific retail product type, in a specific packaging configuration, from a specific manufacturer," according to Wikipedia.
ISBN is the acronym for "International Standard Book Number," which is a unique numeric identifier that is specific to books.
Product codes, like SKU, UPC, EAN and ISBN, are widely used in the ecommerce supply chain because they provide a consistent and unique identifier for products at different points of the supply chain, from manufacturers to distributors/wholesalers to retailers, all the way to the customer.
Ecommerce businesses rely heavily on such product codes to validate the accuracy and completeness of product catalogs, as well as to optimize inventory management, broader supply chain management and order fulfillment operations.
Unique product codes, such as SKU, UPC, EAN and ISBN, enable customers to easily perform precise searches for specific product variations, on search engines or ecommerce websites. Such precision is particularly useful to a customer when searching for a specific product type of a product that has several variations.
Web data extraction of SKU, UPC, EAN and ISBN codes enables ecommerce businesses to efficiently compile and reconcile product data from multiple sources.
Product return and warranty policy
The average ecommerce return rate is 16%, according to Forbes. So, close to 1 out of 5 ecommerce sales results in a return.
Therefore, a product's return and refund policies, as well as its warranty policy, are crucial factors that impact a company's product sales volume and help customers make informed purchase decisions.
Ecommerce retailers must ensure that their product listings correctly reflect up-to-date return, refund and warranty information.
Ecommerce retailers can extract such data from their supply chain upstream partners, such as manufacturers and wholesalers/distributors, in order to ensure that the retailers' own policies are up-to-date and consistent with their suppliers' policies.
In the event that a retailer needs to return products to its supplier, consistency between the retailer's and its suppliers' policies can prevent confusion and reduce the risk of disputes.
Competitor pricing and product offerings
Ecommerce businesses can leverage data extraction to generate competitive intelligence by tracking competitors' current prices, as well as ongoing price changes and promotions. The ecommerce retailer can then adjust its own pricing and promotional strategies accordingly.
Additionally, ecommerce companies can use the data they collect from monitoring competitor products and promotions to identify gaps in their own product lines and expand their product offerings.
Such data-driven decision-making on product and promotion offerings empowers an ecommerce business to stay ahead of its competitors.
Consumer reviews and ratings data
Ecommerce businesses can gain valuable insights into their brand perception and customer sentiment by collecting and analyzing consumer reviews and ratings.
Web data extraction can efficiently gather such reviews and ratings data from multiple sources, enabling an ecommerce business to achieve several outcomes, such as to:
- Use customer reviews to identify opportunities for product improvement and to enhance customer experience and satisfaction.
- Analyze customer feedback to understand popular product features and highlight such features in online listings and other marketing efforts.
- Monitor and proactively respond to customer sentiment, whether negative or positive, in order to build brand loyalty and trust.
Search engine results
Web data extraction enables ecommerce companies to track their products' appearance in search engine results pages (SERPs) and identify their web page rankings for specific keywords.
An ecommerce business can then leverage such search engine results information to drive improvements to the company's search engine optimization (SEO) strategy. A well-executed SEO strategy maximizes the odds of potential customers finding your business when they search online.
An ecommerce business can compile search engine results data of competitor product listings and use such information to inform adjustments to or enhancements of product descriptions on its own website; as well other marketing material, in order to highlight the competitive benefits of the company's products.
Additionally, ecommerce companies analyze search data to inform them on popular, high-search-volume keywords that are adjacent to the companies' products. An ecommerce company can then leverage such additional keyword information to optimize its website content and boost its visibility on search engines.
Market trends and consumer behavior data
Data extraction enables ecommerce businesses to capture key inputs from market trends that drive powerful strategic insights.
Examples of such market trends are:
- Technological advancements: Such as the surge of innovation in and the use of Artificial Intelligence (AI) and, in particular, Generative AI.
- Digital marketing trends: For example, the rise of influencer marketing; the increasing use of Tiktok for marketing; and the growth of social commerce, such as via Instagram.
- Shifting consumer demand: Examples include the growth of online shopping triggered by the COVID-19 pandemic, particularly the growth in areas such as food and grocery delivery; and an increasing consumer focus on health and wellness, such as the increasing demand by consumers for natural and organic food.
- Demographic and population changes: For example, aging population and stagnant or declining population sizes in much of the Western world; a young and growing population in much of the so-called Global South, which refers to countries in the southern hemisphere, mainly in Africa, Asia and Latin America; increasing diversity due to immigration and ease-of-travel; and rising urbanization, as people move to large urban areas for employment, education and social opportunities.
Interestingly, the COVID-19 pandemic triggered a surge in remote work that has led to some degree of de-urbanization, with professionals moving to rural areas, the exurbs or even smaller urban areas that have a higher quality of life.
By leveraging data extraction to actively monitor market trends, ecommerce companies empower themselves with critical inputs to enhance product offerings, while optimizing their sales and marketing strategies.
Shipping and delivery information
Ecommerce companies can leverage data extraction to collect shipping times, delivery costs and other related shipping and delivery details from competitors' websites. An ecommerce company can then use such information to optimize its logistics operations and to provide customers with even faster or cheaper shipping.
By extracting shipping and delivery information from competitors, ecommerce businesses can identify areas to outperform their competitors, while ensuring the business meets or exceeds industry standards.
Promotional and discount data
Ecommerce companies can use web data extraction to collect data on their competitors' promotional and discount offerings, to drive informed decision-making on the kinds of and timings for promotions and discounts that the ecommerce company offers.
Ecommerce businesses can actively monitor their competitors' promotions, in order to adjust their own promotional strategies in real-time or close to real-time.
Monitoring such promotions data over time equips an ecommerce company with the insights on competitor and customer behavior patterns, enabling the ecommerce business to stay ahead of its competition.
Customer contact information
Ecommerce companies can extract individual and company information from several sources, such as from social media, blogs and lead generation sites.
Examples of information that companies extract are:
- For individuals: First Name, Last Name, Email Address, Title, Current Company, Phone Number, Past Companies, Geography, Education, Years of Experience and lots more.
- For companies: Company Name, Sector, Industry, Location, Revenue, Headcount, Growth Rate, etc.
Companies can then leverage such information for lead generation, which is the process of identifying and cultivating potential customers for a business. An ecommerce company can leverage individual and company information to identify high-value targets and to personalize sales and marketing efforts.
Additionally, an ecommerce company can use additional information it extracts about individuals and companies to enrich the profile data for accounts, customers, leads and prospects that the company already possesses.
The ecommerce company can leverage such additional data to enrich its CRM (Customer Relationship Management) database; such as Salesforce, HubSpot or Zoho; with more detailed and accurate information, enabling more effective sales pipeline execution and better targeted marketing efforts.
Ecommerce companies leverage individual contact information to personalize marketing and promotional messaging, such as via emails; in order to maximize the conversion rates of such messaging.
A database of deep and rich individual and company data can be an invaluable tool and is a massive competitive advantage for any ecommerce business.
Such a database empowers the company to perform robust, multi-dimensional analytics that enable the business to tailor its sales and marketing strategy, optimize pricing and identify opportunities for upselling and cross-selling; ultimately boosting revenue and growth for the company.
Social media: sentiment analysis and monitoring
Web data extraction can provide ecommerce businesses with valuable inputs from social media platforms; such as Twitter and Instagram; to perform sentiment analysis.
By analyzing feedback and comments related to the company and its products, a business builds a more complete and up-to-date understanding of market and customer perceptions and opinions of the company and its product offerings.
The company can then leverage such insights to inform product development, marketing strategies and pricing decisions.
Additionally, ecommerce companies can monitor activity of or related to competitor brands and their product offerings. Such monitoring is a form of competitive intelligence that an ecommerce company can use to understand how customers are responding to its competitors.
Ecommerce companies can then leverage such competitive intelligence to refine their own sales, marketing and customer engagement strategy and execution.
Finally, social media monitoring enables an ecommerce company to measure follower count growth of its competitors over time. A company's social media follower count is an important indicator of the brand awareness for that company. Social media follower count can also be an input to estimating relative revenue and growth rates across multiple companies in an industry.
Best Practices for Web Data Extraction in Ecommerce
- Identify the best data sources
- Understand and comply with legal and ethical guidelines
- Implement robust anti-blocking measures
- Continuously monitor and update data extraction pipelines and workflows
- Secure your data
- Make use of APIs, where available
- Ensure the quality and accuracy of extracted data
- Properly store and organize extracted data for efficient use
#1. Identify the best data sources
To ensure the success of your data extraction efforts, it's critical that you identify and use only reliable and comprehensive data sources.
Choosing the right data sources for your business needs will enable you to make informed decisions, based on correct and accurate data.
#2. Understand and comply with legal requirements and ethical guidelines
It is critical that you understand and adhere to pertinent legal requirements and ethical guidelines when extracting data on the web.
#3. Implement robust anti-blocking measures
If your data extraction involves web scraping, you should implement robust anti-blocking measures, such as IP proxies, IP address rotation, and CAPTCHA solving, to ensure that source websites do not block you and you maintain uninterrupted access to your source web pages.
#4. Continuously monitor and update data extraction pipelines and workflows
Continuously monitoring and keeping up-to-date your data extraction pipelines and workflows is crucial to ensure the ongoing success of your data extraction efforts.
Web pages constantly evolve and so it is critical that your data pipelines continue to extract data from source web pages, even if the structures of the web pages change.
The best data extraction companies leverage AI to automate the generation of data extraction algorithms in real-time, in order to eliminate or mitigate odds of a data extraction pipeline no longer being compatible with its source web page, due to a structural change of the web page.
Therefore, it is critical that, at the very minimum, you set up automated monitoring of all your data extraction pipelines and workflows to ensure that you are proactively alerted in real-time, if your pipeline suffers a failure or experiences degradation of performance or data quality.
It is essential that you consistently monitor and update your data extraction pipelines to maintain your Service Level Agreements (SLAs) and data quality; as well as to avoid potentially costly disruptions to your business operations.
To ensure your data extraction pipelines and workflows are optimized for maximum stability and flexibility, consider embracing DataOps best practices, such as to:
- Ensure scalability and flexibility in the data ingestion and extraction infrastructure to accommodate changing business needs and growth.
- Implement automated monitoring and error detection to ensure data quality and data integrity.
- Leverage cloud-based services for scalable, cost-effective and efficient data ingestion and extraction.
- Establish clear data ingestion and extraction protocols, including version control and documentation, to improve collaboration and knowledge sharing among your teams.
- Implement automated testing to catch data ingestion and extraction issues early in the engineering lifecycle.
- Implement a monitoring system to detect and alert on any failures in data ingestion or extraction processes in real-time.
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#5. Secure your data
To ensure the security and privacy of your extracted data, it's vital that you implement proper security measures to safeguard all of your data, particularly sensitive and confidential data, such as personally-identifiable information (PII).
Use secure methods for extracting, storing and handling your data, to ensure that only authorized personnel have access to your data.
It's also important that you implement strong data retention policies, to ensure that you are only holding on to data that you need and that your data retention is in compliance with legal and regulatory requirements, as well as the requirements of your partners and other stakeholders.
Regularly audit your security architecture and processes to ensure that you are in compliance with legal, regulatory, industry and ethical standards.
Implement robust authentication processes to ensure that only authorized personnel can access your data. Use authorization methods such as attribute-based access control (ABAC) or role-based access control (RBAC), to ensure that users can see only the data they are authorized to access.
Protect sensitive data, such as passwords or PII, by hashing them.
Encrypt your data to ensure it is secure when in-transit and at-rest.
Consider using tokenization to replace certain sensitive data; such as email addresses and phone numbers; with non-sensitive substitutes, while still maintaining the usability of such data for analytics purposes.
Finally, ensure you are in compliance with applicable regulations, such as GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act ) and CPRA (California Privacy Rights Act).
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#6. Make use of APIs, where available
Consider using API endpoints for data extraction, when possible.
API use can improve the performance and data quality of your data extraction efforts.
Furthermore, using APIs, as an alternative to scraping web pages, effectively eliminates the risk of the data provider blocking you.
Some of the benefits of using an API are:
1) Consistent data format
APIs provide data that is in a consistent and predictable format, which makes it easier for you to work with the data and integrate it into your systems.
With web scraping, the data format may vary from page to page and even from request to request.
Therefore, when you perform web scraping, you need to put in extra effort to validate that your data is accurate and complete. You will most likely need to perform additional data transformation after you perform web scraping, in order to clean, standardize and shape the data into the structure you want.
With APIs, the quality of the data is the responsibility of the API provider. Therefore, if you identify data quality issues with an API's data, you can raise the issues with the API provider and expect the provider to resolve the issues.
2) Reduced legal and ethical risks
With APIs, the data provider has officially granted API consumers access to the API's data. With APIs, you effectively eliminate the risk of violating the data provider's terms of service or copyright laws.
3) Improved reliability
Sustained web scraping from a web page is dependent on the structure of the web page remaining consistent. Changes to the structure of a source website can break a web scraping program that extracts data from the site.
However, the best web scraping service providers leverage AI to automate the generation of web page extraction algorithms to match the current structure of source web pages.
Still, there is still some effort; even if negligible, in the case of AI-powered data extraction; on the part of web scraping service providers to update their extraction algorithms to be compatible with the current structure of source websites.
APIs are designed to be stable, reliable, fast and scalable.
In fact, any good API provider has SLAs (Service Level Agreements) to which the API provider adheres.
An SLA "sets the expectations between the service provider and the customer and describes the products or services to be delivered, the single point of contact for end-user problems, and the metrics by which the effectiveness of the process is monitored and approved," according to Gartner.
Examples of API SLA metrics are:
- Uptime: Percentage of time the API is available and responsive. A typical API uptime target is 99.9% or "three nines". "Three nines" is equivalent to 43.8 minutes of downtime per month or 8.8 hours of downtime per year, according to Wikipedia.
- Response Time: Time duration it takes for the API to respond to a request. API providers typically set response time targets based on the type of API request and the complexity of the data being returned.
- Error Rate: Percentage of API requests that result in errors. API providers usually set an acceptable error rate target, below which the API should operate.
- Scalability: Ability of the API to handle increased traffic and usage over time. API providers typically set targets for scalability to ensure the API can accommodate growth and usage spikes.
- Support: Level of support provided by the API provider to users. Support targets may include response time duration targets for support requests, availability of support channels and resolution time duration for support issues.
API providers will typically communicate API changes well in advance, giving API consumers enough time to update their data extraction pipelines accordingly.
In fact, most API providers will enable versioning (Such as latest, v1, v2, v3, etc) of their endpoints, with each endpoint version being backward-compatible with a published set of existing features and capabilities.
When using an API, be sure to follow best practices for making API requests and consuming API responses, such as to:
- Use pagination to control data volume: Use pagination to control the amount of data you receive for any given request, and to avoid overwhelming your systems.
- Implement caching to improve performance: Use caching to reduce the number of requests you need to make to the API, and to improve response times.
- Monitor API response performance: Monitor API response performance to identify any issues or potential problems early on, and to ensure that you are staying within the limits set by the API provider.
- Handle errors gracefully: Implement error handling that enables your data extraction application to elegantly handle errors that may occur while consuming data from an API. Graceful handling of API responses enables your application to maintain its stability and reliability, in spite of API response errors.
#7. Ensure the quality and accuracy of extracted data
To ensure the quality and accuracy of your extracted data, validate it against the source and your data quality requirements before using the data.
Using low-quality or inaccurate data can lead to costly mistakes and lost sales for your business.
Data testing and validation can help to ensure your data is high-quality, by enabling you to eliminate or reduce the risk of using outdated, incorrect or incomplete information.
Examples of data quality tests and validations you should perform on your data are to:
1) Check for missing or incomplete data fields and values to ensure the completeness of your data. E.g., Check that all product listings have required attributes, such as product title, description, price, images, etc.
2) Validate row counts to ensure all expected data was collected. For example, if you are extracting data for a product catalog and you expect to have 10,000 rows of data but your extraction returns only 9,950 rows, then you need to investigate why 50 rows are missing. You also need to take steps to ensure that all the expected rows are included in your extract or somehow accounted for downstream.
3) Validate data accuracy by comparing it against known and trusted data sources, such as Systems of Authority (SoAs) or Systems of Record (SoRs). E.g., An ecommerce retailer extracts product pricing data from a supplier's website and then validates the accuracy of the data; by comparing it to another trusted pricing source; or measuring the difference/delta between the new prices and existing prices.
4) Check for duplicate entries in the data set. For example, verify that the same product is not listed twice under different categories or with slightly different names, as such duplication can lead to inaccurate data analytics and faulty decision-making.
💡 Tip: Use unique product codes; such as SKU, UPC, EAN or ISBN; to match product types and variations across product names that may be slightly different; or across product categories and subcategories.
5) Ensure data conformity to data types, formats and structures, as defined by your required schema or data model. E.g., Validate that all phone numbers are in the correct format (E.g., (123) 456-7890); and that email addresses follow the standard format (E.g., [email protected]).
6) Perform anomaly detection to identify outliers in data and validate them against specific rules or logic. For example, use standard deviations (or multiples thereof) from the mean periodic sales to detect unusual spikes in sales data for a particular product over a given period, such as a day, week, quarter, month, etc.
Data quality, data testing and data validation is a broad area. We have barely scratched the surface here.
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#8. Properly store and organize extracted data for efficient use
💡 Tip: Use the cloud.
Properly storing and organizing your extracted data is crucial to enabling your efficient use of the data.
The options you have for storing your data for efficient data processing and data analytics include transactional databases, analytics data warehouses and data lakes.
Your choice of data storage depends on several factors, such as your performance requirements for data creation, reads, updates and deletes; data volume; data velocity; data retention; data structures and data formats.
For transactional databases, you have a handful options, like PostgreSQL, MySQL, Oracle and SQL Server.
For analytics data warehouses, it's best to go with an offering from one of the cloud platforms. Your options include Amazon Redshift, Google BigQuery, Azure Synapse Analytics, Snowflake and Databricks Data Lakehouse.
If you are interested in taking the data lake route, you will need to store your data files in Amazon S3, Google Cloud Storage (GCS), Azure Blob Storage or Azure Data Lake Storage (ADLS).
To perform computation on your data lake, you have several options, such as Amazon Redshift Spectrum, Amazon Athena, Amazon EMR, AWS Glue, Google Cloud Dataproc, Azure Data Lake Analytics, Azure HDInsight, Snowflake and Databricks Data Lakehouse.
Finally, regardless of what approach you take to store and process your data, you will need to structure your data into tables with specified fields and a defined schema, i.e., data types for each field.
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Choose the Best Web Data Extraction Service for Your Ecommerce Business
When it comes to data extraction for your ecommerce business, finding a reliable service provider that meets the specific needs of your business is a critical decision for your company.
Here are some key points to consider when selecting a data extraction service provider:
- Scalability: Ensure that the provider can scale up or down as needed, so you don’t have to worry about outgrowing the provider's capabilities.
- Pricing: Make sure you understand the provider's pricing structure and compare quotes from different service providers before making your decision.
- Data Security: It is essential to select a service provider that has secure data transfer and storage protocols and processes in place to protect all of your data.
- Customer Service: Ensure that the provider offers strong customer service; and, even better, a strong customer success team; for any challenges you face or questions you may have.
- Data Quality: Make sure your service provider has strong data quality and assurance systems and mechanisms in place to ensure that your extracted data is accurate, complete and up-to-date.
Finally, make sure you are dealing with a reputable service provider that has extensive experience in data extraction for ecommerce businesses, to ensure maximum returns for your investment.
By taking all of these factors into account, you can rest assured that your company will select the best data extraction service provider for your particular needs; enabling you to make the data-driven decisions you need to stay ahead of your competition.
Take Your Ecommerce Business to the Next Level with a Web Data Extraction Service
A web data extraction service is invaluable for ecommerce businesses looking to gain a competitive edge.
By leveraging the power of web data extraction for your ecommerce business, you can enjoy several benefits; such as:
- Acquiring key inputs to perform robust sales data analytics.
- Optimizing your inventory levels and supply chain management.
- Shrinking fulfillment times for your customers and enhancing their customer experience.
- Identifying the best opportunities for promotions and discounts, maximizing your marketing return-on-investment (MROI)
All of the above are critical components for your ecommerce business to generate higher revenue, increase profit margins and scale growth.
When choosing the best data extraction service provider for the specific needs of your ecommerce business, consider factors such as scalability, pricing, data security, customer service and data quality.
With careful selection of an experienced data extraction partner that understands how to leverage technology, to address the specific needs of your ecommerce business, you will be able to drive effective decisions that take your business to the next level.
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