The ecommerce industry is in the midst of a data revolution. With millions of customers browsing and buying online every day, retailers have access to vast troves of valuable data. But extracting actionable insights from this data deluge is a complex undertaking. Enter web scraping – an essential tool for ecommerce companies looking to harness the full power of big data.
Web scraping refers to the automated process of extracting data from websites using bots or software. It allows retailers to efficiently gather large volumes of structured data from their own sites as well as competitors‘ sites, marketplaces, reviews platforms, and more. This external data can then be integrated with internal data sources to fuel advanced analytics initiatives.
In this article, we‘ll explore the pivotal role web scraping plays in enabling next-level big data analytics for ecommerce. From dynamic pricing to product optimization to customer sentiment analysis, web data integration is driving force behind many of the most impactful ecommerce use cases today.
Web Scraping: A Pillar of the Ecommerce Big Data Ecosystem
Ecommerce companies have no shortage of internal data, from transaction records to customer profiles to marketing campaign metrics. However, this first-party data only tells part of the story. To gain a truly comprehensive view of the market and their customers, retailers must look beyond their own walls.
As HiChee Ong, Head of Analytics at Shopee, explains: "External data is important because it provides insights that internal data alone cannot provide. For example, internal data can show you how your own products and campaigns are performing, but external data can show you how your competitors are performing and what tactics they are using."
Web scraping allows ecommerce companies to automatically collect this valuable external data at scale. Some common data sources and use cases include:
- Competitor websites: Scrape pricing data, product details, stock levels, promotions, and more to inform pricing strategies and stay competitive
- Marketplaces and retailer sites: Gather product sales ranks, ratings, reviews, and other key performance metrics to optimize product assortment and positioning
- Social media: Mine customer feedback, trending topics, and influencer posts for sentiment analysis and trend forecasting
- Forums and blogs: Extract discussions, opinions, and experiences to understand consumer behavior and preferences
- Public data sources: Collect demographic, economic, weather, and location data to enrich customer profiles and models
For example, beauty retailer Sephora uses web scraping to monitor competitors‘ prices, promotions, and product assortment on a daily basis. By integrating this external data with internal sales and inventory data, Sephora can quickly identify opportunities to adjust prices, run promotions, or tweak product pages to maximize performance.
Leading ecommerce companies are investing heavily in web scraping capabilities to feed their ever-growing big data ecosystems. A Deloitte survey of retail executives found that 65% were using web scraping and 44% planned to increase its use in the next 12 months.
The High ROI of Web Scraping for Ecommerce Analytics
The business benefits of web scraping for ecommerce are manifold, from increased sales to improved margins to enhanced customer experiences. By enabling richer insights and faster decision-making, web data integration can impact virtually every area of an ecommerce organization.
Pricing Optimization
Dynamic pricing is one of the most common and impactful applications of web scraping in ecommerce. By collecting competitors‘ pricing data in real time, retailers can continuously optimize their own prices to maximize profitability while staying competitive.
Research by Deloitte found that using web scraping to feed dynamic pricing algorithms can increase gross margins by up to 10%. For a $1 billion retailer, this translates to $100M in additional annual profit.
The impact is clear in the results of early adopters like Amazon, which updates prices millions of times per day based on data scraped from across the web. A study by Profitero found that Amazon averages a 13% price lead over competitors.
Product Assortment and Positioning
Web scraping also enables ecommerce companies to make data-driven decisions about which products to stock, how much inventory to carry, and how to position items on their site and across channels.
By analyzing product assortment, sales ranks, and ratings across retailer sites and marketplaces, ecommerce companies can predict demand for specific SKUs. Scraping product reviews helps identify both popular product features to highlight as well as common issues to improve.
This external data is even more powerful when combined with internal sales and inventory data in what‘s known as market basket analysis. This process uncovers which products tend to be purchased together, informing cross-sell and bundle strategies.
Furniture retailer Wayfair used this approach to increase revenue by identifying the most popular items to group into room sets based on web sales data. After promoting the trending room sets, the average order value of room set purchases jumped by 35%.
Customer Experience Optimization
Today‘s consumers expect highly personalized, seamless experiences across channels and touchpoints. Web scraping provides the comprehensive view of the customer needed to deliver these experiences at scale.
By mining data on customer sentiment, behavior, and preferences from social media, reviews, and forums, ecommerce companies can better understand the needs and expectations of different segments. These insights can inform everything from product recommendations to content personalization to proactive customer service.
Beauty brand Glossier is known for its personalized approach, which is powered by web-scraped customer data. The company famously mines social media and blog discussions to identify trending ingredients, formulate new products, and create targeted marketing campaigns.
The results speak for themselves: Glossier‘s average order value is 3X the industry average and its customer repurchase rate is above 50% within one year of the first order.
Competitive Intelligence
In the fast-paced world of ecommerce, staying ahead of the competition is crucial. Web scraping provides real-time visibility into competitors‘ every move, from pricing changes to product launches to marketing campaigns.
By setting up automated web scrapers to track competitor activity 24/7, ecommerce companies can quickly identify threats and opportunities. For example, if a competitor drops prices, an alert can be triggered to enable a rapid response. Or if a competitor‘s new product page is gaining search traction, the insights can inform SEO strategy.
This type of competitive intelligence is becoming a must-have for ecommerce companies. A study by Crayon found that 78% of ecommerce decision-makers say competitive intelligence is critical to their success. The stakes are high: ecommerce brands that use advanced competitive intelligence techniques report 2-3X higher revenue growth.
Ecommerce Web Scraping: Techniques and Considerations
While the benefits of web scraping for ecommerce are clear, the process itself can be complex. Ecommerce sites are known for their dynamic page structures, inconsistent HTML tags, CAPTCHAs, and bot-blocking tools. As a result, ecommerce scraping often requires more advanced techniques and tools than general web scraping.
Some key considerations and best practices for ecommerce web scraping include:
AI and machine learning: Applying AI to web scraping, known as intelligent web scraping, enables the scraper to handle dynamic website changes and extract data more accurately. Machine learning models can also be used to classify and structure product data post-scraping.
API integration: Many ecommerce platforms and marketplaces offer APIs that provide direct access to product and pricing data. Integrating API calls into the web scraping workflow can simplify data collection and ensure data integrity.
Cloud-based infrastructure: Running large-scale web scraping jobs requires significant computing resources. Leveraging cloud services like AWS for data scraping and storage allows for on-demand scaling and reduces the risk of bottlenecks.
Data quality checks: Raw web-scraped data often contains errors, inconsistencies, and missing values. Building in automated data quality checks and cleanup rules is critical to ensure the data is fit for analysis and use.
Legal and ethical compliance: When scraping external websites, it is important to do so legally and ethically. This includes honoring robots.txt files, being mindful of website owners‘ terms of service, and not overwhelming servers with requests. Consulting legal counsel and establishing web scraping guidelines is recommended.
While there are many web scraping tools and services available, not all are well-suited for the complexities of ecommerce. Working with a provider that specializes in ecommerce web data integration and offers customized solutions can save significant time and resources.
The Future of Web Scraping and Big Data in Ecommerce
As ecommerce continues to grow and evolve, web scraping and big data analytics will only become more essential. We are already seeing ecommerce companies push the boundaries of what‘s possible with web data integration.
For example, some retailers are using computer vision and natural language processing to scrape images and videos from social media and automatically tag them with relevant products. This user-generated content can then be spliced into personalized product pages and emails, driving engagement and conversion.
Web scraping is also enabling new forms of predictive analytics and real-time optimization in ecommerce. By combining web-scraped data with machine learning, retailers can anticipate customer needs, estimate lifetime value, and dynamically adjust experiences on the fly.
The rise of headless commerce and microservices is further expanding the possibilities for web data integration. With ecommerce platforms decoupled from front-end experiences, web-scraped data can be more easily ingested and activated across touchpoints.
Looking ahead, the winners in ecommerce will be the companies that can most effectively harness the power of big data – and web scraping will be a key enabler. As Gartner advises, "Digital commerce analytics is quickly becoming a point of differentiation for many organizations. Data and analytics leaders must prepare now to adopt new data analytics technologies, skills and processes to remain competitive."
However, realizing the full potential of web scraping and big data in ecommerce requires more than just tools and technology. It demands a cultural shift towards data-driven decision making at all levels of the organization. Ecommerce leaders must break down data silos, invest in data talent, and empower teams to experiment and iterate based on insights.
As Wharton professor and author Kartik Hosanagar notes, "The biggest challenge in becoming data-driven is not technical. It‘s organizational. It requires aligning people, processes, and culture around data. It‘s a journey that requires leadership and long-term commitment."
For ecommerce companies that get it right, the rewards are substantial. The most advanced data-driven retailers are seeing up to 30% higher operating margins and 6-10% higher shareholder returns. In an increasingly competitive ecommerce landscape, the effective use of big data is becoming a matter of survival.
Web scraping is the unsung hero of ecommerce big data analytics. By unlocking a wealth of external data and enabling real-time insights, web data integration is driving the next wave of ecommerce innovation. As the data arms race heats up, the companies with the most robust web scraping capabilities will be the ones left standing.