- By Web Screen Scraping
How E-commerce Companies Use Scraped Data for Demand Forecasting & Inventory Planning?
E-commerce brands benefit from scraping data from websites and other sources to improve their forecasting accuracy for current and future customer demand and inventory levels.
Table of Contents
Introduction
Demand forecasting in E-commerce may seem clear-cut, as you are simply predicting what people will purchase and when they will purchase. However, actual demand forecasting in E-commerce is a very complex and difficult operational challenge. Demand is influenced by promo price reductions, social media trends (e.g., Influencer promotions), seasonality fluctuations, safety stock levels, product availability at retailers and wholesalers (which is rapidly changing), and competitor and other e-commerce activities. Even tiny errors in demand forecasting can be nonsensical. Under-forecasting could result in missed sales, stockouts, lower search rankings on marketplaces, and customer dissatisfaction. Conversely, over-forecasting can lead to excess cash tied up in unnecessary inventory, increased storage costs, and markdowns that erode margins.
Traditional demand forecasting methods include using past orders, website analytics, marketing calendars, and other internal sources. It is helpful, but only part of the forecasting total. Forecasting with internal data alone can also produce “insular” views of demand; that is, companies do not fully consider what’s occurring in their or their competitors’ marketplaces.
Companies are missing out on opportunities to improve forecasting accuracy since they are not considering competitor pricing actions, product availability (across multiple e-commerce marketplaces), and customer sentiment regarding reviews and product popularity. It is where scraped data benefits e-commerce businesses by providing near-real-time visibility into external demand signals and by allowing e-commerce teams to combine them with traditional internal data to improve forecasting accuracy.
In this blog post, we will define what e-commerce companies scrape data for and how it can be used to create practical forecasting and replenishment decisions.
What does scraped data mean in e-commerce?
Web scraping (scraping) is the process of gathering publicly available information from various sources on the Internet and putting it into a format for easy access. In e-commerce, scraped data is primarily used to understand competitors through product-level metrics such as Price, Availability, Promotions, Shipping Interpretation, Ranking, and Review Activity. Scraping’s purpose isn’t to duplicate what you’ve found, but rather to identify signals from the marketplace and convert them into measurable inputs to inform future planning.
A category manager may use scraping to determine competitors’ daily prices for a specific product. At the same time, a Supply Planner may be interested in stock availability for their highest-selling SKUs by Marketplace each week, and a Marketing Team might be curious about whether specific keywords or product types are trending. All of these types of external indicators can impact demand and/or Inventory Requirements.
From where will I collect this scraped data? Competitor Websites, Marketplaces, Search Result Pages, Social Media Forums, or Review Forums? (What am I allowed to gather?) After collecting and scraping data from various sources, I can clean and deduplicate it and map it to an Internal Catalog so I can include it in Analytics and Forecasting workflows. The best time to extract value from scraped data is when it is consistent, frequent, and addresses real-time planning questions.
Which types of scraped data matter most for forecasting?
Not all scraped data will improve your forecast. The best types of signals to look for are those that indicate potential shifts in Demand or highlight possible Supply Chain Constraints. Commonly Utilized Types:
- Competitor Pricing & Discounting: Price is a strong demand driver. Competitor discounts = decrease demand; your discounts = increase demand.
- Availability & Stock Status: “Out of stock” denotes when supply in the market is constrained. If competitors’ products are out of stock, your demand may increase; if you’re out of stock, demand may go to a competitor.
- Shipping Promise & Speed: Speedy shipping may change your conversion rates. Delivery estimates can be updated to reflect shifts in demand, even when pricing remains unchanged.
- Product Assortment Changes: Introduction of new products and/or degradation of existing merchandise can affect demand. Tracking changes to your product assortment can help plan/replenishment substitutes.
- Reviews, Ratings & Sentiment: A surge in reviews may signal rising demand. Declining ratings can indicate declining demand.
- Search Rank & Category Placement: Many online platforms provide “best seller” lists and/or category rankings, which can also indicate which products are becoming popular.
The most effective forecasting systems treat these topics as “features” that affect demand, not “demand.” They are used alongside sales history to help models adapt more quickly to market changes.
How does scraped data become usable through cleaning, mapping, and enrichment?
In most cases, raw scraped data requires extensive preparation for forecasting. Raw data typically contains inconsistent product names, missing values, duplicate product listings, and constantly changing website layouts; therefore, e-commerce businesses invest in building a processing pipeline to convert raw, scraped data into reliable datasets. When using data gathered via scraping to develop models to forecast product demand, e-commerce companies will take several steps. The first step is to normalize the several different ways in which price, currency, size, and timestamp information have been provided. Deduplication and error filtering will follow, ensuring all products are correctly matched.
The next most critical step is to match information sourced from external organizations, e.g., through scraping, to an internal SKU. It is known as catalog matching or Product Mapping. There are several methods for matching external sources to an internal SKU; examples include UPC, EAN, GTIN, manufacturer part numbers, and combinations of brands and models, among others. When IDs are inconsistent across marketplaces, matching will be done using rules, human judgment, and machine learning.
Once the mapping is complete, companies enrich the data by providing additional context regarding the category, brand tier, pack size, and margin band. They will also offer market-related context, e.g., holidays, payday, weather (for some categories), and the marketing campaign calendar. Finally, they will create engineered features that indicate how other retailers’ prices are tracking relative to their own, how much inventory is available in the marketplace to buy compared to competitors’, and how many reviews are generated per day/week.
Once companies have processed the raw data in this manner, the data will be sufficiently complete and qualified for use as a forecasting input. If companies do not conduct sufficient quality checks on data acquired through scraping, the noise introduced by “scraped signals” may undermine the accuracy of their forecasting models rather than improve it.
How do e-commerce companies use scraped data to build better demand forecasting models?
Today’s forecasting is based on machine learning; however, even in the past, using traditional Statistical Models, having additional information from external sources will contribute to more accurate forecasts. When using scraped data for forecasting, it is typically treated as an explanatory variable that provides context for changes in product demand.
For example, a brand’s sales may suddenly drop by 12% from one week to the next. The retailer’s internal data indicates there have been no changes in marketing. However, using information gathered through scraping, the retailer discovers that a competitor has reduced its price by 15% and improved its delivery speed from 5 days to 2 days. Consequently, the retailer’s computerized model can learn that price and speed are significant determinants of demand. Estimates demand elasticity over time and predict the impacts of future price changes.
Scraped signals help with cold-start forecasting for new products. When a new product has little or no historical sales data, insights can be drawn from similar products, such as category trends, pricing ranges, and early review activity in the market.
Also, they aid promotion forecasting. The demand changes quickly during promotions. Scraped data indicates the discount intensity across the whole market and how it correlates with demand spikes.
When scraper data is used effectively, it doesn’t “replace” internal data; it combines internal sales, traffic, conversion rates, and marketing calendars with external scraper signals, then validates the results through back-testing to create a quicker-reacting forecast with fewer surprises.
How is scraped data applied to inventory planning and replenishment decisions?
Forecasting is just one aspect; inventory planning makes the numbers valuable by determining quantities to order, stock positions, and reorder times. When using scraper data to inform these decisions, it is easier to interpret and assess market conditions and risk.
If competitors begin to exhaust stock in an essential category due to stock-outs, it may be advantageous to raise safety stock levels (within cash flow constraints) so you do not miss out on capturing sales demand. Conversely, when competition offers deep discounting across a category, conservative replenishment methods should be employed to avoid flooding inventories and driving lower margins through forced markdowns.
Scraper data can also help determine reorder points and safety stock levels. As demand variance grows—such as during holiday seasons or large-volume sales events—external signals, such as increased ranking movement or the presence of a sudden discount, may enable you to justify raising the safety buffer. On the other hand, if demand is softening and competitors have too much inventory (e.g., they consistently have ample inventory available and/or are offering discounts on these products), it may signal that you should reduce your replenishment volume to safeguard your business’s profitability.
Scraped data is also helpful for multi-warehouse networks because it provides visibility into where inventory should be allocated at the regional level. If you have an overall idea of which regions/products have greater demand, you can place your inventory closer to these key markets (reducing your shipping costs, improving your delivery speeds, etc.).
In summary, using scraped data allows e-commerce businesses to make better inventory decisions based on their actual market competition. Using scraped data can help companies minimize their stockouts and overstock risk.
What are the real-world use cases of scraped data in e-commerce operations?
Some examples of practical applications for how e-commerce businesses are using scraped data to gain an advantage are as follows:
- Using competitor pricing data for demand forecast adjustments: For example, if your item has high price sensitivity and your competitor drops their price, you can adjust your demand forecast to account for the expected increase in sales; therefore, your planner should begin preparing your inventory before the competitor’s price drop.
- Identifying early sales trends using best-seller pages: Many platforms publish best-selling or category rankings every few hours or days. Keeping an eye on changes in rankings can help you identify trends earlier than you would with only your internal sales data.
- Planning for substitution demand: If a competitor’s leading product becomes out of stock, demand may shift to the substitute product(s). Data acquired through scraping competitors’ product availability will help you identify what substitute products to keep on your shelves.
- Vendor negotiations and lead time planning: If you see an increase in supply chain shortages across many vendors in the market, your purchasing team can place orders for items ahead of their regular scheduled orders and negotiate higher production priority with their existing vendors.
- Review Velocity Alerts: If a product is experiencing a sudden increase in the number of reviews it has received, this could indicate that it is going viral, which means it’s time for your team to make quick decisions to replenish projected stock before the sales spike occurs.
- Assortment Gap Analysis: By using scraping tools to scrape your competitors’ assortments, you can find out what sizes, colors & product types they are not carrying that are helping to increase long-term demand and provide a more stable forecast of sales.
These examples present a strong use case for Scraped Data because they allow you to translate an outside signal (Rapid Increases in the Number of Reviews) into a specific operational recommendation (Make Fast Replenishment Decision).
What risks, compliance issues, and best practices apply to using scraped data?
Scraped data must be handled responsibly. The key risks are legal, ethical, and operational.
The most important aspect of using scraped data is ensuring compliance with the legal, ethical, and operational regulations governing it.
Legally, companies must be aware of whom they have access to scrape, have the website owner’s permission to scrape their website, comply with all laws governing scraping activities, and comply with all other requirements established under data governance best practices. A general rule is to scrape only publicly available, non-sensitive, and legally protected data (e.g., no Personal Identifiable Information), and a legal review of the particular situation is recommended when scraping data on a large scale or across multiple jurisdictions.
From an operational perspective, scraped data can be volatile at best. Once a planning team has begun to depend on scraped data feeds, they need to put in place monitoring and alerting functions, as well as fallback logic for the demise of their primary data feeds. Data quality checks for scraped data would include validating price ranges, detect gaps and missing values, and detect anomalies.
Another risk associated with scraping data is that many companies misinterpret the external signals scraped as actual demand. For example, an increase in ranking positions could result from a short-term promotion and not be attributed to the scraped product. Likewise, when a competitor goes out of stock on a particular product, it may be due to a listing issue rather than an actual stock shortage. To ensure that actual sales conversions are associated with scraped signals, it is necessary to validate the scraped signals against actual sales and conversion data.
Best practices for using scraped data include establishing a clear use case(s) for which to scrape; using a data dictionary; setting accuracy targets for SKU mapping; performing model back-testing to validate performance; and creating role-based access to scraped data. Following these best practices will provide companies with a sustainable advantage from the use of scraped data.
Final thoughts
Demand Forecasting and Inventory Planning have evolved beyond ‘operations’ executed within a company. Today’s environment is dynamic, and consumers respond to price, availability, delivery speed, and social validation the moment they receive those signals. As such, companies can use scraping technology to continuously collect information from external sources and structure that data for their forecasting needs related to demand and replenishment.
By integrating the items detailed above with your internal historical sales performance data (Sales History, Traffic/Conversion Rates, Campaign Calendar, Lead times) companies will significantly reduce their forecast error rates and give their planning departments the ability to predict surges in demand better, identify category slowdowns earlier, and see the competitive dynamics that drive the performance variations seen in the marketplace. By operationalizing the above knowledge, your company will see fewer stockouts, less excess inventory, improved cash flow, and higher profit margins.
Many of the best-performing organizations have developed a repeatable process for creating a scalable scraping and data quality pipeline, mapping scraped data to their internal SKUs, generating meaningful market indicators, and validating performance through back-testing. They also have data governance in place to ensure that data is used consistently and responsibly by all users.
Clients receive complete documentation covering data sourcing methodology and legal basis for each feed. A compliant pharma data scraping company makes this documentation a standard deliverable, not an optional add-on.
If you are interested in developing the capabilities listed above without creating all new processes and systems, I would suggest developing a partnership with a provider of specialized ecommerce data scraping services to assist you in building a solid data collection and structuring environment that will provide your employees with the ability to turn your external signals into actionable data sets for forecasting and inventory planning.
