Search Engine Companies

These are typically crawlers or spiders belonging to large search engine providers. They index content from websites all over the internet so they can help users of their search engines to find things on the internet. Google, Bing, Facebook, Amazon, Baidu, etc. all have scrapers that regularly visit every single website on the public internet. The business model for these scrapers is selling ads that are placed alongside search results. The search ad business pays for the scraping activity.

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Business Competitors

In many industries, competitors will constantly scrape content and data from each other. This practice takes a number of forms. The business model for competitors is to win customers, market share and revenue by using the information that they scrape from competitors to their advantage.

Competitive Pricing

This involves scraping pricing and availability of competitor products and undercutting competitor prices to win increased market share. In other instances, like airline tickets, knowing that competitors are sold out for a particular route will allow an airline to increase prices and maximize revenue.

Network Scraping

This involves scraping the names, addresses and contact details of a company’s network partners. This is common in the insurance space, where details of repair shops, doctors, hospitals and clinics, insurance agents and brokers are scraped. This can be done by competitors looking to identify gaps in the market or poach partners from competitors. It is also done by other third parties, for example, pharmaceutical companies whose representatives want to pitch new drugs to doctors in a particular region and specialty. Scraping health insurer websites for in-network doctors is an easy way to get this data.

Inventory Scraping

This involves stealing valuable content and data from a competing site for use on your own site. This typically includes things such as product imagery, descriptions, specifications, prices, and reviews. These can be stolen from one website and used on a competing website selling the same items. In one extreme case, we have seen a start-up classified ad company going to the website of the incumbent market leader and scraping entire categories of listings to populate their own website. Scrapers can also collect product reviews from one website to use to populate reviews on a different site.

Researchers and Investment Firms

Academics researching a given topic often design scrapers to go out onto the internet to gather content and data for their research. These academics are motivated by getting the data needed to complete their research and get it published. They usually have research funding they use to pay for their scraping activities.

We have also observed the activities of hedge funds and other investment firms using web scraping as a means to estimate the revenues and profitability of companies in various sectors. For example, by scraping flight prices, availability and seat maps, an investment firm can determine how full flights are going to be and the average prices that passengers are paying for those tickets. This allows them to estimate what revenues they expect the airline to bring in, and hence, buy or short the airline’s stock.

Market research firms also fall into this category. They use web scraping for research purposes and generate revenue by publishing and selling the results of their market research.

Intellectual Property Owners

We have observed scraping from organizations that use scrapers to scour the internet in search of counterfeit goods for sale in order to identify possible trademark or copyright infringements. Brand owners also use scraping to ensure that distribution partners and resellers are following pricing and discounting guidelines, as well as other contractual stipulations like use of correct imagery, placement, product descriptions, and branding. Scrapers allow these brands to ensure compliance with their requirements, while enabling them to identify counterfeit goods.

Data Aggregators

These are third parties who collect and aggregate data from many different sources and then sell the data to interested parties. Some data aggregators specialize in particular kinds of data or data for specific industries. An example is companies which specialize in scraping LinkedIn data and selling the names and contact information of executives and business leaders to sales and marketing people who want to target these individuals. Their business model is to simply sell the aggregated data.

Other examples of data aggregators include price comparison websites and apps which are very common in the retail, airline, car rental, hospitality, and insurance spaces. These price comparison services scrape prices and insurance quotes from various providers and give users the ability to compare and contrast offerings all in one place. These price comparison services generate revenue via referral commissions for business they drive towards a particular provider, or by charging a service fee or selling ads on their websites and apps.

Other Kinds of Data Aggregators

News aggregators also fall into this data aggregator category. These entities use scrapers to pull news feeds, blogs, articles, and press releases from many different websites and APIs. This includes public/private company websites, public relations firms, government and government agency websites, non-profit agencies, and more. The news is aggregated and published on their websites, apps, newsletters, or social media pages, or sold to other news agencies.

A more subtle example of a data aggregator is the Wayback Machine. This organization keeps a historical archive of the top sites on the internet so users can see what content was on those sites on specific dates in the past. They do this by scraping the content of the website, archiving it, and making it available to their users. The Wayback Machine is a non-profit organization and does not have a profit motive for their scraping activities.

Artificial Intelligence (AI) Companies

Since the launch of Open AI’s ChatGPT, VC funding of generative AI startups has exploded, fueling additional scraping activity by AI companies. These companies need large amounts of text, image, and video data to train their models. They obtain this data by scraping content from the internet. Facial recognition startups like ClearView AI[1] have made headlines in the past for scraping billions of user photographs from the internet,[2] including the most popular social media sites. They used this scraped data to create an AI system that can put a name to a person’s photo within seconds. The resulting AI models are then sold to governments and law enforcement agencies.

We commonly see AI companies scraping all kinds of data across a variety of different industries. Sometimes these companies identify themselves, but often they do not. As the AI space continues to grow, we expect this to be an increasing source of scraping traffic.

Criminal Organizations

There are a number of malicious reasons why criminals might want to scrape a given website or application.

Phishing

Criminals scrape website content, layout, and data so that they can create replicas of the site or app to use for phishing. They register domains that are very similar to the victim domain and apply the scraped layout, content, and data to create a convincing phishing website. Users are then asked to hand over personally identifiable information (PII), including usernames, passwords, and MFA codes.

Vulnerability Scanning

Criminals use scrapers to test for and identify vulnerabilities in a website or application that can be exploited. For example, by changing the input into an insurance quote, hotel room reservation, or airline flight search application, attackers may find vulnerabilities that allow them to get discounted rates or access to back-end systems. Web scrapers are designed to supply millions of variations of inputs into applications and capture the resulting millions of outputs. This makes them ideal for vulnerability scanning.

Identity Theft

By scraping information, including PII, from social media and other sites, criminals can gather all the information they need to steal identities and enable fraud schemes such as spear phishing, social engineering, and account take over. This scraping also might provide answers to commonly used security questions and PIN numbers based on common choices such as a child’s birthday, which can then be used for credit card fraud.

Direct Costs of Scraping

There are several direct costs of scraping to the victims, leading to an impact to an organization’s bottom line.

Infrastructure Costs

Scrapers are typically very high volume and we commonly observe more than 50% of all traffic to scraped websites originating from scrapers. This means that more than half of the operating costs of the website are going towards servicing requests from scrapers.

Server Performance and Outages

Scraper transaction request volumes tend to be high and may use computationally intensive search features. Together, this can cause scrapers to pull so much compute resources that they cause the origin servers to fail, leading to an outage and effective denial of service on the website. These outages can cost a company substantial lost revenue, as legitimate customers cannot conduct transactions due to site outages or slowness.

Loss of Revenue and Market Share

Scraping activities driven by competitors result in loss of competitive advantage, and thereby lost sales revenue and market share. This loss of revenue and market share can also be a result of scraper-driven intermediation.

Intermediation

This occurs when a third party becomes an intermediary between a business and its customers. A significant proportion of scraping is done by intermediaries across many different sectors, including insurance, airlines, hotels, retail and government. The scraping is what provides these intermediaries with the data needed to intermediate the service providers, potentially leading to reduced revenue, profits, market share and customer dissatisfaction. Companies prefer to have direct relationships with their customers for a number of good reasons:

Selling and Marketing

By having a direct relationship with the customer, companies can send marketing emails and special offers directly and generate additional sales that would not happen if the customer’s relationship was with the intermediary.

Customer Retention

An intermediary can move customers from one provider to another depending on various incentives and this can lead to the loss of business revenue. Having a direct relationship allows companies to retain more of their customers.

Cross Selling, Up Selling

By having direct relationships with the customers, business can cross-sell and upsell additional products and services to the users, increasing revenue and profitability. For example, an airline can sell trip insurance, car rentals and hotel rooms to a customer. However, if the customer uses an intermediary, the airline will only sell the airline ticket, while the intermediary can provide additional services from other providers or from themselves, removing the airline’s opportunity to generate additional revenue.

Customer Experience

Having a direct relationship with customers allows companies to ensure the end-to-end customer experience. When intermediaries are involved, the service provider loses control of the experience, which can lead to dissatisfied customers. For example, in the airline industry, an intermediary may book connections that are too tight, or where bags do not automatically transfer. This may result in bad experiences and customers blaming the airline, tarnishing their reputation online when the fault lies with the intermediary. Companies therefore prefer to manage the entire experience by having direct relationships with their customers.

Indirect Costs of Scraping

Victims of scraping also experience costs that do not immediately affect their profits, but instead represent a loss of investment, harm to elements of a business that are difficult to quantify, or introduce other risks.

Loss of Intellectual Property

This can include proprietary pricing algorithms, data and statistics, product and user reviews and ratings. These take years and resources to develop, but can be stolen in a few hours by determined scrapers.

Reputational Damage

Users whose information is scraped may blame the website for sharing their information or even accuse them of selling their data to third parties, which can lead to brand damage.

Legal Liability

In the event that scraped data is later used in a way that causes direct harm to the data subject (the owner of the PII or other data), this may expose the scraped organization to potential legal liability for violation of user privacy if it turns out they did not take reasonable steps to prevent the scraping of this data by unauthorized third parties.

Questionable Practices

Sometimes scraped data can be used to discover and publicize discriminatory data models and business practices that can leave businesses in legal and reputational trouble. For example, researchers who scraped retailer prices for select products were able to identify instances of price collusion.

Conclusion

Scraping is a big deal and affects enterprises all over the world in many different industries. F5 Labs’ research across the enterprises from which we have data shows that almost 1 in 5 search and quote transactions are generated by scrapers. Scraping is usually done by various entities including search engines, competitors, AI companies, and benign or malicious third parties. Scraping leads to several direct and indirect costs for victim organizations, ultimately resulting in the loss of revenue, profits, market share and customer satisfaction.