50 Ways Web Scraping Helps Organizations Create Value

June 1993 marked the creation of the first public web traversing bot on record. The World Wide Web Wanderer was meant simply to measure the size of the internet. A mere seven months later, the first web crawler-enabled search engine was born. Today an estimated 37% of web “users” are bots, with a solid portion of these bots interacting with sites and grabbing content.

Manual Data Gathering Replacement

• Manual data gathering is still one of the most common shared tasks among knowledge workers and even data teams. In fact, data finding, gathering, and structuring comprise the largest portions of data team’s workflows. Boosted efficacy of data gathering is the most universal way in which teams find value in web scraping. This makes this value gain shared among almost all of the remaining forty nine value gains.

• Manual data gathering simply doesn’t scale past a certain point. Put another way, the unit cost of manual data gathering doesn’t lower at increased scale. There’s simply only so quickly that a human can read and gather information from the public web. Web scraping – and even moreso “ruleless” web scraping that works on any page type – lower the unit cost of fact accumulation astronomically.

• Data gathering, structuring and wrangling comprises the majority of data teams’ time. By replace any manual gathering, structuring, or wrangling steps with properly tuned web scraping, these teams can focus on the more value-added portions of their workflows.

• Sure, humans know how to re read the web. But did you know that for many common fact types machine vision and natural language processing enabled-bots outperform or match human accuracy at fact identification and retention? Common benchmarks among natural language APIs that can be applied to scraped content match humans ability to identify and pull out facts from text.

Market Intelligence

• Hundreds of millions of organizations have some sort of digital footprint. While finding the location of all of these orgs to scrape may be a challenge (one you should leave to our public web data-sourced Knowledge Graph), many single industries have singular domains that consolidate valuable market intel about orgs of interest.

• Some market signals - like stock prices - need to updated in realtime to a greater extent than web scraping can support. But many market signals are more nuanced and presented in text. Pairing web scraping with natural language processing can help you to surface strategic events from across the web without having to read through reams of news.

• Web scraping is often the only source of ecommerce-related data such as product availability, pricing specs, or reviews (for yourself or a competitor). Because many common ecommerce platforms don’t provide a pre-built way to access this data in bulk, web scraping is crucial for specific forms of competitor intel.

• Track dynamic pricing by scraping from different locations by proxy. Many multi-national organizations present themselves entirely differently from other geographic regions. Scraping can help you to gain a fuller picture than what a consumer browser and manual research can provide.

• Explore scraped web data as a knowledge-as-a-service solution in Diffbot’s Knowledge Graph. Built in search and filter functions perform many market intelligence tasks on our database of hundreds of millions of organizations.

Lead Generation

• Locating contact information is likely the single most common form of data gathering online for many sales and marketing teams. There are a range of services that provide contact information, often through scraping. Couple tools like Hunter.io or Zoominfo with Knowledge Graph data including job titles, skills, and firmographic data in our Excel Add-In.

• Even if you’ve defined your account based marketing personas, you’ll need to search for some extent to fill out your outreach list. Scraping can help to pull in key industry information like revenue, hiring trends, company size, and industries. For a look at the largest firmographic database in the world (220M+ organizational profiles scraped from the web), check out our Knowledge Graph.

• Both sales and marketing teams benefit from keeping track of qualifying events for outreach. Maintaining a cache of events within your industry of interest can be achieved by scraping company or industry blogs and parsing with NLP.

• By scraping selective parts of a page – such as terms of service and privacy information – you can gain a technographic reading of a given organization’s web presence. Doing this at scale can inform selling opportunities for tech services or solutions.

Data Enrichment

• Data becomes stale as soon as you commit a value to a database. Data enrichment via web scraping can connect your increasingly stale data to live updates crowdsourced from wherever you point your scrapers.

• It’s commonly said that having incorrect data is more detrimental than having no data at all. Build off of historical data gathering by correcting and updating fields from across the public web.

• Avoid the overhead of rule-based scrapers breaking, proxy upkeep, and technical talent by utilizing Diffbot’s Enhance data enrichment product. Enhance draws from the world’s largest Knowledge Graph to match your data to entities and fill out with tons of public web data sourced fact types.

Media Monitoring

• Scraping is potentially the only data source for the full range of public-facing organizational indicators including press releases, news coverage, and social media. By extracting and structuring various media-derived fields into entities, results fit into your business intelligence tools of choice.

• With a compound annual growth rate of 13.2%, the online media monitoring market is expected to double between 2021 and 2028. The use of AI and NLP to automatically parse and derive insights from social media data hoses is driving massive productivity gains for organizations.

• Retail and ecommerce are the industries that most regularly employ social intelligence platforms (often powered by scraping and structuring social feeds). These industries are followed by banking, financial services, and insurance, IT and software companies, and media and entertainment companies. The growing acknowledgement that nearly every industry has key indicators available on the public web is changing how old school businesses work.

Machine Learning Training Data

• Some machine learning tasks require millions of training examples every time they’re (re)trained. Few data sources can keep up like the public web extracted and structured with web scraping.

• For multi-lingual or multi-modal machine learning training sets, you may need a web data extraction service that can crawl the entire web. Diffbot’s Automatic Extraction APIs work on pages of any language. Additionally, you don’t need to know the structure of the page you’re going to extract in advance for all of the most prevalent page types.

• Using web scraping paired with robust natural language processing can provide your own ML or NLP models with hyper tuned and incredibly large training data sets from the public web.

Ecommerce

• Competitors and vendors don’t benefit from giving you price and availability transparency. But this data is often available on the public web. Web scraping can provide an almost-live feed of product page details as they change.

• By using proxies and multi-lingual rule-less product data extraction, you can escape the geo-based results in your browser to gain global pricing trends.

• Many important retail and ecommerce signals are buried online in industry publications. Routinely scraping and extracting data related to key events such as acquisitions, mergers, supply chain issues, and new product launches can free up analysts for higher value add analyses.

• Review data is often one of the most valuable and plentiful forms of connection with a core audience for brands. At a certain scale, reviews can’t be understood without scraping and structuring data for examination in a non-web form.

Knowledge Graph Creation

• Public web sourced Knowledge Graphs often rely on starter taxonomies created from crawling and scraping all Wikipedia head entities.

• Knowledge graphs are one of the most flexible database types, allowing entities with different taxonomies to coexist and link to one another. This makes knowledge graphs a great choice for structuring unstructured (and sometimes expredictable) public web data.

• Knowledge graphs sourced from the public web can be a great way to obtain scraped information at scale without any technical overhead. Organizations of all sizes and levels of technical proficiency choose Diffbot’s Knowledge Graph for this reason.

Sentiment Analysis

• Web data extraction that can pull out quotes, speakers of quotes, members of discussions, or entities mentioned can help to provide a much more granular view about sentiment expressed.

• The huge velocity of customer conversations on the public web can help brands to discern the product or service customers care about. Intention analysis builds off of this data to catagorize interactions by their intent (opinion, complaint, suggestion, appreciation, and so forth).

• Theme analysis using web scraping and natural language processing can help to discern what a conversation means in your business context. For example, we analyzed restaurant reviews to discern how safe diner’s felt at varoius locations during the Covid-19 pandemic.

Journalism

• Rule-less or automatic web scrapers can be a low-tech way to harvest data much more efficiently for data journalism-based projects.

• Web scraping at scale can lead to valuable media monitoring dashboards so you can stay at the forefront of developments that may be mentioned across the web.

• Public web data knowledge graphs paired with web scraping have been successfully used to automatically vet posts to combat fake news.

Fraud Detection

• Many of the largest ecommerce sites utilize web scraping of their own domains to detect duplicate or fraudulent product pages.

• Web scraping paired with natural language processing is often the only feasible way to detect fake news occuring across many domains.

• Knowledge graphs created from entities pulled out of unstructured data (often web based) can be used to see relationships, helping investigators to spot locations to investigate further.

• Diffbot’s Natural Language API can be custom trained to pull out entities and relationships of interest. Examples related to fraud detection include the ability to pull out the relationship “defrauded” or to pull out entities germaine to regulatory announcements.

DaaS, IaaS, and KaaS

• DaaS, IaaS, or KaaS constitute a spectrum of service adoption that allows data teams to focus less on the gathering, structuring, and cleaning of data and spend more time on higher value added tasks.

• Many cloud-based data scraping services provide all the benefits of data-as-a-service including data storage, integrations, and extraction processing in the cloud.

• Web scraping built to automatically pull out and augment fields from a page – like Diffbot’s Automatic Extraction APIs – provides the value associated with information as a service arrangements. Not only is data scraped from a given web page returned, but also augmented fields and inferred fields that provide additional context and value.

• Diffbot’s Knowledge Graph is a category-defining example of knowledge-as-a-service. While the basic building blocks are crawled and scraped web pages, the construction of a graph database filled with billions of entities (each structured with facts pertinant to their entityy type), exemplifies the value of KaaS.

Back Office Automation

• Robotic process automation simply moves pixels across a screen to automate back office tasks. This means when the pixels change, RPA often breaks. Smart web data extraction can structure data from within your apps regardless of page structure. This promotes resiliency in back office automation.

• Scraping paired with natural language processing can exponentially boost efficiency at lead routing, support ticket classification, or answering questions posted to the public web.

• Many large organizations have years of unstructured or semi-structured data in the forms of emails, pdfs, presentations and more. Web scraping and NLP can make this data actionable and help to flesh out internal knowledge bases.

• Web scraping services that can provide not only raw extracted data – but also structure and context – can slot into many back of office workflows. For example, web scraping-powered data enrichment can provide additional details about a current customer list. Or news monitoring via web scraping can lead to early knowledge of supply chain difficulties.

Finance

• AI-enabled fact extraction can infer many fields of interest from public web articles on organizations and people. Our new revenue field within the Knowledge Graph does just this by providing a predictive revenue value for millions of private companies based on public web data.

• For a large or varied investment portfolio, scraping (or scraping paired with other APIs) is likely the only way to obtain a news feed of all applicable public web-based signals.

• Knowledge Graph data structured into organization and person entities can help provide connections otherwise available through human-curated content. Hiring trends, skill sets of employees, quotes circulating the public web on a topic, and more are available from pages that were originally surveyed through web scraping.