Data scraping is a technique in which a tool or application extracts the information from the output coming from another program. Web scraping is the most known form of scraping where an application is used to extract data from a website. Say, xml contains information, which might not be in human readable format, tools like python are used to scrape the data off the website. Many businesses use web scraping to improve their overall operations and services. In real estate industry, web scraping comes in handy as the scraped information builds databases of vacant flats or houses on sale, in general the available listings.
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Lead Generation
In today’s world, having a contacts database makes you a valuable asset. Extracting information from millions of websites and platforms with the help of data or web scraping techniques is not a simple task, it would require right set of skills and tools to generate loads of contact information within a short span of time. It is a great solution as with the contact database in hand, gaining potential leads or customers by sending email campaigns and newsletters at once.
Machine learning
Scraping is data scientist’s best friend. When a data scientist is asked to classify the houses based on multiple variables, the data set must be ready. Where does the data come from? Scraping the websites. The predictive models are built on a larger dataset which is extracted from a number of websites.
When the processes are automated, the time savings of the resources as there is limited or no manual intervention. This helps in better allocation of time and resources to the actual analysis as it frees data analysts and engineers from manual reporting to more productive tasks.
Branding
A brand reputation speaks volumes about the company. It is essential to know how is the brand perceived in the market. Looking into the social media data on the particular brand name, sentimental analysis report can be created after the data is scraped. This helps the decision makers understand their strengths and weaknesses.
Data driven decisions
Say, a new brand of make up is in the market, traditionally the product owner runs focus groups, creates ad copies and then takes it to the PR to know the market. But now, the customer reactions are clearly voiced in social media and gains the feedback of it. Scraping this information provides valuable insights to enable the decision makers take data backed up decisions.
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