10 Best Practices for Implementing Automated Web Data Mining using BOTS

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10 Best Practices for Implementing Automated Web Data Mining using BOTS A famous manufacturer of household products, working with a number of retailers across the globe, wanted to capture product reviews from retail websites. The objective was to understand consumer satisfaction levels and identify retailers violating the MAP (Minimum Advertised Policy) policy. The manufacturer partnered with a web scraping and distributed server technology expert to get an accurate, comprehensive and real-time overview of their requirements. It took them no time to get complete control over the retailers and pre-empt competitors with a continuous sneak peek into their activities. This example underscores the importance of web scraping as a strategic business planning tool. Web scraping is the process of extracting unique, rich, proprietary and time sensitive data from websites for meeting specific business objectives such as data mining, price change monitoring contact scrapping, product review scrapping and so on. The data to be extracted is primarily contained in a PDF or a table format which renders it unavailable for reuse. While there are many ways to accomplish web data scraping, most of them are manual, and so, tedious and time-consuming. However, in the age of automation, ​automated web data mining has replaced the obsolete methods of data extraction and transformed it into a time saving and effortless process.

How is Web Data Scraping Done Web data scraping is done either by using a software or writing codes. The software used to scrap can be locally installed in the targeted computer or run in Cloud. Yet another technique is hiring a developer to build highly customized data extraction software to execute specific requirements. The most common technologies used for scraping are Wget, cURL, HTTrack, Selenium, Scrapy, PhantomJS and Node.js.

Best Practice for Web Data Mining 1) Begin With Website Analysis and Background Check


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