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How Can AI Be Used in Web Scraping?

If data influences every industry and niche on earth, we would not be further from the truth.

For instance, during the last decade, the automotive industry was heading towards a state of comatose, and sales were plummeting.

But in the wake of big data and web scraping skills, we have seen the industry not only recover but move to become one of the biggest industries in the world.

Manufacturers are now making vehicles that align with what buyers want, and market demand is growing by the day.

This is all thanks to data and data extraction. These companies can understand what consumers want by scraping data and then launch products that enjoy easy assimilation into the market and buyers’ lives.

The more data a company has, the better they perform in the market. And this doesn’t seem to be abetting anytime soon as we see AI-based tools join the foray of tools used for web scraping.

These tools are faster and deliver more accurate data while increasing productivity, and it is not only changing web scraping but the industries that use web scraping as well.

What Is Web Scraping?

Web scraping can be defined as the method used to frequently harvest large amounts of data from the internet.

The process entails automatically visiting multiple data sources and extracting their content in large proportions.

It needs to be automatic to eliminate the stress and pains of manually extracting data from millions of web pages daily.

The automation also helps to improve data quality and accuracy as it now needs minimal human interference.

And because it is one of the easiest ways to get quality data, we see web scraping being put to use in several industries and for several reasons.

The Role of AI in web Scraping

AI-based tools play many significant roles in web scraping, but in this section, we will focus on the most common two:

  • Evading Blocking

There is no better way to bypass blocking and bans on the internet than to use AI tools. These tools can perform web scraping and quickly learn what prompts blocks and what doesn’t.

This new knowledge is then used by the tools to self-improve themselves with almost no human input.

We see this in the evolution of the Residential Proxies known as Next-Gen Residential Proxies that witness the least possible blocking by studying the scraping paths and adapting new knowledge.

  • Adaptive Data Parsing

Another common area in which we see AI-based software in full manifestation is data parsing. Manually parsing large amounts of data is nearly impossible, and automatically doing this does not cut it anymore.

This is because building a data parser requires too many labeled parts, and there have to be different parsers for different data types.

However, with AI in the mix, parsing can become more adaptive as the tools can learn from the data and use that knowledge to become more specialized.

That way, not only will the need for human interference during data parsing be eliminated, but we can also have parsers that learn on the job and self-improve with every new data extraction. 

Use Cases of Web Scraping in the Automotive Industry

The new and improved web scraping using AI tools has so many important applications in the automotive industry, and below are some of the most common use cases:

  • Monitoring Market Trends

The automotive market is influenced largely by buyers’ preferences and decisions. This affects the trends that dominate the market at every particular point.

These preferences determine what vehicles a company makes and when new products are pushed into the market.

Brands that use this data to decide their production are more productive and profitable than companies that ignore this type of data.

  • Setting Price Intelligence

A brand that knows how to use an auto web scraper efficiently will end up getting a better pricing intelligence than a brand that does not. Learning how to use an auto web scraper is therefore of paramount importance. To learn more about how to extract data from a website within the automotive industry, visit this blog.

Prices influence every buyer’s decision, with too high prices chasing more customers away.

On the flip side, prices that are set too low may attract more buyers but can have a negative impact on revenue and profit margin.

An appropriate way to manufacture products and dominate the automotive industry is to collect sufficient data and set prices following the insights provided by this data.

To make the process even more automatic and seamless, web scraping can be performed using AI-based technologies.

  • Aggregated Car Listing Websites

Web scraping also plays an important role in aggregation websites. These websites list cars and their availability to allow customers to access this data more readily.

These aggregate websites can also serve sellers as it provides a platform to market their vehicles.

But regardless of who is using them, they can provide lists of available vehicles and other information such as their prices to help people find the best solutions.

To make these websites get updated easily, advanced AI software is used to scrape other websites regularly.

Conclusion

Web scraping can benefit the automotive industry by helping companies build and retain brand success while inspiring faster innovations of different solutions.

Using AI tools makes the process faster, more cost-effective, and more efficient.

 

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