— November, 19 2018

11 reasons why you should use web scraping

Here are our top 11 reasons why you should use web scraping for your next project and 10x your business growth with web data.

11 reasons to use web scraping

Web scraping, or web content extraction, can serve an unlimited number of purposes.

Whether you're a new business or a growing one, web scraping helps you 10x your business growth with web data.

Here are our top 11 reasons why you should use web scraping for your next project.

Technology makes it easy to extract data

Access to technology is probably the most important factor of all, because it enables pretty much anyone to do web scraping at scale very easily.

There's a lot of content on the web to help you master web scraping and probably even more service providers such as Captain Data to help you collect data.

As websites are getting more complicated to scrape (like scraping a single page application), new tools such as Puppeteer make it possible to scrape virtually anything.

Furthermore, deploying bots at scale has becoming increasingly accessible. It enables companies to extract data at any scale.

Innovation at the speed of light

One of the things we really like is how scraping and crawling are enabling businesses to create new products and innovate faster.

Take for example a price comparison website like Kayak, a technical SEO product like Botify or even a job board that is built from multiple sources. Without being able to extract web data, these companies would not be able to exist.

The use cases are unlimited. And it really puts the bar higher in terms of innovation; by enabling easy access to web data to everyone, web scraping forces you to enhance your value proposition.

It helps you innovate faster because you can test and execute new ideas faster. Let’s say you want to build a product referencing independent artists and their music … but you need a database! Well, you better start scraping.

Better access to company data

Over the past decade, governments in many countries like France decided to open their data to the world. But ... (there's always a but!) it's not quite useful, or at least it needs to be enriched with other sources.

In France we have the Sirene database. They have an API (a bit sluggish) but it's a great start. Let's say you have a SIRET (a unique company identifier), here's what you could do:

  • Enrich the SIRET with the Sirene API
  • Find the company website's domain thanks to its name by searching and cross-referencing multiple search engines
  • Look up the company on various websites depending on the company's typology: LinkedIn, AngelList, YellowPages and so on
  • Aggregate the results, by attributing scores (this could be a bit tricky)

And voilà, you have a fully enriched company profile with everything you need: number of employees, date of creation, business category, etc. This is typically what any sales team dream to automate for their CRM.

Lead generation to build a sales machine

Well, I think you see it coming: if you have better access to company data, it also means you can build an automated sales machine.

If we get back to the previous example, before enriching a company profile ... you need to find these companies!

There are many tricks you could use (growth hacks). Among others, you can automate:

  • A LinkedIn search to find pretty much any company given your criteria
  • A PagesJaunes search for small companies in France
  • A Google Maps search to find local businesses
  • An AngelList companies search to find trendy and growing tech companies

Depending on the level of data accuracy and quality you need, you can aggregate each search :) .

It's a HUGE timesaver for any SDR. I mean, who likes to do manual search anyway? With these techniques, you're left with qualifying the prospects. And you have tons of data to do so.

You could also fully automate your lead generation with our LinkedIn Sales Navigator Company Recipe.

Marketing automation without limits

This topic is actually really fun! We're often told how marketers are (or should be) creative, right?

Well, since you can do pretty much what you want with web scraping ... you have carte blanche!

Let's say you have spotted one of your competitors on Instagram. And damn, they have a nice community of 15K+ followers . But you're convinced your product is far better and that users could switch to you.

What do you do? You scrape! Find their Instagram page and start extracting every followers.

With that list, you'll be able to follow and DM them. It's generally highly qualified since you know the profiles you collect are interested in what you do.

As a matter of fact, you could do the same on Twitter or on any other social network.

Brand monitoring for everyone

The brand monitoring market is growing very fast. And for once, I think that we can all agree that checking other customers reviews has becoming a basic step when buying online.

Consumers are more and more educated: they like to be recommended products and to be reassured that they're making the "right choice".

Strangely enough, businesses do not always check reviews and ratings.

Why? Well, it's not that easy. There are so many platforms gathering reviews and ratings that you need to extract reviews from each website and then aggregate them.

You could also monitor social networks and combine it with sentiment analysis to quickly respond to haters or reward users who love you.

The outcome of improving your brand image in terms of ROI is just clear!

Here's how we collected reviews for a brand monitoring SaaS.

Market analysis at scale

Everyone talks about Big Data and Business Intelligence. But in the end, what really matters is quality over quantity.

You don't need big data, but rather smart data.

Let's say you sell machines and spare parts. There's obviously a "used" market. But how do you know a specific spare part is sold for? I mean, if you could just optimize the price by 10% ... imagine the additional revenues at scale!

Web scraping to the rescue: you "just" need to collect data on specific websites that distributors use. And voilà, you can build an argus that is fed from the data you extract.

Although in this case, data processing might be a bit tricky since product references are not always the same!

Here's how we automated market analysis at scale for a corporate client.

Data(base) enrichment on demand

I’ve already covered this topic a bit in the previous examples. But you have to understand that the possibilities are endless:

  • You can’t post a small ad in a platform like Craigslist? There’s a bot for that
  • You need to build a database for your new product
  • You can add search or product metrics from other platforms
  • Data provided by your users aren’t enough? Well, you get it

Again, at the risk of repeating myself, web data is not only a mean to boost your business from a sales or marketing point of view. It also enables you to enhance your product and foster innovation.

Machine learning and large datasets

You've been tasked with building a model that will classify houses. Your product owner wants you to use deep learning, because they think it's a great option for such a use case.

You need a large volume to build your training set. And you're definitely not going to do this by hand.

Want to predict the stock market? Web. Scraping.

You need to predict your competitor's pricing? Scrape that data!

Web scraping is actually the data scientist’s best friend. But you're a data scientist, not a freaking bot! You want to analyze and build predictive models, not clean and extract web data.

So don't reinvent the wheel, use a platform or ask us to do it for you.

SEO loves data extraction

If you’re serious about SEO, you probably use tools such as SEMrush or keywords finder like Ubersuggest. It’s simple: these simply won’t exist without data extraction :) .

Using such tools, you can quickly find out your SEO competitors for a particular search term.

You can determine the title tags and the keywords they are targeting to get an idea of what is driving traffic to their website.

If you have a website with lots of content (1K+ URLs), you could also perform a technical SEO analysis to check out broken links and verify how is your content performing across your entire website.

End-to-end testing

Finally, you have to know that one of the best uses of web scraping is testing. If you’re a developer, I’m sure you heard of Selenium.

If you want to build user testing scenarios or monitor a website’s performance, you need a bot.

Companies like Ip Label have built products that automate this kind of testing.

Start scraping!

So, are you ready to scrape like a boss? Start here. Or ask us anything.