Few things are stronger leverage than having up-to-date and detailed information you can rely on and utilise to manage your business accordingly. What exactly this information is depends on the nature of your industry, of course.
Two small examples to draw a mental picture: Imagine you can view the prices of your competitors in real-time on a dashboard. You can also see their latest price adjustments along with changes of product availability. This would, undoubtedly, give you an insight into the market beyond all competitors, who do not have access to this kind of information.
While the previous example is rather sophisticated, it can also be much simpler: You could scrape a government website to get a weekly email report of government tenders matching a keyword. This would save you a few hours of work every week and ensure you aren't missing any business opportunities. The possibilities are only limited by the information you can find on the Internet and your imagination.
Bots, the relentless data collectors, already make up more than half of all internet traffic. Make sure you aren't staying behind. In the following article, you'll get the required insights to find out where to apply scraping most efficiently and how to find the right contractors for the job.
Sounds great! How do I get started with scraping?
Before you start scraping data from websites, you should find out if this is the easiest way. The first choice should always be an API - but not all websites and services provide the option to access their data using an API. If you aren't sure if a website offers an API, consult their support or contact your go-to developer.
Scraping the data directly from the website is usually the second choice after using the official API. In developer circles a website is sometimes considered "an ugly API" because it pretty much serves the same purpose: to share information - but it comes with lots of "clutter" for human-readability: layout, fonts, styling, etc. This "clutter" makes work and needs to be cleaned up. There is also another downside: while APIs are usually stable and seldom change, websites change more frequently. This means you might have to get your scraper a little tweaked now and then.
What you would use scraped data for
As mentioned initially, the uses of scraped data end only with your imagination. You could use the information to drive more leads or sales to your business. Collect information about your competitors. Alternatively, you could use it to protect your company from issues arising. The following gives some common uses for scraped data.
Keeping an eye on your brand
From Yelp to TripAdvisor, to Twitter and Instagram: The internet is filled with an endless number of boards, communities, blogs and other websites. On any of these, people might mention your company or brand. This doesn't need to be bad. Often it is a positive mention as well. Either way, you want to know about it to make sure you are able to reply and drive premium customer support. A dashboard to collect all mentions can help a great deal to keep an eye on your brand.
Competitor and market analysis
Keeping an eye on a few competitors or products is doable by hand. Doing this for hundreds of competitors and products every day isn't doable by hand anymore. The only way to run your business and still have a comprehensive market analysis available when you need it is to run data scraping on auto-pilot. You will need a software which runs in the background and works while you concentrate on running your business. Ideally you want to have a dashboard with the needed information ready on demand.
One way or another, every business needs to make money. As mentioned at the beginning, this is a great way to apply data scraping. You could collect leads, be it government tenders, official company registers or simply company contacts from sites such as LinkedIn. The data could be collated, for example, in a Google Sheet. From there you are in business-owner known territory. You can manipulate and export data, build dashboards or invite team members to collaborate.
What is the difference between data scraping and data collection?
Both bots and humans find their respective areas of competency here: Scraping is automated, while data collection is manual. Data Collection is usually applied when the data sources vary widely and the amount of data is limited. Scraping is used to aggregate very large datasets as well as for on-going jobs.
How you could benefit from data scraping
Day-by-day business is often too demanding to keep manual data collection up. This leads to data gaps, which often increase over time. Automation can help here: Bots, short for robots, scrape data in the background and don't get tired, ever.
Bots also work with an accuracy human workers are unable to match. While humans provide the benefit of flexibility and on-demand problem-solving over repetitive work, manual scraping has another downside: If, for example, a date format changes it would still get copied and pasted as before, leading to follow-up problems when the data is processed further. A well-developed bot would notice the different date format and respond with an error message to request human interaction.
With automation, another aspect comes into play: it makes collecting data affordable for many. Bots work for cents' worth of electricity for computer power compared to hourly wages of employees. The affordability increases also as bots parse structured data much faster than any human ever could. Both play into the hands of automated scraping instead of manual data collection.
How to find a freelancer to do data scraping
Thanks to more and more people shifting to online work, it's become much easier to find a freelancer for your scraping project. Depending on the difference between scraping and collecting, you can find both here on Freelancer.
Manual data collection using a freelancer
If your sources vary largely and the datasets are small enough to be human-collected, you might want to consider hiring a virtual assistant. That's a modern version of a mix of an office worker and classic assistant you might remember. Basically, it's a person you can train and assign simple tasks to. Data collection is a typical task for virtual assistants.
If your job is very large or by nature on-going you should strongly consider investing in a developer to build a web-scraper to your needs. This ensures you can collect data at scale and with high accuracy. Screenshots with marked areas explain the requirements well. Make sure to discuss how the scraper would be hosted. Your ideal agreement includes the ability to get fixes, if needed, as well.
Agree also on the format in which the data should be provided. If you want to have a dashboard too, mention this from the start to see if the developer can assist you with this. Any visual, even a scratch on paper made by hand, can assist to tell your story and explain your needs better here.
With website scraping you've got the chance to gain an edge on your competition. Start with identifying repetitive checks on online sources, identify which particular pieces of information you are looking for while checking and track the time involved in manually checking these. Once you have a clear picture on the time demand you can set out to either find a virtual assistant for data collection or a software engineer to build a custom data scraper.
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