Scraping for Advanced Data Analytics: How It Works
The amount of data captured, created, copied, and consumed has exploded in the past decade. According to Statista, the world only used two zettabytes of data in 2010. However, by 2020, the globe generated a whopping 64.2 zettabytes of data. Estimates suggest that by 2025, global data creation will grow more than 180 zettabytes.
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That is an astounding amount of information. To stay ahead of the curve and remain competitive, businesses need to use advanced data analytics software and other state-of-the-art tools to capture, create, collect, and organize data. These tools will give companies profound insights into their customers and industries that they would’ve never gained using traditional tools.
Read on to learn more about advanced data analytics and how you can use these techniques to derive insights for your business.
What Is Advanced Data Analytics?
Advanced data analytics is an umbrella term for statistical techniques that can help you analyze business information from different sources. It covers various tools and methods, including deep learning, predictive modeling, and machine learning.
Often used by data analysts and data scientists, advanced analytics enables businesses to predict patterns and customer behavior. This, in turn, gives businesses a firmer grasp of what their customers want and boosts their accuracy in decision-making.
How To Conduct Advanced Data Analytics
The list of advanced data analytics tools and methods is constantly growing. Some of the most popular advanced data analytics techniques include:
Predictive analytics
This advanced data analytics technique analyzes historical data to predict future outcomes using statistical methods such as predictive modeling, data mining, and machine learning. Predictive analytics typically mines data from systems and databases such as enterprise resource planning (ERP) systems, marketing automation stacks, and customer relationship management (CRM) software. The method then presents the information in easy-to-understand formats such as line and bar graphs.
Many industries use predictive analytics to future-proof their offerings. For instance, Human Resources (HR) departments often use predictive analytics to predict how employees will perform in the future. Hospitals and other healthcare providers also use predictive analysis to predict negative health events.
Prescriptive analytics
While predictive analytics tells us what will probably happen next, prescriptive analytics tells us what we should do. As the most actionable type of advanced data analytics, prescriptive analytics plays a large role in data science. Data analysts often use prescriptive analytics in deep learning, applied statistics, computer vision, and other methods to derive insights from unstructured and structured data.
Since leveraging prescriptive analytics requires specialized data science knowledge, prescriptive analytics isn’t as popular as the other types of data analytics on this list. As such, predictive analytics will remain the to-go advanced data analytics method for most businesses.
Artificial intelligence and machine learning
This technique leverages artificial intelligence (AI) technology to recognize patterns in large datasets and produce results from those patterns. Many eCommerce companies use AI and machine learning to create responses for retail chatbots.
Sentiment analysis
Also known as opinion mining, sentiment analysis is the process of collecting and analyzing text such as social media posts and reviews to identify customers’ emotions. eCommerce and retail businesses often use sentiment analysis to learn how their customers perceive them, their products, and their marketing campaigns.
Data mining
Data mining is the process of sifting through large datasets to uncover trends, patterns, and other insights. This is achieved through statistics and machine learning.
There are many ways to mine data. Here are some of the most common methods:
- Regression analysis: Looks at the effect one variable has on others. Most businesses use regression analysis to explain an event they want to understand. For example, why did social media responses drop last month?
- Anomaly detection: Looks at data points that deviate from the norm. Also known as outlier detection, anomaly detection is often used to identify critical incidents or opportunities, such as changes in consumer behavior.
Big data analytics
Big data analytics uses advanced techniques on big datasets that include unstructured, structured, and semi-structured data from different sources and in different sizes.
Essentially, advanced big data analytics is the analysis of “big data,” which refers to data sets that are so large and varied that traditional relational databases can’t capture, process, or manage them. Big data is so varied because it encompasses data created through AI, social media, mobile devices, and the Internet of Things (IoT). For example, data from Instagram, TikTok, and Amazon are considered big data because most of the information is generated on an enormous scale in real time.
Since big data contains a lot of key insights about customer behavior and industry trends, many businesses use high-level solutions such as web scraping to harness the information contained in big data analytics.
How Can Advanced Data Analytics Help Your Business?
Advanced analytics tools provide many benefits for businesses. Because you can use it on almost any kind of data, you can use it to make future-proof decisions that can increase your Return on Investment (ROI). You can also use it to anticipate potential problems and opportunities, make faster decisions and get deeper insight into trends, business processes, and customer preferences.
For example, let’s say you’re an eCommerce store selling gaming laptops. You can use the following advanced data analytics methods to your advantage:
- Data mining: Collect information about your competitors and your industry to stay ahead of the curve.
- Sentiment analysis: Collect and analyze your gaming laptops’ social media posts and reviews to understand how consumers see your website, marketing, and products.
- Predictive analytics: Predict how consumers will behave in the future and how future trends in the gaming and tech industries will affect sales.
- Prescriptive analytics: If you’re a small shop, you may not have the resources to hire data scientists and data analysts to perform prescriptive analytics. But if you do, you can use prescriptive analytics to determine your next step. Prescriptive analytics can help you answer questions such as:
- What types of gaming laptops should I include next year?
- Should I invest more in social media marketing? If yes, which social media platform(s) should I focus on?
- Big data analytics: Use big data analytics solutions such as web scraping to analyze trends and customer behavior from IoT, social media, Amazon, AI, and mobile devices.
Why You Should Use Web Scraping For Advanced Data Analytics
If you’re interested in harnessing the power of advanced data analytics, consider using web scraping.
Web scraping — also known as web data extraction or web harvesting — is the process of harvesting large amounts of data from websites and saving it to a database or file. It is done through automated programs called web scrapers or bots, which automate the tedious process of scraping data from websites, so you don’t have to visit each webpage yourself.
Web scrapers also make the data collection process faster and more precise, particularly when dealing with large datasets and big data. Instead of copying and pasting information into Excel sheets, all you have to do is instruct your bot to collect the information you want it to collect. Your bot will always be faster and more accurate than you since it doesn’t need to wait for a webpage to load to start the scraping process. Within seconds, it can read a webpage’s HTML, extract the information you want, and move on to the next page. Additionally, a web scraper can organize extracted data for you. It can automatically output information in your preferred format or directly upload it to another software for further processing.
How Scraping Robot Can Help You Conduct Advanced Data Analytics
Finding the right scraping bot or web scraper for your project can be challenging, particularly if you’re new to advanced data analytics and scraping. Many scrapers are overpriced, hard to use, and lack frequent updates.
That’s why you should get Scraping Robot. Powerful and intuitive, Scraping Robot is a full-suite web scraper anyone can use to conduct advanced analytical research on competitors and trends. By creating an account with us, you’ll automatically receive 5,000 scrapes per month. You’ll also get access to all of Scraping Robot’s features, including:
- 24/7/365 customer support: At Scraping Robot, your experience is our top priority. Our customer support team is available at any time of the day to answer any questions you have,
- Frequent improvement and module updates: Unlike many other scrapers, Scraping Robot is frequently updated — we proactively add new modules every month for you to use. You can also request new modules. If we think that your request will benefit other users, we’ll add on the module for free!
- 7-day storage: You can store your scrapes for seven days, download, or export your data for further use.
If you want more than 5,000 scrapes per month, our pricing will have your back:
- Business tier: Offers up to 500,000 scrapes per month at only $0.0018 per scrape.
- Enterprise tier: Offers more than 500,000 scrapes per month at rates as low as $0.00045 per scrape. You’ll also be able to make custom API requests.
Interested? Get started today.
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