Enhance Predictive Analysis with Web Scraping

Predictive Analysis with Web Scraping: Stay Ahead of the Competition

Predictive analytics is a process of analyzing existing data to determine patterns and predict future outcomes or trends in the business. Although predictive analytics cannot accurately forecast the future, it is about predicting what the probabilities are. Considering this analysis is implemented on a huge amount of existing data, web scraping can extract and make available large amounts of data that can then be used in predictive analytics.

Why is predictive analytics a key process of analyzing existing data to determine patterns and predict future outcomes or trends? Predictive analytics cannot accurately forecast the future, but rather is about forecasting what the probabilities are with sophisticated techniques. Apart from other fields, predictive analysis has its application mainly in the business world. Predictive analytics is used to study and understand customer behavior, products, competition, and other factors to determine risks and opportunities.

As is evident, this is a type of analysis that is carried out based on a large amount of existing data. For this reason, web scraping has gained importance because it can extract and make available large amounts of data used in predictive analysis. In other words, web scraping is essential for predictive analysis.

Predictive analysis for corporate strategy

Companies must use different predictive analytics to remain competitive. Predictive analytics is still necessary to examine quality data using business intelligence. This is because it is used in forecasting and modeling. It is a pattern prediction approach with numerous applications in different industries, especially finance, banking, insurance, and general commerce.

In this sense, credit evaluation is the most typical application of integrating predictive analysis and web scraping. Using historical data to forecast the future of a company and the market is a crucial aspect of any organization.

Business intelligence is critical to helping marketing teams proactively anticipate and prepare for customer needs rather than simply reacting to them. Information based on demographic data that may have been ignored in the past can be presented using web scraping. Combining customer demographic data can be valuable in determining which marketing platform to employ, as well as which marketing strategy to use and when.

Executives can use a combination of web scraping and predictive analytics to increase sales while saving resources and time. The goal of any business is to reduce losses and increase profits. As a result, web scraping and predictive analytics are essential for businesses, whether online or offline.

Types of analysis

Predictions based on an analysis of data collected over some time take different forms, according to the behavior of the variables, data, and patterns to be found:

  1. Analysis of descriptive data: One of the most fundamental types of analysis is descriptive analysis. These analyses represent historical events, no matter how recent or distant, after the data has been processed. This is beneficial because companies can use this information to learn from past actions and make better actions or strategies in the future. Examples of this type of analysis include quantitative and visual data analysis, data analysis on a company’s customer base, production information, finances, records, and sales.
  2. Predictive data analysis: As its name indicates, predictive analysis precisely manages to forecast future events. While the future can never be completely predictable, the probability of a variety of different scenarios can be predicted. This allows companies to consider the risk factor when making decisions, effectively putting these probabilities into their probabilities. Forecast supply trends to make decisions about what type of supplies to use or where they are purchased from, or predict your sales revenue in the coming years; are some examples of predictive analysis.
  3. Diagnostic analysis: The most abstract phase of analysis is sometimes called diagnostic analysis. It may explain some events with sales, customer demand, or why specific manufacturing regions had so little supply or so much demand. Recognizing correlations between events and determining whether they have a positive, negative, or zero correlation is also part of the diagnostic analysis. When both variables increase at the same time, there is a positive association. When one variable increases while the other decreases, this is known as a negative correlation. The term “zero correlation” refers to the fact that none of the variables are related to each other, nor are they impacted by each other.
  4. Prescriptive data analysis: Prescriptive analytics is becoming more common in companies of all sizes. It involves combining all the aforementioned data analysis methodologies and providing various possibilities for a specific business decision. It is possible to anticipate the implications of these actions and prospective results, as well as explain why these results occur. In this way, this analysis will accurately forecast events, explain why they might occur, and provide options for making business decisions based on these events. In this sense, it is important to have solutions specifically built to adapt to the demands of some companies and company departments.

Types of models

Predictive analytics is based on models that allow users to use historical and current data to find insights, which means achieving long-term beneficial results. Predictive models come in a variety of shapes and sizes:

  • Customer lifetime value model: Discover customers who are most likely to invest more in products and services.
  • Customer segmentation model: group customers based on similar characteristics and purchasing behaviors
  • Predictive maintenance model: forecast the chances of essential equipment breaking down.
  • Quality Assurance Model: find and prevent defects and additional costs when providing services to customers.


Web scraping is the perfect way to collect useful data for analysis. Below are some ways you can take advantage of web scraping to make your analysis as accurate as possible.

  1. Trend analysis: Similar to tracking market changes, trend analysis helps you understand how styles, opinions, and behaviors change in a given market (from what type of promotion or offer you need to launch in an online store to how customers feel about e-commerce versus buying in a physical store). Understanding trends is crucial to understanding how they will change in the future. We all know that fashion trends change quickly, but so do other markets. Searching for trending topics on social media sites can give you insight into how consumers think about a given topic or style trend. You can pull Google search results to see the top sites for a given keyword. Search results data tells you which brands or online stores customers see first when searching for your product. Having data from Google search results makes it easier to understand how to best appear in search results and what the competition looks like for your organization.
  2. Social Analysis: Social networks host tons and tons of free data. People express opinions about products, services, entertainment, or news. Searching for social data helps you understand what your customers want so you can offer it to them in the future. While this may seem more applicable to the business world, predictive social media analytics is useful for creators of all types to understand how public interest is changing. For those looking to start a small business, you can use social data to discover what is missing in the market.
  3. Custom scraping solutions: Each project to expand your organization requires different sets of data. Creating a custom scraping solution allows you to get data specific to your needs while scraping at a higher capacity. We know that the future may be uncertain. We can never really know what the future holds, but data can give us an idea. Custom predictive analytics services, such as web scrapers, help organizations collect and analyze data to help form predictions. These predictions can be as general as the future of an industry or as specific as a customer’s likely next purchase. Either way, mastering web scraping skills and understanding predictive analytics is a sure way to help your organization handle whatever comes next.

Why Scraping Pros

One of the great advantages of hiring Scraping Pros is being able to provide you with a flexible and personalized web scraping service, adapted to the changes in your business and the competition: you can feed your business with a large volume of integrated data from different websites, and then use them in all types of predictive analysis. Our knowledge of the characteristics, opportunities, and potential of each industry means that we can deliver personalized data daily, according to the unique needs of each project.