Leverage Black Friday Pricing Insights With Data Scraping

Aim for optimal pricing strategies in black friday with data scraping

E-commerce is an industry that revolutionized the business world and today continues to generate innovation and potential benefits for the market. Today, decision-makers can extract data from e-commerce and obtain important insights, especially in annual consumption milestones such as Black Friday. In this post, we explore how Data Scraping can help set dynamic prices and analyze certain consumer data during Black Friday.

When e-commerce gains ground in the retail consumer industry there is no stopping it. In fact, according to industry sources, the estimated global value of retail e-commerce could be $4 trillion or more. And it is expected to continue growing to the point where the total value of e-commerce in 2024 could exceed $6 trillion.

One of the key moments in the sector has to do with the dates of large discount offers for limited days, actions that usually encourage sales and facilitate access to various consumer segments. Millions of people fill the shopping centers, stores offer enormous discounts to attract customers, and Internet sites crash and break sales records: it is what is now known worldwide as Black Friday, a consumer ritual that is repeated every year.

Black Friday is the Black Friday tradition that originated in the United States. It has been celebrated for decades after Thanksgiving. This is a great phenomenon in retail trade that represents a great increase in the economy of shops. Years ago, it meant a great movement of people when it came to buying gifts, in many cases anticipating Christmas shopping. This year Black Friday was not far behind and in the case of the United States, it was loaded with offers that continued throughout the month.

With 91% of manufacturers indicating their intention to increase prices in the second half of 2023, the value of sound purchasing decisions has never been higher. The strong competition that exists in the industry makes product price analysis and dynamic pricing one of the keys to success in E-commerce.

Data Scraping as an Opportunity Enhancer

How can we determine offers, discounts, and prices, improving our performance in the face of the high competition that exists on e-commerce platforms? Currently, many executives are unaware of the potential of Data Scraping to manage e-commerce data. They also don’t know that their competition could be up to a hundred times more expensive, depending on the products they plan to promote. It is not practical to perform the manual task of acquiring mass product information by copying and pasting data from web pages. Not only is excess money wasted, but data is also exposed to human error. That’s where Data Scraping comes into the picture, providing us with enormous value-added opportunities.

To meet these expectations, one of the main applications of Data Scraping is dynamic analysis and pricing. The main advantage of Data Scraping for websites is that monitoring the prices of competing websites benefits e-commerce companies of any size, large or medium. Businesses can set rates for the same product or service in different locations. It allows the customer to decide the price of their product and investigate trends in new products.

Above all, companies selling highly competitive products need to know if the prices charged are adequate or if they need to be reshaped to ensure the right balance between return and market parameters.

In this case, Data Scraping is used to identify precise data and the objective is to create an always updated database with which to perform comparative analyses and which can be referenced for the definition of pricing strategies that change dynamically according to the target market.

An activity of this type can also be especially useful to propose discounts, promotions, and offers or in periods in which the propensity to buy becomes stronger, such as Black Friday, Cyber ​​Monday, or Christmas shopping in general.

To review some examples, some consulting companies used Data Scraping to analyze and compare the most requested products in 2022 and explain how to crawl different e-commerce websites (see example). Other agencies conducted market research analyzing the sample of more than 15,000 products from Walmart, Nordstrom, Amazon, Target, JCPenney, and Macy’s (see example).

The metrics and indicators are excellent since with these public data extraction tools you can set the most competitive prices and adjust them according to more price analysis of thousands of products and real consumer behavior. With these analyses, you can scale your business, in an unprecedented and unprecedented way, and make dynamic pricing increasingly viable.

However, one of the biggest challenges will be being able to adapt your company culture to the benefits of Data Scraping and Web Data Mining, since you will somehow have to find a business partner that allows you to transform your data processes into commercial assets. Being aware that these tools will transform the value of your assets is how you will be able to take advantage of each business opportunity that is presented to you for your company, venture, or platform.

Scraping Pros: the partner for your E-commerce

Web Data Mining and competitor research with automated methods can reduce the time spent analyzing large amounts of data. In this sense, Data Scraping is essential for your business: it can automate operations such as data collection and analysis, drastically accelerating the development of e-commerce. Instead of performing mundane, manual, or tedious tasks, as an executive, you can channel your efforts towards more creative and product development tasks.

Using ScrapingPros for your E-commerce you will obtain real-time information, comparative metrics between products, analysis of consumer demand, and new insights to make faster decisions aimed at improving profitability. By hiring our solutions, you will be able to customize your analysis of various sites and sources of information, with a structure to handle any large-scale data mining project.