Every year, we witness significant growth in traffic for automated public data collection at Oxylabs. While some businesses have yet to find a way to effectively apply analytics, others are becoming creative with how they interpret data for maximum impact. 

Data volumes are growing exponentially and will continue to do so for the foreseeable future. Scholars and industry professionals are keen to learn more about how these new opportunities affect businesses’ priorities. 

Data-driven businesses often have adequate tools to verify that their insights are correct (i.e., even simple measures like an increase in traffic positively correlating with Ideal Customer Profile inbound leads). However, big data may be much more than a tool for basic analytics. Researchers are already employing unique mathematical tools to find novel correlations in massive data sets. 

Though it’s possible that businesses may get by without using such sophisticated analytical models, novel ways to make sense of the information at hand are still needed. Consequently, we may be able to discover unique or improved methods of revenue generation by integrating linked data sets. 

RESEARCH IS COSTLY 

All sound business decisions are supported by evidence. While research is still the gold standard for proof, it has a number of limitations. Most significantly, well-designed research is costly and time-consuming, therefore, the ROI may be ambiguous. It is frequently unwise to perform academic-level research in business, particularly in dynamic and ever-changing areas like online retail. So, large-scale consumer research is unsustainable for most firms. 

Data that is only a few years old in ecommerce may already be utterly obsolete and erroneous. Unfortunately, that is typically the period in which well-designed research is carried out. 

However, if you ask a researcher what the most challenging component of performing any study is, they would almost always reply – the data gathering stage. Acquiring and entering all of the necessary data takes significant time (and, in many cases, resources). Recent developments in data gathering, transmission, and Data-as-a-Service companies make it possible to collect massive amounts of information on almost any topic. Taking advantage of such prospects requires businesses to conduct meticulous research around existing data to test assumptions. 

USING BIG DATA FOR TESTING 

Instead of going over abstract cliches of using big data to test revenue-generating hypotheses, let’s look at scholarly studies. Sinan Aral, an information technology and marketing professor, has published two essays on the possibilities of big data. As we will see, there are several opportunities for digital firms to use these insights or comparable research methodologies to improve their customer connections. Aral explored many assumptions about the influence of social contagion on runners in “Exercise contagion in a global social network.” Social (behavioral) contagion is the tendency for people to mimic each other’s behavior based on the socially transmitted information. 

Testing regularly by conducting research, gathering people, and dividing them into various groups would be time-consuming, inefficient, and costly. They, however, obtained behavioral information from a smartwatch business that focuses on runners. Furthermore, the company supplied users with a social network via which they could send notifications to their friends regarding exercise-related activities. They would still need to randomize the users in some way, so Aral used weather data from stations throughout the United States. Weather is connected with general running activity (i.e., when it rains, people are less likely to run). Their findings indicate that social contagion has a causal link. It means that jogging an extra kilometer one day will cause your pals to run an additional 4/10 of a kilometer the next. Social contagion effects will last into the latter days, albeit with a reduced effect (e.g., your 

buddies will run more than usual, but only for 4/10 of a kilometer). Prof. Aral and his colleagues also published a research paper titled “Digital Paywall Design: Implications for Content Demand and Subscriptions,” which aimed to optimize revenue earned by paywall structures. They examined how alternative paywall structures (for example, the New York Times requiring users to buy a subscription to view more articles) affected reading across platforms, ad income, and subscription rates. They put their assumptions to the test by altering the amount and type of free material offered. 

The research findings verify that the newspaper’s paywall design had a substantial effect on content demand, subscriptions, and net income. By using this research’s framework, digital paywall administrators might arrive at the most efficient pricing plan and increase income. While paywalled digital content is unique to the publishing and journalism industries, it’s easy to see how a similar technique might be used for any subscription business that offers free trials, plans, or any service. 

USING WHAT WE’VE LEARNED TO IMPROVE ECOMMERCE BIG DATA ANALYTICS 

Massive amounts of internal data are already being collected in ecommerce and other digital businesses. Yet, companies should not confine themselves to internal data as the benefits provided by external collection are immense. 

Web scraping is a popular method for acquiring external data. It may be used to obtain previously inaccessible data, which can then be readily absorbed into a data warehouse for subsequent research. Ecommerce is an excellent target for novel techniques for data interpretation since a large amount of data on various interactions is collected daily with no additional effort. However, various offline and off-site online elements can significantly impact user experience. As a result, we should make daring interpretations to maximize revenue and optimize the user experience. To capitalize on these insights, ecommerce, SaaS, and other digital organizations may need to reconsider their data gathering strategy. However, by combining external and internal data and doing creative analysis, companies may maximize customer experience, which will significantly impact overall growth. 

WAYS BIG DATA ANALYTICS IS CHANGING ECOMMERCE 

The vast amounts of data that must be processed and analyzed to reap the advantages of ecommerce are one of the key challenges brought about by the information revolution. Analyzing and making sense of massive volumes of information is the principal goal of big data analytics, which aims to enhance decision-making. Sazu revealed a tradeoff between big data and its analytics capabilities, with the optimal balance depending on the level of analytics potential. The study showed that a company’s capacity for analytics amplifies the influence of big data on gross margin and sales growth. According to Gopal et al., big data analysis can substantially increase online transactions reliability, ameliorate personalization, improve pricing strategy, boost revenue, and help companies to adjust their offering to the market’s demand. Ecommerce businesses may benefit from big data analytics by examining historical market patterns in light of the present situation. Therefore, companies tailor their advertising to the tastes of their clientele, create brand new products to meet the needs of their buyers, and ensure that their employees provide the high standard of service to which they have been used. Due to the highly competitive nature of the sector, ecommerce has always been data-hungry. Oxylabs chose to explore the shifting interest in online scraping and other data collecting methods. According to the findings, web scraping has firmly established itself within the ecommerce business, with more than three quarters (75.7%) of enterprises using it in their everyday operations. Furthermore, most have already experienced great returns from the data collecting strategy, with web scraping generating the highest money (32.4%).

There are numerous ways to obtain pricing data, including manual collection and purchased data sets. While most methods provide the information required, price monitoring architecture that leverages web scraping tops the list by providing the automation needed to collect massive data volumes at scale – in seconds. 

Price monitoring helps enterprises stay agile, giving them the flexibility needed to pivot their strategy and quickly adapt to changing market conditions. In addition, pricing intelligence via web scraping catalyzes dynamic pricing strategies, fuels competition analysis, and enables companies to monitor Minimum Advertised Price (MAP) policies.

How web scraping works

Web scraping obtains information via specially-programmed scripts (also known as “bots”) that crawl ecommerce shops, marketplaces, and other online public spaces to extract data. Besides pricing intelligence, web scraping has numerous alternative uses that include cybersecurity testing, illegal content detection, extracting information for databases, obtaining alternative financial data, and many more. 

4 ways online businesses leverage price intelligence

Pricing intelligence has been fundamental to businesses since humans began buying and selling products and services. However, unlike traditional marketplaces, web scraping amplifies the process exponentially by enabling enterprises to extract thousands of data points in seconds. Some applications of scraped data for product and pricing intelligence include: 

1. Digital Marketing 

Digital marketing comprises a set of practices designed to target your ideal customers and guide them through the buying process. Successful strategies depend significantly on the ability to collect timely, accurate data to enhance marketing practices. 

Some digital marketing applications of data include: 

• Profit-maximizing pricing strategies.
• Customer avatar creation.
• SEO-optimized content marketing.
• Email marketing.
• Sales funnel optimization.

Public sources of product, service, sales, and marketing data include online stores, marketplaces, search engines, social media platforms, and forums. 

Some types of data available to online enterprises from these sources include: 

• Product titles.
• Current and previous prices.
• Product descriptions.
• Image URLs .
• Product IDs from URLs.
• Currency information.
• Consumer sentiment.
• Brand mentions.

Digital marketing strategies vary significantly from sector to sector, however, success greatly depends on the quality of data extracted and insights obtained. Web scraping provides a targeted method for acquiring that information, customized for your business. 

2. Competition Analysis 

Competition analysis is fundamental to online sales success. Scraped data from public websites gives businesses the vital information required to pivot their marketing strategy to outperform the competition and gain a greater market share. 

Web scraping can be used to obtain competitor information that includes: 

• Historical pricing data 
• Detailed product and service information 
• Complete product catalogs 
• Inventory/stock information 
• Shipping policies 
• Anonymized reviews from competitor websites and marketplaces 

Competition analysis is essential to any ecommerce strategy. Web scraping provides the data required to refine your product catalog, pricing, branding strategy, and email marketing to beat competitors and adapt to ever-changing market conditions. 

3. Dynamic pricing strategies

Dynamic pricing refers to the strategy of shifting prices according to product or service demand. Most consumers are familiar with the practice from transacting with travel websites to book flights and hotel rooms. 

Price monitoring via web scraping has amplified the practice via process automation. As a result, enterprises across additional sectors can leverage dynamic pricing to quickly adjust prices based on real-time supply and demand data. 

4. Minimum Advertised Price Monitoring 

Minimum Advertised Price (MAP), MSRP (Manufacturer’s Suggested Retail Price), or RRP (Recommended Retail Price) refer to the lowest price allowed for a retailer to advertise a product. 

MAP policies are implemented to protect a brand by preventing retailers from excessively lowering the price and reducing consumer confidence in a product. Price monitoring architecture is used to crawl the internet to collect pricing data and identify online businesses that may be violating MAP policies.

Web scraping challenges while collecting pricing intelligence 

Web scraping is a complex process that requires expertise to select the most relevant target websites, effectively program scripts, and choose the most appropriate proxies to distribute requests and prevent server issues. 

As mentioned previously, extracting large volumes of data via web scraping requires automation to collect data at scale. The process requires consistent monitoring because web scraping algorithms must be adjusted to account for numerous challenges that include: 

• Failure to differentiate same or similar products – even if product titles and images don’t match.
• Constantly changing website layouts and HTML structure.
•  Server issues such as blocking and captchas 

How web scraping works within price monitoring architecture

Price monitoring is based on an entire architecture that includes price tracking, monitoring, and analysis. The process requires four main steps that include: 

Step 1: Collecting target URLs 

The first step is to analyze competitors and identify target URLs. Following URL selection, a database containing the URLs is created either by manual collection or automated web crawling. 

Step 2: Web scraping 

Configuring the web scraper is the next part of the process, requiring three steps that include: 

Selecting and configuring proxies – intermediaries between the scraper and server to provide anonymity and prevent blocks. 

Creating a browser “fingerprint” – configuring identification data that relays information to the server, allowing a scraper to submit requests and extract data successfully. Sending HTTP requests – the actual data requests sent to the server to scrape the desired information. 

Step 3: Data parsing 

Data parsing transforms extracted raw HTML data into a readable format that can be analyzed for insights. Learn more about the process by listening to episode 3 of the OxyCast – Data Parsing: The Basic, the Easy, and the Difficult. 

Step 4: Data cleaning and normalization 

Data cleaning and normalization is an optional step that refines the scraped data by removing inaccurate or corrupt records, converting currencies, and translating foreign language text. 

Get an inside look at price monitoring architecture

This article is a valuable introduction for anyone interested in price monitoring architecture. To get a detailed explanation of how it works, download our free white paper Real-Time Price Monitoring System Architecture. 

Here’s what you’ll learn: 

• Detailed pricing architecture concepts.
• More technical steps and sub-steps to configure and operate price monitoring architecture.
• Different proxy types and how to choose them.
• Different proxy types and how to choose them.
• Overcoming price monitoring challenges.
• Next steps to get started.

Price is the critical factor that can make or break your online business. Download Real-Time Price Monitoring System Architecture to discover how to unlock the power of data for creating pricing strategies that outperform the competition.