How Dynamic Pricing Helps Ecommerce Companies Maximize Profits

Black Friday is coming up and competition is higher than ever. Leverage dynamic pricing with the power of web scraping to increase sales and maximize profits.

Black Friday – the first Friday after the US Thanksgiving holiday – is one of the busiest shopping days of the year. Everything from website hosting to jewelry seems to go on sale, and it’s gaining more importance as time goes on.

According to statistics from the National Retail Federation, nearly 190 million people shopped over the Black Friday weekend in 2019, up 14% from 2018.  While 2020 experienced a slump, bringing the spending back down to 2018 levels, overall spending in 2021 has increased by nearly 30% when compared to 2020, according to Harvard’s Recovery Tracker. It wouldn’t be surprising to experience a Black Friday surge that would blow 2018, 2019, and 2020 out of the water.

Big data trends show the increasing importance of Black Friday

Whether it’s online or offline, Black Friday is a big deal and our work at Oxylabs verifies this. Data from our 2020 web scraping data and trend report suggests that marketers took data insights more seriously by increasing web scraping resources by nearly 30% in 2018 and 13% in 2019. This represents a massive 43% increase in two years.

No longer a luxury for the bigger players, data extraction is becoming a necessity – particularly for those organizations that want to survive the current recession. Data by McKinsey suggests that organizations using advanced marketing techniques outperform their competition by 85% in sales growth and an impressive 25% in gross margin.

Besides sales, businesses grow faster and obtain a larger market footprint when big data is leveraged. Research confirms this, verified in a recent report by Forrester Research that revealed that businesses engaged in web scraping outperform their competition through increased average growth by at least 30% annually. 

Leverage web scraping through dynamic pricing

Imagine being able to find out about a competitor’s sale and swooping in with a better deal at a critical time. Now picture being able to do this in real time on an automatic basis. This idea is central to dynamic pricing – a strategy that uses flexible prices based on real-time supply, demand and competitor prices. 

While the notion of changing prices may seem strange or unfair to some people, dynamic pricing is everywhere on the internet. Consumers see this, although they might not notice it, constantly on travel websites, where the prices for hotels and flights often change several times within hours. It is also seen in flash sales on electronics or other consumer goods on price aggregator websites.

Web scraping is central to any strategy involving dynamic pricing, and the quality of the data obtained is critical to its success.

Key factors to consider for dynamic pricing

Dynamic prices depend on several changing factors. Web scraping is leveraged to track some of these factors in order to arrive at the most profit-maximizing price.

Internal factors are those that govern the supply side in dynamic pricing. They include factors such as the amount of stock, production costs and shipping charges.

Different rules also apply to different businesses. For example, some items that are rare become more valuable when stock is low (like a limited edition designer clothing item) while other items are best to sell off as quickly as possible (like older phone models). Internal web scraping can be targeted to account for different business models according to their industry and sector.

External factors that affect prices are typically outside the organization and can include holiday events (like Black Friday), increased competition from emerging brands, technological innovation and seasonal changes. Web scraping typically tracks several factors simultaneously, ensuring that a seller’s prices are competitive at all times.

Web scraping is actually (very) complex

Web scraping is quite complicated, comprising several processes that include: crawling the internet with predefined targets and data points, extracting the data, parsing the data (so it can be read), and then adjusting the prices.

The dynamic nature of data on the internet can make the process particularly challenging because website structures and formats are always changing due to different coding methodologies. As a result, web scraping scripts need to be adjusted continuously and implemented appropriately – although technology is evolving to combat this issue.

Newest developments and research have shown that focusing on a specific category of pages or websites is the more efficient approach. These insights were the reason we separated our Real-Time Crawler solution into three APIs, each dedicated to a specific industry (e.g. E-Commerce Scraper API).

Another serious issue is when scrapers get blocked. Data-rich sites have data request limits, often using algorithms to detect bot-like activity. When requests are exceeded, servers are triggered to ban specific internet protocol (IP) addresses in an effort to prevent server overload.

Requesting parties often respond by using datacenter or residential proxies – intermediaries that have a specific IP that can mimic human activity or a separate device. This allows scrapers to distribute the requests in order to collect data in a way that doesn’t overload servers. Along with ensuring that servers aren’t overwhelmed, ethical web scraping typically employs practices for obtaining proxies in a transparent manner that rewards participating users.

Ethically-procured proxies are of higher quality, resulting in more stable connections for increased success and improved efficiency for the web scraping process. The end result is a win-win for both sides where users are compensated while clients are rewarded with better data extraction results.

Dynamic pricing can level the playing field for smaller players