Ready, Set, Web Scrape: Winning E-Commerce Pricing Strategies
By: Adelina Kiskyte
Tags: Data Acquisition, Pricing Strategy, Web Scraping, Ecommerce, Dynamic Pricing

Photo by Scott Graham on Unsplash

Consumers who have ever purchased plane tickets, booked hotel rooms or used taxi services have experienced dynamic pricing. Adjusting prices based on market demands is not a new concept, but it is more relevant than ever.

Not that long ago, dynamic pricing was based on past sales data. Market demands were forecasted by analyzing seasonal and cyclical trends. Today, dynamic pricing involves more sophisticated procedures, such as large-scale data gathering and analysis.

In a broader sense, dynamic pricing is part of pricing intelligence: a process where businesses gather and process data to adjust pricing strategies and grow profit.

In this article, you will find out what dynamic pricing is and its main strategies. You will also learn more about the process of acquiring data to optimize pricing strategies and drive growth in profits.

What is dynamic pricing?

Dynamic pricing allows companies to set flexible prices for their goods or services based on real-time demand. Prices are adjusted based on supply and demand changes, competitor prices, and other market conditions.

Introducing dynamic pricing into business strategy is sometimes seen as a controversial decision. If companies abuse real-time pricing, it might damage the brand. This will be further explained under dynamic pricing challenges.

Dynamic pricing explained (Source: Oxylabs’ design team)

What is a dynamic pricing strategy?

The dynamic pricing model allows marketers to adjust product or service pricing based on demand (or other factors mentioned earlier). Instead of having one price point, companies introduce multiple price points in order to increase their revenue.

Dynamic pricing introduces multiple price points (Source: Oxylabs' design team)

What are the benefits of dynamic pricing?

Dynamic pricing has a number of advantages, and here are the main ones:

Stock management (supply and demand control)

Dynamic pricing allows managing demand in case of operational bottlenecks. Adjusting prices also helps to sell out surplus products, if required. Acquired data on market trends allows companies to foresee a peak in demand and take care of required product supplies.

Insights into consumer behavior

Having extensive data on consumer behavior allows companies to get to know their customers. For example, data analysts can extract competitor information and calculate what minimum and maximum price a customer would pay for a product or service. This information can then be used to adjust prices and generate more sales.

Revenue

Introducing multiple price points generates more profit than having a single price point. The revenue increase is one of the main benefits of real-time pricing.

Dynamic pricing challenges

Price discrimination

Real-time pricing brings healthy competition to various markets. However, real-life examples show that some businesses may take it too far and even harm their brand reputation.

Raising prices during peak times might backfire as it did for Uber, a ridesharing company. The company introduced higher prices during times when people needed transport the most, for example, during a snowstorm in New York City. The company was accused of exploiting its customers.

Solution: Dynamic pricing should not turn into price discrimination. A healthy competition may benefit both companies and their clients, but trying to profit in, for example, force majeure situations may have a negative impact on a brand’s reputation.

Customer satisfaction

Introducing a variable pricing strategy is sometimes seen as a controversial business decision. Customers might find it unfair and choose other brands.

Solution: Being transparent about real-time pricing is the way to go. Customers appreciate honesty and freedom of choice.

Data acquisition

In order to implement surge pricing, companies need to constantly follow their competitors’ prices in real-time. Data acquisition, especially on a large scale, is one of the main dynamic pricing challenges.

Solution: Web Crawler tool can be a solution that allows gathering large-scale data even from the most challenging targets in real-time. Such tools do not require any extra management or resources.

Is dynamic pricing legal?

Dynamic pricing is legal, but it has some exceptions. For example, price discrimination based on gender, race, religion, or nationality is illegal, according to findlaw.com. Also, in order not to compromise consumer rights, companies should be transparent about employing dynamic pricing.

Other sources state that: “If different prices are charged to different customers for a good faith reason, such as a [sic] an effort by the seller to meet the competitor’s price or a change in market conditions, it is not illegal price discrimination.”

Optimizing Dynamic Pricing for E-commerce

Dynamic pricing requires companies to track many external and internal factors related to the valuation of a product or service. Internal factors include:

Stock

Production costs

Shipping costs

Meanwhile, external aspects are harder to track as they might vary due to time (seasonal weather changes, holidays, etc.), consumer behavior (such as trends and brand loyalty), competition, and many other factors. Yet, most of all, efficient dynamic pricing requires an accurate and effective process of data crawling and parsing.

Acquiring accurate data can be challenging due to the constantly changing nature of consumer information. Data extraction can be divided into several different processes that allow for the continuous scanning and scraping of the Internet. These processes are:

Building a scraping path

Creating data extraction scripts

Proxy management

Data storage

Data parsing

Each of these steps brings its own challenges, although implementing a pricing data extraction process is not difficult in itself. Issues often arise whenever websites start fighting back against data scraping.

Implementing the pricing data extraction process

In order to apply dynamic pricing, accurate and real-time data is required. Implementing a pricing data extraction process is one of the few ways to continuously acquire such data as only fresh information can be used for dynamic pricing strategies.

Data Acquisition

Data acquisition is an umbrella term for a 4-step process:

Developing data extraction scripts and implementing them Using (headless) browsers and their automation Data parsing Data storage

Developing data acquisition tools is a critical part of dynamic pricing strategies (Source: Oxylabs’ design team)

Building a pricing data acquisition tool is generally not too difficult for teams with dedicated and experienced developers. Nowadays at least several languages have easily accessible libraries that make building data extraction scripts and parsing acquired information significantly easier. Additionally, open source tools (e.g. Beautiful Soup) can serve as good starting grounds for some projects.

On the other hand, maintaining a constant flow of pricing data is challenging. Most website owners are not too keen to give large swaths of data to anyone with a Python script. As such, most data extraction targets (especially ones that are critical to pricing intelligence) have built algorithms to detect bot-like activity.

Avoiding anti-bot detection algorithms relies on two key factors — understanding how websites detect bot activity and consistently rotating IP addresses through proxies.

Avoiding blocked IPs

There are two ways to consistently scrape the web for pricing intelligence — using residential proxies or using a web scraping solution such as web crawler tools.

Residential proxies allow web scrapers to acquire IP addresses of physical devices which makes detection significantly harder. These proxies need to be consistently changed in order to avoid bot detection algorithms. Exact details on IP change frequency, work volume will depend on the scraping target.

Efficient web scraping requires not only a large pool of residential proxies in the world but also a large degree of customization:

Backconnect entries

City and country-level targeting

Session control

Sticky ports

Managing proxies without a dedicated developer team is a daunting task, therefore many customers choose to outsource their data acquisition pipeline. After all, it is often significantly more time and cost efficient.

Out-of-the-box solutions like web crawler tools bring many benefits to those looking for a reliable way to extract data. Proxy management, data extraction script development and other resource-intensive tasks are done on the side of the provider, allowing companies to focus making use of the data.

Additionally, such tools can extract data from challenging targets without the requiring increased resource allocation. Scraping large e-commerce websites or some search engines can require additional development time as these targets often create sophisticated anti-bot algorithms. In-house solutions would have to create workarounds through trial and error which means inevitable slowdowns, blocked IP addresses and an unreliable flow of pricing data.

Conclusion

Dynamic pricing strategies are becoming more popular. The main benefits of real time pricing include increased control in pricing strategy, better stock management, and more revenue. However, implementing pricing intelligence without the appropriate tools requires extensive resources. For example, large scale data acquisition raises challenges that can only be solved with powerful data gathering solutions.

Choosing between residential proxies or a web crawler tool might be tough as they both have their benefits. If you are looking for more efficient ways to extract data from large e-commerce websites and search engines, a web crawler solution will be a game changer as it allows to cut down on data extraction costs.