In this post we will cover:
- Be prepared for the upcoming holiday season with a data-driven market strategy
- The challenges of real-time product matching is hard during buyer peaks
- How to gain a competitive advantage by buying ready-to-use Amazon Datasets
Be prepared for the upcoming holiday season with a data-driven market strategy
Scraping Amazon is probably the most effective tactic to improve your data-driven marketing strategy in preparation for peak seasons such as Black Friday, special promotions and holiday seasons such as Golden Week in China. Such a strategy depends very much on your business and the niche in which you operate as well as your unique challenges.
- Some companies have a blind spot when it comes to introducing new-to-market products based on competitor catalog data.
- While yet others find it difficult to gauge current consumer sentiment based on review and conversions data.
Whatever hurdles you are facing, Amazon Datasets can help close the gap, helping you significantly increase your market share.
The challenges of real-time product matching is hard during buyer peaks
Vendors are facing the following challenges when trying to collect competitor/prices/products public web data from Amazon or other marketplaces in real-time:
One: Geolocation- based restrictions
Companies in country A, say China, trying to sell products in country B, say the United status, are oftentimes blocked due to their geolocation. Many American websites block Chinese IPs making it impossible for these retailers to collect competitor pricing in real-time. This can make successfully entering new target markets nearly impossible, especially for vendors/manufacturers located in the East or outside of Europe, and North America.
Two: The danger in IP blocks and ‘data cloaking’
IP blocks can come in many forms, yet instead of blocking you sometimes Amazon and other large vendors simply feed you misinformation. This can be catastrophic to your business plan as you may have the wrong pricing ‘based on competitors’ and end up losing not only business but also having your reputation tarnished.
Three: Classic techniques are so slow they become irrelevant
Some classic web scraping techniques include using:
- Using Java for web scraping
- Scrapy and Beautiful Soup
- Collecting data with PhantomJS
These techniques can be very effective, but are code-heavy, and typically require a technical team or individual to dedicate considerable time and effort in order to take out valuable information. Seeing how quickly modern consumers can make decisions and how slowly code-based collection jobs retrieve data, these methods have become the sub-par option for businesses looking to actually compete and win market share.
Four: Website structure is hard to navigate and constantly changing
Many small and medium businesses waste a lot of resources on mapping out a target site’s structure (in this case Amazon), only to find out that a week later things have been ‘reorganized’: categories classifications have changed, new ASINs are trending while older ones have expired, and in the meanwhile your code remains the same . This of course is something that marketplaces, and other sites do methodically in order to make web scraping more challenging.
How to gain a competitive advantage by buying ready-to-use Datasets
Buying Amazon Datasets is now a viable option that companies in the digital commerce space are implementing. What this essentially does is shift the entire burden of GEO-based restrictions, website blocks/architecture, and complex code-based scraping to a third party.
What your team receives is a Dataset which can include Amazon’s entire website or something more targeted. This may include:
- All the customer reviews for retailers selling baby toys
- The pricing of a certain brand of women’s shoes in the London metropolitan area
- The characteristics of the top-performing listings (such as product images and descriptions)
Datasets can be tailored to the format in which your team members prefer to work in such as HTML, JSON or CSV. And most importantly they have ‘Dataset refresh settings’ that can be built around your company’s sale cycles (meaning your team/algorithms can get updated pricing information hourly, while consumer sentiment can be analyzed on a season-by-season basis).
