In this article we will discuss:
Why localize?
Vendors are looking for web data collection guides to help them gain concrete insights into new markets they are looking to expand to. The reason for this is that consumer preferences and trends differ from country to country:
- Price points that convert American consumers will drive Asia-Pacific (APAC)-based customers to run in the opposite direction, and vice versa
- Different consumers from different regions start their buying journey with different Google search queries. They also respond differently to marketing campaigns.
- The types of products that trend in China are widely different than those that are popular in the UK for example. Adult women in China buy Hello Kitty paraphernalia on a much larger scale than their British counterparts.
These kinds of insights are crucial to a business that is looking to create a successful market entry strategy.
Collecting localized eCommerce web data
Scraping data from Amazon, eBay, Walmart, or even Etsy’s local sites can help you paint a clear picture of consumer trends in your target GEO. The main issue is that sites will block you or serve you incorrect data if the origin IP differs geographically from the target site IP. If for example, you are looking to collect data using your IP (say you are located in Germany), yet your target audiences are located in the United States and Great Britain, you will most likely not be able to gain access to consumer data trends.
In this case scenario, you should consider using United States proxies, and UK proxies. By doing this you will be able to leverage Residential, ISP, Mobile, and Datacenter IP addresses that originate in the US/UK.
Say you decide to use Residential proxies; these are real IPs that belong to people who live in Dallas, New York, London, and Birmingham. When you request to see how Amazon is displaying the price of a laptop bag to a consumer in London, for example. The request will be routed through a real Londoner who opted into the proxy network’s computer, and you will retrieve a highly accurate response. Say 12 GBP. If, however, you tried obtaining this information using your German IP address, there is a high probability that the information would be distorted, and you would see that the average price is 15 GBP, for example.
High impact eCom data points to collect
Many eCommerce use cases can benefit from open-source web data. Some of the most popular datasets that businesses in the digital commerce space collect include:
#1: Pricing strategy web data
When entering a new market, you will want to collect data on how your competitors are pricing similar items, which price point is converting at an above-average rate, and when discounts are being applied. In China, for example, Chinese New Years, and Golden Week are major holiday seasons for digital retail. Data will point to an increased number of sales, and holiday promotions; you can use this information and incorporate it into your own pricing strategy.
You will also want to collect data on specific SKUs (Stock Keeping Units). For those of you who are not familiar, an SKU is a numerical/alphabetical code that helps identify products based on specific characteristics, such as:
- Color
- Manufacturer
- Style
- Color
- Brand
- Size
Very often, there is a correlation between a specific characteristic, the price, and the region. In Japanese culture, for example, the color blue is considered to be lucky, and as such, very often, blue products can command higher retail prices. In ‘The West’ you have the infamous ‘pink tax’. For those of you who are not familiar, this is the price discrepancy between products, and their identical pink counterparts.
#2: Inventory and product discovery web data
When looking to sell products in a new country, it can be extremely useful for your business to collect your competitors’ catalogs. This can help you discover new products, and categories that you may not have ever thought of dabbling in, in the first place. By cross-referencing hundreds of competitor catalogs, you can paint a picture of which items are especially popular in a specific city or country, and then work to include them in your offering as well. For example, according to Shopify, false eyelash accessories are trending in the United Kingdom, with YoY (Year over Year) sales growth of 1,823%, as of 2022.
You can also start collecting new product information in real-time, so if a competitor in a target GEO introduces a new item, you know about it and can react.
Additionally, if a competitor is out of stock, you can get an alert and use this information to grab market share by proving yourself ‘more reliable’ to new customers.
#3: Consumer sentiment and search trend web data
Social media is extremely important in terms of trend-setting, and social influence. If someone who is respected in the fashion industry starts posting pictures of tiger-themed bags on social media, and there is a lot of engagement and hype around among Spanish consumers, you may want to jump on that bandwagon.
Beyond influencers, and user engagement data with posts (i.e. likes, shares, and comments), one major source of buying trends is search engines. Based on the same Shopify article mentioned above, surprisingly enough, ‘belt buckle’ searches are trending with customers located in the US. When the data is reviewed, these are the most popular search phrases along with their monthly search volume:
- ‘Western belt buckles’: 9,900
- ‘Custom belt buckles’: 14,800
- ‘Unique belt buckles’: 1,300
- ‘Belt buckles for men’: 27,100
Once this type of search data is obtained, fashion brands can work to include these products in their digital catalogs. They can also use these phrases in product listings, blog posts, as well as alt text on listing images – all of which will help channel high-intent traffic.
#4: Product ranking, and market share web data
When looking to discern how a product is performing in a specific target market, there are two key data points which vendors should be collecting and analyzing:
- How products rank on local, and global eCommerce websites utilizing local IPs (both in terms of product search results as well as in product category page display).
- Shopper listing/product engagement data including reviews, and likes.
The sum total of the above data when correlated, and cross-referenced can paint a very clear picture of a product’s market share in a given market. A sausage cooker, for example, may have minute market share in Brazil, yet enjoy widespread popularity in Germany.
The bottom line
Collecting eCommerce data from different countries can be extremely effective when deciding which products to stock, how to choreograph marketing campaigns, as well as how to best price items for a specific target audience. All of this is made possible by collecting consumer/competitor-generated web data.
