How to Boost eCom Sales with These 5 Web Data Points for Product Matching

In this article, we will discuss how digital-focused retailers use web data from various sources to compare their own product listings with those of competitors. 

The main problem currently arising is the inability to collect and compare data sets in different formats across multiple marketplaces. And also comparing products precisely to the models/styles that competitors sell. Especially since no seller (intentionally) uses the same product identifiers, names, and images, trying to confuse competitors. 

Here are 5 specific ways to use Web Scraper IDE, an automated solution for collecting e-commerce data that can be easily scaled in real-time based on changing needs: 

#1: Intelligent Price Comparison 

Challenge: Most companies face difficulties in determining the exact price for their products in a competitive space. Simply finding the lowest price and undercutting competitors is generally not the best strategy. Retailers have to consider a multitude of data so that algorithms set a price at a level that will convert target customers. 

Solution: Collecting and comparing multiple data points before making a decision to change pricing can help increase revenue by:

• Receiving notifications that competitors are running a promotion, adding a free offer to a product bundle, or offering a discount at checkout. Competitors may not even lower prices, but if they sweeten the deal with a free laptop case, they might well take advantage of that offer. 
• Comparing specific features and quality indicators that help you avoid "undervaluing" your products. For example, comparing fabrics in the context of two black winter coats, one made of handcrafted cashmere and the other of synthetic material. Classic price comparison tools do not account for such product attributes.  

Furthermore, by determining the stock availability of similar products among competitors, one can identify where there is a supply chain-driven shortage and find opportunities to raise prices (in other words, identify products that are currently priced too low).

#2: Mapping Product Specifics 

Challenge here lies in understanding which specific products convert, for what reasons, and in which geographic regions. For example, sellers may collect data showing that when selling women's shoes, the ideal is to have 7 specific attributes. Adding the brand name (e.g., Gucci) and country of origin (e.g., Italy) is paramount for listings with high sales-to-rate (STR). However, these indicators may differ based on other factors such as buyer geolocation, price range, and brand, greatly complicating the matter.

Solution involves collecting and comparing all relevant data to more precisely determine the ideal set of attributes for achieving high STR on a specific market. 

For example, women's shoes sold to buyers in India, produced by a high-end brand like Christian Dior, and priced between 50,000 and 70,000 rubles, may convert best when three specific elements are present: 'brand', 'color', 'fabric'. 

While Russian consumers looking for running sneakers in the 5,000 ruble range will want much more information, since they will use this product daily and, as budget-conscious buyers, do not want to be forced to buy another pair if it falls short. Such buyers might expect to see the following product identifiers: "condition", "upper material", "model", "color", "style", and "material composition".

#3: Scanning Customer Reviews 

Challenge is that "consumer perception" and "subjective perception" of a product are things that are difficult to collect and analyze. However, understanding how customers react to competitors' products similar to yours is crucial for identifying where competitors fall short and where you can improve to increase market share.  

Solution involves collecting consumer reviews and analyzing them using natural language processing (NLP) technology. For example, customers in Australia might be annoyed that delivery of specialized dog food takes up to a week. Moreover, you can correlate this review data with another dataset showing increased sales of organic pet food among the same consumer group. Cross-referencing seemingly unrelated customer sentiments can allow you to increase market share. For instance, you could stock up on organic dog food, vividly describe its health benefits in the product description, and offer free overnight delivery — thereby solving a range of issues and increasing the appeal of your offering to potential buyers. 

#4: Listing Title Analysis

Problem is that suppliers intentionally do not use the same model numbers and names, in order to muddy the waters for competitors. This makes it difficult to compare apples to apples and oranges to oranges. 

Solution is to collect data on the best-performing products in your niche/category, and then analyze them:

• Title length
• Sentence structure
• Specific product features mentioned in the title.

When you cross-reference all this data, companies can derive a "winning formula" to increase click-through rates (CTR) and ultimately conversions. For example, companies selling mobile phones might find that headlines in the 7-10 word range, mentioning condition (e.g., new), brand (e.g., iPhone), and color (e.g., rose gold) in that order, capture the attention of 86% of buyers who click and purchase.

#5: Impact of Visuals 

Problem is that images are perhaps one of the most important aspects of the digital shopping experience. If images are wrong, products don't sell. However, there are many aspects of an image that need to be analyzed simultaneously, making choosing the right visuals a real headache. 

Solution is to collect and compare multiple data points from competitor listings, including:

• The number of images in the listing (e.g., 5)
• Determine if the images include people or are strictly product-focused?
• Understand the angles of the shots (low/high/close-up/zoom).
• Find out if most shots are "lifestyle" oriented? Or "technical" showing how the product is used or its various dimensions?

Having a clear picture, companies can make specific decisions on how best to visually present a product. For example, high-STR listings in the watch industry might feature 3 images (one lifestyle, one showing dimensions, and one close-up of materials, such as a precious metal dial). 

Conclusion 

Product matching can be timely and tedious if done manually or using a single data point. But when you start using an automated solution that provides your systems with multiple data sets that can then be cross-referenced for deeper insights, companies can better position themselves for success.