The answers to all the questions included in this white paper are with numbers, descriptions and live examples.
Whether a retailer monitors competitors to make sure its prices are optimal or uses an aggressive pricing strategy to win the market share, accurate data is quite significant.
It’s hard to estimate the number of data variables retailers need to consider. One of them—mostly underestimated—product matches. Percentage of comparisons is one of the primary indicators of data quality; indirectly it affects customer loyalty, market share, and therefore, margins.
There are several components successful, or unsuccessful, matches consist of: the name of the product (title), product descriptions with attributes (fixed or unfixed set of attribute values), product images and price. Each of these components may vary for the product by different retailers.
Let’s say the retailer is to set a price for Pixel 2 XL smartphone. With incorrect available data on product matches, it might set its prices based for not identical products, e.g. based on competitors’ refurbished products, different storage settings, color, etc. Thus, the prices would not be optimal from a customers’ perception. Therefore, sales and overall retailer’s metrics will suffer.
To get correct matches at the speed of changes, the retailer needs to combine the best of automated and manual methods: set the process of automated product matching in eCommerce and enhance it with an accuracy of manual matches.
In this free white paper you’ll find a detailed information on the differences between manual and automated matching approaches, and how to estimate product mathes accuracy, how much does it cost to create an in-house solution, hire freelancers to do the matches, or hire a third-party service.
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Disclaimer: We were not discussing data collection in this whitepaper, but for both approaches, raw data quality is the most crucial part. Retailers need to collect as much information as possible about their competitors and do it in a right way. We’ll discuss this topic in our next whitepaper, “Competitive data bottlenecks of losing money.”