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Competitive pricing intelligence is a cornerstone for retailers aiming to evaluate their pricing strategies vis-à-vis rivals. It's instrumental in ensuring their offerings remain competitive, aligning with consumer preferences and market dynamics. Central to this intelligence is product matching, a complex endeavor given the diverse formats products assume across online platforms, from varying titles to distinct images and descriptors. A notable challenge in this arena is the inconsistent units of measurement displayed for products across different sites, adding complexity to the task.
Across product segments, retailers frequently present items in varied capacities or sizes. For instance, apparel might be sold as individual pieces or in sets of 2 or 3, and groceries often offer eggs in configurations of 6, 12, or 24.
To illustrate, a cursory look might indicate that an 800g pack of Kellogg’s Corn Flakes priced at $5 offers better value than a 950g pack of Nestle Cornflakes at $5.1. Yet, this initial perception can be misleading. In truth, the latter provides a more economical option, a nuance discernible only upon unit-standardized price evaluation.
This becomes especially pertinent given the rise of "shrinkflation," where brands subtly reduce product quantities or sizes to respond to inflation, maintaining ostensibly unchanged prices. Thus, it's crucial to account for such variations in product dimensions or quantities when assessing competitive pricing using Amazon product data collection.
For retailers to make meaningful price contrasts across competitors, it's imperative to standardize the diverse units of measurement they encounter using e-commerce data scraping services. This normalization is vital as price evaluations should encompass more than individual product SKUs, accounting for package volumes and quantity variations. From "each" for singular items to "dozen" for multiples and from "pounds" and "kilograms" to "liters" and "gallons," units must be consistently defined across different product categories.
For instance, using a consistent base unit, like 100 grams for cornflakes, offers a uniform benchmark. Thus, the normalized price for any cornflake variant becomes its cost per 100 grams. Applying this method, it's evident that while Kellogg’s is priced at $0.57 per 100 grams, Nestle offers a rate of $0.52 for the same quantity.
Retailers commonly display product weights using units such as kilograms (kg), grams (g), ounces (oz)or pounds (lbs).
Items may be presented with diverse quantities or package sizes within a single SKU.
Products can differ in volume or capacity, often quantified in liters (L) or fluid ounces (fl oz).
With Actowiz Solutions' advanced product matching mechanism, identical or analogous products are seamlessly identified, and their units of measurement are harmonized. This precision ensures precise and actionable competitive pricing analytics by standardizing variables such as weight, quantity, and volume, facilitating equitable product comparisons using e-commerce data collection.
Retailers can opt to scrape Amazon product data using either specific or standardized units. This feature equips retailers and analysts with the tools to conduct precise and comprehensive evaluations of pricing data on a product-specific basis.
In certain situations, unit-standardized pricing analysis provides a more precise depiction of pricing trends and competitive positioning than merely examining retail prices. This holds especially true for sectors like CPG, characterized by products available in varied units of measure. For example, in the illustration provided, a comparison of price positioning trends within the Fruits and Vegetables category can be evaluated using retail and unit-based pricing metrics.
The contrast is evident: while the initial analysis based on retail prices indicates a stagnant pricing trend, the unit-standardized pricing approach reveals a more fluid and evolving pricing landscape.
With Actowiz Solutions, retailers can define which units to juxtapose, ensuring meticulous comparisons. For instance, a retailer might compare unit prices exclusively for packs of 8, 12, or 16 ounces and 1 or 3-pound packages, excluding 10 and 25-pound sacks. This meticulous approach guarantees accurate product matching and provides insights based on the most relevant normalized units, enhancing the precision of pricing analytics.
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