GrowByData
Ecommerce data services for online retailers
We provide intelligence into your designated marketing areas - Country, Regions, County, Metro, ZIP Codes
We support languages from around the world and monitor country specific TLD
The new frontier of pricing is here. Advanced Machine Learning (ML) models now enable retail category managers to fine-tune SKU-level pricing by leveraging a variety of signals such as current demand, demand trends, weather, seasonality, share of voice, competitor prices, competitor promotions, oversupply, supply shocks, and geopolitical risks. These factors can work together to enable optimum pricing to maximize sales and gross margins. A critical ingredient in retail pricing models is competitor pricing at the SKU level, and having high-quality SKU-level pricing data is vital to this. Without high-quality pricing data, the junk-in-junk-out principle applies, and even the most sophisticated pricing models will suggest inaccurate results.
This article aims to assist professionals involved in building, optimizing and safeguarding pricing strategies such as:
Accurate and reliable SKU-level competitor pricing data is crucial for developing effective pricing strategies. Below, we share the key components needed to obtain high-quality competitive pricing data.
Modern e-commerce sites typically provide unique URLs per variant SKU (such as varied sizes, colors, and case packs), each with its own SKU number, GTIN/UPC number, or ASIN number. However, some e-commerce sites still use a single URL for parent SKUs. In these cases, pricing intelligence solutions must be sophisticated enough to extract child SKU prices from the same parent URL. For example, shoes may come in 10 different sizes and 4 colors accounting for 40 child SKUs for the same parent SKU. Now if the shoe is available in two different fabric types, then the number of variant SKUs doubles to 80. Pricing solutions must be able to pull the pricing of the 40 variant SKUs.
Ideally, you can use automated data collection algorithms to collect SKU-level competitive pricing data intra-day, daily, or multiple times a week across different geographies from e-commerce sites, marketplaces, search engines, and product catalogs. When automation is not feasible due to contractual or technology reasons, retail-trained e-commerce data analysts can manually record the pricing data visible on the Product Detail Page (PDP). Although not ideal for the manual effort required, trained retail analysts can offer this.
Another crucial aspect is capturing zonal pricing. The same SKU may be priced differently based on location. For instance, a product’s price in Southgate might differ from its price in Hooksett. Additionally, shipping charges and promotions can vary by location. Since competitors frequently change product prices, promotions, and shipping terms, it is vital to capture these subtleties by the market to run dynamic pricing models effectively and generate accurate zonal pricing for your products. The price may even vary by zip code in the same metro.
Getting unit of measure pricing is essential to make relative comparison between competitor brands and private label brands. This means that to compare the prices of similar products from different stores accurately, you must look beyond the total price and focus on the price per unit. The unit of measure could be per item, per weight, per volume, or any other standard unit that makes comparison meaningful.
Let’s take an example, considering Pepperoni sold by two different wholesale retailers:
To understand which product offers better value, one must calculate the price per ounce:
Even though Amazon Fresh’s total price is lower, the price per ounce is higher than BJs. Therefore, BJs offers a better price per unit. Thus, for pricing algorithms to effectively determine product competitiveness, they must consider both the total price and the unit of measure price. This approach ensures that comparisons are accurate and reflect the true value offered by each brand.
Investing in high-quality competitor pricing data is essential for robust dynamic pricing algorithms. The true measure of these algorithms’ success is improved revenue and margins. If these metrics decline, the input data for the dynamic pricing algorithm might be an issue. Hence, we advise you to invest in a comprehensive price intelligence solution that delivers high-fidelity data at scale across millions of SKUs. While quality competitor pricing data comes at a premium price, the returns on this investment will be substantial. We know coupling data with your model is a huge effort and you don’t want to go through this effort repeatedly as onboarding a new pricing data provider is a significant effort on your end.
If you are looking for comprehensive pricing solutions, it is best to evaluate the solutions providers’ expertise. The provider’s competence in monitoring competitive pricing data across various sections and markets is crucial. The provider must understand the subtleties of competitive price monitoring and use this data to run dynamic pricing algorithms. Reliable high-quality competitive pricing data from search engines like Google, marketplaces like Amazon and Walmart, and direct e-commerce sites worldwide is vital for both e-commerce and store sales.
Vendors must also be flexible and agile, adjusting their competitive pricing data collection methods to meet changing needs and deliver competitive pricing intelligence in the desired format. The source platforms change regularly, and the data vendor needs the expertise, resources, and eagerness to change rapidly. The vendor needs rigorous data quality checks to minimize errors that cause inaccurate uncompetitive pricing suggestions.
GrowByData has been monitoring and offering competitive retail pricing data to the SKU level from 2016 across retailers in apparel, food, parts, toys, fashion, shoes, and many other categories. We also offer these data in various countries such as the US, Canada, Japan, the UK, France, Italy, Singapore, and other parts of the world.
For retailers, we offer high-quality algorithms and human-verified SKU-level competitor pricing data. This is vital for dynamic pricing and Minimum Advertised Price monitoring. For brands, we monitor pricing set by retailers across sites to understand channel mix and compliance with terms such as MAP pricing, channel territory, promotions, and others. We also offer relative competitive pricing data to compare products serving the same purpose, ensuring a comprehensive competitive analysis.
Investing in high-quality competitive pricing data is pivotal for maintaining the accuracy and effectiveness of dynamic pricing algorithms, essential for driving robust sales and margins. This investment ensures that pricing decisions are based on precise and up-to-date market insights, allowing algorithms to adapt swiftly to changes in competitor prices and demand dynamics. With accurate data as the foundation, pricing strategies can resonate better with consumers, leading to increased sales and improved profitability. By leveraging reliable pricing data, brands and resellers can develop optimal pricing to balance competitiveness and profitability and maximize margins while staying agile in response to market shifts.
If you are seeking high-quality pricing data or insights, please contact us today. We will happily share our knowledge and support your custom competitive pricing needs.
