Competitive Pricing Data for Powerful Pricing Strategy & Dynamic Pricing
The new frontier of competitive pricing strategy 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 price models is competitive 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 and pricing software will suggest inaccurate prices.
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 corporate pricing strategies. Below, we share the key components needed to develop high-quality competitive pricing optimization.
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. Competitive Pricing tools must be able to pull the pricing of the 40 variant SKUs.
Ideally, you can use automated data collection tools 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, trained retail analysts can compare competitor’s prices manually and offer this for your analysis.
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. So you must monitor zonal pricing across competitors.
Getting unit of measure pricing at a SKU level is essential to make relative competitor price comparison between competitive brands and private labels. This means that to compare the prices of similar products from different stores accurately, you must look beyond the total price and get the price per unit. The unit of measure price could be per item, per weight, per volume, or any other standard unit that makes the comparison meaningful and logical.
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 dynamic pricing software models to effectively determine product competitiveness, they must consider both the total price and the unit of measure price. This strategy ensures that price 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 the end brand’s improved revenue and margins. If these end business goal metrics decline, the raw competitor’s pricing data to run the dynamic pricing algorithms may be an issue. Hence, we advise you to invest in a comprehensive top-notch price intelligence platform that delivers high-fidelity data at scale across millions of SKUs. While quality comes with price, your returns on this investment will be substantial. We know combing competitive data with your sophisticated pricing models is a huge effort, and you don’t want to go through this effort repeatedly. Onboarding a new competitive pricing data vendor is a significant effort on your end, so you want to select judiciously.
If you are looking for comprehensive and excellent competitive pricing solution, it is best to evaluate the solution providers’ expertise. The provider’s competence in competitive monitoring and pricing intelligence across various digital channels and markets is crucial. The provider must understand the subtleties of competitive price monitoring and use this dataset to run dynamic pricing models. 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 implementing correct pricing optimization strategy for both e-commerce and store sales.
The data vendor must also be flexible and agile, adjusting their competitive pricing data collection methods to meet changing needs to deliver competitive pricing intelligence in the desired pricing data format. The source platforms change regularly, and the data vendor needs the expertise, resources, and eagerness to change their data collection methods rapidly. The vendor needs rigorous data quality checks to minimize data errors that cause inaccurate uncompetitive pricing suggestions.
GrowByData has been monitoring and offering competitive retail pricing software and data to the SKU level from 2016 across retailers in apparel, food, parts, toys, fashion, shoes, and many other categories. We also offer the pricing data and analysis across countries such as the US, Canada, Japan, the UK, France, Italy, Singapore, and others.
For retailers, we offer high-quality pricing insights and human-verified SKU-level competitor pricing data. This high quality is vital for dynamic pricing and Minimum Advertised Price monitoring. For brands, we track pricing set by retailers across sites to understand channel mix and compliance with terms such as MAP pricing, channel territory, price promotions, and others. We also offer relative competitive pricing data and comparative insights for comparable products.
Watch our YouTube video on the importance of high quality competitive pricing intelligence solution, analysis and data to drive your successful corporate pricing strategy.
Investing in high-quality competitive pricing dataset is pivotal for maintaining the accuracy and effectiveness of your dynamic pricing algorithms. This is essential for driving strong sales growth and margins. This investment ensures that your price change decisions are based on precise and up-to-date market and competitor insights. With this, you can confidently allow pricing algorithms to reprice swiftly to changes in competitor market prices and demand dynamics. With accurate price data as the foundation, dynamic repricing strategies will resonate better with consumers, leading to higher eCommerce sales and gross margins. By leveraging this reliable market and competitive pricing datasets, brands and resellers can develop optimal pricing to maximize gross margins while staying agile in response to market shifts.
If you are seeking high-quality pricing data or competitive price insights, please contact us today to learn best practices on competitor pricing data. We will happily share our knowledge and support your custom competitive pricing insight and data needs.