One of the most important elements of succeeding in eCommerce is making sure products are in stock. The impacts of being out of stock are multiple and significant. First, there is the lost sale itself. If a shopper visits a detail page and a product is not available for purchase, the brand will almost certainly lose the purchase. And, being out of stock has long-term impacts. The brand has also lost repeat sales on the product. The lost sale reduces the relevance of the product to future searches, reducing future sales overall. So, maintaining a good in-stock rate is critical for any brand wanting to succeed in eCommerce.
The metric Amazon uses to track how well a vendor is keeping products available is Replenishable Out of Stock % (RepOOS), which is the number of detailed page views for which products were not in stock and available for purchase. It’s the “detail page views part” which makes RepOOS a poor measure for brands, because the metric treats the impact of every detail page with the same weight. That makes sense for the retailer because every customer visit to a detail page with an unavailable product is what Amazon calls a “poor customer experience” that will lower customer engagement with Amazon. But every detail page view is not the same for the brand. 1000 OOS page views on a low-priced or low-converting product simply aren’t as great an impact as the same number of OOS views on a high-converting or high-priced product.
For a brand, a much better metric is % of Total Sales lost to Out of Stock. This does not necessarily correlate to RepOOS %. For example, here are RepOOS % numbers over time for two different prominent brands on Amazon, compared to the actual % of Order Product Sales each brand was losing to OOS products over the same time period.
This data clearly shows that brands can experience extreme impacts from OOS products while showing minimal increases or even decreases in Amazon’s RepOOS metric.
Understanding the actual sales impact of OOS requires integrating the current and projected velocity and ASP of every OOS product and using that data to determine the actual sales loss that OOS is causing.
This is exactly the kind of insight that CommerceIQ provides by matching up multiple data points from Amazon.
But this is not merely a trailing metric that tells brands how they are doing now. It functions as a key leading metric that shows a brand where it needs to prioritize adjusting stock levels. Having this data both historically and projected enables brands to move their availability, ordering, and forecast conversations with Amazon out of anecdotal demands that never go anywhere to a data-based conversation that can produce real action.
CommerceIQ provides other important insights and automated actions that help brands solve the availability issues that $ of OPS lost to OOS reveals, including
• past forecast and purchase order quantities relative to actual consumption to create more accurate internal forecasts
• automated identification and correction of purchase order product data errors
• automated identification and dispute of OOS and unavailable ASINs with inventory
• automated identification and dispute of suppressed ASINsIf you have questions about how availability on Amazon works or how a machine learning based ecommerce platform can help manage the thousands of decisions a brand must make to successfully run an Amazon business, contact me at firstname.lastname@example.org.