The challenges of multi-period markdowns and the use of AI

Much more than a market hype...

In our April publication, we introduced the concept of multi-period markdowns. This month, we delve deeper, tackling the core challenges that arise when optimizing markdowns over multiple periods. In June, we plan to conclude this series with our final discussion on "The Holy Grail" of markdown optimization.

So, what are the main challenges of markdown optimizations? And how do these challenges intensify with multi-period markdowns?

1. Dynamic Pricing Complexity
   • Multiple Interdependent Variables: Factors such as remaining shelf life, product-specific decay, cross-competition, inter-competition, shelf capacity, timing, buying prices, and the original “catalog” price heavily influence the appropriate markdown.
   • Data Overload: Managing the massive amounts of data required for true dynamic pricing is a staggering challenge, especially in environments where demand continually changes due to seasonality and other variables, as well as store-specific considerations.
2. Short Shelf Life and Perishability
   • Predicting the Sell-Through Date: Accurately determining when an item will no longer be desirable to consumers is one of the biggest challenges. Slight variations in temperature, handling, or initial quality can result in significantly different lifespans for a single batch of produce.
   • Balancing Waste vs. Lost Sales: Setting markdowns too steep too early can erode profit margins. Conversely, waiting too long may result in products spoiling before they sell, even at a discount. This delicate balance is constantly shifting for each item. Multi-period markdowns address this issue, which we will explore in next month's issue.
3. Operational Difficulties
   • Inconsistent Implementation: Manual markdown processes that rely on individual judgment can lead to varied discount levels and timing among staff and across stores, hindering optimization efforts.
   • Timing Mismatches: Staff may struggle to identify and apply markdowns at the optimal time, especially during peak shopping hours.
   • Increased Workload: Incorrectly changing prices on fresh items can lead to surges in printing/replacing labels, updating point-of-sale systems, and managing potential customer inquiries. Fortunately, Wasteless offers multiple solutions specifically designed to tackle just that.

1. Consumer Perception and Fairness
   • Trust: Dynamic markdowns across stores must follow logical and reasonable patterns to be executed correctly and win consumer loyalty.
   • Associations: In the past, particularly during the 1950s, customers associated markdowns with lower-quality products. Although this perception has diminished over time, ensuring pricing accurately reflects product quality is still essential. For example, excessively deep discounts might lead consumers to suspect there is something wrong with the product.

1. Technological Limitations
   • Legacy Systems: Supermarkets often rely on systems not designed for real-time dynamic pricing, which struggle to handle the vast data input and speed required for optimizing fresh food markdowns.
   • Integration Challenges: Despite the advancements in new software, integration with existing point-of-sale systems, inventory management, and other platforms can still present significant technical hurdles. Wasteless has recognized this challenge and developed an independent solution to streamline these complex integrations.
   • Perception Barriers: Traditional retailers often find it challenging to adopt sophisticated markdown optimization technologies, especially those utilizing AI, like Wasteless. This significant shift can be daunting, as it requires a departure from familiar practices, sometimes leading to hesitation in embracing and progressing with new technology.