Markdowns with Artificial Intelligence
Retail Markdowns or “Clearance Sales” are a great way to get rid of unwanted inventory of products that the retailer might not want to carry or sell in the future. Often times, this occurs as a result of ordering more than the actual demand and the retailer is under a time constraint to sell through the leftover stock of the products.
Like Promotions, Markdowns can be made intelligent. The components needed to make a live, intelligent, markdown system are as follows:
1. Data Collection:
The retail store can provide a wealth of information, especially when relevant data is made available quickly for consumption. Data sources can include point of sales, expiry dates, live inventory levels and salvage values (value of the product if it remains unsold) which become important during a markdown process.
2. Live Algorithms:
Historic data is analyzed to estimate demands at various price levels in different circumstances. For example, a discounted price for a product before Christmas might have a very different reaction when compared to a similar discount for the product after the Christmas season. The algorithms are configured to make live decisions based on in-store conditions and the algorithms learn from “feedback” to make a better pricing decision.
3. Electronic Shelf Labels/ Websites:
The quick data gathering and instant output action as a computer by the neural networks ultimately need to be implemented in-store or online. Retailers will need a system of Electronic Shelf Labels, Display Units or Store employee devices to be able to execute the outputs. It is relatively easier to implement the output when it comes to online pricing.
Once the connections are made in the Artificial Intelligence-based system, an autoplay button can be pressed to markdown the prices at the product-store level. The goal of the optimization is profit maximization with consideration to the salvage value (and the deadline by which the product has to sell out.
The category manager or pricing manager plays a key role is approving the products selected for the markdown and to set the parameters of the markdown pricing. These typically include constraints such as the maximum allowed discounts, the time between changed markdown levels and the last allowed digits on the discounted prices.
A deep learning-based markdown system can be further trained to trigger markdowns based on a combination of in-store factors for maximum effectiveness. While Artificial Intelligence-based pricing and promotion system can help reduce the need for markdown in the first place. The markdown system can be deployed to rescue margins on overstocked products that need to be cleared before a certain date.
A well designed and automated markdown system has the capability to achieve a margin increase by 4% to 6%.
RapidPricer helps retailers to increase margin and reduce waste by automating pricing and promotion in real time unlike traditional consulting solutions.