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Technique of identifying and ranking markdown candidates for retailers Disclosure Number: IPCOM000238008D
Publication Date: 2014-Jul-25

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The Prior Art Database


There are techniques to optimize the markdown price plan given a particular product. However, how to identify products which have to be marked down, by examining the entire catalog of products in various stores of a retailer The artice describes factors to consider and technique of ranking markdown candidates.

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Technique of identifying and ranking markdown candidates for retailers
Disclosed is a technique of ranking and identifying products which are ideal markdown candidates. Today, systems exist which calculate the best markdown optimization plan for a given product. However, the question that still remains is how to identify these products in the first place?

* Presently at retail outlets, category managers manually short-list products to be markdown based on inventory levels / past experiences / less-scientific / rudimentary methods. The manual methods are not only non-optimal and laborious (attach a heavy markdown cost) but also they could lead to marking down incorrect products and cause loss of margins. Existing automated methods, that recommend markdown candidates based solely on expiry dates or product's phase out dates, are again very user driven and not optimal.

- Consider an example of two perishable products A and B. Let's say that the current date is Sept 2013. Product A has an expiry date of June 2014, whereas B has an expiry date of Jan 2015. The traditional methodology would consider product A to be a better candidate for markdown because it expires first. The current technique may not take into account that A might be a seasonal product and in the upcoming season of Jan-March 2014, may have good sales anyway (not warranting a markdown). However, in spite of having a longer term period before its expiry, Product B may have a slower sales curve (with better elasticity - sensitivity to price drops - i.e. sells better when its cheaper) and may need a markdown immediately to drive better sales.

- Let's take another example of a winter product X. It is a seasonal product that has the best seasonal sales from Nov-March. Traditional methods will recommend markdown at the end of the season i.e. starting in March. However, this may not be optimal because this type of product may not sell in the off season even if there is a 75% discount. Therefore, It may warrant a markdown much earlier for optimal results.

- There may also be products which are inexpensive but very bulky (take up more space), or require special storage and handling that could be markdown first to free up space for newer incoming stock.

The proposed technique would use an analytics engine to examine product-store metrics such as seasonality, elasticity, future sales forecasts, future product roll-outs, product return trends etc. and executes a ranking algorithm to short-list products at specific stores that are candidates for markdown. After the initial short listing, the products would be sent through a sample markdown simulation engine to evaluate and forecast the revenue impact of the markdown. Finally, the products are ranked in the order of the best revenue to markdown dollar ratio.

1. Implementation

1.1 Understanding factors that warrant markdowns, assigning weights

The proposed technique works by assigning weights to each of the factors that increase the...