A markdown is a reduction in price, usually to clear out inventory before it becomes obsolete or must be removed to make way for new stock. It is a planned reduction in the selling price of an item.

Let’s take an example of fireworks for a holiday. The retailer wants to run out of old stock at the end of the holidays. Fireworks that are not sold by the end of the holiday season will have to be sold at prices often below cost to clear inventory levels. Markdown management helps reduce shrinkage by weeding out the latest products that may lose value if they don’t sell.

While ideally the best approach would be to order only enough items that can be sold at full price, sometimes retailers inevitably end up with unsold inventory. This problem is most common in the fashion industry and e-retail, where products quickly become out of date. The scientific approach to markdown management helps retailers leverage their pricing capabilities and drive new levels of performance in retail capabilities.

The excitement in the world of retail software over the past two years has been focused on providing retailers with markdown optimization tools. Every retail product has three phases of the product life cycle; launch, growth and end of life of the product. More attention is paid to product launch and growth, rather than end of life, which is an overall process that often incurs considerable expense. Too often, the product is overpurchased at the end of its useful life and requires a significant markdown to eliminate excess inventory. Markdown comes with two significant responsibilities. First, Markdown uses an understanding of price elasticity to generate the most profit when liquidating an inventory. Also, such rebates tend to be priced too low, resulting in a lower margin; or too high, resulting in lost revenue and leftover inventory.

Proper markdown analysis helps retailers determine which items should be marked down, when, and which markets to cover. Markdown Analysis requires base factor information such as inventory level, average sales volume; price elasticity; salvage value and time frame in which retailers must sell. The data must first be prepared for analysis, typically through data analysis software such as SAS or even Excel (for a small retailer) and then mathematical techniques must be applied to the data to extract the coefficients and factors needed to plan an analysis. effective markdown management plan. Through price changes at the correct time intervals, a schedule can be automatically developed for each product to maximize revenue and profit.