A markdown is a reduction on price, usually to clear inventory before it becomes obsolete or needs to be removed to make way for new stock. It is a planned reduction in the selling price of an item.
Let us take an example of fireworks for a holiday. Retailer wants to finish old stocks at the end of the holiday. Fireworks left unsold at the end of the holiday season will have to be sold at prices often below costs to clear inventory levels. Markdown management helps cut the losses by pushing out the last goods which may lose value if unsold.
While ideally the best approach would be to order just enough items which can be sold at full price sometimes retailers inevitably end up with unsold inventory. This problem is more rampant in the fashion and electronic retail industry where products get outdated quickly. Scientific markdown management approach help retailers to leverage their pricing capabilities and drive new levels of performance in retail capabilities.
The buzz in the retail software world in the last couple of years has been focused on providing retailers with markdown optimization tools. Every Retail product has three phases of product life cycles; product launch, growth and end of life. More attention is paid to product launch and growth, rather than end of life, which is a general process that often incurs considerable expense. All too often, product is overbought at end of life and requires significant markdown to clear excess inventory. Markdown comes with two significant liabilities. Firstly markdown utilizes an understanding of price elasticity to generate the most profit in liquidating an inventory. Moreover, such markdowns tend to be priced either too low, resulting in lower margin; or too high, resulting in lost revenues and leftover inventory.
Proper markdown analysis help retailers to determine which items should be markdown, 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 by which the retailers need to sell out. Data should first be prepared for analysis usually through data analysis software like SAS or even excel (for a small retailer) and then mathematical techniques be applied on the data to extract the coefficients and factors required to plan an effective markdown management plan. Through price changes at the right intervals of time, a schedule can be developed automatically for each product to maximize revenue and profits.