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Calculated ideal store-level discounts to reduce sales loss and maximize margins

71%

Increase in ROS

~6%

Margin improvement

2x

Increase in decision-making frequency

A leading designer lifestyle brand used Increff Markdown Optimization to calculate ideal store-level discounts and determine the reordering of bestsellers for maximum ROS. Based on the ongoing performance, the tool was able to suggest an increase or decrease in discount percentages, for the right set of styles. It was able to access the correct selling price of the style, across all points of sales, to achieve higher sales (by increasing discounts), and better margins (by reducing discounts). To reduce sales loss and improve margins, bestsellers or outperformers were identified at each point of sale so accurate quantities can be reordered on a timely basis for brands to capitalize on the ongoing trend and upcoming demand.

Challenges: 

  1. Identify products that deserve a change in selling price and select the right set of products that need to be discounted to deliver a higher rate of sale.
  2. Excessive discounting of products to be avoided to ensure set margins are not severely devalued if further discounting does not bring an increase in sales.
  3. Identify which styles are good designs, or pick up trends, so they can be re-ordered for better sales.
  4. Make discounting and re-ordering a regular efficient activity for each store.

Solutions: Increff Markdown Optimization automated data-driven decision-making by taking granular scientific inputs, at a store-level, to deliver concrete measurable output in the form of more accurate discount percentages and appropriate reorder quantities.

  1. Dynamic pricing – Styles that were already at a discount, Markdown Optimization helped identify if the price point was right or not. The tool accessed important factors of a style, like ROS, availability, health, stock cover, age, etc. to identify which needed an increase in discounts, at a comparative level, and recommended the right discount percentage that would boost sales and revenue. It also helped the brand identify fast movers that were running low on stock, where a cut in discount percentage would result in higher margins. The brand had the flexibility to cap discounting to prevent further reduction in price if discounting was not impacting sales. This helped them prevent the devaluation of products.
  2. Bestseller reordering – As the tool monitors the daily performance and stock levels of the style, it helps identify styles that are fast movers and bestsellers. It suggested designs that should be capitalized and kept longer on the shelf for higher returns. It helped identify a style’s true sales potential by correcting for unavailability and liquidation scenarios. It took into account the lead time required to procure a style and factored that when suggesting reordering quantities so orders were placed well in advance and sales loss was minimized. It helped determine the accurate quantity that needed to be reordered for the desired shelf life- e.g 60 days, 90 days, to improve overall margins. It also helped identify styles that were picking up and where the brand could increase profits by selling more at different points in sales. 

Impact delivered in 14 days of implementation:

  • 71% ROS improvement with only a 10% discount increment for low ROS styles at the offline stores. The same set of styles had seen degrowth in other stores. 
  • ~6% margin improvement for fast movers at online Point of Sales.  
  • 2x increase in the frequency of decision making. They moved from making monthly decisions to making discounting decisions every 14 days. 
  • Successfully handled in-season, Event, and Old season liquidation scenarios
Industry:
Lifestyle
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