April 15, 2024

Improving markdown pricing for end-of-life products in consumer electronics

Using machine learning to support markdown decisions

Improving markdown pricing for end-of-life products in consumer electronics

At a glance

Challenge

A consumer electronics retailer sought to optimize the markdown pricing of end-of-life products, which had previously relied on manual, empirical decision-making. The existing process overlooked key factors, leading to inefficiencies, potential errors, and biases. To address these challenges, the project aimed to develop an analytical model for clearance pricing, delivering a more consistent, reliable, and time-efficient decision-making process while improving data visibility.

Solution

A tailored Decision Support System was developed to optimize markdown pricing for end-of-life products. It features a machine-learning model to predict demand variations based on price reductions and an optimization algorithm that recommends markdowns aligned with the retailer's margin and sellout goals. The system’s user-friendly interface allows product managers to validate recommendations and access key data, ensuring informed and strategic decision-making.

Results

The tool enabled accurate sellout predictions and quicker responses for end-of-life items, with over 90% of recommended markdowns accepted by managers. This led to improved sellout rates without compromising margins. Product managers unanimously praised the tool for enhancing lifecycle management and streamlining the markdown pricing process.

Challenge

A consumer electronics retailer sought to optimize the markdown pricing of end-of-life products, which had previously relied on manual, empirical decision-making. The existing process overlooked key factors, leading to inefficiencies, potential errors, and biases. To address these challenges, the project aimed to develop an analytical model for clearance pricing, delivering a more consistent, reliable, and time-efficient decision-making process while improving data visibility.

Approach

Solution

A tailored Decision Support System was developed to optimize markdown pricing for end-of-life products. It features a machine-learning model to predict demand variations based on price reductions and an optimization algorithm that recommends markdowns aligned with the retailer's margin and sellout goals. The system’s user-friendly interface allows product managers to validate recommendations and access key data, ensuring informed and strategic decision-making.

Results

The tool enabled accurate sellout predictions and quicker responses for end-of-life items, with over 90% of recommended markdowns accepted by managers. This led to improved sellout rates without compromising margins. Product managers unanimously praised the tool for enhancing lifecycle management and streamlining the markdown pricing process.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

A retailer in the consumer electronics industry was facing a challenge in the markdown pricing process of end-of-life products. Decisions were made empirically and, although criteria had been set as guidance for the discounts to apply based on average sales and stock, most prices were set by hand as they didn’t account for many other factors, resulting in a time-consuming task that was prone to error or bias.

They felt, as a result, the need for a more consistent and reliable process of decision-making, with added time efficiency and improved data visibility. The project’s main goal was to develop an analytical model to optimize clearance pricing of discontinued items and address the challenges mentioned above.

A customized Decision Support System was developed to improve the markdown pricing process of end-of-life products. The solution relied on the implementation and integration of several components, including a machine-learning model to predict the variation in demand caused by different price reductions and a clearance pricing optimization algorithm to recommend the optimal markdown to be applied to each product.

Our AI-generated summary

Our AI-generated summary

Finally, the Decision Support System comprises an easy-to-use interface in which product managers validate the recommended markdowns and have access to all revelant information needed to make the best possible decisions.

The new tool allowed for accurate sellout predictions until the end of each campaign and faster reaction on end-of-life items. On average, more than 90% of the recommended price markdowns were accepted by the managers. As a result, the sellout rate increased compared to previous campaigns, without compromising margin targets

All product managers agreed that the new tool facilitated and improved lifecycle management and made the markdown pricing process much more efficient.

The markdown recommendation can be automatically adjusted according with the retailer’s strategy and priorities, as it considers the margin and sellout targets set by the product managers.

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