Embark on a transformative journey as the challenge of maximizing profitability in retail promotions comes to the forefront. With current SKU-level promotions offering slender margins, the need for efficient calendar promotion plans becomes paramount. Join us as we unravel the complexities of demand interaction between different products and the cross-product effects that shape promotion outcomes. Witness the power of cutting-edge techniques such as the Autoregressive Distributed Lag Model and Mixed-Integer Non-Linear Programming as they optimize category-level long-term profitability. Through the implementation of a Genetic Meta-Heuristic Algorithm, optimal prices, promotional planning, and accurate sales & profit forecasts materialize. The impact is undeniable as a practical SKU-level promotion Decision Support System (DSS) tool is productionalized within the client's environment. Experience the success firsthand as profits soar, with a 16.7% increase over manually scheduled promotions.
The current promotion program offers SKU-level promotions with minimal margins, typically around 1%. However, it is important to note that SKU-level promotions can have cross-product effects, leading to changes in demand for other promoted SKUs. Consequently, there is a need to develop efficient calendar promotion plans that not only maximize short-term sales but also consider the long-term profitability of the entire category. By taking into account the demand interactions among different products, organizations can ensure optimal resource allocation and achieve sustainable profitability in the category.
To address the challenges of cross-product and cross-period effects in promotions, a combination of advanced modeling techniques can be employed. Autoregressive Distributed Lag (ADL) Model: This model allows for the analysis of cross-products and cross-period effects of promotions. By incorporating these factors into the equation, it becomes possible to optimize category-level long-term profitability effectively. Mixed-Integer Non-Linear Programming (MINLP): MINLP techniques can be utilized to generate optimal prices, devise promotional planning strategies, and provide accurate sales and profit forecasts. This approach takes into account the nonlinear relationships between variables and enables the identification of optimal solutions. Genetic Meta-Heuristic Algorithm: A genetic meta-heuristic algorithm can be applied to further optimize the promotion planning process. This algorithm leverages evolutionary principles to find the best possible solutions by iteratively refining and adapting strategies to maximize category profitability. By employing these sophisticated modeling techniques in tandem, organizations can unlock insights, make informed decisions, and enhance their ability to drive profitable growth through effective promotional planning.
By implementing a practical SKU-level promotion Decision Support System (DSS) in the client's environment, there has been a significant increase in profits. Specifically, the use of this tool resulted in a remarkable 16.7% increase in profits compared to manually scheduled promotions. Showcasing the tangible benefits of adopting an automated and data-driven approach to promotion planning and execution.
Intrigued by the accomplishments highlighted in this case study? Do you see parallels with your own organization's challenges? At Blend, we are experts in delivering clear, concise and engaging solutions that meet your unique business needs. We're eager to apply our strategic thinking and domain expertise to help transform your business operations, just as we did for our esteemed client. Reach out to us today – let's collaborate to drive impactful business results and craft your own success story. Contact us today.
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