A leading Consumer Product Goods (CPG) client needed to optimize the allocation of their nearly billion-dollar promotion budget across more than 6,000 customer-brand combinations while meeting corporate growth and trade objectives for each brand. Automating the trade spend allocation will reduce time and resources, allow scenarios to be planned and quickly tested to inform the optimal trade fund allocation plan, and provide a scalable foundation to optimize trade spend across retail channels, customers, and brands.
Given the number of brands and customers, the potential mix of combinations is nearly infinite, which makes this an ideal problem for linear optimization. Leveraging a previous solution we developed for this client, we used lpSolve to develop a linear model to optimize budget allocation. The model produced results based on targeted growth rates and customer priorities for over 6,000 decision variables and tens of thousands of constraints in under two minutes.
The result provided our client with an algorithm that optimally allocates the trade budget to all customer-brand combinations while meeting corporate objectives, customer prioritizations, and the client’s preferences for consistent solutions. The algorithm also provides information that can help the client determine future corporate objectives where data analytics can improve decision making.