Marketing Mix Optimization with Practical Constraints
This work addresses a practical challenge for marketing managers in retail and CPG industries by making marketing mix optimization more computationally feasible under real-world constraints.
This paper tackles the marketing mix optimization (MMO) problem, incorporating practical constraints such as minimum spend change and maximum number of activity changes. The authors reformulate the resulting mixed integer nonlinear program (MINLP) to improve computational efficiency, demonstrating significant speedups in solution time.
In this paper, we address a variant of the marketing mix optimization (MMO) problem which is commonly encountered in many industries, e.g., retail and consumer packaged goods (CPG) industries. This problem requires the spend for each marketing activity, if adjusted, be changed by a non-negligible degree (minimum change) and also the total number of activities with spend change be limited (maximum number of changes). With these two additional practical requirements, the original resource allocation problem is formulated as a mixed integer nonlinear program (MINLP). Given the size of a realistic problem in the industrial setting, the state-of-the-art integer programming solvers may not be able to solve the problem to optimality in a straightforward way within a reasonable amount of time. Hence, we propose a systematic reformulation to ease the computational burden. Computational tests show significant improvements in the solution process.