Published inTowards Data ScienceUnderstanding Bayesian Marketing Mix Modeling: A Deep Dive into Prior SpecificationsExploring model specification with Google’s LightweightMMMJun 24, 20231Jun 24, 20231
Published inTowards Data ScienceExploring Different Approaches to Generate Response Curves in Marketing Mix ModelingComparing Saturation Function and Partial Dependence for Response Curve GenerationJun 14, 20232Jun 14, 20232
Published inTowards Data SciencePractical Approaches to Optimizing Budget in Marketing Mix ModelingHow to optimize the media mix using saturation curves and statistical modelsFeb 28, 20232Feb 28, 20232
Published inTowards Data ScienceMetrics for uncertainty evaluation in regression problemsHow to evaluate uncertainty with Validity, Sharpness, Negative Log-Likelihood, and Continuous Ranked Probability Score (CRPS) metricsAug 12, 20223Aug 12, 20223
Published inTowards Data ScienceModeling Marketing Mix Using Smoothing SplinesCapturing non-linear advertising saturation and diminishing returns without explicitly transforming media variablesJul 15, 20221Jul 15, 20221
Published inTowards Data ScienceModeling Marketing Mix with Constrained CoefficientsHow to fit a SciPy Linear Regression and call R Ridge Regression from Python using RPy2 InterfaceJun 22, 20223Jun 22, 20223
Published inTowards Data ScienceImproving Marketing Mix Modeling Using Machine Learning ApproachesBuilding MMM models using tree-based ensembles and explaining media channel performance using SHAP (Shapley Additive Explanations)Jun 8, 202210Jun 8, 202210
Published inTowards Data ScienceLearning product similarity in e-commerce using a supervised approachA practical solution to finding similar products using deep learning. A product-centric approach.Apr 27, 20223Apr 27, 20223
Published inTowards Data ScienceModeling Marketing Mix using PyMC3Experimenting with priors, data normalization, and comparing Bayesian modeling with Robyn, Facebook’s open-source MMM packageFeb 23, 20225Feb 23, 20225
