This study explores the retail success in developing markets by examining the media and retail patterns of the Sri Lankan consumer electronics industry. By leveraging a time series dataset covering sales, media spending, holidays, and intricate economic factors such as the Consumer Price Index, this study applies advanced analytical approaches through media mix modeling to uncover complex relationships between advertising and sales performance. To proceed with this study, the trend and seasonality in sales were decomposed using Prophet by Meta to recognize seasonal effects on sales, particularly during New Year’s and Christmas. Optima hyper parameter optimization estimates ad stock effects in advertisements. It can mitigate human bias in estimating how long an advertisement will be remembered by consumers. The random forest machine learning model with time series cross-validation captures interactions between variables and sales. Further, we use the SHapely Additive exPlanations (SHAP) to enhance the interpretability of the black-box nature in random forest. Thereby, the relative effect of each variable on sales was estimated. This study contributes to improving the understanding of the interplay between sales, media, socio-economic, and seasonal implications in developing markets. These insights provide a framework for marketers, business owners, and analysts to leverage data-driven decision-making in order to optimize marketing campaigns and budgets to achieve sustainable growth in sales.