Optimizing sales forecasting: an industry-specific framework for methodology selection

An important data drive phenomenon that describes the organizational function is determined by sales forecasting. Technological advancement introduced a methodology counting on deep learning approaches with conventional statistical techniques. This creates a gap in the forecasting models specific to industries. The current study tries to address this research gap, using a mixed methodology of data analysis. The generalized forecasting model is built on three major industries: Financial Services and Insurance (BFSI), pharmaceutical, finance and retail. Further, the study recommends a framework such as time series models in the pharmaceutical sector, causality-based approach for the finance sector, and a hybrid approach for retail sector based on temporal and causal factors. These insights provide actionable guidelines for practitioners and contribute to the theoretical understanding of industry-specific forecasting optimization.