Sales Forecasting forms the heart of effective data driven decision making and affects every function in a business. There has been a quantum leap in technology choices available for predicting sales. Novel methods in deep learning and SVM have evolved while conventional time-series models like ARIMA, ARCH and Holts-Winter continue to be leveraged. Extensive progress has also been made on aspects such as explain ability and outlier treatment. There is however a gap in applying technology to business areas such as industries. Each industry is unique in terms of nuances and challenges it faces. These impact the forecasting process and hence should impact technology choices as well. This paper addresses that gap by proposing a novel recommendation framework comprising algorithms, accuracy metrics and seasonality treatment that have highest chance of successful application for respective industry. Since the top 3 industries that leverage Forecasting are BFSI (Banking, Financial Services, and Insurance), Pharmaceutical and Retail, we look at a cross- section between them. This paper analyses differences in forecasting through historic research, interviews with industry professionals and 3 experiments.