Machine Learning for New Product Forecasting

Forecasting the demand for new products is crucial given the level of investment required for a launch. It is also challenging and risky in an environment of vigorous economic competition, evolving customer expectations, and the emergence of new technologies and innovations. Given the high failure rate of new launches (70–80 percent for consumer-packaged goods), the accuracy of demand forecasts is a top priority for decision-makers. Underpredicting demand leads to a loss of potential salesSales; overpredictingOverpredicting it leads to costly excess inventory. Forecasting new product demand has traditionally been done using a variety of techniques: judgmental methods, market researchMarket research like surveys of buyers’ intentions, marketMarket testing, expert opinion methods like the Delphi method, diffusion models like the Bass model, and statistical modelingStatistical modeling through a variety of time seriesTime series and/or multivariate techniquesMultivariate techniques. More recently, machine learning has been added to the mix. The selection depends somewhat on whether the new product is: (a) new to the world, (b) new to the firm, (c) an addition to existing product lines, or (d) an improvement or revision to existing products. Machine learning is a good candidate when we have lots of data, including the salesSales history, on existing products that are similar to the new one. Although humans use this approach too, the idea is that machine learning should be able to do it faster and more accurately. Many papers and case studies are available on using machine learning to forecast existing products with historical data. However, when it comes to new products with little or no history, the literature is very limited. In this chapter, we will review the main techniques for predictingPredicting new product demand, focusing on machine learning. We also review four recent case studies that confirm that machine learning can improve accuracy of demand forecasts for new products.