Forecasting New Product Success: A Methodology Combining Consumer Preference Analysis and Machine Learning Prediction Models
Keywords:
Predictive new product development, Demand Forecast, Gaussian Process Regression, KANO, Product DifferentiationAbstract
In an increasingly competitive market with shortening product lifecycles, developing successful new products is more critical than ever. Traditional methods, which assess product performance post-launch, often lead to missed opportunities and inefficiencies. This study introduces a novel methodology that combines consumer preference analysis with advanced predictive modeling using Gaussian process regression to forecast new product success. By integrating the Product Differentiation Index with the Demand Creation Index and incorporating user satisfaction data from the KANO model, this approach offers a robust tool for predicting market demand before a product even hits the shelves. Tested in the dynamic smartwatch industry, the model demonstrated high accuracy, with a MAPE value of 0.13, and identified pulse detection as the feature most likely to drive sales in future products. This innovative methodology not only predicts early-stage demand but also equips companies with the insights needed to make strategic, data-driven decisions that maximize market impact and profitability.
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