Increasingly diversified market preferences have driven companies to offer multiple product variations that provide a similar set of basic functions but differ on various dimensions such as features and prices. This is the case in traditional consumer goods markets, fashion, home appliances, as well as high-tech markets for consumer electronics. While a broad product offering increases the chance that customers will find a product that matches their tastes, it also increases the chance of cannibalization. As the complexity of product lines increases and as consumers’ tastes diversify and shift, managers start to lose their grip on forecasting and pricing due to two major hurdles: (1) the frequent updating of product lines as a firm introduces new products and retires old products (i.e., demand models and product prices need to be adjusted each time such an event occurs); (2) the heterogeneity in the customer population (i.e., different customer segments use the products differently and consequently value them differently). As such, a decision tool is in order to systematically forecast demand and optimize prices. This solution would account for past sales and price information as well as adjust to changes in the product line and heterogeneity in the customer population.
Researchers at Arizona State University have developed a novel toolset that fills this market need by optimizing prices with consideration of both product features/attributes and customer preferences/tastes within each segment. The tool uses historical data to calibrate predictive models of demand, and generates the efficient frontier along the dimensions of market share and profit. It allows decision makers to identify prices for a product line that balance market share and profit goals. With this tool, a company can predict how customers will respond to product line changes and price adjustments, and project future sales, profit, and market shares with a segment-by-segment breakdown.
• Marketing analytics
• Pricing strategy
• Product line development
Benefits and Advantages
• Award-winning – Won first-prize in the 2017 Manufacturing and Service Operations Management Practice-Based Research Competition, one of the highest honors for practice-based research
• Cost-effective – Provides predictions based on prior purchases instead of on expensive market studies
• Superior – Developed methods solve a challenging multinomial logit demand problem and does so with high efficiency