Product lines allow firms to diversify feature sets across a range of prices, with the end goal of maximizing total profits. The interplay between product appeal, feature options, and price constraints presents a multi-dimensional, time-sensitive optimization challenge. Maintaining a balanced product portfolio requires preserving the appeal of existing products even alongside attractive new options. With proper quantitative tools, addressing these considerations effectively can greatly affect the short-term and long-term success of a product line.
Researchers at Arizona State University have developed a novel toolset that caters to three common product line decision scenarios: (1) optimization of prices for pre-determined features, (2) optimization of features for pre-determined price points, and (3) simultaneous optimization of features and prices. These adaptive tools provide solutions for strategic fine-tuning, introduction and discontinuation of products, and development of entirely new product lines.
Multinomial logit (MNL) is used with real sales data to predict consumer purchasing probabilities as a function of price and features. This captures, for example, the commonly observed phenomenon that consumers become less sensitive to price changes as product quality increases. Even for offerings with identical price sensitivity and cost functions, this toolset can pinpoint optimal spreads in product features and markups. Similar toolsets documented in the literature neither account for existing products nor prioritize in-depth analysis of price-feature relationships within a product line.
• Marketing analytics
• Pricing strategy
• Product line development
Benefits and Advantages
• Versatile – Covers scenarios ranging from product line refinement to complete redesign
• Comprehensive – Integrates existing products when modeling consumer choice
• Simplifying – Uses sales data to provide quantitative solutions for a complex, highly qualitative problem