Christensen Clayton's “The Innovator’s Dilemma” author has a theory for product competition. Products may pass through cycles of evolution (birth, growth, maturity, and demise). Although, researchers who support another method of a well-linked product lifecycle with the firm's marketing investment have challenged Clayton's model of product lifecycle of evolution. In this new theory, a product life depends directly on the firm continuous support and investment in that product category. A classical example of counteracting shorter product life cycle is Campbell's condensed soup. This effect was the strategic measure taken by Campbell soup company to sustain its legendary premium brand, Campbell's soup. But Clayton's theory about the regularity in how the basis of competition evolves has given us a deep insight how companies should make their investment dollars in product innovation. He claims that a shifted on the basis of competition is needed when the performance of a product overshoots the market ability to absorb. Those bases are divided into four dimensions: functionality, reliability, convenience, and price.
Its key to a sustained profitability and growth that the firm commits in the search for patterns in the product evolution and aggressively invest into replacing mature product by new ones with improved performance. The Clayton's model for product evolution is based on plotting two trajectories (technology improvement and market needs) over time and studying its intersecting trajectories, where technology improvement overshot particular tiers of market need segments and its ability to absorb any increased performance. Those intersecting points represent a shifting of the basis of competition into the next dimension, i.e., from reliability to price dimensions, which is illustrated in Exhibit 3, p.128 “Patterns in the evolution of product development.” But because of the multi-tiered characteristic of most markets, modeling a market-need trajectory by using simple linear regression is an oversimplification of a very complex, highly dimensional problem. (Actually, I am highly interested in this topic and how machine learning algorithms and analytics could drive more intuitive model for product innovation and development.)