# 🟩D1': Learn β then Set Quality (Pull) The pull strategy embodies "measure twice, cut once" adapted for perishable commitment environments—prioritizing learning stakeholder response parameters (βr, βc) before committing to quality levels. Tesla could have pursued this path: extensive market research with luxury car buyers, months of supplier negotiations to map battery partner constraints, detailed competitive analysis of Porsche and Ferrari owner preferences. This approach builds confidence through data, using tools like conjoint analysis to precisely estimate how customers trade off acceleration versus range, or technical deep-dives to understand exactly how battery costs scale with energy density. The pull model's analytical rigor offers clear benefits: decisions are grounded in evidence rather than assumptions, reducing the risk of catastrophic misalignment. A methodical Tesla might have discovered that customers cared more about charging infrastructure than raw performance, or that battery partners needed different contract structures than initially assumed. By investing upfront in parameter learning, pull strategies minimize the chance of building the wrong product for the wrong market. However, in environments where commitment is perishable, pull strategies suffer three critical limitations. First, the opportunity cost of learning time proves enormous—while Tesla hypothetically surveyed customers and negotiated with suppliers, Fisker or established automakers could have launched their own electric sports cars, capturing early adopters and locking up battery supply chains. Second, parameters shift during learning—customer expectations evolve as they see new possibilities, battery technology advances quarter by quarter, making carefully gathered data obsolete. Third, lack of real market engagement means missing crucial feedback that only emerges from actual transactions—customers might claim they'll pay $100,000 for 300-mile range in surveys but balk when writing checks. The pull approach implicitly assumes parameter stability and patient stakeholders—assumptions that rarely hold when innovation creates winner-take-all dynamics. Tesla's actual choice to move faster reflected recognition that in rapidly evolving markets, perfect information arrives too late to be useful. The irony of pull strategies in perishable commitment contexts: by the time you know exactly what to build, the window to build it has often closed.