Purpose
Uncertainty in the early development of digital business startups can benefit from data-driven testing of hypotheses. Startups face uncertainty not only in product development, but also over the structure of the business model and the nature of the customer or market to address. The authors conceptualize a new model, the Lean Discovery Process (LDP), which focuses on market-based testing from the early business idea through to fully realized product stages of an innovation. The purpose of this paper is to highlight a methodology to help digital business reduce uncertainty and apply lean principles as early as possible in the development of a business concept.
Design/methodology/approach
Examining literature in lean startups, lean user experience and lean software development, the authors highlight underlying assumptions of existing lean models. The authors then examine the LDP using the case of raiserve, a social entrepreneurship startup that focuses on the management of cause-based voluntary service.
Findings
Existing literature focuses on product development against an assumed customer base. Early hypothesis testing can be applied to business concept development to substantially reduce cost and time to market.
Research limitations/implications
Further investigation of different forms of uncertainty in digital startups can open up opportunities to further apply lean methodologies. A more extensive empirical study to measure the potential impact is warranted.
Originality/value
The authors conceptualize the minimum viable customer and support early testing with concepts from market research and collective intelligence. The authors demonstrate early opportunities to apply lean principles and rigorous hypothesis testing in an LDP that results in significant reductions in time and expense of product development.
