An Integral and Practical Framework for the Customer Discovery Phase

Abstract

Much research has been conducted regarding which steps to take to enhance the likelihood of entrepreneurial success. However, entrepreneurs often turn to practical books for insights on what steps to take, resulting in a research-practitioner gap. This thesis aimed at combining different models and methods into one integral yet simple framework that is practically applicable for startup founders in the early phase: customer discovery. The scientific literature for these models and methods was included. The framework included the Customer Development Model, Diffusion of Innovations Model, Technology Acceptance Model, the Mom Test method and the Lean method. This framework was then tested on a practical use case to test the applicability of the different aspects in a real-life scenario. This use case was the company Plense Technologies, a startup I recently co-founded and still in the customer discovery phase. The metric of learnings and insights was used to assess the framework during this phase. Although the initial framework proved to be insightful, some additions were made. These four additions were (1) the distinction between directive- and non-directive interviews at the different phases of the customer development insight cycle, (2) interviewing suppliers, (3) the benefit of startup coaches and (4) doing an internship to get a better understanding of the customer. With these additions, the resulting framework proved to be integral and simple to apply in a practical context. Entrepreneurs can use the models and methods in the framework to rapidly iterate and improve the business model, ultimately increasing the chances of entrepreneurial success. Factors mentioned that may influence how the framework can best be applied are the financial climate, regulatory environment, culture, market segment, market type, product type, the role of the customer and the relevance of deep tech. These should be further assessed in future research to validate their applicability in different contexts.