Description
Business models have gained much interest in the last decade to analyze the potential of new business ventures or possible innovation paths of existing businesses. However, the business model concept has only rarely been used as basis for quantitative empirical studies. This paper suggests the concept of a Business Model DNA to describe the characteristics of specific business models. This concept allows to analyze business models in order to identify clusters of business models that outperform others and calculate future prospects of specific business models. We used 181 startups from the USA and Germany and applied data mining techniques, i.e. cluster analysis and Support Vector Machines, to classify different business models in regards to their performance. Our findings show that 12 distinct business model clusters with different growth expectations and chances of survival exist. We can predict the survival of a venture with an accuracy of 83.6 %.
The Business Model DNA: Towards an Approach for Predicting Business Model Success
Business models have gained much interest in the last decade to analyze the potential of new business ventures or possible innovation paths of existing businesses. However, the business model concept has only rarely been used as basis for quantitative empirical studies. This paper suggests the concept of a Business Model DNA to describe the characteristics of specific business models. This concept allows to analyze business models in order to identify clusters of business models that outperform others and calculate future prospects of specific business models. We used 181 startups from the USA and Germany and applied data mining techniques, i.e. cluster analysis and Support Vector Machines, to classify different business models in regards to their performance. Our findings show that 12 distinct business model clusters with different growth expectations and chances of survival exist. We can predict the survival of a venture with an accuracy of 83.6 %.