Analyze dynamic value of strategic partners using Markov chain

Abstract

With rapidly growing and increasingly competitive marketplace, companies are constantly performing strategic planning for growth strategy and repositioning itself for even more disruptive changes to achieve or regain market leadership, which makes selection of strategic partners play a very critical role in the corporate growth strategy. How to reallocate limited resources to maximize return on investment (ROI), significantly reduce budget overruns and effectively leverage customer value analysis has become a key research topic for both pragmatic and academic research groups. Previous strategy research findings are mostly focused on resource based view (RBV) and transaction cost to explore the selection strategy on strategic partners, while shed very little light on the value of strategic partners as this becomes an inevitable component in strategy management. In order to bridge such a gap, this research is in an attempt to perform a thorough value analysis of strategic partners using both Markov chain and Hierarchical Bayesian. With Markov chain as a base, this research performs data collection and analysis using previous value status of strategic partners, and their migration pathways, along with Hierarchical Bayesian, to establish individualized transition probability matrix and further predict the future migration pathways of strategic partners, eventually providing key insights for companies to develop strategies on how to manage and strengthen key partnerships.