Big data analytics and international market selection: An exploratory study


  • Jonathan Calof
  • Wilma Viviers



Market Market Intelligence, Business Intelligence, Competitive Intelligence, Information Systems, Geo-Economics


A great deal of information is available on international trade flows and potentialmarkets. Yet many exporters do not know how to identify, with adequate precision, thosemarkets that hold the greatest potential. Even if they have access to relevant information, thesheer volume of information often makes the analytical process complex, time-consuming andcostly. An additional challenge is that many exporters lack an appropriate decision-makingmethodology, which would enable them to adopt a systematic approach to choosing foreignmarkets. In this regard, big-data analytics can play a valuable role. This paper reports on thefirst two phases of a study aimed at exploring the impact of big-data analytics on internationalmarket selection decisions. The specific big-data analytics system used in the study was theTRADE-DSM (Decision Support Model) which, by screening large quantities of marketinformation obtained from a range of sources identifies optimal product‒market combinationsfor a country, industry sector or company. Interviews conducted with TRADE-DSM users aswell as decision-makers found that big-data analytics (using the TRADE-DSM model) didimpact international market-decision. A case study reported on in this paper noted thatTRADE-DSM was a very important information source used for making the company’sinternational market selection decision. Other interviewees reported that TRADE-DSMidentified countries (that were eventually selected) that the decision-makers had not previouslyconsidered. The degree of acceptance of the TRADE-DSM results appeared to be influenced byTRADE-DSM user factors (for example their relationship with the decision-maker andknowledge of the organization), decision-maker factors (for example their experience andknowledge making international market selection decisions) and organizational factors (forexample senior managements’ commitment to big data and analytics). Drawing on the insightsgained in the study, we developed a multi-phase, big-data analytics model for internationalmarket selection.


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