@article {1188,
	title = {Editorial: Insights (October 2018)},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {10/2018},
	pages = {3-4},
	publisher = {Talent First Network},
	address = {Ottawa},
	keywords = {customer foresight, data analysis, data mining, design thinking, digital platforms, industry{\textendash}academia collaboration, machine learning, market entry, Open innovation, service design},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1188},
	url = {https://timreview.ca/article/1188},
	author = {Chris McPhee}
}
@article {1189,
	title = {Strategic Foresight of Future B2B Customer Opportunities through Machine Learning},
	journal = {Technology Innovation Management Review},
	volume = {8},
	year = {2018},
	month = {10/2018},
	pages = {5-17},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {Within the strategic foresight literature, customer foresight still shows a low capability level. In practice, especially in business-to-business (B2B) industries, analyzing an entire customer base in terms of future customer potential is often done manually. Therefore, we present a single case study based on a quantitative customer-foresight project conducted by a manufacturing company. Along with a common data mining process, we highlight the application of machine learning algorithms on an entire customer database that consists of customer and product-related data. The overall benefit of our research is threefold. The major result is a prioritization of 2,300 worldwide customers according to their predicted technical affinity and suitability for a new machine control sensor. Thus, the company gains market knowledge, which addresses management functions such as product management. Furthermore, we describe the necessary requirements and steps for practitioners who realize a customer-foresight project. Finally, we provide a detailed catalogue of measures suitable for sales in order to approach the identified high-potential customers according to their individual needs and behaviour. },
	keywords = {action research, B2B industries, customer base analysis, customer foresight, customer knowledge, customer profile, data mining, machine learning, strategic foresight},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1189},
	url = {https://timreview.ca/article/1189},
	author = {Daniel Gentner and Birgit Stelzer and Bujar Ramosaj and Leo Brecht}
}
