00710nas a2200253   4500008004100000022001400041245003900055210003600094260004200130300000800172490000600180653002300186653001800209653001600227653002000243653002200263653003800285653002100323653001700344653002000361653001900381100001800400856003800418       2018                        eng d  a1927-032100aEditorial: Insights (October 2018)0 aEditorial Insights October 2018  aOttawabTalent First Networkc10/2018  a3-40 v810acustomer foresight10adata analysis10adata mining10adesign thinking10adigital platforms10aindustry–academia collaboration10amachine learning10amarket entry10aOpen innovation10aservice design1 aMcPhee, Chris  uhttps://timreview.ca/article/118802071nas a2200289   4500008004100000022001400041245008600055210006900141260004200210300000900252490000600261520120700267653002001474653001901494653002701513653002301540653002301563653002101586653001601607653002101623653002401644100002001668700002001688700001901708700001601727856003801743       2018                        eng d  a1927-032100aStrategic Foresight of Future B2B Customer Opportunities through Machine Learning0 aStrategic Foresight of Future B2B Customer Opportunities through  aOttawabTalent First Networkc10/2018  a5-170 v83 aWithin 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. 10aaction research10aB2B industries10acustomer base analysis10acustomer foresight10acustomer knowledge10acustomer profile10adata mining10amachine learning10astrategic foresight1 aGentner, Daniel1 aStelzer, Birgit1 aRamosaj, Bujar1 aBrecht, Leo  uhttps://timreview.ca/article/1189