TY - GEN
T1 - Human Intelligence Versus Artificial Intelligence
T2 - International Conference on Marketing and Technologies, ICMarkTech 2020
AU - Stadlmann, Christian
AU - Zehetner, Andreas
N1 - Funding Information:
The authors like to acknowledge the contributions of the research team consisting of S. Baumberger, M. Halama, M. Huber, D. Kutsam, A. Narli, B. Yüksel and J. Neumüller. Moreover, we would like to express our thankfulness to M. Seferovic of the Austrian Trade Commission in Chicago and G. Berend of the University of Szeged for their generous support with the database research and the data mining research.
Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - This contribution deals with a comparison of two traditional approaches and one AI-based data mining tool to collect and interpret data for prospect generation. Traditional prospect generation methods like manual web search or using purchased data from external providers may create high costs and efforts and are subject to failures and waste coverage through outdated and untargeted data. In contrast, AI-based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI-based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.
AB - This contribution deals with a comparison of two traditional approaches and one AI-based data mining tool to collect and interpret data for prospect generation. Traditional prospect generation methods like manual web search or using purchased data from external providers may create high costs and efforts and are subject to failures and waste coverage through outdated and untargeted data. In contrast, AI-based methods claim to provide better results at lower costs. Based on a real case, the authors compare effects of these three prospect generation methods. AI-based data mining tools compensate for some weaknesses of other methods, especially because they do not need pre-defined selection criteria which might bias the results. In addition, they involve less effort from the researcher. However, the results in generating concrete prospects may be still weaker than with traditional methods if web crawling activities are influenced by underlying databases. For academic research in the field of prospect generation, this study provides a fact-based comparison of approaches. Implications for businesses include the advice to combine methods rather than to rely on a single approach. The time available for research and the complexity of the target market have an influence on the selection of the prospect generation approach.
KW - Commercial data
KW - Data mining
KW - Method comparison
KW - Prospecting
KW - Systematic manual web research
UR - http://www.scopus.com/inward/record.url?scp=85103464458&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4183-8_2
DO - 10.1007/978-981-33-4183-8_2
M3 - Conference contribution
SN - 9789813341821
T3 - Smart Innovation, Systems and Technologies
SP - 11
EP - 22
BT - Marketing and Smart Technologies - Proceedings of ICMarkTech 2020
A2 - Rocha, Álvaro
A2 - Peter, Marc K.
A2 - Loureiro, Sandra
A2 - Reis, José Luís
A2 - Cayolla, Ricardo
A2 - Bogdanovic, Zorica
PB - Springer
Y2 - 8 October 2020 through 10 October 2020
ER -