| Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity M Platzer, T Reutterer Marketing Science 35 (5), 779-799, 2016 | 116 | 2016 |
| Holdout-based empirical assessment of mixed-type synthetic data M Platzer, T Reutterer Frontiers in big Data, 43, 2021 | 42 | 2021 |
| Leveraging purchase regularity for predicting customer behavior the easy way T Reutterer, M Platzer, N Schröder International Journal of Research in Marketing 38 (1), 194-215, 2021 | 32 | 2021 |
| A deep recurrent neural network approach to learn sequence similarities for user-identification S Vamosi, T Reutterer, M Platzer Decision Support Systems 155, 113718, 2022 | 28 | 2022 |
| Customer Base Analysis with Recurrent Neural Networks J Valendin, T Reutterer, M Platzer, K Kalcher International Journal of Research in Marketing, 2022 | 20 | 2022 |
| Stochastic Models of Noncontractual Consumer Relationships M Platzer Vienna University of Business and Economics, 2008 | 11 | 2008 |
| Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data M Platzer, T Reutterer arXiv preprint arXiv:2104.00635, 2021 | 6 | 2021 |
| Customer Base Analysis with BTYDplus M Platzer | 4 | 2016 |
| AI-based Re-identification of Behavioral Clickstream Data S Vamosi, M Platzer, T Reutterer arXiv preprint arXiv:2201.10351, 2022 | 3 | 2022 |
| Strong statistical parity through fair synthetic data I Krchova, M Platzer, P Tiwald arXiv preprint arXiv:2311.03000, 2023 | 2 | 2023 |
| AI-Based Privacy Preserving Census (like) Data Publication J Gussenbauer, A Kowarik, K Kalcher, M Platzer | 2 | 2021 |
| Rule-adhering synthetic data--the lingua franca of learning M Platzer, I Krchova arXiv preprint arXiv:2209.06679, 2022 | 1 | 2022 |
| Market Response Models M Platzer Technical University of Vienna, 2002 | 1 | 2002 |
| Synthesizing Mobility Traces K Kalcher, M Platzer, D Soukup US Patent App. 17/449,993, 2022 | | 2022 |
| Special Session:“Marketing Analytics and Privacy” R Laub, PK Kannan, M Platzer | | |