Abstract
Since the early 1910s, managers have been using a simple recency-based decision strategy, the hiatus heuristic, to identify valuable customers. This study analyses the role of recency using a library of 60 data sets from business and other areas including weather, sports, and medicine. We find that the hiatus heuristic outperforms complex algorithms from machine learning, stochastic and econometric models in many of these environments. Moreover, if one includes further variables apart from recency in the complex algorithms, their performance does not improve. We show that the results are not so much driven by limited sample size than by the dominant role that recency plays in most of these environments. We conclude that less can be more, that is, relying on smart data such as recency can yield powerful predictions.
Cite
@inproceedings{artinger2018recency,
title={Recency: Prediction with Smart Data},
author={Artinger, F. and Kozodoi, N. and Wangenheim, F. and Gigerenzer, G.},
booktitle={{AMA} Winter Academic Conference Proceedings},
year={2018}
}
