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Optimising newspaper sales using neural-Bayesian technology

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Abstract

We describe a software system, called just enough delivery (JED), for optimising single-copy newspaper sales, based on a combination of neural and Bayesian technology. The prediction model is a huge feedforward neural network, in which each output corresponds to the sales prediction for a single outlet. Input-to-hidden weights are shared between outlets. The hidden-to-output weights are specific to each outlet, but linked through the introduction of priors. All weights and hyperparameters can be inferred using (empirical) Bayesian inference. The system has been tested on data for several different newspapers and magazines. Consistent performance improvements of 1 to 3% more sales with the same total amount of deliveries have been obtained.

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Authors and Affiliations

  1. SMART Research BV and SNN, University of Nijmegen, Geert Grooteplein 21, 6525 EZ , Nijmegen, The Netherlands

    Tom Heskes, Jan-Joost Spanjers, Bart Bakker & Wim Wiegerinck

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  1. Tom Heskes
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  2. Jan-Joost Spanjers
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  3. Bart Bakker
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  4. Wim Wiegerinck
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Correspondence to Tom Heskes.

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Heskes, T., Spanjers, JJ., Bakker, B. et al. Optimising newspaper sales using neural-Bayesian technology. Neural Comput & Applic 12, 212–219 (2003). https://doi.org/10.1007/s00521-003-0384-x

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  • DOI: https://doi.org/10.1007/s00521-003-0384-x

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