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Exploiting visual and text features for direct marketing learning in time and space constrained domains

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Abstract

Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audiences. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the direct marketing industry has been under increased pressure to further optimize learning, in particular when facing severe time and space constraints. Taking into account the multimedia nature of offers, which typically comprise both a visual and text component, we propose a two-phase learning strategy based on a cascade of regression methods. This proposed approach takes advantage of visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial multimedia messaging service show the effectiveness of the proposed methods that improve on classical learning techniques. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. The proposed methods can be used in any multimedia direct marketing domains in which offers are composed by image and text.

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Notes

  1. By definition, a holistic cue is one that is processed over the entire human visual field and does not require attention to analyze local features [10]

  2. Taking into account the overall simulation settings, 30 offers per day is an arrival rate value comparable to the mean arrival rate values observed in our real system.

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Acknowledgments

The authors would like to thank Daniele Ravì for helping in the implementation of the simulation studies. The authors would also like to thank Neodata Group for giving access to the mobile messaging dataset, and for helping in the implementation and testing of the proposed approach.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy 

    Sebastiano Battiato, Giovanni Maria Farinella, Giovanni Giuffrida & Giuseppe Tribulato

  2. Tanaka Business School, Imperial College London, South Kensington, Campus, London, SW7 2AZ, UK

    Catarina Sismeiro

Authors
  1. Sebastiano Battiato
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  2. Giovanni Maria Farinella
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  3. Giovanni Giuffrida
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  4. Catarina Sismeiro
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  5. Giuseppe Tribulato
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Corresponding author

Correspondence to Sebastiano Battiato.

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Battiato, S., Farinella, G.M., Giuffrida, G. et al. Exploiting visual and text features for direct marketing learning in time and space constrained domains. Pattern Anal Applic 13, 143–157 (2010). https://doi.org/10.1007/s10044-009-0145-2

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