A Deep Learning-Based Decision Support System for Mobile Performance Marketing

Luís Miguel Matos (), Paulo Cortez, Rui Mendes () and Antoine Moreau ()
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Luís Miguel Matos: ALGORITMI/LASI, Department of Information Systems, University of Minho, Guimarães, Portugal
Paulo Cortez: ALGORITMI/LASI, Department of Information Systems, University of Minho, Guimarães, Portugal
Rui Mendes: ALGORITMI/LASI, Department of Informatics, University of Minho, Braga, Portugal
Antoine Moreau: OLAmobile, Spinpark, Guimarães, Portugal

International Journal of Information Technology & Decision Making (IJITDM), 2023, vol. 22, issue 02, 679-703

Abstract: In Mobile Performance Marketing (MPM), monetary compensation only occurs when an advertisement results in a conversion (e.g., sale of a product or service). In this work, we propose an intelligent decision support system (IDSS) to automatically select mobile marketing campaigns for users. The IDSS is based on a computationally efficient mobile user conversion prediction model that assumes a novel Percentage Categorical Pruning (PCP) categorical preprocessing and an online deep multilayer perceptron (MLP) reuse model (MLPr). Using private (nonpublicly available) business MPM data provided by a marketing company, the MLPr model outperformed an offline multilayer perceptron and a logistic regression, obtaining a high quality class discrimination when applied to sampled (85% to 92%) and complete (90% to 94%) data. In addition, the MLPr compared favorably with other machine learning (ML) models (e.g., Random Forest, XGBoost), as well as with other deep neural networks (e.g., diamond shaped). Moreover, we designed two strategies (A — best campaign selection; and B — random selection among the top candidate campaigns) to build the IDSS, in which the predictive deep learning model is used to perform a real-time selection of advertisement campaigns for mobile users. Using recently collected big data (with millions of redirect events) from a worldwide MPM company, we performed a realistic IDSS evaluation that considered three criteria: response time, potential profit and advertiser diversity. Overall, competitive results were achieved by the IDSS B strategy when compared with the current marketing company ad assignment method.

Keywords: Big data; categorical transformation; classification; Conversion Rate (CVR); deep multilayer perceptron; intelligent decision support system (IDSS) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S021962202250047X

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