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

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

ARTIFICIAL neural networks

Big data

BIG data

categorical transformation

classification

Conversion Rate (CVR)

DECISION support systems

DEEP learning

deep multilayer perceptron

intelligent decision support system (IDSS)

MACHINE learning

MARKETING

MOBILE learning

RANDOM forest algorithms

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. [ABSTRACT FROM AUTHOR]

679-703

2

22

Matos, Luís Miguel

Cortez, Paulo

Mendes, Rui

Moreau, Antoine

International Journal of Information Technology & Decision Making

2023

journalArticle

2196220

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