<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.</p>
</abstract>

Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.

Decision Making Support System for Managing Advertisers By Ad Fraud Detection

Faculty of Mechanical Engineering and Computer Science

Faculty of Management and Social Sciences

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Decision Making Support System for Managing Advertisers By Ad Fraud Detection"}

Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and...

Decision Making Support System for Managing Advertisers By Ad Fraud Detection

Data publikacji: 08 paź 2021

Zakres stron: 331 - 339

Przyjęty: 22 wrz 2021

DOI: https://doi.org/10.2478/jaiscr-2021-0020

Słowa kluczowe
lead management, feedforward neural networks, embedding, online marketing

© 2021 Marcin Gabryel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Decision Making Support System for Managing Advertisers By Ad Fraud Detection

Data publikacji: 08 paź 2021

Zakres stron: 331 - 339

Przyjęty: 22 wrz 2021

DOI: https://doi.org/10.2478/jaiscr-2021-0020

Słowa kluczowelead management, feedforward neural networks, embedding, online marketing

© 2021 Marcin Gabryel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
lead management, feedforward neural networks, embedding, online marketing