Digital Advertising Traffic Operation: Machine Learning for Process Discovery

TL;DR

This paper introduces a machine learning-based process discovery method for digital advertising traffic operations, enabling automatic identification of process variations, delays, and issues to improve efficiency and customer service.

Contribution

It proposes a novel machine learning approach for process discovery in digital advertising traffic management, enhancing problem detection and process optimization.

Findings

Identified process delays and loops using machine learning.

Enabled automatic detection of process variations.

Improved process problem communication.

Abstract

In a Web Advertising Traffic Operation it's necessary to manage the day-to-day trafficking, pacing and optimization of digital and paid social campaigns. The data analyst on Traffic Operation can not only quickly provide answers but also speaks the language of the Process Manager and visually displays the discovered process problems. In order to solve a growing number of complaints in the customer service process, the weaknesses in the process itself must be identified and communicated to the department. With the help of Process Mining for the CRM data it is possible to identify unwanted loops and delays in the process. With this paper we propose a process discovery based on Machine Learning technique to automatically discover variations and detect at first glance what the problem is, and undertake corrective measures.