MACHINE LEARNING FOR PROCESS DISCOVERY IN DIGITAL ADVERTISING TRAFFIC OPERATION
Keywords:
Process Mining, Business process management, Declarative process models, Digital Advertising, Online Advertising, Trafficking, Adv Operations Management, Mixed Integer Linear Programming (MILP), Optimization, Scheduling, Stakeholders, Project Management, End-To-End management, Campaign Workflow, Analytical Skills, People management skills, Relationships, CRM, Operations, Ad Platforms, Creative delivery, Campaign Performance, KPIsAbstract
The daily trafficking, pacing, and optimisation of digital and sponsored social campaigns must be managed in a web advertising traffic operation. The Traffic Operation data analyst is able to deliver answers in a timely manner, communicate with the Process Manager in their native language, and visually represent any process issues that have been identified. The department has to be informed of the process's shortcomings in order to address the increasing volume of complaints filed through the customer support channel. Process loops and delays can be found in the process with the use of Process Mining for CRM data. In this research, we present a machine learning-based process discovery technique to automatically identify variances, quickly identify the issue, and take appropriate action.
