Design and Implementation of an Online Marketing Prediction System

https://drive.google.com/file/d/16uwe2aV0jJnHe2hcMEk6_y2S4kOOpzIo/view?usp=drive_link

In online marketing, everything is about the Customer Acquisition Costs (CAC), which indicate how much money has to be spent to acquire a new customer [13]. Especially in the software industry, CAC are high and increasing [2][7]. Therefore, solutions are required to reduce the CAC and keep them low as quickly as possible after a product launch. In this thesis, it was researched what data can be exported from online marketing ad platforms (e.g., Google Ads) and how it can be connected to the data collected by the promoted mobile application. With this knowledge, the goal was to find out whether and to what extent analyses and predictions regarding the performance of future online mobile app campaigns can be made by using the aggregated data and calculated Key Performance Indicators (KPI) based on the connected data from the different sources. With the implementation of a prototype, the system operating costs were evaluated and several challenges encountered in implementing such a system were identified. The main challenge is that the export of data from mobile app campaigns is restricted in several ways, and therefore the data volume is too low to train the machine learning models in most cases. The designed prediction system component is affordable in terms of operation costs and therefore worth a try if enough data is available. Future work could test the system on data from campaigns that promote web applications, as the data extraction capabilities are better for non-app campaigns and the low data volume might be less of an issue.