@inproceedings{0921f81b45674482b347033fe529df9b,
title = "SiloSolver: Developing an Algorithm for Automatic Aggregation and Testing of Isolated Customer Segments in Facebook Ads Campaigns",
abstract = "Data silo problem refers to related datasets located in different databases, systems, or files. In the case of online advertising, performance metrics are fragmented in different campaigns and ad sets, making it difficult to compare customer segments. In this research, we present SiloSolver, an algorithm that (a) retrieves performance metrics for different customer segments across all campaigns, (b) aggregates the values for each customer segment in mean values, and (c) runs a statistical comparison (Student's t-Test) on the performance differences between the segments. The algorithm is evaluated using a real Facebook Ads dataset from an e-commerce company consisting of hundreds of campaigns from over five years. Using SiloSolver, advertisers using Facebook Ads are better able to understand their market segments across multiple seemingly disparate campaigns.",
keywords = "Facebook Ads, customer segmentation, digital marketing, online advertising",
author = "Joni Salminen and Tommi Salenius and Jansen, {Bernard J.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021 ; Conference date: 15-11-2021 Through 16-11-2021",
year = "2021",
doi = "10.1109/IDSTA53674.2021.9660798",
language = "English",
series = "2021 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5--10",
editor = "Mohammad Alsmirat and Mohammad Alsmirat and Yaser Jararweh and Feras Awaysheh and Moayad Aloqaily",
booktitle = "2021 2nd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2021",
address = "United States",
}