Predictive Online Digital Sales (PODS) and Marketing

About The challenge

Digital advertisements of products and services are commonplace in almost every online platform used for distributing and/or selling content, goods, and services. We usually come across these paid digital ads when we visit a website or use an app or watch TV (among other ad-delivery platforms) while interacting with the content. Vendors usually run digital campaigns to manage these ads and pay the online platforms that deliver their ads. In 2024, digital ads spent about $750 billion worldwide. By 2028, it is expected to cross $1 trillion.

Running a digital marketing campaign efficiently involves collecting the performance data about the ads, analyzing those, and adapting the parameters (e.g. ad content, bids for ads) of the campaigns accordingly. When an ad shows up in front of the user on the screen, we say that the ad got an impression. If the ad is of interest to the user, she may click on the ad (for those platforms that allow user interaction). The ratio of the total number of clicks and impressions is called the Click Through Rate (CTR). CTR of an ad is often used by the platform to measure the level of interest of the users to that ad and possibly to the associated product/services it is trying to promote. Therefore, predicting CTR is an important problem in digital campaign management. Conversion from ads is also important particularly in e-commerce platforms where selling goods and services is one of the primary objectives of the vendors.

The objective in this Discovery Challenge is to optimize sponsored ad targeting in e-commerce platforms where ads show up in response to keyword-based search by the users. The first task will involve predicting future CTR for a keyword based on campaign performance data containing keyword bid, cost-per-click (CPC) for thousands of related keywords among others. The second task is to predict future ad-conversion using the provided data set. Participants will develop scalable algorithms that can be used for large scale online campaign management. Agnik is releasing campaign management data for the first time to support this competition and advance machine learning research in this emerging field.

The Discovery Challenge Problem

Consider sponsored ads in online e-commerce platforms where ads show up in response to keyword-based searches by the users.

Dataset

Agnik is releasing campaign management data for the first time to support this competition and advance machine learning research in this emerging field:

  • It will contain 30 days of campaign management data sampled every few minutes round the clock.
  • It will contain the impression, click, spending, CPC, and conversion data among other items for various search keywords.

The task

There are three tasks:

  • Predict the future CTR as a function of the previous values of the CTR and bids allocated to the keywords.
  • Predict future conversion at least one hour in advance.
  • Optimize the scalability of the algorithm. The task is to keep the execution time growth manageable as the dataset size expands.

Communication Plan

  • Online platforms will be used for promoting the competition.
  • Agnik’s digital marketing team and user population worldwide will also be used to publicize the competition.For any question, please contact us pods2025@agnik.com

Technical Details

We will use CodaBench for managing the communications.

Here's the Codabench competition link: https://www.codabench.org/competitions/7588/

Evalution

  • CTR Prediction - one hour into the future. (The average RMSE error for all the provided keywords will be the chosen metric).
  • Conversion Prediction - one hour in the future. (The average RMSE error will be the chosen metric).
  • Scalability - will be measured based by tracking the duration (execution time) on the growth of computing time as more keywords are added to the prediction algorithm!

Date, Time, and Place

  • Start Date: April 21, 2025 at 05:30 UTC
  • End Date: June 30, 2025 at 05:30 UTC
  • Results will be published by July 8, 2025 at 05:30 UTC
  • Competition Report will be submitted by July 31, 2025 at 05:30 UTC
  • Place: Online

Prizes

  • The top three competitors will receive a time slot to present their work at the conference!
  • The winner at the first place will receive free registration for the ECML-PKDD 2025.
  • Moreover, we will offer prize money to the top three winners. The team in the first place will receive 500€, the second-place 300€, and the third-place 200€!

Reporting Requirements

  • External data cannot be used.
  • Teams must be composed of at most five people.
  • Participants must release the code publicly to ensure compliance and verify the results.
  • A technical report no longer than 4 pages must be provided.

Privacy and Ethics

  • The data and the keywords will be anonymized using unique non-identifiable words.
  • No human subjects will be involved in the competition.
  • Participants will maintain professionalism towards other competitors and organizers.
  • Any form of cheating, plagiarism, or unethical behavior will result in disqualification.

Organizers

Anwesha Pal, Agnik International

Anwesha Pal is a member of the Technical Staff at Agnik International. She has an undergraduate degree in Mathematics and a graduate degree in Data Science. She has been working on real-time data analytics, ML for Natural Language Processing (NLP), and machine performance modeling.

Hillol Kargupta, Agnik

Dr. Hillol Kargupta is a co-founder and President of the Agnik Group of Companies. He is an IEEE Fellow. He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1996. He was a full Professor of Computer Science at the Univ. of Maryland, Baltimore County until 2014. Dr. Kargupta won the 10-Year Highest Impact Paper Award from the IEEE Data Mining Conf. in 2013, IBM Innovation Award in 2008, and NSF CAREER award in 2001. He and his team received the 2010 Frost and Sullivan Enabling Tech. of the Year Award. Other awards include the 2016 Fleet Logistics Tech Outlook Top-10 Fleet Management Solution Provider, CIO Review 2015 20 Most Promising Auto. Tech. Solution Providers, best paper award for the 2003 IEEE Int. Conf. on Data Mining for a paper on privacy-preserving data mining, the 2000 TRW Foundation Award, and the 1997 Los Alamos Award for Outstanding Technical Achievement. His dissertation earned him the 1996 Society for Industrial and Applied Mathematics annual best student paper prize. He published more than one hundred peer-reviewed articles. His research has been funded by the NSF, US Air Force, US Dept. of Homeland Security, NASA, DOT among others. He co-edited several books. He served as an associate editor of the IEEE Trans. on Knowledge and Data Engg., IEEE Trans. on Systems, Man, and Cybernetics, Part B and Statistical Analysis and Data Mining Journal. He was the Program Co-Chair of 2009 IEEE Int. Data Mining Conf., General Chair of NGDM Symposiums, Prog. Co-Chair of 2005 SIAM Data Mining Conf. and Assoc. Gen. Chair of the 2003 ACM SIGKDD Conference. (https://en.wikipedia.org/wiki/Hillol_Kargupta)

Codrina Lauth, EPIC Resilience AI Center

Dr. Codrina Ana Maria Lauth is Director of the EPIC Resilience AI Center at Perton HPC Park in Bratislava, Slovakia, where she leads research at the intersection of high-performance computing (HPC), artificial intelligence, and climate resilience. A former researcher at Fraunhofer and Industrial PhD alumna of Copenhagen Business School and Grundfos, Dr. Lauth brings extensive expertise in sustainable digital infrastructures. Her current work focuses on enabling net-zero HPC architectures and advancing AI resilience strategies for critical infrastructure and industrial applications, positioning the EPIC Center as a hub for innovation in green technology and adaptive computing systems.

Michael May, Siemens

Dr. Michael May is Head of Artificial Intelligence at Siemens, where he leads the strategic integration of AI technologies across the company’s global operations. Prior to joining Siemens, he served as Head of the Knowledge Discovery Department at Fraunhofer IAIS, one of Europe's leading applied research institutions. Dr. May played a pivotal role in shaping Europe’s AI and data mining landscape through his leadership of major EU projects such as KDNet and KDUbiq, advancing knowledge discovery in distributed and ubiquitous computing environments. His work bridges cutting-edge research and industrial innovation in AI, big data, and digital transformation.

Ernestina Menasalvas, Universidad Politécnica de Madrid

Dr. Ernestina Menasalvas is a Computer Scientist and has a PhD in Computer Science. She leads the MIDAS “Data Mining and data simulation group” at the Center of Biotechnology in UPM and she is databases and data mining professor at UPM. Associate Dean of studies and Associate Rector for Graduate Studies (2004-2012). Co-leads the task force on skills in the BDVA-DAIRO. Her research integrates different aspects of data analytics; with the involvement in different real-world problems with special emphasis on health. She has participated actively in project development (H2020, FP7, EIT). She has published more than 40 papers in journals including Data and Knowledge Engineering Journal, Physics Reports, Information Sciences, Expert Systems with applications and Journal of Medical Systems and International and actively participated in International Program Committees. (https://medal.ctb.upm.es/team/ernestina-menasalvas/)

Katharina Morik, Technische Universität Dortmund

Dr. Katharina Morik is a full professor for computer science at the TU Dortmund University, Germany. She earned her Ph.D. (1981) at the University of Hamburg and her habilitation (1988) at the TU Berlin. Starting with natural language processing, her interest moved to machine learning ranging from inductive logic programming to statistical learning, then to the analysis of very large data collections, high-dimensional data, and resource awareness. Her aim to share scientific results supports strongly open source developments. For instance, RapidMiner started out at her lab, which continues to contribute to it. Since 2011 she is leading the collaborative research center SFB876 on resource-aware data analysis, an interdisciplinary center comprising 12 projects, 19 professors, and about 50 Ph D students or Postdocs. She was one of those starting the IEEE International Conference on Data Mining together with Xindong Wu, and was chairing the program of this conference in 2004. She was the program chair of the European Conference on Machine Learning (ECML) in 1989 and one of the program chairs of ECML PKDD 2008. She is in the editorial boards of the international journals “Knowledge and Information Systems” and “Data Mining and Knowledge Discovery”.

Sanghamitra Bandyopadhyay, Indian Statistical Institute

Prof. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph. D. in Computer Science from Calcutta University, IIT Kharagpur and Indian Statistical Institute respectively. She then joined the Indian Statistical Institute as a faculty member, and is currently the Director of the Institute. Her research interests include computational biology, soft and evolutionary computation, artificial intelligence and machine learning. She has authored/co-authored several books and over two hundred articles in international journals. Prof. Bandyopadhyay has worked in many Institutes and Universities worldwide. She is the recipient of several honors and awards including the Padma Shri from the Government of India, Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, JC Bose Fellowship, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni Award, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, young engineer/scientist awards from INSA, INAE and ISCA, and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India. She is a Fellow of the Indian National Science Academy (INSA), Indian Academy of Sciences, Bangalore (IASc), National Academy of Sciences, India (NASI), Indian National Academy of Engineers (INAE), Institute of Electrical and Electronic Engineers (IEEE), The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology. She is a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC).

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