Customer Intelligence
Research Group

Customer Intelligence (CI) is key in effective Customer Relationship Management (CRM) systems and relies on artificial intelligence (AI), gathering, analyzing and modeling information regarding customers and their activities, incl. human-computer interaction (HCI) and human-AI interaction (HAX)

Research Highlights

ZUT+ Customer Intelligence Research Group aims to bridge the gap between computational technology, artificial intelligence (AI), human-computer interaction (HCI), and electronic commerce. Our current research addresses the invention of AI frameworks and software to help companies better understand customers and improve their user experience with e-commerce. In particular we propose general and shop-specific advice on presentational aspects of products in recommending interfaces etc.

We have a 15+ years’ experience in designing and implementing research and building AI, CRM, customer intelligence, HCI and churn analytical solutions, both for purely scientific purposes, whose results are made available to general public, and in R&D projects initiated by e-commerce companies.

Our team consists of scientists and students in the areas of computer science, information technology, data science, ergonomics, biomedical engineering, and psychology/sociology. Our newest projects focus on applying computational technology in e-commerce to improve the quality of online shopping experience and enhance revenues. We always seek ways to translate our research results into products to help customers and companies benefit from our work.

Human-computer interaction

We work on analyzing and optimizing human-computer interaction (HCI), including interactions with popular commercial websites and artificial intelligence (HAX).

Customer behavior monitoring

Our browser plug-ins and other software are able to continuously monitor human behavior online, such as browsing paths, mouse moves, mouse clicks and key strokes, as well as assess user experience (UX).

Wearable sensors

In particular eye trackers, but also heart rate and skin galvanic response sensors, EEG etc. can be used to support us with useful research data.

Recommender systems

Some of our research is connected with layout and visual intensity of websites, in particular recommending interfaces and AI presence in e-commerce.

AI and analytical frameworks

Sophisticated algorithms track all the data coming from various sources and we analyze the data looking for patterns that might become recommendations for further development.

Our team is open to many kinds of research collaboration, including joint projects, joint publications, contract research, consulting and new ventures.

APPROACH
TALK
PLAN
DESIGN
IMPLEMENT
TEST
LAUNCH
SUCCESS

RESEARCH & DEVELOPMENT

E-commerce
Human-Computer Interaction (HCI)
User Experience (UX)
Machine Learning (ML)
Artificial Intelligence (UI)
Recommender Systems
Customer Relationship Management (CRM)
Churn Analysis
Website Design
E-health
Business Development
Eye Tracking
Mouse Tracking
Event Tracking
R&D Strategy

We have experience in R&D projects in the fields of AI, e-commerce design and enhancement, recommender systems, CRM, HCI and UX optimization, financed by National Science Centre (NCN), National Center for Research and Development (NCBR), Ministry of Education and Science (MEiN), Chancellery of the President of the Republic of Poland (KPRP), and private companies, including a leading e-commerce developer in Poland.

Members of the team gained experience at Stanford University, IBM TJ Watson Research Center in New York, Fraunhofer Institute in Lepizig and other renowned research institutions, as well as the Board of the National Center of Research and Development (NCBR), NCN/NCBR Joint Program Tango K Phase Evaluation Team, R&D Investment Committees, POIR Project Evaluation and Audit Committees and others.

team members

Prof. Piotr Sulikowski, Ph.D.

Team Leader

West-Pomeranian University of Technology

Tomasz Zdziebko, Ph.D.

University of Szczecin

Konrad Ryczko

West-Pomeranian University of Technology

Michał Kucznerowicz

West-Pomeranian University of Technology

Cooperating Researchers and Students

Faculty

Prof. Iwona Bąk, Ph.D.

West Pomeranian University of Technology

Prof. Kristof Coussement, Ph.D.

IESEG School of Management

Prof. Krzysztof Dyczkowski, Ph.D.

Adam Mickiewicz University

Prof. Omar Hussein, Ph.D.

University of New South Wales

Prof. Jarosław Jankowski, Ph.D.

West Pomeranian University of Technology

Prof. Eliasz Kańtoch, Ph.D.

AGH University of Science and Technology

Krzysztof Kluza, Ph.D.

AGH University of Science and Technology

Prof. Marek Postuła, Ph.D., M.D.

Warsaw Medical University

Prof. Małgorzata Przybyła-Kasperek, Ph.D.

University of Silesia in Katowice

Karina Sachpazidu-Wójcicka, Ph.D.

Wrocław University of Economics and Business

Prof. Anna Wilbik, Ph.D.

Maastricht University

Soojeong Yoo, Ph.D.

University College London

Łukasz Zadrożny, Ph.D., M.D.

Warsaw Medical University

Students and PhD Students

Jakub Baran

West Pomeranian University of Technology

Łukasz Dobrowolski

West Pomeranian University of Technology

Piotr Jabłoński

Adam Mickiewicz University

Kacper Milan

AGH University of Science and Technology

Joanna Piotrowska

West Pomeranian University of Technology

Dominik Turzyński

Pomeranian Medical University

Adrian Widłak

AGH University of Science and Technology

Marcin Wysocki

West-Pomeranian University of Technology

cooperation

We are proud to participate in projects and/or cooperate with representatives of the following institutions:

publications

Selected publications:

Przybyła-Kasperek M., Sulikowski P.: Rough Set Decision Rules for Usage-Based Churn Modeling in Mobile Telecommunications. In: Nguyen et al. (eds): Advances in Computational Collective Intelligence. 16th International Conference on Computational Collective Intelligence - ICCCI 2024. Communications in Computer and Information Science, vol 2165. Springer, Cham, 2024.

Przybyła-Kasperek M., Marfo K.F., Sulikowski P.: Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns. Applied Sciences 2024, 14, 9226.

Marfo K.F., Przybyła-Kasperek M., Sulikowski P.: Fragmented Image Classification Using Local and Global Neural Networks: Investigating the Impact of the Quantity of Artificial Objects on Model Performance. In: Franco L. et al. (eds): Computational Science – ICCS 2024. 24th International Conference on Computational Science (ICCS). Lecture Notes in Computer Science, vol 14838. Springer, Cham, 2024.

Zdziebko T., Sulikowski P., Sałabun W., Przybyła-Kasperek M., Bąk, I.: Optimizing Customer Retention in the Telecom Industry: A Fuzzy-Based Churn Modeling with Usage Data. Electronics 2024, 13, 469.

Bortko K., Fornalczyk K., Jankowski J., Sulikowski P., Dziedziak K.: Impact of changes in chatbot’s facial expressions on user attention and reaction time. PLOS One 2023, 18(7): e0288122.

Kromołowska K., Kluza K., Kańtoch E., Sulikowski P.: Open-Source Strain Gauge System for Monitoring Pressure Distribution of Runner’s Feet. Sensors 2023, 23, 2323.

Bąk I., Barej-Kaczmarek E., Sulikowski P.: Impact of Information and Communication Technologies on the Tourism Sector. European Research Studies Journal 2022, Volume XXV, Issue 3, 595-606.

Sulikowski P., Kucznerowicz M., Bąk I., Romanowski A., Zdziebko T.: Online Store Aesthetics Impact Efficacy of Product Recommendations and Highlighting. Sensors 2022, Vol. 22, 9186.

Sulikowski P., Ryczko K., Bąk I., Yoo S., Zdziebko T.: Attempts to Attract Eyesight in E-Commerce May Have Negative Effects. Sensors 2022, Vol. 22, 8597.

Sulikowski P., Zdziebko T., Hussain O., Wilbik A.: Fuzzy Approach to Purchase Intent Modeling Based on User Tracking For E-commerce Recommenders. 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg, 2021, pp. 1-8.

Sulikowski P., Zdziebko T.: Churn factors identification from real-world data in the telecommunications industry: case study. Procedia Computer Science 2021, Vol. 192, pp. 4800-4809.

Czerniachowska K., Sachpazidu-Wójcicka K., Sulikowski P., Hernes M., Rot A.: Genetic Algorithm for the Retailers' Shelf Space Allocation Profit Maximization Problem. Applied Sciences 2021, Vol. 11(14), 6401.

Sulikowski P, Zdziebko T., Coussement K., Dyczkowski K., Kluza K., Sachpazidu-Wójcicka, K.: Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase. Sensors 2021, Vol. 21, 1381.

Sulikowski P, Zdziebko T. Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking. Applied Sciences 2021, Vol. 11(1), 56.

Sulikowski P., Zdziebko T.: Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing. Electronics 2020, Vol. 9(2), 266.

Sulikowski P.: Evaluation of Varying Visual Intensity and Position of a Recommendation in a Recommending Interface Towards Reducing Habituation and Improving Sales. In: Chao KM., Jiang L., Hussain O., Ma SP., Fei X. (eds.): Advances in E-Business Engineering for Ubiquitous Computing, Proceedings of the 16th International Conference on E-Business Engineering, ICEBE 2019, Shanghai, China, 11–13 October 2019- Lecture Notes on Data Engineering and Communications Technologies, Springer: Cham, Switzerland, 2020, Volume 41, pp. 208–218.

Sulikowski P., Zdziebko T., Turzyński D.: Modeling Online User Product Interest for Recommender Systems and Ergonomics Studies. Concurrency and Computation: Practice and Experience 2019, Vol. 31, Issue 22; e4301, pp. 1-9.

Zdziebko T., Sulikowski P., Turzyński D.: Concept of Customer Relationship Management in E-commerce [Koncepcja zarządzania relacjami z klientami w serwisach e-commerce]. Studies & Proceedings of Polish Association for Knowledge Management. 2018, Vol. 89, s. 97-104.

Sulikowski P., Turzyński D., Zdziebko T.: Knowledge Discovery in the Information Society [Zagadnienia eksploracji danych i odkrywania wiedzy w świetle literatury]. Studies & Proceedings of Polish Association for Knowledge Management. 2018, Vol. 89, s. 74-83.

Sulikowski P., Zdziebko T., Turzyński D., Kańtoch E.: Human-Website Interaction Monitoring in Recommender Systems. 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems – Belgrad, Serbia 3-5 September 2018. Procedia Computer Science (2018), Vol. 126, pp. 1587-1596.

Zdziebko T., Sulikowski P.: Classification Trees in E-commerce Customer Preferences Identification [Drzewa klasyfikacyjne w identyfikacji preferencji klientów e-handlu]. Studies & Proceedings of Polish Association for Knowledge Management. 2016, Vol. 78, pp. 98-107.

Zdziebko T., Sulikowski P.: Monitoring Human Website Interactions for Online Stores. In: Rocha A., Correia A., Costanzo S., Reis L. (eds), New Contributions in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 354. Springer, Cham, 2015, pp. 375-384 (WorldCIST Conference).

contact

Piotr Sulikowski, Team Leader
Customer Intelligence Research Group
West-Pomeranian University of Technology
ul. Żołnierska 49
71-210 Szczecin
Poland
intelligence@zut.edu.pl

Declaration of accessibility