Customer Intelligence
Research Group

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

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Research Highlights

ZUT+ Customer Intelligence Research Group aims to bridge the gap between computational technology, human-computer interaction 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 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, 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, including interactions with popular commercial websites.

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.

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

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

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 e-commerce development.

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

  2. TALK
  3. PLAN
  6. TEST


  • 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 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

photo of Piotr Sulikowski

Prof. Piotr Sulikowski

Team Leader

West-Pomeranian University of Technology

Photo of Tomasz Zdziebko

Tomasz Zdziebko, PhD

University of Szczecin

Photo of Konrad Ryczko

Konrad Ryczko

West-Pomeranian University of Technology

Photo of Michał Kucznerowicz

Michał Kucznerowicz

West-Pomeranian University of Technology


Cooperating Researchers and Students


Prof. Iwona Bąk

West Pomeranian University of Technology

Prof. Kristof Coussement

IESEG School of Management

Prof. Krzysztof Dyczkowski

Adam Mickiewicz University

Prof. Omar Hussein

University of New South Wales

Prof. Jarosław Jankowski

West Pomeranian University of Technology

Prof. Eliasz Kańtoch

AGH University of Science and Technology

Krzysztof Kluza, Ph.D.

AGH University of Science and Technology

Prof. Marek Postuła

Warsaw Medical University

Karina Sachpazidu-Wójcicka, Ph.D.

Wrocław University of Economics and Business

Prof. Anna Wilbik

Maastricht University

Students and PhD Students

Jakub Baran

West Pomeranian University of Technology

Łukasz Dobrowolski

West Pomeranian University of Technology

Piotr Jabłoński

Adam Mickiewicz University

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


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

  • logo The National Centre for Research and Development
  • logo of National Science Centre
  • logo of West Pomeranian University of Technology in Szczecin
  • logo of University of Szczecin
  • logo of Maastricht University
  • logo of UNSW Sydney
  • logo of IESEG School of Management
  • logo of LEM-CNRS 9221
  • logo of AGH University of Science and Technology in Krakow
  • logo of Pomeranian Medical University
  • logo of Medical University of Warsaw
  • logo of Adam Mickiewicz University in Poznan
  • logo of Wrocław University of Economics and Business


Selected publications:

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.
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.: Churn factors identification from real-world data in the telecommunications industry: case study. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems – Szczecin, Poland, 8-10 September 2021. Procedia Computer Science 2021, Vol. 192, pp. 4800-4809.
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).


Piotr Sulikowski
ZUT+ Customer Intelligence Research Group
West-Pomeranian University of Technology
ul. Żołnierska 49
71-210 Szczecin