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Магистратура 2022/2023

Прикладные исследования в области Product management

Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус: Курс обязательный (Управление развитием компании)
Направление: 38.04.02. Менеджмент
Когда читается: 2-й курс, 2, 3 модуль
Формат изучения: без онлайн-курса
Охват аудитории: для своего кампуса
Преподаватели: Бутрюмова Надежда Николаевна, Новикова Алиса Андреевна, Папишвили Анастасия Зурабовна
Прогр. обучения: Управление развитием компании и бизнес-аналитика
Язык: английский
Кредиты: 6
Контактные часы: 38

Course Syllabus


The discipline "Applied research in Product management" consists of two parts: 1. active work on writing a master's thesis. Students will report on the results of the consistent implementation of tasks of the master's thesis. 2. Guest lectures, excursions and master classes from invited practitioners on the topics of students' master's theses.
Learning Objectives

Learning Objectives

  • Build skills for applied research in product management
Expected Learning Outcomes

Expected Learning Outcomes

  • Explains the principles of a human-centered approach to product development
  • Describes the place of user/client research in product management
  • Organizes a UX-research
  • Chooses suitable UX-research method
  • Asks the right questions in an interview
  • Composes an interview guide
  • Analyzes the data obtained from the interview.
  • Explains what tasks CJM is used for
  • Plots a CJM
  • Explains most common product metrics
  • Matches metrics to objectives relevant to the task
  • Develops a system of standardized indicators
  • Describes approaches to hypothesis prioritization
  • Explains when and why to use AA tests, A/B tests
  • Applies basic segmentation methods
Course Contents

Course Contents

  • Introduction to UX-research
  • Types of UX research
  • Interviews as a research method
  • CJM (Customer Journey Map)
  • What are metrics and why are they needed?
  • Hypothesis testing
  • Consumer segmentation
Assessment Elements

Assessment Elements

  • non-blocking Inclass activity
  • non-blocking Project
Interim Assessment

Interim Assessment

  • 2022/2023 3rd module
    0.3 * Inclass activity + 0.4 * Project


Recommended Core Bibliography

  • Croll, A., & Yoskovitz, B. (2013). Lean Analytics : Use Data to Build a Better Startup Faster. O’Reilly Media.
  • Dolnicar, S., Grün, B., & Leisch, F. (2018). Market segmentation analysis: Understanding it, doing it, and making it useful. Germany, Europe: Singapore: Springer Open. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3880CA9
  • Fritz, M., & Berger, P. D. (2015). Improving the User Experience Through Practical Data Analytics : Gain Meaningful Insight and Increase Your Bottom Line. Amsterdam: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=961223
  • MCKNIGHT, C. (2017). Customer Journey Maps: A Path to Innovation and Increased Profits. EContent, 40(6), 20.
  • Seidman, I. (2019). Interviewing As Qualitative Research : A Guide for Researchers in Education and the Social Sciences (Vol. Fifth edition). New York, NY: Teachers College Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2168597
  • Tullis, T., & Albert, B. (2013). Measuring the User Experience : Collecting, Analyzing, and Presenting Usability Metrics. Amsterdam: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=486121

Recommended Additional Bibliography

  • A. Bogner, B. Littig, & W. Menz. (2009). Interviewing Experts. Palgrave Macmillan.
  • Beasley, M. (2013). Practical Web Analytics for User Experience : How Analytics Can Help You Understand Your Users. Amsterdam: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=485273
  • Brown, D. (2013). Agile User Experience Design : A Practitioner’s Guide to Making It Work. Waltham, Mass: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=485894
  • Burk, S. (2006). A Better Statistical Method for A/B Testing in Marketing Campaigns. Marketing Bulletin, 17, 1.
  • Eric Benjamin Seufert. (2014). Freemium Economics : Leveraging Analytics and User Segmentation to Drive Revenue. Morgan Kaufmann.
  • Gothelf, J., & Seiden, J. (2013). Lean UX : Applying Lean Principles to Improve User Experience: Vol. 1st ed. O’Reilly Media.
  • Handbook of interview research context & method ed. Jaber F. Gubrium; James A. Holstein. (2002).
  • Knaflic, C. N. (2015). Storytelling with Data : A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1079665
  • Kohavi, R., & Thomke, S. (2017). The Surprising Power of Online Experiments: Getting the Most out of A/B and Other Controlled Tests. Harvard Business Review, 95(5), 74–82.
  • Kuniavsky, M., Goodman, E., & Moed, A. (2012). Observing the User Experience : A Practitioner’s Guide to User Research (Vol. 2nd ed). Burlington: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=472263
  • Malthouse, E. C., & SAS Institute. (2013). Segmentation and Lifetime Value Models Using SAS. Cary, N.C.: SAS Institute. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=607170
  • Mario D’Arco, Letizia Lo Presti, Vittoria Marino, & Riccardo Resciniti. (2019). Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework. https://doi.org/10.21511/im.15(4).2019.09
  • Moon, H., Han, S. H., Chun, J., & Hong, S. W. (2016). A Design Process for a Customer Journey Map: A Case Study on Mobile Services. Human Factors & Ergonomics in Manufacturing & Service Industries, 26(4), 501–514. https://doi.org/10.1002/hfm.20673
  • Silver, N. (2012). The Signal and the Noise : Why So Many Predictions Fail-but Some Don’t. New York: Penguin Books. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1122593
  • Steve Portigal. (2013). Interviewing Users : How to Uncover Compelling Insights. Rosenfeld Media.
  • Tsiptsis, K., & Chorianopoulos, A. (2009). Data Mining Techniques in CRM : Inside Customer Segmentation. Chichester, West Sussex, U.K.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=310209
  • Turner, P. (2017). A Psychology of User Experience : Involvement, Affect and Aesthetics. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1651402
  • Weinstein, A. (2004). Handbook of Market Segmentation : Strategic Targeting for Business and Technology Firms, Third Edition: Vol. 3rd ed. Routledge.
  • Yankelovich, D., & Meer, D. (2006). Rediscovering Market Segmentation. Harvard Business Review, 84(2), 122–131. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=19406199