Startup Engineering

Complementary elective course in “Business & Management”

andreas baetz, pixabay, cc0


  • 2 ECTS

Target Audience

  • Master students with “Business & Management” courses in their curriculum
  • Students with a strong interest and motivation in acquiring the skills required to build new technology startups.



Startups are temporary, team-based organizations, which can form independently, but also within established companies. They pursue one central objective: taking a business idea to market by finding and designing a repeatable and scalable business model. This entrepreneurial process involves gathering and combining resources that you do not (yet) possess and dealing with high uncertainty about what combinations of resources actually generate value. This course module is designed to introduce students to a systematic Startup Engineering approach to master the process of taking a business idea to market in light of resource contraints and uncertainty.

Startup Engineering takes an iterative approach, in that it favors variety and alternatives over one detailed, linear five-year business plan to reach steady state operations. From a problem solving and systems thinking perspective, Startup Engineers create different possible versions of a new venture and alternative hypotheses about value creation for customers and value capture vis-à-vis competitors. To test critical hypotheses early on, Startup Engineers engage in an evidence-based, experimental trial-and-error learning process that measures real progress.

The workflow in this course module is comprised of three elements:
  1. (Flipped) classroom: learning about and discussing concepts and tools currently prevailing in theory and practice of modern technology entrepreneurship.
  2. Problem-based learning: deepen an understanding of the concepts and tools by seeing them applied and applying them to real company cases.
  3. Experiential learning: applying the concepts and tools in teams to an own new startup project.

Students are invited to apply to this course module already with a startup idea and/ or team, but this is not a requirement. We will form teams and ideas in the beginning of the course.


Upon completion of this course module, students will be able to:

  • Apply a modern innovation toolkit relevant in both the startup & corporate world
  • Analyze business opportunities in terms of its constituent elements
  • Design new business models by gathering and combining relevant ideas, facts and information
  • Evaluate business opportunities and derive judgment about next steps & decisions

This course module can prepare students for the following career paths:

  • Startup founder
  • Early employee in a startup
  • New business development in established corporations
  • Venture capital investing

Grading/ Evaluation

  • Startup Engineering: Project (team deliverable): Development and pitch of an own startup project


Time & Location

  • Monday, May 9th, 09.00 - 17.00, Building Startup Port, Harburger Schloßstrasse 6-12, Room Hertz Hörsaal
  • Tuesday, May 10th, 09.00 - 17.00, Building Startup Port, Harburger Schloßstrasse 6-12, Room Hertz Hörsaal
  • Friday, May 13th, 09.00 - 17.00, Building Startup Port, Harburger Schloßstrasse 6-12, Room Hertz Hörsaal

Course Notes & Materials

Access to course notes & materials here.

Preliminary Schedule

1May 9thIntroduction
2May 9thCustomer Segmentation
3May 9thTechnology & Product Development
4May 9thCompetition & Market Analysis
5May 10thPlatform Business Models
6May 10thRevenue Models & Pricing
7May 10thLean Startup & Experimentation
8May 10thMarketing for Startup Growth
9May 13thSales for Startup Growth
10May 13thOrganizing for Startup Growth
11May 13thFinancial Analysis
12May 13thFinal Startup Pitches
13May 27thSubmission of Final Pitch Deck
Prof. Dr. Christoph Ihl
Professor & Head of Institute

My research interests include cultural entrepreneurship, social networks and natural language processing.

Dr. Hannes W. Lampe
Research Associate & Post-Doc

My research interests lie at the intersection of Technologie, Innovation and Entrepreneurhip (TIE) and Public and Nonprofit Management (PNP).

Oliver Mork
Research Assistant & Doctoral Student

My research interests lie at the intersection of Econometrics & Machine Learning.