MSc: Unit-economics for IT startups

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Unit-economics for IT startups: metrics-based decision making

  • Course name: Unit-economics for IT startups: metrics-based decision making
  • Code discipline:
  • Subject area: Technological Entrepreneurship

Short Description

Together with theoretical knowledge, the course has the practice of analyzing a project's unit economics using the key metrics which are necessary to understand the financial efficiency of the product. The course uses the actual cases of a developing business. Key concepts of the class Product metrics Unit economics and cohorts analysis Data-driven decision making Reporting for investors

Prerequisites

Prerequisite subjects

  • HSS309

Prerequisite topics

  • Metrics and indicators required to assess the effectiveness of a product;
  • Tools for collecting and analyzing product metrics and indicators;
  • Decision making methods based on the analysis of a product metrics and indicators;

Course Topics

Course Sections and Topics
Section Topics within the section
Product approach
  1. what is the product (idea, pain, customer, value);
  2. determination of the unit economics (unit, business model, types of models)
  3. formation of training cases;
Metrics, unit economics
  1. types of units, selection of the right ones, comparison;
  2. fixed and variable costs;
  3. basic metrics: CAC, ARPU, ARPPU, LTV, ROI, ROMI;
  4. metrics: why we collect them, what are the proc and cons;
Identifying metrics along the customer journey
  1. CJM, onboarding, retention;
  2. DAU, MAU;
  3. sales funnel, model AARRR;
  4. North star metric;
Cohorts
  1. how to divide into cohorts, what it is, why it may be important, how to deal with the flow of clients between cohorts, how to identify;
Reporting for investors
  1. budgeting, PnL and key financial reporting;
  2. what is the difference between a startup and a traditional business;
  3. the minimum required for regular analytics;
  4. regularity of reporting, focus from the report to the analytics process;
  5. PnL, Cashflow, budget, data room, dashboard;
  6. reports for investor and owner;

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

The main goal of this course is to teach students to understand what metrics to collect about a product, analyze them, and make decisions based on the metrics. Students will learn approaches to defining metrics and gain practice in applying this knowledge in practical classes as well as in their research projects.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

  • • Metrics, cohort metrics and indicators required to assess the effectiveness of a product;
  • • Tools for collecting and analyzing product metrics and indicators;
  • • Decision making methods based on the analysis of product metrics and indicators.

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

  • • Ability to measure and manage economic efficiency of a business;
  • • Creating data acquisition models for analyzing products metrics;
  • • Calculating unit economics;
  • • Ability to use a cohort analysis of user behavior;
  • • Ability to specify key and secondary indicators for the business and work with them in dynamics;
  • • Ability to make metric-based decisions.

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

  • • Creating the product metrics pyramid and communicate it’s meaning in the team;
  • • Designing data collection system and specifying the requirements for developers of the product;
  • • Researching traffic funnel – identifying weak points in it and the ways to eliminate them
  • • Identifying growth drivers - levers of influence over the product economic success of the product;
  • • Using the cohort analysis to deeper understand the results of changes in product and predicting future effects;
  • • Presenting the results unit-economics researches.

Grading

Course grading range

Grade Range Description of performance
A. Excellent 85-100 -
B. Good 75-85 -
C. Satisfactory 60-74 -
D. Fail 0-59 -

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Weekly student reports 30
Lab №1 20
Lab №2 20
Lab №3 30

Recommendations for students on how to succeed in the course

The student is recommended the following scheme of preparation for classes:
Students can be joined to the groups to prepare and complete the course assignment.
Teams from 2 to 5 students are allowed.
It is possible to work with a team that is not part of the study group.
It is highly recommended to treat the written assignments of the course as a tool to help students to make decisions about the development of their own business.
The university classes format shouldn’t influence the students' attitude to the value of the research in business.

Resources, literature and reference materials

Open access resources

  • Textbook: Бланк Стив Стартап: Настольная книга основателя / Бланк Стив, Дорф Боб. — Москва : Альпина Паблишер, 2019. — 623 c. — ISBN 978-5-9614-1983-2. — Текст : электронный // Электронно-библиотечная система IPR BOOKS : [сайт]. — URL: (дата обращения: 01.07.2021). — Режим доступа: для авторизир. Пользователей

Closed access resources

  • Textbook: McClure D. Startup metrics for pirates. Slideshare. net. 2007 Aug.
  • Textbook:Maurya A. Scaling lean: Mastering the key metrics for startup growth. Penguin; 2016 Jun 14.
  • Textbook: Croll A, Yoskovitz B. Lean analytics: Use data to build a better startup faster. " O'Reilly Media, Inc."; 2013 Apr 15.
  • Textbook: Masters B, Thiel P. Zero to one: notes on start ups, or how to build the future. Random House; 2014 Sep 18.

Software and tools used within the course

  • MSTeams License,
  • Moodle,
  • Excel or other table editor.

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Teaching and Learning Methods within each section
Teaching Techniques Section 1 Section 2 Section 3 Section 4 Section 5
Problem-based learning (students learn by solving open-ended problems without a strictly-defined solution) 1 1 1 1 1
Project-based learning (students work on a project) 1 1 1 1 1
Differentiated learning (provide tasks and activities at several levels of difficulty to fit students needs and level) 1 1 1 1 1
Contextual learning (activities and tasks are connected to the real world to make it easier for students to relate to them); 1 1 1 1 1
Business game (learn by playing a game that incorporates the principles of the material covered within the course). 1 1 1 1 1
inquiry-based learning 1 1 1 1 1
Just-in-time teaching 1 1 1 1 1
Task-based learning 1 1 1 1 1
Activities within each section
Learning Activities Section 1 Section 2 Section 3 Section 4 Section 5
Lectures 1 1 1 1 0
Interactive Lectures 1 1 1 1 0
Modeling 1 1 1 1 0
Cases studies 1 1 1 1 0
Individual Projects 1 1 1 1 0
Group projects 1 1 1 1 0
Quizzes (written or computer based) 1 1 1 1 0
Peer Review 1 1 1 1 0
Discussions 1 1 1 1 0
Presentations by students 1 1 1 1 0
Written reports 1 1 1 1 0
Oral Reports 1 1 1 1 0
Lab exercises 0 1 1 1 0
Experiments 0 1 1 1 0
Simulations and role-plays 0 0 1 1 0

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type Content Is Graded?
Discussions 1. Unit economics - what it is and how to calculate it;
2. Basic metrics - CAC/ARPU/ARPPU/LTV;
3. Single customer economy, AARRR model;
4. ROI/ROMI;		 1

Section 2

Activity Type Content Is Graded?
Discussions • What is cohorts and types of the cohorts;
• Purposes of cohort analysis;
• Time-based cohorts;		 1

Section 3

Activity Type Content Is Graded?
Homework and group projects Typical questions for ongoing performance evaluation within this section
Select an appropriate methodology and data collection method for product analytics of the research project or simulated product;
Typical questions for seminar classes (labs) within this section
1. Automatic services for collecting product metrics (online analytics tools Google and Yandex);
2. Manual data collection techniques (Python, Google dashboards, power BI/Tableau);		 1

Section 4

Activity Type Content Is Graded?
Homework and group projects Typical questions for ongoing performance evaluation within this section
1. Forecast the product evolution and identify growth drivers;
2. Make a few hypotheses for the product improvement, fix the changes in the forecasting of product metrics.

Typical questions for seminar classes (labs) within this section
1. Behavioral cohorts;
2. DAU/MAU, stickyness;
3. North star metric - how to work with it?
4. Forecasting;
5. Budgeting;
6. Product planning and prioritizing;		 1

Section 5

Final assessment

Section 1

  1. 1. What type of metrics does exist? Which of them are the most important and why?
  2. 2. How to calculate marketing cost effectiveness in the product metrics analysis?
  3. 3. Calculation of the unite economics for the student's choice product.

Section 2

  1. 1, What are cohorts? What types of cohorts do you know?
  2. 2. What are the purposes of cohort analysis?

Section 3

  1. 1. Describe the analytic model of the data collection method for product analytics of the research project or simulated product;
  2. 2. Which data should be collected and for which metrics are going to be used?

Section 4

Section 5


The retake exam

Section 1

  1. 1. What type of metrics does exist? Which of them are the most important and why?
  2. 2. How to calculate marketing cost effectiveness in the product metrics analysis?
  3. 3. Calculation of the unite economics for the student's choice product.

Section 2

  1. 1. What is the main idea of the cohorts? When it is needed to make cohort analysis?
  2. 2. What are the purposes of cohort analysis?

Section 3

  1. 1. Describe the analytic model of the data collection method for product analytics of the research project or simulated product;
  2. 2. Which data should be collected and for which metrics are going to be used?

Section 4

Section 5

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