MSc: Unit-economics For IT startups

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

• Course name: Unit-economics for IT startups: metrics-based decision making
• Course number:

Course characteristics

Shor description

Together with theoretical knowledge course has the practice of analyzing project's unit economics using the key metrics which are necessary to understand the financial efficiency of the product. As a basis, the course uses the cases of a successfully developing business

Key concepts of the class

• Product metrics
• Data-driven decision making
• Unit economics and cohorts analysis
• Metrics collection automation tools
• Product’s backlog planning

What is the purpose of this course?

The main purpose of this course is to teach students to collect the product data, analyze it and make metrics-based decisions. Students will learn the tools for gathering and analyzing data and get the practice of applying this knowledge in simulated practical classes as well as in their research projects.

Course Objectives Based on Bloom’s Taxonomy

What should a student remember at the end of the course?

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

• 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;

What should a student be able to understand at the end of the course?

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;

What should a student be able to apply at the end of the course?

By the end of the course, the students should be able to apply:

• Calculating and analyzing product metrics;
• Identifying growth drivers - levers of influence over the product economic success of the product;
• Defining a business unit for calculating indicators and a sales funnel;
• Researching the client's traffic funnel, identifying weak points in it and the ways to eliminate them;
• Identifying business bottlenecks based on key product metrics;
• Presenting the results of business research;

Course evaluation

The course has two major forms of evaluations:

Course grade breakdown

Component	Points
Weekly student reports	20
Lab №1	10
Team brainstorm	20
Lab №2	20
Final presentation	30

Grades range

Course grading range

A. Excellent	85-100
B. Good	75-85
C. Satisfactory	60-74
D. Poor	0-59

If necessary, please indicate freely your course’s grading features.

Resources and reference material

• 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: 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: http://www.iprbookshop.ru/82518.html (дата обращения: 01.07.2021). — Режим доступа: для авторизир. Пользователей
• Textbook: Masters B, Thiel P. Zero to one: notes on start ups, or how to build the future. Random House; 2014 Sep 18.

Methodological guidelines

The student is recommended the following scheme of preparation for classes: 1. Students can be joined to the groups to prepare and complete the course assignment. 2. Teams from 2 to 5 students are allowed. 3. It is possible to work with a team that is not part of the study group. 4. 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 own business. 5. The university classes format shouldn’t influence on the students attitude to the value of the research in business.

Course Sections

The main sections of the course and approximate hour distribution between them is as follows:

Course Sections

Section	Section Title	Teaching Hours
1	Metrics, unit economics, cohort analysis	10
2	Metrics collection automation tools	10
3	Hypotheses, management reports and forecasting	10

Section 1

Section title: Metrics, unit economics, cohort analysis

Topics covered in this section

• Unit economics - what it is and how to calculate it;
• Basic metrics - CAC/ARPU/ARPPU/LTV;
• Customer economics, AARRR model;
• ROI/ROMI;
• Cohorts and cohorts analysis;

What forms of evaluation were used to test students’ performance in this section?

	Yes/No
Development of individual parts of software product code	0
Homework and group projects	1
Midterm evaluation	0
Testing (written or computer based)	0
Reports	0
Essays	0
Oral polls	0
Discussions	1

Typical questions for ongoing performance evaluation within this section

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;
5. Cohorts and cohorts analysis;

Typical questions for seminar classes (labs) within this section

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;
5. Cohorts and cohorts analysis;

Test questions for final assessment in this section

1. What type of metrics does exist? Which of them are the most important and why?
2. How to calculate marketing cost effectiveness in the product metrics analysis?
3. What is the main idea of the cohorts? When it is needed to make cohort analysis?
4. Calculation of the unite economics for the student's choice product;

Section 2

Section title: Metrics collection automation tools

Topics covered in this section

• Automatic services for collecting product metrics (online analytics tools Google and Yandex)
• Manual data collection techniques (Python, Google dashboards, power BI/Tableau)

What forms of evaluation were used to test students’ performance in this section?

	Yes/No
Development of individual parts of software product code	0
Homework and group projects	1
Midterm evaluation	0
Testing (written or computer based)	0
Reports	0
Essays	0
Oral polls	0
Discussions	0

Typical questions for ongoing performance evaluation within this section

1. 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);

Test questions for final assessment in this section

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

Section 3

Hypotheses, management reports and forecasting

Topics covered in this section

Data-driven hypotheses

• Behavioral cohorts;
• DAU/MAU, stickyness;
• North star metric - how to work with it?

Forecasting and budgeting

• Forecasting;
• Budgeting;
• Product planning and prioritizing;

What forms of evaluation were used to test students’ performance in this section?

	Yes/No
Development of individual parts of software product code	0
Homework and group projects	1
Midterm evaluation	0
Testing (written or computer based)	0
Reports	0
Essays	0
Oral polls	0
Discussions	0

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;

Test questions for final assessment in this section

1. Prepare the proposals and a rationale for a list of proposed product solutions in order of priority that are recommended for implementation.