Difference between revisions of "MSc: Unit-economics For IT startups"

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= Unit-economics for IT startups: metrics-based decision making =
  
= Unit-economics for IT startups: metrics-based decision making =
  +
* '''Course name''': Unit-economics for IT startups: metrics-based decision making
  +
* '''Code discipline''':
  +
* '''Subject area''':
   
  +
== Short Description ==
−
* <span>'''Course name:'''</span> Unit-economics for IT startups: metrics-based decision making
  
  +
This course covers the following concepts: Product metrics; Data-driven decision making; Unit economics and cohorts analysis; Metrics collection automation tools; Product’s backlog planning.
−
* <span>'''Course number:'''</span>
 
   
 
== Prerequisites ==
  
== Prerequisites ==
−
== Course characteristics ==
  
   
−
=== Shor description ===
 +
=== Prerequisite subjects ===
−
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 ===
  
   
  +
=== Prerequisite topics ===
−
* Product metrics
  
−
* Data-driven decision making
  
−
* Unit economics and cohorts analysis
  
−
* Metrics collection automation tools
  
−
* Product’s backlog planning
  
   
   
  +
== Course Topics ==
−
=== What is the purpose of this course? ===
  
  +
{| class="wikitable"
  +
|+ Course Sections and Topics
  +
|-
  +
! Section !! Topics within the section
  +
|-
  +
| Metrics, unit economics, cohort analysis ||
  +
# 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;
  +
|-
  +
| Metrics collection automation tools ||
  +
# Automatic services for collecting product metrics (online analytics tools Google and Yandex)
  +
# Manual data collection techniques (Python, Google dashboards, power BI/Tableau)
  +
|-
  +
| Hypotheses, management reports and forecasting ||
  +
# Behavioral cohorts;
  +
# DAU/MAU, stickyness;
  +
# North star metric - how to work with it?
  +
# Forecasting;
  +
# Budgeting;
  +
# Product planning and prioritizing;
  +
|}
  +
== Intended Learning Outcomes (ILOs) ==
   
  +
=== What is the main 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.
  
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 ===
 +
=== ILOs defined at three levels ===
   
−
==== What should a student remember at the end of the course? ====
 +
==== Level 1: What concepts should a student know/remember/explain? ====
  +
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;
   
−
By the end of the course, the students should be able to
 +
==== 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? ====
−
*Metrics and indicators required to assess the effectiveness of a product;
  
  +
By the end of the course, the students should be able to ...
−
*Tools for collecting and analyzing product metrics and indicators;
  
−
*Decision making methods based on the analysis of a product metrics and indicators;
 +
* 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;
  +
== Grading ==
   
  +
=== Course grading range ===
−  
  +
{| class="wikitable"
−
==== 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:
  
−  
−
<div id="tab:OSCourseGrading">
  
−  
−
{| style="border-spacing: 2px; border: 1px solid darkgray;"
  
−
|+ '''Course grade breakdown'''
  
−
!align="center"| '''Component'''
  
−
! '''Points'''
  
 
|-
  
|-
  +
! Grade !! Range !! Description of performance
−
| Weekly student reports
  
−
|align="center"| 20
  
 
|-
  
|-
  +
| A. Excellent || 85-100 || -
−
| Lab №1
  
−
|align="center"| 10
  
 
|-
  
|-
  +
| B. Good || 75-85 || -
−
| Team brainstorm
  
−
|align="center"| 20
  
 
|-
  
|-
  +
| C. Satisfactory || 60-74 || -
−
| Lab №2
  
−
|align="center"| 20
  
 
|-
  
|-
  +
| D. Poor || 0-59 || -
−
| Final presentation
  
−
|align="center"| 30
  
 
|}
  
|}
   
  +
=== Course activities and grading breakdown ===
−  
  +
{| class="wikitable"
−
=== Grades range ===
  
  +
|+
−  
  +
|-
−
<div id="tab:OSCourseGradingRange">
  
  +
! Activity Type !! Percentage of the overall course grade
−  
  +
|-
−
{| style="border-spacing: 2px; border: 1px solid darkgray;"
  
  +
| Weekly student reports || 20
−
|+ Course grading range
  
 
|-
  
|-
  +
| Lab №1 || 10
−
| A. Excellent
  
−
|align="center"| 85-100
  
 
|-
  
|-
  +
| Team brainstorm || 20
−
| B. Good
  
−
|align="center"| 75-85
  
 
|-
  
|-
  +
| Lab №2 || 20
−
| C. Satisfactory
  
−
|align="center"| 60-74
  
 
|-
  
|-
  +
| Final presentation || 30
−
| D. Poor
  
−
|align="center"| 0-59
  
 
|}
  
|}
   
  +
=== Recommendations for students on how to succeed in the course ===
−
If necessary, please indicate freely your course’s grading features.
  
   
−
=== Resources and reference material ===
  
   
  +
== Resources, literature and reference materials ==
−
* '''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 ===
 +
=== Open access resources ===
  +
* Textbook: McClure D. Startup metrics for pirates. Slideshare. net. 2007 Aug.
−
The student is recommended the following scheme of preparation for classes:
  
  +
* Textbook:Maurya A. Scaling lean: Mastering the key metrics for startup growth. Penguin; 2016 Jun 14.
−
1. Students can be joined to the groups to prepare and complete the course assignment.
 
  +
* Textbook: Croll A, Yoskovitz B. Lean analytics: Use data to build a better startup faster. " O'Reilly Media, Inc."; 2013 Apr 15.
−
2. Teams from 2 to 5 students are allowed.
 
  +
* 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
−
3. It is possible to work with a team that is not part of the study group.
  
  +
* Textbook: Masters B, Thiel P. Zero to one: notes on start ups, or how to build the future. Random House; 2014 Sep 18.
−
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 ==
 +
=== Closed access resources ===
   
−
The main sections of the course and approximate hour distribution between them is
  
−
as follows:
  
   
  +
=== Software and tools used within the course ===
−
<div id="tab:OSCourseSections">
  
  +
  +
= Teaching Methodology: Methods, techniques, & activities =
   
  +
== Activities and Teaching Methods ==
−
{| style="border-spacing: 2px; border: 1px solid darkgray;"
  
  +
{| class="wikitable"
−
|+ Course Sections
  
  +
|+ Activities within each section
−
!align="center"| '''Section'''
  
−
! '''Section Title'''
  
−
!align="center"| '''Teaching Hours'''
  
 
|-
  
|-
  +
! Learning Activities !! Section 1 !! Section 2 !! Section 3
−
|align="center"| 1
  
−
| Metrics, unit economics, cohort analysis
  
−
|align="center"| 10
  
 
|-
  
|-
  +
| Homework and group projects || 1 || 1 || 1
−
|align="center"| 2
  
−
| Metrics collection automation tools
  
−
|align="center"| 10
  
 
|-
  
|-
  +
| Discussions || 1 || 0 || 0
−
|align="center"| 3
  
  +
|}
−
| Hypotheses, management reports and forecasting
  
  +
== Formative Assessment and Course Activities ==
−
|align="center"| 10
  
−
|}
  
−  
−  
−
</div>
  
−
=== 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? ====
 
   
  +
=== Ongoing performance assessment ===
−
<div id="tab:OSSectionEval1">
  
   
  +
==== Section 1 ====
−
{| style="border-spacing: 2px; border: 1px solid darkgray;"
  
  +
{| class="wikitable"
−
|''' '''
  
  +
|+
−
! '''Yes/No'''
  
 
|-
  
|-
  +
! Activity Type !! Content !! Is Graded?
−
| Development of individual parts of software product code
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Unit economics - what it is and how to calculate it; || 1
−
| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Basic metrics - CAC/ARPU/ARPPU/LTV; || 1
−
| Midterm evaluation
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Single customer economy, AARRR model; || 1
−
| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || ROI/ROMI; || 1
−
| Reports
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Cohorts and cohorts analysis; || 1
−
| Essays
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Unit economics - what it is and how to calculate it; || 0
−
| Oral polls
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Basic metrics - CAC/ARPU/ARPPU/LTV; || 0
−
| Discussions
  
−
|align="center"| 1
  
−
|}
  
−  
−  
−
</div>
  
−  
−
==== Typical questions for ongoing performance evaluation within this section ====
 
−  
−
#Unit economics - what it is and how to calculate it;
  
−
#Basic metrics - CAC/ARPU/ARPPU/LTV;
  
−
#Single customer economy, AARRR model;
  
−
#ROI/ROMI;
  
−
#Cohorts and cohorts analysis;
  
−  
−  
−
==== Typical questions for seminar classes (labs) within this section ====
  
−
#Unit economics - what it is and how to calculate it;
  
−
#Basic metrics - CAC/ARPU/ARPPU/LTV;
  
−
#Single customer economy, AARRR model;
  
−
#ROI/ROMI;
  
−
#Cohorts and cohorts analysis;
  
−  
−
==== Test questions for final assessment in this section ====
 
−
#What type of metrics does exist? Which of them are the most important and why?
 
−
#How to calculate marketing cost effectiveness in the product metrics analysis?
  
−
#What is the main idea of the cohorts? When it is needed to make cohort analysis?
 
−
#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? ====
 
−  
−
<div id="tab:OSSectionEval2">
  
−  
−
{| style="border-spacing: 2px; border: 1px solid darkgray;"
  
−
|''' '''
  
−
! '''Yes/No'''
  
 
|-
  
|-
  +
| Question || Single customer economy, AARRR model; || 0
−
| Development of individual parts of software product code
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || ROI/ROMI; || 0
−
| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Cohorts and cohorts analysis; || 0
−
| Midterm evaluation
  
  +
|}
−
|align="center"| 0
  
  +
==== Section 2 ====
  +
{| class="wikitable"
  +
|+
 
|-
  
|-
  +
! Activity Type !! Content !! Is Graded?
−
| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Select an appropriate methodology and data collection method for product analytics of the research project or simulated product; || 1
−
| Reports
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Automatic services for collecting product metrics (online analytics tools Google and Yandex); || 0
−
| Essays
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Manual data collection techniques (Python, Google dashboards, power BI/Tableau); || 0
−
| Oral polls
  
  +
|}
−
|align="center"| 0
  
  +
==== Section 3 ====
  +
{| class="wikitable"
  +
|+
 
|-
  
|-
  +
! Activity Type !! Content !! Is Graded?
−
| Discussions
  
−
|align="center"| 0
  
−
|}
  
−  
−  
−
</div>
  
−  
−  
−
==== 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 ====
  
−
#Automatic services for collecting product metrics (online analytics tools Google and Yandex);
  
−
#Manual data collection techniques (Python, Google dashboards, power BI/Tableau);
  
−  
−  
−
==== Test questions for final assessment in this section ====
  
−  
−
#Describe the analytic model of the data collection method for product analytics of the research project or simulated product;
  
−
#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? ====
 
−  
−
<div id="tab:OSSectionEval2">
  
−  
−
{| style="border-spacing: 2px; border: 1px solid darkgray;"
  
−
|''' '''
  
−
! '''Yes/No'''
  
 
|-
  
|-
  +
| Question || Forecast the product evolution and identify growth drivers; || 1
−
| Development of individual parts of software product code
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Make a few hypotheses for the product improvement, fix the changes in the forecasting of product metrics. || 1
−
| Homework and group projects
  
−
|align="center"| 1
  
 
|-
  
|-
  +
| Question || Behavioral cohorts; || 0
−
| Midterm evaluation
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || DAU/MAU, stickyness; || 0
−
| Testing (written or computer based)
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || North star metric - how to work with it? || 0
−
| Reports
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Forecasting; || 0
−
| Essays
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Budgeting; || 0
−
| Oral polls
  
−
|align="center"| 0
  
 
|-
  
|-
  +
| Question || Product planning and prioritizing; || 0
−
| Discussions
  
  +
|}
−
|align="center"| 0
  
  +
=== Final assessment ===
−
|}
  
  +
'''Section 1'''
−  
  +
# What type of metrics does exist? Which of them are the most important and why?
−  
  +
# How to calculate marketing cost effectiveness in the product metrics analysis?
−
</div>
  
  +
# What is the main idea of the cohorts? When it is needed to make cohort analysis?
−  
  +
# Calculation of the unite economics for the student's choice product;
−  
  +
'''Section 2'''
−
==== Typical questions for ongoing performance evaluation within this section ====
 
  +
# Describe the analytic model of the data collection method for product analytics of the research project or simulated product;
−  
  +
# Which data should be collected and for which metrics are going to be used?
−
#Forecast the product evolution and identify growth drivers;
  
  +
'''Section 3'''
−
#Make a few hypotheses for the product improvement, fix the changes in the forecasting of product metrics.
  
  +
# Prepare the proposals and a rationale for a list of proposed product solutions in order of priority that are recommended for implementation.
−  
−  
−
==== Typical questions for seminar classes (labs) within this section ====
  
−
#Behavioral cohorts;
  
−
#DAU/MAU, stickyness;
  
−
#North star metric - how to work with it?
  
−
#Forecasting;
  
−
#Budgeting;
  
−
#Product planning and prioritizing;
  
−  
   
  +
=== The retake exam ===
  +
'''Section 1'''
   
  +
'''Section 2'''
−
==== Test questions for final assessment in this section ====
  
   
  +
'''Section 3'''
−
#Prepare the proposals and a rationale for a list of proposed product solutions in order of priority that are recommended for implementation.
  

Latest revision as of 12:06, 29 August 2022

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

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

Short Description

This course covers the following concepts: Product metrics; Data-driven decision making; Unit economics and cohorts analysis; Metrics collection automation tools; Product’s backlog planning.

Prerequisites

Prerequisite subjects

Prerequisite topics

Course Topics

Course Sections and Topics
Section Topics within the section
Metrics, unit economics, cohort analysis
  1. Unit economics - what it is and how to calculate it;
  2. Basic metrics - CAC/ARPU/ARPPU/LTV;
  3. Customer economics, AARRR model;
  4. ROI/ROMI;
  5. Cohorts and cohorts analysis;
Metrics collection automation tools
  1. Automatic services for collecting product metrics (online analytics tools Google and Yandex)
  2. Manual data collection techniques (Python, Google dashboards, power BI/Tableau)
Hypotheses, management reports and forecasting
  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;

Intended Learning Outcomes (ILOs)

What is the main 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.

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 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;

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 ...

  • 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;

Grading

Course grading range

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

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Weekly student reports 20
Lab №1 10
Team brainstorm 20
Lab №2 20
Final presentation 30

Recommendations for students on how to succeed in the course

Resources, literature and reference materials

Open 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: 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.

Closed access resources

Software and tools used within the course

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Activities within each section
Learning Activities Section 1 Section 2 Section 3
Homework and group projects 1 1 1
Discussions 1 0 0

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type Content Is Graded?
Question Unit economics - what it is and how to calculate it; 1
Question Basic metrics - CAC/ARPU/ARPPU/LTV; 1
Question Single customer economy, AARRR model; 1
Question ROI/ROMI; 1
Question Cohorts and cohorts analysis; 1
Question Unit economics - what it is and how to calculate it; 0
Question Basic metrics - CAC/ARPU/ARPPU/LTV; 0
Question Single customer economy, AARRR model; 0
Question ROI/ROMI; 0
Question Cohorts and cohorts analysis; 0

Section 2

Activity Type Content Is Graded?
Question Select an appropriate methodology and data collection method for product analytics of the research project or simulated product; 1
Question Automatic services for collecting product metrics (online analytics tools Google and Yandex); 0
Question Manual data collection techniques (Python, Google dashboards, power BI/Tableau); 0

Section 3

Activity Type Content Is Graded?
Question Forecast the product evolution and identify growth drivers; 1
Question Make a few hypotheses for the product improvement, fix the changes in the forecasting of product metrics. 1
Question Behavioral cohorts; 0
Question DAU/MAU, stickyness; 0
Question North star metric - how to work with it? 0
Question Forecasting; 0
Question Budgeting; 0
Question Product planning and prioritizing; 0

Final assessment

Section 1

  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

  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

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

The retake exam

Section 1

Section 2

Section 3

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