This course is designed for:

• Professionals in Product, Engineering, and Data roles who already have an understanding of Product Analytics. Ideal candidates are those who regularly use data and feel comfortable conducting analyses.

• Startup Founders and Entrepreneurs looking to deepen their understanding of how to measure and optimize the effectiveness of their products using advanced analytical tools and techniques.

To enroll in the Advanced Course on Product Analytics, you should be proficient in analyzing data sets, familiar with pivot tables, formulas, and capable of deriving insights from data. If you are not yet comfortable with these skills or they are not part of your daily routine, it is recommended that you first complete the Fundamentals course.

If you are unsure which course is right for you, feel free to contact me. I am here to help you choose the learning path that best suits your needs and goals.

This course lasts six weeks and follows a flipped classroom methodology, where students are expected to lead their own learning. The course consists of four modules within these six weeks. You'll learn at your own pace through reading lessons, watching videos, and completing multiple analytical exercises. Additionally, you'll collaboratively solve an analytical case study involving product and business data in a spreadsheet before each Live Session. There will be four Office Hour sessions available for you to attend and get your doubts resolved.

In the Live Sessions, we'll explore different real-world scenarios of advanced analytics presented by several senior professionals. These sessions will include live exercises, addressing your questions, and jointly solving the weekly product and business analytical case.

The dates for the live sessions are as follows:

• Wednesday, April 23 from 9 to 10 CET – Introduction

• Wednesday, April 30 from 9 to 12 CET – Module 1

• Wednesday, May 14 from 9 to 12 CET – Module 2

• Wednesday, May 28 from 9 to 12 CET – Module 3

• Wednesday, June 4 from 9 to 12 CET – Module 4

Expect to dedicate 6 to 10 hours per week outside of the live sessions for content learning and exercise completion. It's advised not to enroll in the course if you cannot commit this amount of time. Mastering analytics requires hours of practice.

All written content is in English. Videos are in Spanish. The live sessions will be in Spanish or English, depending on the invited professional. If you prefer to take the course entirely in English, please let me know, as I am planning to organize an English edition soon.

We will have a group where you can ask questions during and after the course. This allows you to resolve any doubts as you begin implementing the learnings in your company. You will have access to the course content for at least one year.

Module 1: Product Analytics and Data Visualization Best Practices

• Self-paced

  • Establishing a Data-Driven Culture: Introduction, Components, and Best Practices

  • The Role of Product Analytics across Stages: Discovery, Prioritization, Research, Shaping, Design, Launch, Iteration, and Retirement

  • Exploring Different Data Types: Transactional vs. Behavioral (Event) Data

  • Best Practices for Data Collection during Feature Development

  • Case Study: Requesting a Trip on Uber

  • Building Measurable Products: Effective collaboration among Product, Engineering, and Data Teams

  • Unmasking the Full Picture: The Symbiosis of Qualitative and Quantitative Data

  • Principles and Best Practices for Data Visualization: Transforming Data into Insights and Narratives

• Live Session 

  • Real-life scenario: "Leveraging Quantitative and Qualitative Research on Product Decisions" (Diana Lipcanu, Senior PD at Ankorstore)

  • Resolution of Case Study: Adoption of a feature in a B2C company

Module 2: Diving Deeper into Data Analysis from Multiple Angles

• Self-paced

  • Metric Selection Best Practices: Lagging vs. Leading Indicators, Vanity Metrics vs. Actionable Metrics

  • Delving into Total and Growth Measures

  • Decoding Averages: Utilizing Median, Modes, Percentiles, Standard Deviation, and Interquartile Range to Unveil Hidden Information

  • Utilizing Ratios and Percentages for Constructing Effective Leading KPIs

  • Customer Segmentation: Uncovering Hidden Insights and Distinguishing Correlation from Causation

  • Funnel Analysis: Conversion and Drop-off Rates, Segmentation and Analytical Techniques, Funnel Optimization

  • Strategies for Complex Funnel Design and Analysis: Optional Steps, Multiple Outcomes, Loops, Parallel Paths

  • Harnessing Cohort Analysis to Monitor Customer Retention and Usage Frequency

• Live Session

  • Real-life scenario: "Best Practices on a Product Analysis" (Juan Luis Hernández, Data Manager at Mafre)

  • Resolution of Case Study: Revenue increase in a SaaS company.

Module 3: Connecting Product and Business Metrics

• Self-paced

  • Deeper Dive into Customer Satisfaction and Retention Metrics: NPS, CSAT, CES, Retention Rate, Repeat Purchase Rate, Churn Rate, Aha Moment

  • Churn Analysis and Causal Identification

  • Growth and Retention Models: AARRR ("Pirate") Metrics, RARRA Retention Model,

  • Flywheel Model for Marketplaces, and Hook Model for Habit Formation

  • North Star Metric: Identifying and Harnessing your Company's and Products' Guiding Light

  • Business KPIs Introduction: Distinguishing Metrics from KPIs, High-level and Low-level KPIs, and Standard KPIs

  • Review of Frequently Used High-level KPIs: Understanding Key Executive Trackers

  • Exploring Business KPIs in Marketing, Sales, Customer Service, Operations, Finance, and Engineering

  • Case Study: Pricing and Customer Support at Cabify

  • Basic Concepts of Business Plan Modeling: Reading and Unveiling Insights from a Business Plan

  • Leveraging Driver Trees to Connect Product and Business Metrics: Uncovering Product Levers for Business Targets and Aligning KPIs across Departments

  • Strategies for Simplifying Complex Analytical Problems

  • Creating a Product Roadmap Aligned with Business Objectives

• Live Session 

  • Real-life scenario: "Building products which optimize processes" (Iván Martinez, CEO at an AI stealth startup)

  • Resolution of Case Study: Reaching Profitability through Product Improvements

Module 4: Experimentation, Data Management, and Advanced Analytics

• Self-paced

  • Scientific Method: The Foundation of Product Experimentation

  • A/B Testing Introduction: Appropriate Use Cases, Sample Size Determination, Key Best Practices, and Advanced Techniques

  • Case Study: Experimenting with Route Providers

  • Roles and Responsibilities in the Data Ecosystem: Optimizing Collaboration with Data Analysts, Data Engineers, and Data Scientists

  • Data Management Introduction: Ensuring Data Accessibility and Quality by Understanding Collection, Storage, and Management

  • Crucial Tools for Product Analytics: Data Visualization, Web Analytics, Product Analytics, Customer Data Platforms, A/B Testing, and Data Warehousing

  • Predictive Analytics Introduction: Using Historical Data to Forecast the Future

  • Prescriptive Analytics Introduction: Recommending Actions for Optimal Outcomes

  • Exploring New Branches of Artificial Intelligence: Text, Audio, Image, and Video Analytics, Social Network Analysis and Generative AI

  • Best Practices for 'Data as a Product'

• Live Session

  • Real-life scenario: "Leveraging AI in our products" (Roberto Cruz, CTO at Idoven)

  • Resolution of Case Study: Reduction of costs in a B2B company