customer analytics | customer segmentation | customer journey map | retention | lifetime value | churn | nps and csat
What You Will Learn:
- Segment a population by characteristics and find the insight inside a segment
- Calculate lifetime value and the return on what you spend to acquire
- Build a journey map and attach a questionnaire to every stage of it
- Measure retention properly, including intent to stay and reasons for leaving
- Run a key driver analysis and a regression in a spreadsheet
- Design an experiment with a control group instead of guessing at causation
- Read NPS, CSAT, CES and first-contact resolution without confusing them
- Cost the churn you have, and build the business case to reduce it
- Learn alongside Mike’s 1.6 million students from 185 countries
- Draw on the author’s work at Preply, Wargaming, iDeals and Alfa-Bank
Beyond the Vanity Metrics: A Deep Dive into Mike’s Customer Analytics
I’ve been in the tech game for over a decade, and if there’s one thing that keeps C-suite executives up at night, it isn’t “how much data do we have,” but rather “what the hell is this data telling us about our revenue?” Most customer analytics courses I’ve seen are either dry academic lectures on statistics or “coding-lite” tutorials that leave you with a syntax error and no business insight. Mike’s course, Customer Analytics: Segmentation, Journey and Value, is a different beast entirely. It’s clearly built by someone who has sat in the trenches at high-growth companies like Preply and Wargaming, where if you miscalculate lifetime value (LTV), you’re essentially flushing millions in marketing spend down the toilet.
The standout insight here isn’t just the math—it’s the psychology. This isn’t just about certification prep; it’s about shifting your mindset from seeing customers as rows in a database to seeing them as moving targets with evolving needs. The course treats customer segmentation not as a one-time exercise, but as a living strategy to find the hidden “whales” in your population. If you’ve ever felt like your company is just guessing at why users are leaving, this curriculum provides the clinical framework to stop the bleeding.
Prerequisites
You don’t need a PhD in statistics or a background in software engineering to get value out of this. If you can navigate a spreadsheet and understand basic arithmetic, you’re ready. Mike pitches this as beginner to advanced, and he hits that sweet spot by explaining the logic before diving into the hands-on labs. Familiarity with business concepts like “revenue” and “acquisition” is helpful, but honestly, as long as you have a curious mind and access to Excel or Google Sheets, the barrier to entry is low.
Skills & Tools
- Industry-standard tools: Primarily spreadsheets (Excel/Google Sheets) for regression and key driver analysis.
- Data Synthesis: Learning how to build a customer journey map and, more importantly, how to attach actionable questionnaires to every touchpoint.
- Financial Modeling: Calculating LTV and Customer Acquisition Cost (CAC) ratios to determine the health of a business.
- Experimentation: Designing control group experiments to prove causation rather than just correlating random spikes in data.
- Sentiment Analysis: Properly interpreting NPS, CSAT, and CES without falling into the common trap of treating them as identical metrics.
Career Benefits & Job Roles
If you’re looking for career growth, this is the kind of job-ready skills training that makes you indispensable during a layoff cycle. Companies don’t fire the people who can prove they are saving the company money by reducing churn. This course is a goldmine for:
- Product Managers: Who need to justify feature roadmaps based on retention data.
- Data Analysts: Looking to transition from “SQL monkeys” to strategic partners who provide real-world projects and insights.
- Growth Marketers: Who need to optimize spend and understand the return on investment for every dollar spent on acquisition.
- Customer Success Leads: Seeking to quantify the impact of their NPS and CSAT scores on the bottom line.
Pros
- The Pedigree: You’re not learning from a full-time academic. Mike’s experience at Alfa-Bank and Wargaming brings a level of grit to the lessons. He knows what it’s like when the churn numbers are ugly and you have to build a business case to fix them.
- The “Spreadsheet First” Philosophy: By running regressions in a spreadsheet instead of a black-box Python script, you actually understand the relationship between the variables. This is crucial for explaining your findings to non-technical stakeholders.
- Focus on Causation: Most courses skip experimental design. Mike insists on control groups, which is the difference between a “hunch” and a “data-driven strategy.” This focus on rigor ensures your career growth is built on solid results, not luck.
- Comprehensive Retention Frameworks: He doesn’t just tell you to measure retention; he teaches you how to measure the “intent to stay.” That distinction alone is worth the price of admission.
Cons
If I have one gripe, it’s that the course is very “Excel-centric.” While this is great for understanding the logic, if you’re a senior data scientist looking for advanced industry-standard tools like Snowflake integration or automated Python pipelines, you might find the manual spreadsheet work a bit tedious. It’s perfect for the “business-logic” layer of tech, but maybe a bit basic for those who want to spend all day in a code editor.
The Bottom Line: If you want to stop being a “data reporter” and start being a “value creator,” this course is a must. It’s one of the few programs that actually delivers job-ready skills that you can apply at your desk the very next Monday morning.