Product Management line, station 2 of 11
Product Discovery
Find out what users really need before you build, with story-based interviews, field visits, honest synthesis, cheap tests and one-day AI prototypes.
- 5lessons
- 3 hto read and do
- Beginnerlevel
- 5podcast episodes
Tools you'll use in this course
The lessons open these with a template ready. They work for your own job too, no course needed.
About this course
Most products fail because they solve a problem nobody has, or solve it in a way nobody uses. Discovery is the work that prevents this: learning what is true about your users before you commit a team to building. This course teaches the methods strong product teams use every week, step by step, with templates you can copy into your own work.
You will run interviews that surface what people actually did instead of polite opinions, and watch real work in stores, warehouses and small shops to spot the notebooks, side spreadsheets and chat-group workarounds. You will turn everything you collect, including support tickets, app reviews and field chat groups, into an opportunity solution tree, using AI for a first pass without letting it invent findings. Then you will test the riskiest assumptions cheaply with fake doors, concierge tests, Wizard of Oz tests and usability tests, and build a clickable AI prototype in a day without mistaking it for the product.
Every lesson follows Imran, the product manager for Mangrove Market's Store app and Partner app, as he works out why about a third of 1,800 partner shops still order by phoning a sales rep. The examples cover consumer apps and the tools that frontline staff and small shop owners use, including research with people on shift and across languages.
What you will be able to do
- Run interviews that surface past behaviour instead of polite opinions, including with store staff on shift and busy shop owners
- Observe real work and turn it into a journey map or service blueprint that shows the workarounds and what they cost
- Turn interviews, field notes and always-on feedback into an opportunity solution tree with ranked assumptions, using AI safely for a first pass
- Pick and run the cheapest test for the riskiest assumption, including a fake door, a concierge test and a usability test
- Build a clickable AI prototype in a day, test it with real users, and explain what it can't tell you
Syllabus
5 stops, about 3 h in total. Each stop fills in when you complete the lesson.
User interviews that don't lie
People are kind. Ask them about your idea and they will tell you it is great and that they would use it. This lesson teaches you to ask about what people actually did last time, which is much harder to fake, and to do it with store staff on shift, busy shop owners and people who speak a different language from you.
Customer interviewsStory-based questionsRecruiting participantsInterview snapshots
Watching the work
What people tell you about their work and what they actually do are different, and most product opportunities hide in that difference. This lesson shows you how to visit a store or a partner shop, watch real work without getting in the way, spot the workarounds, and turn what you saw into a journey map and a service blueprint.
Field researchJourney mappingService blueprintsSpotting workarounds
From research to opportunities
After a few weeks of interviews and visits you have a pile of notes, photos and tickets. This lesson turns that pile into a short list of customer needs with evidence behind each one, an opportunity solution tree that links them to one outcome, and a ranked list of the assumptions to test first. It also shows where AI speeds this up and where it quietly misleads you.
Research synthesisAlways-on feedbackOpportunity solution treesAssumption mapping
Testing ideas cheaply
Building the real thing is the most expensive way to learn that an idea doesn't work. This lesson gives you four cheaper tests (fake doors, concierge tests, Wizard of Oz tests and usability tests), shows you how to set the pass mark before you start, and how to run each one fairly with shop owners and frontline staff.
Assumption testingSuccess criteriaUsability testingExperiment design
Prototyping with AI
AI tools can now turn a written description into a clickable, working-looking app in an hour. Used well, that puts a realistic prototype in a shop owner's hands the same day you think of it. Used badly, it produces a demo everyone mistakes for a finished product. This lesson teaches the first and protects you from the second.
AI prototypingPrototype briefsUsability testingThrowaway vs production
Capstone: Why do partner shops still phone?
A realistic project with a rubric and a model answer.
Final exam and certificate
10 questions drawn at random, 70% to pass.
Capstone project
Why do partner shops still phone?
About a third of Mangrove Market's 1,800 partner shops still order by phoning their sales rep instead of using the Partner app. Run a two-week discovery to find out why, and what the partner team should do about it. Interview five shop owners (classmates or friends can role-play Ravi and his peers from this brief), observe one ordering session live or from a recording, and tag 200 support tickets, writing a realistic sample set if you have none. Build an opportunity solution tree under the outcome "share of partner orders placed in the app", map the assumptions behind your best solutions, and test the riskiest one with a fake door, a concierge test or an AI prototype. Finish with a one-page decision for the partner team.
You will hand in
- Five interview snapshots and one set of timed field notes from an ordering session
- A tagged sample of 200 support tickets with your codebook, and a service blueprint of phone ordering with the workarounds marked
- An opportunity solution tree with a target opportunity, three rival solutions and an assumptions map
- A test card written before the test, and the results of the test you ran
- A one-page decision: what you learned, what to build next, and which belief you have dropped
Check what you know
Already know some of this? Answer 5 quick questions, one from each lesson, and we will mark the lessons you can skim. It takes about 3 minutes and nothing is graded.
Questions
How long does the course take?
About three hours for the five lessons and their practice tasks, plus the podcast episodes if you listen to them. The capstone takes about two weeks of part-time work, because it involves talking to real or role-played users.
Do I need access to real users for the capstone?
It helps, but it isn't required. Classmates, friends or colleagues can role-play shop owners from the brief, and you can write your own sample of support tickets. If you do have users, people who order stock for a small shop or work shifts in a store are ideal.
Do I need to code for the AI prototyping lesson?
No. The lesson is tool-neutral and uses prompt-to-app tools that write the code for you. You write a question card, a brief and small prompts. What matters is the question the prototype answers, not the code, which you throw away.
Is this course only about consumer apps?
No. Every method is shown for consumer apps and for internal tools used by store staff, warehouse teams and small shop owners, including interviewing people on shift and across languages.
Should I take Product Thinking Foundations first?
It helps, because this course builds on problem statements, users and the four risks from that course. If you already turn feature requests into problem statements comfortably, you can start here.
Is it really free?
Yes. Every lesson, podcast episode, exercise, quiz, final exam and certificate is free. The labs and their templates for real work are free too. Plus and Premium add every lab challenge, weekly extras and a mentor on top, and they never lock the tools or the courses.
How does the final exam work?
You get 15 questions drawn at random from a larger bank. You need 70% to pass. If you do not pass, you can try again after 24 hours. The exam is graded on the server and you never see the answer key.