Kellogg PM: Week4 product discovery

Product discovery, user stories, mvp, test-launch-repeat, product-market fit

Him Apisit
3 min readMar 23, 2022

From the previous chapter, we’re in the search of product opportunities in which we aim to find unmet needs and untapped areas with the Jobs-To-Be-Done framework. This week we concentrate on the product discovery topic, for me it’s closely related to product opportunities. Once we have prioritized ideas then we try to address these ideas with solutions.

Product discovery is a follow up process after we identify unmet needs, it’s the time to think about how we could solve their problems or touch on big opportunities. We start by forming discovery hypotheses with formats outlined below:

My product solves <insert problem> by <insert benefit>

<Users> will adopt my product because <insert why>

<User Group 1> will adopt my product first because <insert why>

This could help you to stop and think a bit even if you’re super excited with new opportunities. Once we have hypotheses, we could either talk to users real quick to get feedback on hot lead or develop as simple product as possible and launch to get real feedback. According to Y Combinator on youtube, the faster the launch the better, and the faster you could learn also the better. Some startup founders feel like they’re going to be celebrities. They need a hall of fame or press conference which in reality it’s just a launch, so no one really cares about what we are doing. Stop overthinking and start executing to make your users or revenue grow, anything except that may be just a waste of your time.

Photo by J. Balla Photography on Unsplash

Key learning

  1. User stories: This is a statement summarizing what happens, who aim to do what, and why they are doing that. Pro tip is to integrate JTBD from our research into user stories which could help strengthen customers’ bond to the jobs.
  2. MVP: This stands for Minimum viable product, this is the state of product where we launch it to test how well we solve customer problems or touch JTBD. You could imagine MVP is a set of relationship mapping between JTBD and solution or product.
  3. Pro tip from Y Combinator: You should launch your product as fast as possible to be able to quickly learn from real users unlike your competitor either big or small. You learn from the frontline and adapt your product to match their needs. You need to pivot if you find out that your assumptions are wrong.
  4. Launch test repeat: This is from Y Combinator, in a startup environment especially for pre product-market fit stage. It’s critical to focus on growing users and building something a lot of people want. It might be something stupid or just half-baked half-broken product but if it solves people problem then no one cares.
  5. Product Market Fit: There’s no pre product-market fit stage, a lot of startup founders tend to claim this according to Y Combinator. If there’s product-market fit you will know and don’t ever have enough time to ponder this question at all.
  6. Product Requirement Documents: This is the last step for a product manager within big corporations to communicate with the engineering team. This comprises 4 sections which include Product Purpose(the why), Product Feature(the what), Release criteria, and Schedule & Constraint(the when). I think it’s a clean and structured way to summarize stuff into a slide deck/page.
Photo by Ivan Diaz on Unsplash

Recommended reading

  1. Minimum Viable Product and the Importance of Experimentation in Technology Startups, Dobrila Rancic Moogk
  2. How to Write a Good PRD, Marty Cagan
  3. Know Your Customers’ “Jobs to Be Done”, Clayton M. Christensen, Taddy Hall, Karen Dillon, and David Duncan
  4. How to Build Rockstar Products: Think Like Airbnb, ClassDojo and Geekie, Ha Nguyen
  5. An MVP is About Smart Learning, Steve Blank
  6. How to Define a Minimum Viable Product
  7. The Specification is Dead, Long Live the Specification, Ben Yoskovitz
  8. 6 Steps To Write Product Specifications
  9. The Beginner’s Guide to Product Specifications

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Him Apisit

Data Scientist @ LMWN | Interested in Tech Startup, Data Analytics, Social Enterprise, Behavioral Economics, Strategy.