This document discusses what product market fit (PMF) is not through examples of several companies. It summarizes that PMF is not constant and can change as the needs of the market change. Getting PMF right requires finding the right combination of product, package, user, and timing, which may evolve over time. Assumptions about the market can be wrong, and it's important to test assumptions and not quit in the search for PMF.
2. Disclaimer
1. My Goal is to teach you to fish, not give you fishes
2. This is catered more towards entrepreneurs
3. What is PMF
• What is Product Management
It is working towards getting the Right Product in the Right
Package for the Right User
4. What is PMF
• What is Product Management
It is working towards getting the Right Product in the Right
Package for the Right User
• Based on that, Product Market Fit according to me
becomes :
Getting the Right Product in the Right Package for the Right
User at the Right time.
7. Vserv (2012)
What did Vserv do
Built AppWapper which put in Entry & Exit ads in J2ME & Android
Apps, with out code
Why did it have PMF?
Companies had a large portfolio of J2ME apps, which now ran on
Java. Ads with no additional coding effort got more revenue.
When did PMF break? MoPub, then Facebook. The needs of the
market changed.
Lesson: PMF is not constant.
8. MoEngage (2014)
• PMF Early to Marketing Automation on Mobile, Retention
& Engagement a new problem people had, and
MoEngage provided a solution.
• Today: Market is massive, different companies have niche
PMFs in individual markets.
Lesson: PMF is not constant, and not the same across
competition.
10. PureMetrics
What was PM? Mobile App Analytics [ Cheaper & more insightful]
• People struggled at analytics.
• We assumed a market existed, and we assumed we spoke
to the "right user" and it seemed like the "right time"
• People found analytics expensive.
• It could be cheaper, We assumed there could be a better
"right package"
• We just need to build the "right product"
11. PureMetrics
In retrospect, we got all the assumptions wrong.
• Right User Assumption: Analytics, in India, is still a firefighting tool
• True story: At an event, I was asked, if all is going well, do we still
need analytics
• Right Package Assumption: Lower cost = lower trust
• Right Time: Firebase came out with "free" "app analytics". We were
too late to the market, products selected. DataStudio also went Free.
• Right Product Mistake: We looked at a lot of SaaS metrics and
wanted to get it for Mobile users. Bad idea.
13. Odiocast
• Right Market: Podcasts were gaining popularity. Listeners
growing, creators not so much. Competition existed. We
knew we were early. FB,Insta etc did not compete in this
space.
• Right Package: Allow people to create podcasts on the
phone itself, no mic, no editing etc needed.
• Right Price: Free, figure out the business model later.
14. Odiocast
• Result. In 3 months we were Acquihired by Yourstory. We
got the tech, they got the reach.
15. Odiocast
• Problem:
• Podcasting Exploded
• Right Product was a little different. Anchor got it right.
16.
17.
18. Odiocast
Lesson: Don’t quit, finding PMF is a journey in itself, a
journey that never ends. It is finding the right combination of
the following:
• Right Product
• Right Package
• Right User
• Right time.