Recently I got the opportunity to talk about Product market fit at the ProductFolks community meetup. Having worked with early stage startups and staring up myself, I decide to talk on What Product Market Fit is not.
Here are my slides from it, with a write up I had prepared before the slides below
My Definition of Product Management
Product Management is working towards getting the Right Product in the Right Package to the Right User
Based on that, Product Market Fit according to me becomes :
Getting the Right Product in the Right Package to the Right User at the Right time.
Why do I have my own definition ? I believe the ones I came across has holes.
When I joined, had great PMF.
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 in a ton of revenue.
When did PMF break. MoPub made mediation popular, then Facebook transformed the market. Thus the need of the market changed.
Lesson: PMF is not constant.
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.
This is where assumptions kicked in. Since we were starting up.
What was PM? Mobile App Analytics [ Cheaper & more insightful]
- We knew people struggled at analytics, many are still bad at it. We spoke to a bunch of folks, who found analytics a challenge. So We assumed a market existed, and we assumed we spoke to the “right user” and it seemed like the “right time”
- We knew 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”
In retrospect, we got all the assumptions wrong.
- Right User Assumption: Analytics, in India, is still a firefighting tool, if you don’t have a problem no one looks at data.
A. True story: At an event I was asked, if all is going well, do we still need analytics. My Response: Imaging if you are driving in the night, and you switch off your headlights, you are going fine, how long will you go without the headlights?
- Right Package Assumption: We knew most folks did not need real time analytics, so we could reduce costs. The problem was, once you lower the cost, you don’t sound premium any more
- Right Time: Firebase came out with “free” “app analytics”. If you though competition with free was hard, try Free + Google. At launch it was a shitty product, people still preferred it. We were also too late to the market, people already had their allegiances.
- Right Product Mistake: We looked at a lot of SaaS metrics and wanted to get it for Mobile users, bad idea
If we had stuck to SaaS analytics we might had done better. If we had targeted the West, we might have done better. We even though of pivoting to
Learning: Cheaper is not a USP, Brand is important, Pick the right first set of users
- Right Market: Podcasts were gaining popularity. Listeners growing, creators not so much. We had competition too, Anchor.fm & Bumpers for example. We knew we were early. Also 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 business model later.
Result. In 3 months we were Acquihired by Yourstory. We got the tech, they got the reach.
Problem: Podcasting Exploded after a few more months, we left too early. Right time was almost there. Also the package and the product was different. Anchor got it right. They pivoted from 2 min Long radio show to a podcast hosting company.
Lesson: Don’t quit.
Also Ujjwal Trivedi for being a great partner at crime and the second speaker. I will link his deck once I get it.