How to Build a Product Market Fit

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Once you have that little experiment built, you want to enter private beta. This just means getting like ten, 20, 30 customers to actually try this product and then having very direct lines of communication open with them. A next a data warehouse is something that we recommend. Used to be expensive, is no longer expensive to day. Once you get big enough, you can start using Google baling for feature analytics. All paseas will help you improve your user experience in custom emarios. The really good cokas are the one-to-one chat tools such as slacka or live chat on the page. And so if you feel product market fit there, then you can start

We've cut down the second week of lectures to be even shorter and combined them into one podcast.

First, a lecture from Michael Seibel. Michael is CEO and a partner at YC. His lecture is about How to Plan an MVP.

Then, a lecture from Adora Cheung. Adora is a YC partner and a cofounder of Homejoy. Her lecture is about How to Set KPIs and Goals.

Last, a lecture from Ilya Volodarsky. Ilya is a cofounder of Segment. His lecture is about Analytics for Startups.

Y Combinator invests a small amount of money ($150k) in a large number of startups (recently 200), twice a year.

Learn more about YC and apply for funding here: https://www.ycombinator.com/apply/

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Topics

00:00 - Intro

00:43 - Michael Seibel - How to Plan an MVP

1:25 - The goal of a pre-launch startup

3:10 - Iterating vs pivoting

3:59 - Lean MVP

5:06 - Heavy MVP

6:19 - Launching

7:35 - Learning is easier with an MVP

8:07 - How to build an MVP quickly

10:17 - Adora Cheung - How to Set KPIs and Goals

11:51 - What are the right KPIs to set?

15:50 - Revenue or active users

16:28 - Why choose active users?

18:55 - Biotech or hardtech KPIs

20:25 - Secondary metric

21:25 - What if you haven't launched?

22:20 - Setting goals

23:47 - How fast should you grow?

25:13 - Defining your own goals

28:40 - Tracking progress

30:27 - Ilya Volodarsky - Analytics for Startups

30:46 - Why analytics?

31:36 - Funnel

32:51 - Collecting data, analytics, and data flow

34:16 - Metric - Signups per week

35:28 - Metric - Retention cohorts

36:30 - Which metric to pick?

36:57 - Have I reached product market fit?

37:27 - Metric - Revenue

37:51 - Dashboards

38:35 - Advisor updates

39:01 - The startup stack

42:13 - Recommendations for the MVP process