1
00:00:00,000 --> 00:00:04,800
Goal on the behaviors, that 
matter, don't goal on your 

2
00:00:04,800 --> 00:00:08,600
vanity metrics, figure out what 
it is that not just works for 

3
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your product. 
But honestly, for you, as a 

4
00:00:10,107 --> 00:00:14,100
person, like, what works for you
as an individual, what makes you

5
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the most successful figuring out
what those behaviors? 

6
00:00:16,800 --> 00:00:19,900
Are that make you the best to 
your abilities? 

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Figure out what those are and 
operate and optimize for that. 

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00:00:28,000 --> 00:00:31,600
Hey, everyone, my Name is Henry 
sura, one. 

9
00:00:32,700 --> 00:00:35,400
And you're listening to the 
tekhelet journal. 

10
00:00:36,000 --> 00:00:39,200
The show will be bringing you 
the greatest technical leaders 

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00:00:39,400 --> 00:00:43,000
practitioners and thought 
leaders in the industry to 

12
00:00:43,000 --> 00:00:47,300
discuss about their Journey 
ideas and practices that we all 

13
00:00:47,300 --> 00:00:51,100
can learn and apply to build a 
highly performing technical team

14
00:00:51,300 --> 00:00:53,800
and to make an impact in your 
personal work. 

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00:00:54,500 --> 00:01:01,200
So let's dive into our Journal. 
Hello, my friend. 

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00:01:01,400 --> 00:01:03,900
Welcome to another episode. 
Sort of the tekhelet journal 

17
00:01:03,900 --> 00:01:06,000
with me or host. 
Henry, Surah one. 

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00:01:06,500 --> 00:01:09,000
Thank you for tuning in and 
spending your time with me 

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00:01:09,000 --> 00:01:11,400
today, listening to this 
episode. 

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If you haven't joined any of the
tech lead Journal social media 

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channels, I would encourage you 
to take a second right now, to 

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00:01:18,200 --> 00:01:20,900
click on the links in the show 
notes where you can find the 

23
00:01:20,900 --> 00:01:24,000
podcast, either on LinkedIn, 
Twitter or Instagram. 

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00:01:24,400 --> 00:01:27,500
I'd really love to see and 
interact with all of you there. 

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Every time you post and share 
something about these podcasts 

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on your social media. 
And it makes me extremely happy 

27
00:01:34,500 --> 00:01:37,400
and I do hope that more people 
will be able to find these 

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podcasts and learn from the 
amazing guest that I have on the

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show. 
If you have other creative ideas

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on how to grow this community 
bigger, please feel free to 

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reach out to me and share your 
ideas. 

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It would really mean a lot to 
me, and if you would like to 

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pledge your support and make 
contribution to the show, you 

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can do so through our patreon 
page at technology, you know, 

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that death / Patron, it would 
help me tremendously to 

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Achieving a goal that I'm 
currently running on the patreon

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page. 
Our Guest for today's episode is

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Crystal with Jaya, crystal is a 
start-up growth advisor. 

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Reforge, advisor partner and 
Sequoia Scout in the San 

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Francisco Bay Area. 
She has been recognized as a 

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Forbes Indonesia 30 under 30 in 
2019 and was most recently, the 

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senior VP of business 
intelligence and growth for Go 

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Jack the leading on-demand 
multi-service platform. 

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In Southeast Asia. 
She is also. 

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So a co-founder of Generation 
Girl, a nonprofit organization 

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that aims to introduce young 
girls to science, technology, 

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engineering, and Mathematics. 
In this episode. 

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I had a fascinating chat with 
Crystal, on many things, about 

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startups and her exhilarating 
Journey with go-jek, as the 

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First Data higher, we started 
our conversation with the recent

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trends of the startup funding in
the US and Southeast Asia, 

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including the impact that covid 
has brought to the startup 

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scenes Crystal then She had her 
in depth and insightful, tips on

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Startup growth strategy, 
including the common Pitfall 

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that startups need to avoid in 
their growth strategy. 

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She also gave practical tips on 
how a start-up can start its 

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data analytics Journey. 
We then talked about her recent 

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go-jek Korea, when see, outlined
her amazing journey, building 

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00:03:22,600 --> 00:03:26,800
gojek data team from just one 
person, which is herself to 

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about two hundred plus people 
the challenges that she had to 

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go through and how she overcame 
them. 

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Them last Crystal shared about 
her nonprofit organization. 

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Generation go. 
And why it is an important cause

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to help Indonesian young girls 
to succeed in stem. 

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I hope that you will enjoy this 
great episode. 

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Please. 
Consider helping the show in the

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smallest possible way by living 
me a rating and review on a 

68
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podcast or the other podcast 
apps that allow you to do. 

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So those ratings and reviews are
one of the best ways to get this

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00:03:58,900 --> 00:04:02,900
podcast to reach more listeners 
and hopefully the show Gets 

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featured on the podcast 
platform. 

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00:04:05,300 --> 00:04:07,600
I'm also looking forward to 
hearing your comments and 

73
00:04:07,600 --> 00:04:11,700
feedback on the social media or 
directly, to me at technology, 

74
00:04:11,700 --> 00:04:15,500
know that death / feedback. 
You can also post your learnings

75
00:04:15,600 --> 00:04:19,100
and even creative pictures of 
you listening to the podcast. 

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00:04:19,200 --> 00:04:22,000
Following the trend that Jerome 
has been doing since the last 

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few episodes on LinkedIn. 
So let's get the episode started

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right after our sponsor message.
Do you want to learn to code? 

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00:05:03,100 --> 00:05:05,300
Hello, Crystal, welcome to the 
technology, you know, very 

90
00:05:05,300 --> 00:05:08,100
excited to have you here. 
Thanks for having me Henry. 

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00:05:08,100 --> 00:05:09,900
It's super exciting to be here 
with you, too. 

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So, where are you in the world? 
Now right now? 

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I'm in Boston. 
I am on the East Coast but I 

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actually just moved back in 
January the point when 

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everything in the world was 
starting to go a little bit 

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crazy. 
I moved back from Singapore and 

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super funny because I remember 
thinking oh, well, there's this 

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virus thing going around and it 
doesn't seem like people in the 

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US are really aware of it yet. 
Is it something that you plan to

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00:05:34,800 --> 00:05:37,200
stay there for long or is it 
just for temporary? 

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00:05:37,600 --> 00:05:39,600
That's a good question. 
I actually moved back to be 

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closer with my family and to 
travel a little bit in the US 

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and then of course, that plan 
was no longer possible. 

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But I've actually started 
working with a couple of 

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different startups in the US and
in latam, so right now, my plan 

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00:05:53,700 --> 00:05:57,000
to do is decompressing and 
understanding and surveilling, 

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the u.s. 
Startup ecosystem, where I came 

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from and it's where I first 
started. 

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And then Move back to Southeast 
Asia and a year to. 

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All right. 
Cool. 

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So let's start with your 
courage, your knee. 

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So Chris you have a very 
wonderful illustrious career so 

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far. 
Would you be able to share your 

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career Journey? 
May be highlighting some of your

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major turning points so far that
are interesting for the 

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listeners to know love to talk 
about that. 

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I guess it would off her start 
when I was actually attending 

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Community College. 
A lot of people don't know that,

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I actually transferred to UC 
Berkeley. 

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00:06:30,200 --> 00:06:34,700
I had initially wanted to do 
something in Rhetoric or logic, 

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00:06:34,700 --> 00:06:38,600
or even philosophy, and I ended 
up majoring or deciding my major

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00:06:38,600 --> 00:06:41,400
in political science because 
that was going to be the fastest

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way to graduate. 
So, based on my test scores and 

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my AP tests, I was able to skip 
a lot of the prerequisites, 

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decided to go to UC Berkeley for
political science. 

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Really? 
The goal was to be like Nate 

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silver and we all know Nate 
Silver's skill set and being 

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able to determine who is going 
to win the next election, how 

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our precincts going to be 
voting. 

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And why that That is 
statistically significant. 

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And so for me that was exciting.
I loved being able to figure out

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and predict what would happen 
based on heuristics and 

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quantitative data sets. 
The initial thinking was I'll do

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some education research. 
I'll learn about how data can be

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used to derive a conclusion and 
I actually ended up in 

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Investment Banking instead, not 
two different, maybe when you're

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00:07:23,400 --> 00:07:26,400
using numbers to figure out what
the best next that is. 

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00:07:26,500 --> 00:07:30,400
So we did VC financing and m&a 
advisory and the Investment Bank

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00:07:30,400 --> 00:07:32,400
that I started working at in San
Francisco. 

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What's interesting is that I 
didn't realize that was 

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directly. 
My first foray into startups. 

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I ended up learning a lot about 
what metrics we should be asking

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different companies. 
We interviewed people like 

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summon which was the Uber, 
before, Uber existed. 

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And it just gave me a really 
good view of what the right 

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00:07:49,700 --> 00:07:52,500
company should be. 
If I were to try to work at a 

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startup. 
So I ended up going to Indonesia

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for the first time in 2015 with 
my parents. 

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And I was exceedingly excited. 
I think there's just so much 

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00:08:00,000 --> 00:08:02,300
opportunity in Indonesia, and 
there's so much growth. 

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At that point in time, I decided
that I did want to join a 

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start-up. 
I wanted to do something within 

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data. 
I tried to create a sequel 

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00:08:09,200 --> 00:08:11,300
database for the bank that I 
worked at. 

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00:08:11,400 --> 00:08:14,300
I actually Googled top startups 
in Southeast Asia. 

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And I did my investment-banking 
thesis. 

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I figured out. 
What were the metrics for each 

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00:08:19,100 --> 00:08:20,700
of these startups. 
I try to figure out their 

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00:08:20,700 --> 00:08:23,500
funding, but their backgrounds 
were, I ultimately ended up 

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00:08:23,500 --> 00:08:27,600
picking a couple that I emailed 
and go Jack happened to reply. 

161
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When I went there. 
I was a first date a person on 

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00:08:29,700 --> 00:08:32,400
the team. 
There was a whole slew of data. 

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Were growing super fast already,
but there was no organization or

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00:08:36,200 --> 00:08:39,500
strategy around how to use this 
data to grow more quickly, more 

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00:08:39,500 --> 00:08:42,299
effectively. 
So, when I first joined, I hired

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00:08:42,299 --> 00:08:45,200
a bunch of different people to 
help build a data warehouse, we 

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built out data infrastructure 
team. 

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And when you have access to all 
this data, you quickly realize 

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00:08:50,800 --> 00:08:54,000
how much fraud is in the system.
And so I ended up also taking up

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00:08:54,000 --> 00:08:57,700
the fraud and risk team building
that team out moved on to 

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00:08:57,700 --> 00:09:01,400
portals Performance Marketing. 
And then of course growth, which

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00:09:01,400 --> 00:09:04,900
I'm primarily Gaston today, so 
it was an incredible journey 

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00:09:04,900 --> 00:09:07,400
over five years. 
I essentially grew the teams 

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00:09:07,400 --> 00:09:12,300
from 0 to about 200 plus across 
bi and growth helped hire our 

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00:09:12,300 --> 00:09:15,800
chief data officer. 
And now I'm working with the 

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00:09:15,800 --> 00:09:19,900
research team on growth and also
a sequoia Scout for the US where

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I help identify precede and 
series a startups for 

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Investments. 
And I'm also advising a handful 

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of companies. 
It's a very wonderful journey. 

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00:09:28,600 --> 00:09:31,000
I didn't know that some of the 
things, for example, you 

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00:09:31,000 --> 00:09:32,400
actually come up with a 
research. 

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00:09:32,500 --> 00:09:34,200
Before you join Go, Jack 
devising. 

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00:09:34,200 --> 00:09:36,800
Some of your knowledge before. 
I think it's very interesting 

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way to find a job. 
Definitely and then what made 

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00:09:39,300 --> 00:09:43,000
you decide to quit project and 
move out to the VC side? 

186
00:09:43,300 --> 00:09:44,400
Yeah. 
It's a good question. 

187
00:09:44,500 --> 00:09:47,900
So I had spoken with the CEO of 
stripe Claire. 

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00:09:47,900 --> 00:09:50,700
She's amazing. 
She spent nearly a decade at 

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00:09:50,700 --> 00:09:53,700
Google. 
She's built stripe to a global 

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00:09:53,700 --> 00:09:55,800
company. 
And one of the things that she 

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00:09:55,800 --> 00:09:59,000
said to me when she was in 
Singapore, was what's really 

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00:09:59,000 --> 00:10:00,900
concerning is? 
Whether or not? 

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00:10:00,900 --> 00:10:03,900
I was just really good. 
Dat working at Google that was 

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00:10:03,900 --> 00:10:07,100
something she didn't want to 
feel given all of her time there

195
00:10:07,200 --> 00:10:09,900
that reminded me a lot of my 
experience at Go Jack. 

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00:10:09,900 --> 00:10:12,600
I'd spent a really long time. 
They're definitely not a decade,

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00:10:12,600 --> 00:10:16,400
but certainly a long enough time
to have felt like my influence 

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00:10:16,400 --> 00:10:21,300
and impact was more of a factor 
of my legacy rather than my 

199
00:10:21,300 --> 00:10:24,000
actual abilities. 
I didn't want to feel like I was

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00:10:24,000 --> 00:10:27,100
primarily working around in the 
politics or that I was 

201
00:10:27,100 --> 00:10:30,600
navigating seniority in the 
right ways, but I wanted to feel

202
00:10:30,600 --> 00:10:33,100
like I was actually being 
impactful and Building my 

203
00:10:33,100 --> 00:10:34,500
skills. 
Not to say that. 

204
00:10:34,500 --> 00:10:36,800
I wasn't building any skills. 
I go jet because certainly I 

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00:10:36,800 --> 00:10:39,800
did, but I wanted to make sure 
that it was transferable enough 

206
00:10:39,800 --> 00:10:42,300
to other companies. 
I felt like I had to leave 

207
00:10:42,300 --> 00:10:44,500
because I felt very comfortable 
to be honest. 

208
00:10:44,500 --> 00:10:46,300
With Go Jack. 
It's such a great company and 

209
00:10:46,300 --> 00:10:48,600
honestly, anyone listening who 
is from Go Jack. 

210
00:10:48,600 --> 00:10:50,800
It's an amazing place. 
I think it's pretty magical. 

211
00:10:51,000 --> 00:10:53,500
But I also knew that it was time
for a change. 

212
00:10:53,500 --> 00:10:54,900
I was starting to feel pressed 
Louis. 

213
00:10:54,900 --> 00:10:57,800
I wasn't sure exactly what I 
wanted to work on next. 

214
00:10:57,800 --> 00:11:01,100
So I actually left without 
really a clear plan of what to 

215
00:11:01,100 --> 00:11:03,700
do next. 
I just Moved back to the u.s. 

216
00:11:03,700 --> 00:11:05,400
Thankfully. 
I have the privilege and the 

217
00:11:05,400 --> 00:11:07,200
luxury of my parents being able 
to. 

218
00:11:07,200 --> 00:11:09,700
Let me come back home and spend 
time with them. 

219
00:11:09,900 --> 00:11:13,600
I worked with a SAS start up for
a couple of months decided that 

220
00:11:13,600 --> 00:11:16,700
I didn't want to do an 
operations role or a head of 

221
00:11:16,700 --> 00:11:20,100
operations role and then ended 
up deciding to just help or 

222
00:11:20,100 --> 00:11:22,700
continue helping the companies 
that were already reaching out 

223
00:11:22,700 --> 00:11:24,700
to me. 
By the time I left Kojak people 

224
00:11:24,700 --> 00:11:27,900
would be asking things like hey,
you love gojek, but I'd still 

225
00:11:27,900 --> 00:11:30,600
like to learn what you did there
and how you did growth. 

226
00:11:30,700 --> 00:11:33,300
My team is growing. 
We don't Know how to look at our

227
00:11:33,308 --> 00:11:35,300
numbers. 
And so after enough time. 

228
00:11:35,300 --> 00:11:38,600
I ended up just helping these 
companies full-time and going 

229
00:11:38,600 --> 00:11:41,000
through their data, advising 
them on where their users were 

230
00:11:41,000 --> 00:11:43,600
getting stuck. 
And where users were retaining. 

231
00:11:43,600 --> 00:11:46,800
What were the behaviors that 
correlated with retention and 

232
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helping advise them. 
There. 

233
00:11:48,600 --> 00:11:50,300
This is Quest. 
Cut program is super 

234
00:11:50,300 --> 00:11:52,300
interesting. 
I think it's a lot of founders 

235
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of Sequoia companies. 
They also have a pre founder 

236
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program that's coming out very 
soon, but it's an amazing 

237
00:11:58,000 --> 00:12:01,400
experience where given your 
network and the people within 

238
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the startup ecosystem. 
Mmm, they know that Sequoia is 

239
00:12:04,800 --> 00:12:08,400
too large of a firm to really 
get access to some of these 

240
00:12:08,400 --> 00:12:11,700
friends and family rounds. 
And so that's where the Sukhoi 

241
00:12:11,700 --> 00:12:14,100
scalp program comes in. 
They're looking for someone who 

242
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has access to these Founders to 
people who are just starting a 

243
00:12:17,008 --> 00:12:20,200
new company and are just 
thinking of building something 

244
00:12:20,200 --> 00:12:21,600
and incorporating. 
It in the u.s. 

245
00:12:21,900 --> 00:12:25,200
My work there is really keeping 
tabs on amazing people that I 

246
00:12:25,200 --> 00:12:27,600
know we're starting companies 
making sure that I can be 

247
00:12:27,600 --> 00:12:30,700
helpful to them whether that's 
in product or just trying out 

248
00:12:30,700 --> 00:12:32,900
there MVP that's been super. 
Settings. 

249
00:12:32,900 --> 00:12:35,000
I've made a couple of 
Investments there so far. 

250
00:12:35,300 --> 00:12:37,700
I think it sounds very 
interesting like accessing the 

251
00:12:37,700 --> 00:12:40,000
friends and family stage 
startup. 

252
00:12:40,000 --> 00:12:42,600
So are you predominantly doing 
it for the US? 

253
00:12:42,600 --> 00:12:45,300
Or is it also Global or 
southeast Asia? 

254
00:12:45,700 --> 00:12:48,100
That's a good question. 
So, this request got program, I 

255
00:12:48,100 --> 00:12:51,200
believe was started in the US, 
and so I'm part of the US 

256
00:12:51,200 --> 00:12:53,400
cohort. 
So, that does mean, I'm only 

257
00:12:53,400 --> 00:12:56,800
allowed to invest in companies 
that are Incorporated in the US,

258
00:12:56,800 --> 00:12:59,900
which is unfair, because, I know
so many amazing Founders who are

259
00:12:59,900 --> 00:13:02,300
starting something in Southeast 
Asia, but I've actually been, 

260
00:13:02,400 --> 00:13:05,900
Unable to help, share the 
southeast Asia Scout team, which

261
00:13:05,900 --> 00:13:08,800
I think just started and 
Sequoias Scout program in 

262
00:13:08,800 --> 00:13:11,200
Singapore and Indonesia. 
So I've sent them a handful of 

263
00:13:11,200 --> 00:13:13,400
startups, as well. 
Hoping that they'll also 

264
00:13:13,400 --> 00:13:16,400
continue to provide support in 
Southeast Asia for new startups.

265
00:13:16,700 --> 00:13:19,700
So you have been in Southeast 
Asia with Kojak for quite some 

266
00:13:19,700 --> 00:13:21,500
time in the startup landscape 
there. 

267
00:13:21,500 --> 00:13:23,100
And all you are in Sequoia is 
called in. 

268
00:13:23,100 --> 00:13:25,600
U.s., What are some of the 
things that you can see like 

269
00:13:25,600 --> 00:13:29,200
maybe trends that are different 
between the two regions or are 

270
00:13:29,200 --> 00:13:32,000
there any similarities or maybe 
just different things? 

271
00:13:32,000 --> 00:13:33,800
So maybe And share your insights
here. 

272
00:13:34,100 --> 00:13:36,800
Definitely. 
There are a couple of things 

273
00:13:36,800 --> 00:13:39,200
that have been immediately 
different. 

274
00:13:39,200 --> 00:13:42,000
I think, first, the fact that 
everyone in the u.s. 

275
00:13:42,700 --> 00:13:46,000
Almost everyone in the u.s. 
Basically speaks English and has

276
00:13:46,000 --> 00:13:49,700
access to online, learning 
platforms, has access to English

277
00:13:49,700 --> 00:13:53,900
documentation and ability to go 
through different tutorials in 

278
00:13:53,900 --> 00:13:57,700
their native language. 
That is such a strong leverage 

279
00:13:57,700 --> 00:13:59,800
point that I think many people 
in the US. 

280
00:13:59,900 --> 00:14:02,100
Do not realize that you have 
this privilege. 

281
00:14:02,400 --> 00:14:06,500
Because the amount of people who
are in non-technical roles, but 

282
00:14:06,500 --> 00:14:08,700
have technical skills. 
Like they can write in 

283
00:14:08,700 --> 00:14:11,700
JavaScript, or they have sequel 
background, or they have skills 

284
00:14:11,700 --> 00:14:14,900
and being able to write quick 
scripts in Python that I think, 

285
00:14:14,900 --> 00:14:18,700
is far more common in the u.s. 
Than it was in Southeast. 

286
00:14:18,700 --> 00:14:20,500
Asia. 
And Southeast Asia, you don't 

287
00:14:20,500 --> 00:14:24,800
have the same resources or 
access or opportunities to the 

288
00:14:24,800 --> 00:14:28,200
same support systems that you 
would in the US where everyone, 

289
00:14:28,200 --> 00:14:32,200
at least in the Silicon Valley 
is very coding first there. 

290
00:14:32,600 --> 00:14:36,300
Resources and opportunities and 
anyone that you can talk to is 

291
00:14:36,300 --> 00:14:39,000
just an arm's length away, at 
least back when we were in the 

292
00:14:39,000 --> 00:14:41,500
office, but you don't really 
have that same opportunity in 

293
00:14:41,500 --> 00:14:44,200
Southeast Asia. 
So I think they don't appreciate

294
00:14:44,200 --> 00:14:45,700
that. 
Quite as much in the US, the 

295
00:14:45,700 --> 00:14:48,100
fact that everyone is so 
Technical and I think that makes

296
00:14:48,100 --> 00:14:51,200
the development work, a little 
bit easier in the US because 

297
00:14:51,200 --> 00:14:53,300
everyone kind of understands how
Tech Works. 

298
00:14:53,500 --> 00:14:57,600
Everyone has an idea about what 
can be automated and what effort

299
00:14:57,600 --> 00:14:59,500
it would take to automate 
something. 

300
00:14:59,600 --> 00:15:02,700
Whereas, in Southeast Asia, what
you do have though, I think Is 

301
00:15:02,700 --> 00:15:06,300
genuine unbridled creativity. 
And if people are inherently 

302
00:15:06,300 --> 00:15:09,100
very creative, they are able to 
solve things. 

303
00:15:09,100 --> 00:15:12,600
Not just with their communities,
but independently as well, but I

304
00:15:12,600 --> 00:15:15,700
think the community aspect is 
one of the most interesting 

305
00:15:15,700 --> 00:15:18,200
parts of Southeast Asia. 
That people are actually willing

306
00:15:18,200 --> 00:15:21,900
to come together and go through 
a solution regardless of the 

307
00:15:21,900 --> 00:15:24,900
backgrounds of whether or not. 
Someone is technical people in 

308
00:15:24,900 --> 00:15:26,500
Southeast. 
Asia seem to be able to get 

309
00:15:26,500 --> 00:15:28,400
things done with far less 
resources. 

310
00:15:28,400 --> 00:15:30,400
I think that's pretty remarkable
as well. 

311
00:15:30,800 --> 00:15:33,800
So what are some of the trends? 
As in your work know like you 

312
00:15:33,800 --> 00:15:36,600
see that the VC is investing 
more in? 

313
00:15:36,600 --> 00:15:39,400
Is there any industrial is there
any technology that are 

314
00:15:39,400 --> 00:15:42,500
outstanding? 
I have a lot of opinions on this

315
00:15:42,500 --> 00:15:45,300
so I have definitely noticed as 
collaborative tooling. 

316
00:15:45,700 --> 00:15:48,800
I don't know if I see this quite
as much in Southeast Asia. 

317
00:15:48,900 --> 00:15:53,100
I'm not sure why but I see a lot
more collaborative tooling being

318
00:15:53,100 --> 00:15:56,600
picked up in the u.s. 
Things like figma for designers.

319
00:15:56,700 --> 00:16:00,300
One of my new personal favorites
is actually Escala draw, which 

320
00:16:00,300 --> 00:16:04,500
is a online whiteboarding. 
Will I see a lot of these new 

321
00:16:04,500 --> 00:16:08,400
startups coming out that provide
remote based collaboration 

322
00:16:08,400 --> 00:16:09,900
tooling and it's a good 
framework. 

323
00:16:09,900 --> 00:16:11,700
I think I see more investment 
here. 

324
00:16:11,700 --> 00:16:16,900
I see more people also entering 
with the consumer first mindset 

325
00:16:16,900 --> 00:16:20,000
things that were usually 
traditionally just a B2B sales 

326
00:16:20,000 --> 00:16:25,000
model is now being driven by a 
slack like growth model where 

327
00:16:25,000 --> 00:16:29,200
you have a certain department or
team that adopts the tool, the 

328
00:16:29,200 --> 00:16:32,900
tool usage ends up becoming 
widespread in the Company. 

329
00:16:33,000 --> 00:16:36,100
And then the company is all but 
forced to pay for the Enterprise

330
00:16:36,100 --> 00:16:38,800
plan, because there's so many 
people at the company already 

331
00:16:38,800 --> 00:16:41,000
using it. 
And I think this is one Trend 

332
00:16:41,000 --> 00:16:44,200
that I see happening much more 
quickly in the u.s. 

333
00:16:44,200 --> 00:16:47,700
Than in Southeast Asia, but I 
hope we'll see that Trend and 

334
00:16:47,700 --> 00:16:50,300
just the better tooling come to 
Southeast Asian soon. 

335
00:16:50,700 --> 00:16:53,400
This is interesting because we 
also see a lot of big 

336
00:16:53,400 --> 00:16:55,800
Enterprises. 
Microsoft obviously has been 

337
00:16:55,900 --> 00:16:59,300
around industry for a long time.
Google has its own tea, sweet or

338
00:16:59,300 --> 00:17:01,200
what is called Google workspace 
or now. 

339
00:17:01,400 --> 00:17:04,500
Why do you think that Some rooms
for these new startups to play 

340
00:17:04,500 --> 00:17:07,599
in this collaboration tools. 
That's very true. 

341
00:17:07,599 --> 00:17:10,500
The mindset of collaboration 
tooling has generally been 

342
00:17:10,500 --> 00:17:13,599
around project management or 
it's been around. 

343
00:17:13,599 --> 00:17:17,500
How do I get alignment and share
the discussions that we're 

344
00:17:17,500 --> 00:17:20,599
having internally with the rest 
of the organization. 

345
00:17:20,800 --> 00:17:23,900
So collaboration management and 
tooling has been very 

346
00:17:23,900 --> 00:17:25,500
traditionally thought in these 
ways. 

347
00:17:25,500 --> 00:17:29,100
It's about getting alignment. 
It's about reaching consensus. 

348
00:17:29,100 --> 00:17:31,700
And that is the type of 
collaboration tooling that many 

349
00:17:31,700 --> 00:17:34,100
companies. 
In Southeast Asia are drawn to 

350
00:17:34,100 --> 00:17:37,000
and are using today already. 
But this new wave of 

351
00:17:37,000 --> 00:17:39,700
collaboration tooling. 
What I've noticed is, it's very 

352
00:17:39,700 --> 00:17:43,500
specific to a specific team. 
It's something that a team can 

353
00:17:43,500 --> 00:17:46,900
put together five to ten. 
People can look at a screen, 

354
00:17:46,900 --> 00:17:50,300
start whiteboarding together. 
People can provide comments on 

355
00:17:50,300 --> 00:17:55,500
design, for example, but it's 
become much more for a unit of 

356
00:17:55,500 --> 00:18:00,100
work rather than an entire teams
collaboration management. 

357
00:18:00,100 --> 00:18:02,200
And so I see less management 
tooling. 

358
00:18:02,500 --> 00:18:05,000
I think is a lot of what 
Microsoft and Google build and 

359
00:18:05,000 --> 00:18:08,900
instead a lot more of these 
real-time synchronous tooling 

360
00:18:08,900 --> 00:18:13,500
systems that let people have 
discussions in A Better Way live

361
00:18:13,500 --> 00:18:15,900
together. 
So I do think this is where I'd 

362
00:18:15,900 --> 00:18:18,600
love to see more people come 
together and adopt these types 

363
00:18:18,600 --> 00:18:20,900
of tooling because I think it 
cuts through a lot of the 

364
00:18:20,900 --> 00:18:23,700
communication that happens on 
video where people are trying to

365
00:18:23,700 --> 00:18:27,200
explain things, but honestly, 
some people have very different 

366
00:18:27,200 --> 00:18:29,500
learning styles. 
When I started using things like

367
00:18:29,500 --> 00:18:32,400
Excalibur draw, I ended up 
realizing that some of The 

368
00:18:32,400 --> 00:18:35,700
people in the team that I work 
with are very visual Learners. 

369
00:18:35,700 --> 00:18:38,500
They don't really see the 
connection of the arguments that

370
00:18:38,500 --> 00:18:41,900
I'm making, unless there's a 
diagram or, like, literally a 

371
00:18:41,900 --> 00:18:45,200
drawing connecting these two 
points of a funnel together and 

372
00:18:45,200 --> 00:18:47,800
illustrating why the drop-off 
rate is so important here. 

373
00:18:47,900 --> 00:18:50,700
So these are the ways that I've 
seen collaboration. 

374
00:18:50,700 --> 00:18:55,700
Evolving from the overall 
company perspective, down to how

375
00:18:55,700 --> 00:18:58,400
can we make individual teams 
more effective? 

376
00:18:58,800 --> 00:19:01,300
So you mention it is growing 
there in the u.s. 

377
00:19:01,300 --> 00:19:04,800
How about in Southeast Asia? 
What is the most recent Trends? 

378
00:19:05,200 --> 00:19:08,700
I think I have actually noticed 
wellness and mental health, 

379
00:19:08,700 --> 00:19:12,000
becoming more widely accepted 
and widely discussed. 

380
00:19:12,000 --> 00:19:14,900
I think this is super important.
It's something that we actually 

381
00:19:14,900 --> 00:19:18,500
tried to implement at go-jek 
early on right before I left. 

382
00:19:18,600 --> 00:19:20,900
It's actually a fairly new 
phenomenon. 

383
00:19:20,900 --> 00:19:22,900
The startup scene has just 
started. 

384
00:19:23,000 --> 00:19:25,300
I know some people will have 
been there for a decade and say 

385
00:19:25,300 --> 00:19:27,000
no. 
It's been here always, but 

386
00:19:27,000 --> 00:19:30,800
really at the point in which 
there was a unicorn in every 

387
00:19:30,800 --> 00:19:33,300
country in Southeast Asia. 
Yeah, that's I think when the 

388
00:19:33,300 --> 00:19:36,500
pressure really becomes 
overwhelming for a lot of people

389
00:19:36,500 --> 00:19:38,500
because there is that pressure 
to compete. 

390
00:19:38,500 --> 00:19:41,800
There is that pressure to be 
compared with your competitor? 

391
00:19:41,800 --> 00:19:45,500
Not just in your own country, 
but then in a neighboring region

392
00:19:45,500 --> 00:19:48,600
as well, so there is a lot more 
pressure these days, I think for

393
00:19:48,600 --> 00:19:52,200
startups and I think that has 
helped introduce or create an 

394
00:19:52,200 --> 00:19:54,400
entry point for these Wellness 
startups. 

395
00:19:54,400 --> 00:19:57,100
That are beginning. 
I think, see Roy at Carousel 

396
00:19:57,100 --> 00:20:00,100
recently, shared one with me 
that he'd recently invested in. 

397
00:20:00,100 --> 00:20:04,100
I think they're super powerful. 
I'd love to See more wellness 

398
00:20:04,200 --> 00:20:08,400
and mindfulness startups, take 
place and grow in Southeast 

399
00:20:08,400 --> 00:20:09,700
Asia. 
I definitely see that Trend 

400
00:20:09,700 --> 00:20:12,300
happening now. 
So I was in a way about that. 

401
00:20:12,300 --> 00:20:14,800
I think it's very interesting 
for me to check out after this, 

402
00:20:14,800 --> 00:20:16,300
the recent covid. 
Pandemic. 

403
00:20:16,300 --> 00:20:19,300
Do you think, is there any 
impact so far with the investing

404
00:20:19,300 --> 00:20:21,800
World PC World and the startup 
growth in general? 

405
00:20:21,800 --> 00:20:25,200
This is such a good question. 
I actually was talking with 

406
00:20:25,200 --> 00:20:28,200
someone recently about this in 
the Philippines that if there 

407
00:20:28,200 --> 00:20:31,900
were any time better for 
Southeast Asia to get funding, 

408
00:20:31,900 --> 00:20:34,900
it should actually be now 
because startups in the u.s. 

409
00:20:35,000 --> 00:20:38,400
Unless they are completely 
remote friendly and primarily 

410
00:20:38,400 --> 00:20:41,400
for a remote workplace. 
They're not able to get to 

411
00:20:41,400 --> 00:20:43,200
scale. 
They're not able to launch and 

412
00:20:43,200 --> 00:20:46,600
do their offline campaigns. 
It's become very difficult to 

413
00:20:46,600 --> 00:20:49,900
reach Mass market and have sales
meetings, and things like that. 

414
00:20:49,900 --> 00:20:53,700
And so where you are, however 
able to do this, is in places 

415
00:20:53,700 --> 00:20:56,200
where the virus has been 
relatively contained, where 

416
00:20:56,200 --> 00:21:00,200
people can go about, the regular
lives can go to restaurants, F 

417
00:21:00,200 --> 00:21:02,200
and B is open again and 
thriving. 

418
00:21:02,300 --> 00:21:04,700
Sammy's are starting to go back 
to work. 

419
00:21:05,000 --> 00:21:07,800
This is actually the right time 
for Southeast Asia to actually 

420
00:21:07,800 --> 00:21:11,200
dominate more of the investment 
pipeline because they're able to

421
00:21:11,200 --> 00:21:14,000
go back to business and show 
results and show growth. 

422
00:21:14,100 --> 00:21:16,200
I've been a part of the 500 
startups. 

423
00:21:16,200 --> 00:21:18,300
Investor discussion groups on 
slack. 

424
00:21:18,300 --> 00:21:22,100
We have a pretty huge directory 
of, I think maybe close to 500 

425
00:21:22,100 --> 00:21:25,000
different VCS, and it's been an 
incredible place where I'm 

426
00:21:25,000 --> 00:21:28,000
noticing more people, provide 
those opportunities and reach 

427
00:21:28,000 --> 00:21:30,800
out to more southeast Asia 
startups because they know 

428
00:21:30,800 --> 00:21:32,100
that's where the growth is right
now. 

429
00:21:32,300 --> 00:21:35,100
Now, I've read somewhere before 
that after these kind of a 

430
00:21:35,108 --> 00:21:36,800
crisis. 
For example, like financial 

431
00:21:36,800 --> 00:21:40,500
crisis last time, there will be 
lots of new startups, especially

432
00:21:40,500 --> 00:21:43,800
growing to become unicorns. 
So, after this pandemic, do you 

433
00:21:43,800 --> 00:21:47,400
foresee some new startups 
popping up and become unicorns. 

434
00:21:47,600 --> 00:21:51,800
I think that you'd have to be a 
very tenacious founder to want 

435
00:21:51,800 --> 00:21:54,700
to start at this time. 
And I think that helps with some

436
00:21:54,700 --> 00:21:58,800
of that self-selection, that 
only the best will be ready. 

437
00:21:58,900 --> 00:22:02,100
And I think determined enough to
want to work. 

438
00:22:02,300 --> 00:22:05,200
And try to build a company in 
this type of environment. 

439
00:22:05,200 --> 00:22:08,800
And so you already have just the
best types of Founders starting 

440
00:22:08,800 --> 00:22:11,400
in this environment with people 
who are starting a company right

441
00:22:11,400 --> 00:22:14,300
now, are likely to do pretty 
well because they are so 

442
00:22:14,300 --> 00:22:16,700
determined. 
And when there isn't a pandemic,

443
00:22:16,700 --> 00:22:19,100
the only do even better. 
So I am excited. 

444
00:22:19,100 --> 00:22:22,000
I think it will see people who 
have had the time to really 

445
00:22:22,000 --> 00:22:25,700
refine their product during the 
quarantine to do. 

446
00:22:25,700 --> 00:22:31,200
Very close user experience, 
surveys product reviews, and 

447
00:22:31,200 --> 00:22:34,700
just have time to Develop heads 
down and then when the pandemic 

448
00:22:34,700 --> 00:22:37,400
is lifted, and hopefully we're 
all back to normal soon. 

449
00:22:37,500 --> 00:22:40,500
Then they'll be able to go full 
force and the next founder could

450
00:22:40,500 --> 00:22:43,200
be creating the world's next 
unicorn for sure. 

451
00:22:43,600 --> 00:22:45,400
I'm looking forward to see that 
coming. 

452
00:22:45,400 --> 00:22:48,700
So obviously hopefully this 
pandemic is ending maybe next 

453
00:22:48,700 --> 00:22:51,200
year or early next year. 
So we all hope so and we'll 

454
00:22:51,200 --> 00:22:54,700
start seeing new startups, new 
kind of startups where probably 

455
00:22:54,700 --> 00:22:57,700
they will dominate some areas. 
Like remote obviously is one 

456
00:22:57,700 --> 00:23:02,100
thing working and then maybe 
digital kind of payment page. 

457
00:23:02,300 --> 00:23:05,400
Then wallet and some countries 
has just started especially in 

458
00:23:05,400 --> 00:23:09,900
Southeast Asia around digital 
banking one interesting place. 

459
00:23:09,900 --> 00:23:14,500
Where there will probably be new
startups needed is in the 

460
00:23:14,500 --> 00:23:17,800
entrepreneurship segment. 
There are a lot more people who 

461
00:23:17,800 --> 00:23:22,300
are now out of work who are 
maybe like me want to go and see

462
00:23:22,300 --> 00:23:25,100
if they can make something of 
their own, maybe they're not 

463
00:23:25,100 --> 00:23:27,600
necessarily building a huge 
company, but they want to 

464
00:23:27,600 --> 00:23:30,400
provide their own Services. 
They want to be an independent 

465
00:23:30,400 --> 00:23:33,400
contractor. 
They want to to try to provide 

466
00:23:33,400 --> 00:23:35,200
and build something 
independently. 

467
00:23:35,400 --> 00:23:38,200
Not unlike the open source 
Community where they see 

468
00:23:38,200 --> 00:23:40,000
problems. 
They want to create something to

469
00:23:40,000 --> 00:23:42,700
fix it. 
And of course, they will want to

470
00:23:42,700 --> 00:23:44,400
be paid for it in many of these 
cases. 

471
00:23:44,500 --> 00:23:48,000
And so, I think platforms that 
help people be self-sustaining 

472
00:23:48,000 --> 00:23:51,100
that help people become may be 
more independent, Better 

473
00:23:51,100 --> 00:23:54,400
Business people collect invoices
on time, Etc. 

474
00:23:54,500 --> 00:23:57,500
I think these platforms will 
also see a new rise, as well. 

475
00:23:58,000 --> 00:24:00,400
So, this is an interesting 
point, you made, because this 

476
00:24:00,400 --> 00:24:04,900
type of I call them in the heck.
Dependent maybe small businesses

477
00:24:04,900 --> 00:24:08,900
doesn't necessarily aspire to 
grow as big as Kojak or ruber, 

478
00:24:09,000 --> 00:24:11,000
but they want to be successful 
as well. 

479
00:24:11,000 --> 00:24:13,400
Maybe million dollars type of 
valuation. 

480
00:24:13,500 --> 00:24:16,300
What are some of the things that
can be done to support these 

481
00:24:16,300 --> 00:24:18,600
types of entrepreneurs? 
Or is there any some kind of 

482
00:24:18,600 --> 00:24:21,200
funding available for them? 
Because I'm not sure whether the

483
00:24:21,200 --> 00:24:24,000
PCS are interested as well, for 
this type of businesses. 

484
00:24:24,400 --> 00:24:27,200
Exactly. 
I think that VC financing is not

485
00:24:27,200 --> 00:24:29,900
the right place for this. 
I think that there are plenty of

486
00:24:29,900 --> 00:24:32,100
good ways for governments to 
support these. 

487
00:24:32,200 --> 00:24:36,100
These industries by providing 
work loans, by providing 

488
00:24:36,100 --> 00:24:39,400
business loans that carry low 
interest rates that help people 

489
00:24:39,500 --> 00:24:42,300
ideate and figure out whether or
not their idea works and to 

490
00:24:42,300 --> 00:24:45,100
provide them with that initial 
investment that will actually 

491
00:24:45,100 --> 00:24:47,800
pay off because they'll be 
building an independent 

492
00:24:47,800 --> 00:24:50,200
employment sector. 
That could eventually be not 

493
00:24:50,200 --> 00:24:52,900
employing maybe thousands of 
people but like you said, 

494
00:24:52,900 --> 00:24:56,300
employing maybe five, ten 
different Freelancers five to 

495
00:24:56,300 --> 00:24:58,800
ten different Engineers or 
designers. 

496
00:24:58,800 --> 00:25:02,100
I think that this is definitely 
one place I see growing there. 

497
00:25:02,200 --> 00:25:06,500
A very good book written just 
about Korean fried chicken 

498
00:25:06,500 --> 00:25:10,800
restaurants that were surging in
97 because of the financial 

499
00:25:10,800 --> 00:25:14,100
crisis, where a lot of these 
Korean families, no longer had 

500
00:25:14,100 --> 00:25:17,300
an opportunity to go to work and
instead decided to open a 

501
00:25:17,300 --> 00:25:19,700
restaurant. 
Now, the problem with that use 

502
00:25:19,700 --> 00:25:21,800
cases, that it was completely 
saturated. 

503
00:25:21,800 --> 00:25:25,100
You had a mom and pop shop on 
every block selling the exact 

504
00:25:25,100 --> 00:25:27,800
same type of food, but I think 
it's southeast Asia, in general,

505
00:25:27,800 --> 00:25:29,900
especially in the Digital 
Services economy. 

506
00:25:30,100 --> 00:25:32,100
You have people who can provide 
a wide range. 

507
00:25:32,200 --> 00:25:35,500
Range of services. 
There is a ton of work and data 

508
00:25:35,500 --> 00:25:40,400
Consulting and designing and 
Brandon and PR and agencies. 

509
00:25:40,500 --> 00:25:44,500
I would hope that we have a lot 
more of these solo entrepreneurs

510
00:25:44,500 --> 00:25:48,100
or small group. 
I guess not really coops, but 

511
00:25:48,100 --> 00:25:50,700
kind of a co-op where it's a 
couple of people who want to 

512
00:25:50,700 --> 00:25:54,100
work together as a cooperative 
and build a product together. 

513
00:25:54,200 --> 00:25:56,400
I definitely encourage this kind
of collaboration. 

514
00:25:56,800 --> 00:25:58,700
Cool. 
So crazy like you just mentioned

515
00:25:58,700 --> 00:26:01,800
you work with reforge. 
So maybe can you tell me about? 

516
00:26:02,800 --> 00:26:06,000
And how do you actually 
interested to join that? 

517
00:26:06,000 --> 00:26:08,700
And what kind of, some of the 
interesting activities that you 

518
00:26:08,700 --> 00:26:10,900
have done with reforge? 
Absolutely. 

519
00:26:10,900 --> 00:26:14,400
I'm super glad you asked, I for 
one really believe that. 

520
00:26:14,400 --> 00:26:18,000
What southeast Asia often needs 
is just opportunity. 

521
00:26:18,000 --> 00:26:20,300
They need resources. 
They need help with 

522
00:26:20,300 --> 00:26:24,200
whiteboarding ideas, bouncing 
ideas off of advisors, working 

523
00:26:24,200 --> 00:26:26,900
with people who have seen it 
before, in Southeast Asia, 

524
00:26:26,900 --> 00:26:30,000
because everything is so new 
because Go Jack was the first 

525
00:26:30,000 --> 00:26:33,500
unicorn in Indonesia. 
There was no One, we could ask. 

526
00:26:33,500 --> 00:26:35,800
Hey how have you solved this 
problem before? 

527
00:26:35,900 --> 00:26:38,800
Because we were the first people
to see this and Indonesia just 

528
00:26:38,800 --> 00:26:41,300
that lack of perspective. 
I think was a real challenge for

529
00:26:41,300 --> 00:26:44,900
us and I always reached out back
when I would travel back home to

530
00:26:44,900 --> 00:26:46,800
the US. 
I would reach out to anyone. 

531
00:26:46,800 --> 00:26:50,100
Who would answer my WhatsApp 
message, my LinkedIn message, my

532
00:26:50,100 --> 00:26:52,000
cold call email. 
I would ask. 

533
00:26:52,000 --> 00:26:53,500
Hey, I have a list of these 
questions. 

534
00:26:53,500 --> 00:26:55,500
Can you help me? 
What do you think? 

535
00:26:55,600 --> 00:26:57,900
And so that actually is why I'm 
really interested in the 

536
00:26:57,900 --> 00:27:01,700
research team because they are 
composed of some of the world's 

537
00:27:01,700 --> 00:27:04,400
greatest. 
Growth companies, we have the CP

538
00:27:04,400 --> 00:27:06,700
0 of Eventbrite. 
There's the head of growth from 

539
00:27:06,700 --> 00:27:11,300
slack and there's just a ton of 
resources available for people 

540
00:27:11,300 --> 00:27:15,500
who are within this community. 
We recently launched a reforged 

541
00:27:15,500 --> 00:27:19,300
beta advisors partner program 
where we were accepting 

542
00:27:19,300 --> 00:27:22,700
applications and we got hundreds
of applications from around the 

543
00:27:22,700 --> 00:27:25,700
world of startups that are 
looking for growth advisory 

544
00:27:25,700 --> 00:27:27,900
help. 
And we're not taking or charging

545
00:27:27,900 --> 00:27:29,900
salary. 
We are taking a very small 

546
00:27:29,900 --> 00:27:34,000
percentage of equity based on 
the We commonly accepted fast 

547
00:27:34,000 --> 00:27:37,000
template agreement. 
But what this allows for is that

548
00:27:37,000 --> 00:27:41,300
startups who are growing who 
just need an hour or two of time

549
00:27:41,300 --> 00:27:44,100
to whiteboard to figure out 
growth Loops to figure out 

550
00:27:44,100 --> 00:27:47,500
growth models, are able to get 
access to this amazing resource,

551
00:27:47,600 --> 00:27:50,800
this team of experts who have 
seen it before. 

552
00:27:51,000 --> 00:27:53,500
That's not to say that we'll 
have all the answers. 

553
00:27:53,500 --> 00:27:56,600
Or that we've seen every problem
that is admittedly going to be 

554
00:27:56,600 --> 00:27:59,200
very different in every country.
But at least they have that 

555
00:27:59,200 --> 00:28:01,000
opportunity with the research 
team. 

556
00:28:01,000 --> 00:28:03,900
We've really been helping. 
Handful of startups that are in 

557
00:28:03,900 --> 00:28:07,600
the growth stage of their life. 
Cycle are trying to meet and 

558
00:28:07,600 --> 00:28:10,900
close that gap between product 
and marketing and I've been 

559
00:28:10,900 --> 00:28:12,100
super thrilled to work with 
them. 

560
00:28:12,100 --> 00:28:15,500
I actually recently published a 
blog post on why most analytics 

561
00:28:15,500 --> 00:28:17,700
efforts fail. 
A lot of it based on the 

562
00:28:17,700 --> 00:28:21,000
learnings that I've had at Go 
Jack about how to track an app 

563
00:28:21,000 --> 00:28:23,600
events. 
How do we create a data 

564
00:28:23,600 --> 00:28:25,700
platform? 
That works not just for the 

565
00:28:25,700 --> 00:28:28,100
engineering team, but for the 
business team so that they can 

566
00:28:28,100 --> 00:28:31,200
also make decisions on the data.
So this has been super exciting 

567
00:28:31,200 --> 00:28:33,700
work the past year. 
So, one of you likes, but these 

568
00:28:33,700 --> 00:28:36,000
obviously is around startup 
growth strategy. 

569
00:28:36,000 --> 00:28:39,500
And I'm sure you play a big role
as well and reforge to advise 

570
00:28:39,500 --> 00:28:42,300
startups on this area. 
Maybe can you share some of your

571
00:28:42,300 --> 00:28:45,900
biggest tips for startups that 
are looking for growth? 

572
00:28:46,200 --> 00:28:50,500
My biggest tip that I see most 
startups get wrong is do not 

573
00:28:50,500 --> 00:28:54,400
goal on retention itself. 
A lot of companies will set 

574
00:28:54,400 --> 00:28:58,600
their okrs around three-month 
retention to month retention. 

575
00:28:58,800 --> 00:29:01,200
And to me it's a metric. 
It's a metric that you should be

576
00:29:01,200 --> 00:29:03,800
monitoring. 
Lie, but it's not something you 

577
00:29:03,800 --> 00:29:06,900
direct a team towards every 
quarter. 

578
00:29:06,900 --> 00:29:10,200
I do this exercise with a 
companies that I advise, we look

579
00:29:10,200 --> 00:29:14,600
at the behaviors that correlate,
with retention, and we use those

580
00:29:14,600 --> 00:29:16,700
as the metrics to goal on. 
Instead. 

581
00:29:16,800 --> 00:29:19,200
You could look at a company. 
Let's say, Facebook. 

582
00:29:19,200 --> 00:29:22,600
They had something like 10 
friends in seven days. 

583
00:29:22,800 --> 00:29:26,300
That is not an aha moment. 
Contrary to popular opinion. 

584
00:29:26,400 --> 00:29:29,100
That's actually what I would 
call a setup moment. 

585
00:29:29,200 --> 00:29:32,500
A setup moment. 
Helps the user in the Product 

586
00:29:32,500 --> 00:29:35,500
get two and reach the aha 
moment. 

587
00:29:35,500 --> 00:29:38,900
And so for me to create a good 
user experience in a platform 

588
00:29:38,900 --> 00:29:42,100
like Facebook, the setup moment 
would be how do I get the user 

589
00:29:42,100 --> 00:29:45,500
to get 10 friends in seven days.
Once they've gotten 10, friends,

590
00:29:45,500 --> 00:29:48,400
in seven days. 
Now, it's time to reach the aha 

591
00:29:48,400 --> 00:29:50,700
moment. 
The aha moment is correlated 

592
00:29:50,700 --> 00:29:54,900
with, let's say seeing three 
Newsfeed posts in the first one 

593
00:29:54,900 --> 00:29:57,500
day, and they have to be 
relevant posts and that's not 

594
00:29:57,500 --> 00:30:00,300
going to happen unless you get 
those ten friends for that user.

595
00:30:00,500 --> 00:30:02,600
And so that's why these 
behaviors Or actually 

596
00:30:02,600 --> 00:30:05,400
correlated. 
The first behavior is getting 10

597
00:30:05,400 --> 00:30:08,900
friends, in seven days, the next
behavior for the aha moment is 

598
00:30:08,900 --> 00:30:12,300
probably something like getting 
three posts in the first day and

599
00:30:12,300 --> 00:30:15,800
then, the long-term retention 
that your is something closer to

600
00:30:15,800 --> 00:30:18,900
seven active days in the first 
10 days. 

601
00:30:19,100 --> 00:30:21,900
What I tend to look at and what 
I tend to do with my companies 

602
00:30:21,900 --> 00:30:26,100
is identify those habits and 
those moments and behaviors that

603
00:30:26,100 --> 00:30:29,800
correlate, with retention rather
than saying how do we get to 

604
00:30:29,800 --> 00:30:31,800
three months of retention 
itself? 

605
00:30:32,100 --> 00:30:35,300
So why do you think three months
retention is not enough? 

606
00:30:35,300 --> 00:30:37,000
Because based on my limited 
knowledge? 

607
00:30:37,000 --> 00:30:40,200
Obviously looking at the three 
months retention is probably 

608
00:30:40,200 --> 00:30:43,600
quite close in terms of seeing 
the growth of your products or 

609
00:30:43,600 --> 00:30:45,800
the growth of your users. 
So, why do you think it's very 

610
00:30:45,800 --> 00:30:49,700
important to see the behaviors 
that probably correlate with the

611
00:30:49,700 --> 00:30:51,400
long-term retention of the 
users? 

612
00:30:51,400 --> 00:30:53,200
You're asking such good 
questions. 

613
00:30:53,200 --> 00:30:56,800
Any most companies will look at 
three month retention and say 

614
00:30:56,800 --> 00:30:59,300
great. 
We have either done it or we 

615
00:30:59,300 --> 00:31:02,100
haven't what happens though. 
Is that when? 

616
00:31:02,200 --> 00:31:05,200
And teams are given this 
pressure to get to three-month 

617
00:31:05,200 --> 00:31:07,900
retention. 
They will start trying anything 

618
00:31:07,900 --> 00:31:11,500
and everything to get there. 
They will spam promotions. 

619
00:31:11,500 --> 00:31:14,200
They will send out tons of push 
notifications. 

620
00:31:14,200 --> 00:31:18,100
They will create irrelevant 
content and send users on this 

621
00:31:18,100 --> 00:31:20,700
wild goose. 
Chase that actually confuses 

622
00:31:20,700 --> 00:31:22,800
them about what the product is 
about. 

623
00:31:22,800 --> 00:31:25,200
And so when I do this 
correlation analysis, what's 

624
00:31:25,200 --> 00:31:28,600
important is to be able to 
identify the behaviors that 

625
00:31:28,600 --> 00:31:31,900
correlate with retention, 
positively have strong. 

626
00:31:32,100 --> 00:31:34,700
On correlation. 
And most importantly that we 

627
00:31:34,700 --> 00:31:39,000
realize at what point, does it 
not really matter if a user gets

628
00:31:39,000 --> 00:31:42,700
to let's say, 8 app opens in the
first 3 days. 

629
00:31:43,000 --> 00:31:45,800
Does it really matter? 
If we get them from 7 to 8, 

630
00:31:45,900 --> 00:31:49,600
probably not what really matters
is that we get them to the aha 

631
00:31:49,600 --> 00:31:53,000
moment at least three times. 
And at that point you then 

632
00:31:53,000 --> 00:31:55,700
realize what the content 
strategy should be that, you 

633
00:31:55,700 --> 00:31:59,500
don't need to spam your users 
that the natural frequency of 

634
00:31:59,500 --> 00:32:01,700
usage of. 
This product is no more than 

635
00:32:01,700 --> 00:32:04,000
three. 
Out of seven days and for a 

636
00:32:04,000 --> 00:32:07,500
company or team to ignore that 
and create a completely 

637
00:32:07,500 --> 00:32:09,200
different experience for the 
users. 

638
00:32:09,200 --> 00:32:11,800
That's disruptive. 
It creates churn people 

639
00:32:11,800 --> 00:32:14,100
uninstall the app. 
You don't want that. 

640
00:32:14,300 --> 00:32:15,800
And so for me, it's really 
important. 

641
00:32:15,800 --> 00:32:20,800
That the team knows the specific
behaviors to go Lon rather than 

642
00:32:20,800 --> 00:32:24,200
retention itself because when 
they goal on retention, they end

643
00:32:24,200 --> 00:32:27,800
up going in a million different 
directions rather than focusing 

644
00:32:27,800 --> 00:32:31,400
their resources on trying to 
identify a specific behavior. 

645
00:32:31,700 --> 00:32:34,600
So this Is the exercise that I 
actually do with these 

646
00:32:34,600 --> 00:32:38,000
companies, I asked them. 
And we can try this to Henry, 

647
00:32:38,300 --> 00:32:41,000
close your eyes and imagine 
everything in the world. 

648
00:32:41,100 --> 00:32:44,200
That is orange. 
So you're thinking maybe a 

649
00:32:44,208 --> 00:32:46,300
basketball. 
Okay, what else? 

650
00:32:46,300 --> 00:32:48,800
An orange itself? 
There's maybe two things that 

651
00:32:48,800 --> 00:32:52,200
you think of in these 30 
seconds, but then let's try this

652
00:32:52,200 --> 00:32:54,100
again. 
Let's think of everything in the

653
00:32:54,100 --> 00:32:56,400
world. 
That's orange, but within a 

654
00:32:56,408 --> 00:33:00,500
construction site, and so now 
when you have this very concrete

655
00:33:00,500 --> 00:33:04,300
and admittedly small. 
Space to ideate on, you actually

656
00:33:04,300 --> 00:33:07,800
come up with more ideas. 
You come up with more concrete 

657
00:33:07,800 --> 00:33:11,800
ways to achieve the goal, the 
difference between saying, let's

658
00:33:11,800 --> 00:33:14,300
think of everything in the world
that would get us to three 

659
00:33:14,300 --> 00:33:17,600
months of retention versus what 
are the things that we can do to

660
00:33:17,600 --> 00:33:21,000
get a user to experience. 
Our Newsfeed three times within 

661
00:33:21,000 --> 00:33:23,500
the first seven days. 
It's a very different experience

662
00:33:23,500 --> 00:33:26,500
that the team comes up with. 
This is very interesting 

663
00:33:26,500 --> 00:33:28,200
exercise. 
Definitely. 

664
00:33:28,200 --> 00:33:31,800
I understand how constrain can 
actually make you creative not 

665
00:33:32,000 --> 00:33:34,500
Just in this type of exercise, 
but basically almost everything 

666
00:33:34,500 --> 00:33:37,300
including, like, technology, 
teamwork or even like the 

667
00:33:37,300 --> 00:33:40,300
company policies, but the 
tendency of these startups, for 

668
00:33:40,300 --> 00:33:43,300
example, if you take e-commerce,
or maybe right sharing or 

669
00:33:43,300 --> 00:33:47,400
whatever that is, they just Spam
promotions or maybe emails every

670
00:33:47,400 --> 00:33:49,400
day. 
I receive tens or maybe 20 

671
00:33:49,400 --> 00:33:52,300
emails everyday, just offering 
promotion, discounts, or 

672
00:33:52,308 --> 00:33:54,500
whatever that is. 
So what are the things that some

673
00:33:54,500 --> 00:33:57,400
of these companies can do 
actually to use your behavior 

674
00:33:57,400 --> 00:33:59,800
correlation analysis. 
Yeah. 

675
00:33:59,800 --> 00:34:01,900
It's a really good question. 
And yes, you'll get email. 

676
00:34:02,100 --> 00:34:06,300
Companies like Zara sending here
is a coupon for a new jacket, a 

677
00:34:06,308 --> 00:34:08,699
new shirt every day. 
And then you have to ask 

678
00:34:08,699 --> 00:34:10,400
yourself. 
What is the natural frequency 

679
00:34:10,400 --> 00:34:12,800
for me to be buying things? 
Is it really supposed to be 

680
00:34:12,800 --> 00:34:14,600
every single day? 
I don't think so. 

681
00:34:14,699 --> 00:34:18,600
And so you get to this level of 
just Spam and I think what these

682
00:34:18,600 --> 00:34:20,900
companies have it done is 
likely. 

683
00:34:20,900 --> 00:34:25,100
They haven't done the analysis 
of what is the short-term gains 

684
00:34:25,100 --> 00:34:28,500
versus long-term gains. 
I am getting with this strategy.

685
00:34:28,600 --> 00:34:31,400
I'm actually hoping to write a 
blog post about this very soon. 

686
00:34:31,400 --> 00:34:32,500
So stay. 
Tuned. 

687
00:34:32,600 --> 00:34:36,300
But if a company were to take 
the time to analyze for every 

688
00:34:36,300 --> 00:34:39,199
message, I send, how many 
uninstalls am I getting how many

689
00:34:39,199 --> 00:34:41,600
people are unsubscribing? 
How many people are turning off 

690
00:34:41,600 --> 00:34:43,800
notifications? 
What does that add up to? 

691
00:34:43,800 --> 00:34:47,000
And the long term if they 
weren't going to purchase today?

692
00:34:47,000 --> 00:34:49,100
They're probably not going to 
purchase tomorrow. 

693
00:34:49,199 --> 00:34:51,900
If I don't even know the right 
frequency of purchases. 

694
00:34:52,000 --> 00:34:53,699
I'm going to lose these 
customers. 

695
00:34:54,000 --> 00:34:56,000
The goal is actually to 
identify. 

696
00:34:56,100 --> 00:34:59,000
How do we balance this mindset 
of? 

697
00:34:59,100 --> 00:35:01,800
I need to send a notification 
because if I don't know, one's 

698
00:35:01,800 --> 00:35:05,300
going to Transact in the short 
term versus if I nurture my 

699
00:35:05,300 --> 00:35:08,600
users give them the right 
frequency of Engagement. 

700
00:35:08,700 --> 00:35:13,200
I acknowledge that the use case 
is defined by the users need. 

701
00:35:13,300 --> 00:35:16,100
And I try to match that need and
slowly nurture. 

702
00:35:16,100 --> 00:35:18,900
It upwards. 
What is then my long-term 

703
00:35:19,000 --> 00:35:21,200
output? 
Because if I have to reacquire 

704
00:35:21,200 --> 00:35:24,100
these users time and time again 
because they are turning, they 

705
00:35:24,100 --> 00:35:25,800
are leaving and I'm annoying 
them. 

706
00:35:26,000 --> 00:35:28,700
The long-term impact of that 
means that I am literally 

707
00:35:28,700 --> 00:35:30,900
pushing people away to my 
competitors. 

708
00:35:30,900 --> 00:35:33,000
They don't want to be Part of my
product. 

709
00:35:33,000 --> 00:35:36,400
So I want teams to actually 
conduct this use case frequency 

710
00:35:36,400 --> 00:35:39,500
analysis, thinking through what 
are the use cases of someone 

711
00:35:39,500 --> 00:35:42,300
wanting to buy a shirt? 
What are the use cases of 

712
00:35:42,300 --> 00:35:45,100
someone wanting to buy ski, 
clothing companies, like 

713
00:35:45,100 --> 00:35:46,800
Patagonia actually do this 
really? 

714
00:35:46,800 --> 00:35:48,500
Well. 
They know that you're not going 

715
00:35:48,500 --> 00:35:51,900
to buy mountaineering clothing. 
I read it instead. 

716
00:35:51,900 --> 00:35:55,900
What they do is this very wise 
use case transition into their 

717
00:35:55,900 --> 00:35:58,100
social media. 
They give you something that you

718
00:35:58,100 --> 00:36:01,600
can consume every day. 
You can always consume super. 

719
00:36:02,200 --> 00:36:05,900
Landscapes through Instagram, 
there's no one who can't do that

720
00:36:05,900 --> 00:36:07,900
every day. 
And so they'll send you there. 

721
00:36:07,900 --> 00:36:10,700
They'll keep you top of mine and
they're providing you with 

722
00:36:10,700 --> 00:36:13,600
actually valuable content. 
And what they do there is that 

723
00:36:13,600 --> 00:36:17,200
they prevent needing to 
reacquire you every season that 

724
00:36:17,200 --> 00:36:20,200
their use case is available. 
So I would encourage companies 

725
00:36:20,200 --> 00:36:21,900
to figure out what are my use 
cases. 

726
00:36:22,000 --> 00:36:24,400
What are the frequencies for my 
product? 

727
00:36:24,400 --> 00:36:27,300
And what can I do to keep my 
user engaged in a way? 

728
00:36:27,300 --> 00:36:30,800
That doesn't cause them to turn 
that would cause me to have to 

729
00:36:30,800 --> 00:36:31,900
reacquire them. 
Later. 

730
00:36:32,000 --> 00:36:35,400
So it's very insightful. 
So the way I think of why these 

731
00:36:35,400 --> 00:36:38,300
startups doing it or maybe even 
like big companies doing it, 

732
00:36:38,300 --> 00:36:41,300
because they come back to your 
three-month retention growth 

733
00:36:41,300 --> 00:36:42,800
strategy. 
You just want to see the 

734
00:36:42,800 --> 00:36:45,300
numbers, keep growing. 
If you spend them enough 

735
00:36:45,300 --> 00:36:47,400
probably people will just need 
an unsubscribe. 

736
00:36:47,400 --> 00:36:49,800
Obviously, that's one thing. 
Or just based on impulsive 

737
00:36:49,800 --> 00:36:52,500
behavior, or based on some 
discount of promotions that they

738
00:36:52,500 --> 00:36:55,100
give, hopefully, the numbers 
will just increase and increase.

739
00:36:55,300 --> 00:36:58,100
And the other thing that 
probably I can guess as well 

740
00:36:58,100 --> 00:37:00,700
because you're saying, it's a 
long-term strategy and probably,

741
00:37:00,700 --> 00:37:04,300
you need to collect a lot of 
Data's and events of how users 

742
00:37:04,300 --> 00:37:06,300
are using your products or your 
systems. 

743
00:37:06,400 --> 00:37:08,600
So this is also coming back to 
the strategy, right? 

744
00:37:08,600 --> 00:37:11,100
How do you actually build your 
data analytics? 

745
00:37:11,800 --> 00:37:14,000
Such a good question? 
And yes, like you said it's 

746
00:37:14,000 --> 00:37:17,300
super hard to do this and be 
patient or use cases that are 

747
00:37:17,300 --> 00:37:22,100
very limited things like buying 
a new car going to the doctor. 

748
00:37:22,200 --> 00:37:24,800
These use cases are certainly 
much more difficult to do this 

749
00:37:24,800 --> 00:37:27,700
analysis for but I think what's 
important is that they start 

750
00:37:27,700 --> 00:37:31,300
building the Frameworks to 
understand how often users 

751
00:37:31,300 --> 00:37:34,200
perform these Behaviors. 
I think I did a Twitter poll. 

752
00:37:34,300 --> 00:37:36,700
Maybe last month. 
Asking people how frequently do 

753
00:37:36,700 --> 00:37:39,300
you go to the doctor? 
I asked do you go every five 

754
00:37:39,300 --> 00:37:42,200
years? 
Every one year every week, every

755
00:37:42,200 --> 00:37:45,100
time you're sick and the amount 
of people who sent five years 

756
00:37:45,100 --> 00:37:47,600
was huge. 
And so when I was talking to the

757
00:37:47,600 --> 00:37:50,000
company that I'm advising 
around, Healthcare was saying 

758
00:37:50,000 --> 00:37:53,100
look, people are saying they 
only really naturally use you 

759
00:37:53,100 --> 00:37:55,600
every five years. 
What does this mean for your 

760
00:37:55,600 --> 00:37:58,800
business and your reacquiring 
people for five years or you're 

761
00:37:58,800 --> 00:38:01,600
trying to figure out how to keep
them engaged for five years. 

762
00:38:02,200 --> 00:38:05,100
The question is then do you 
actually need to reframe? 

763
00:38:05,100 --> 00:38:08,400
The use case is they use case of
going to the doctor? 

764
00:38:08,500 --> 00:38:11,900
The primary use case or is there
actually a different use case 

765
00:38:11,900 --> 00:38:15,300
that has a higher frequency? 
Something like every time I'm 

766
00:38:15,300 --> 00:38:18,800
sick, where do I go to check my 
symptoms to make sure it's not 

767
00:38:18,800 --> 00:38:21,100
serious. 
Now that use case is actually 

768
00:38:21,100 --> 00:38:23,900
far more frequent. 
What we do is it depends on the 

769
00:38:23,900 --> 00:38:28,400
product for companies where the 
use case is extremely infrequent

770
00:38:28,400 --> 00:38:31,700
and what are the use cases that 
are adjacent to our product 

771
00:38:31,700 --> 00:38:33,500
that? 
Can be more frequent that we can

772
00:38:33,500 --> 00:38:36,200
use to collect data on a more 
regular Cadence. 

773
00:38:36,400 --> 00:38:38,700
Now, let's say you have a 
regular Cadence of data. 

774
00:38:38,800 --> 00:38:42,900
What do you then do from here? 
So my starting point is always 

775
00:38:42,900 --> 00:38:45,900
for new users that have 
experienced our core product 

776
00:38:45,900 --> 00:38:47,900
value prop. 
How frequently are they coming 

777
00:38:47,900 --> 00:38:51,300
back to our app within the first
seven days within the first 28 

778
00:38:51,300 --> 00:38:53,300
days? 
And within the first thirty four

779
00:38:53,300 --> 00:38:54,000
days? 
I think. 

780
00:38:54,200 --> 00:38:57,400
And so I've always looking at 
our users coming back to my app.

781
00:38:57,500 --> 00:39:00,900
Let's say in a seven-day window 
period, are they coming back 

782
00:39:00,900 --> 00:39:03,500
three to four days? 
Out of the seven that tells me 

783
00:39:03,500 --> 00:39:05,200
that the use cases every other 
day. 

784
00:39:05,400 --> 00:39:08,800
Are they coming back only one or
two days out of the seven? 

785
00:39:08,900 --> 00:39:11,400
And this told me that the use 
case is actually closer to once 

786
00:39:11,400 --> 00:39:13,400
a week. 
And we open the window in the 

787
00:39:13,400 --> 00:39:16,200
first 28 days of a user 
experiencing, the core value 

788
00:39:16,200 --> 00:39:18,700
proposition. 
Are there only coming back three

789
00:39:18,700 --> 00:39:22,200
to four times and those 28 days.
Then, yes, it's confirmed that 

790
00:39:22,200 --> 00:39:25,200
we have a weekly use case, which
means we should design our 

791
00:39:25,200 --> 00:39:27,700
Communications around that. 
There is a schedule by which the

792
00:39:27,700 --> 00:39:31,800
users need us, whether this is a
product for coffee or Fork. 

793
00:39:31,900 --> 00:39:34,700
Creating jira tickets, there's 
going to be an inherent. 

794
00:39:34,700 --> 00:39:37,900
Use case frequency that we need 
to align ourselves to first. 

795
00:39:38,000 --> 00:39:41,000
So that we know what, Cadence of
communication, we know what 

796
00:39:41,000 --> 00:39:44,700
retention looks like, so that we
can start bowling on the longer 

797
00:39:44,700 --> 00:39:46,900
term retention rates. 
So if we know what the 

798
00:39:46,908 --> 00:39:50,500
short-term retention is, let's 
say it's once a week, every 28 

799
00:39:50,500 --> 00:39:52,700
days. 
They need to be seen on our app 

800
00:39:52,700 --> 00:39:55,300
or our website at least three to
four times. 

801
00:39:55,500 --> 00:39:57,700
Then let's look at what 
long-term retention looks like. 

802
00:39:57,700 --> 00:40:00,700
So this is another kind of data 
analysis that they have to do. 

803
00:40:00,900 --> 00:40:04,600
They'll have to then Expand the 
cohort retention lines, and 

804
00:40:04,600 --> 00:40:07,300
we've all seen these charts 
where it's six, different lines,

805
00:40:07,300 --> 00:40:10,600
all starting on the left hand 
side of the axis and it looks 

806
00:40:10,600 --> 00:40:13,100
like a ski slope going down in 
the best companies. 

807
00:40:13,100 --> 00:40:15,700
It looks like a smile where you 
have retention going up over 

808
00:40:15,700 --> 00:40:18,300
time, but that's very rare to be
honest. 

809
00:40:18,300 --> 00:40:20,800
But let's say we're looking at 
that retention slope at some 

810
00:40:20,800 --> 00:40:23,100
point. 
Either its weekly or monthly. 

811
00:40:23,200 --> 00:40:26,400
You'll see that it starts to 
Flatline it stable. 

812
00:40:26,400 --> 00:40:29,600
It holds. 
Let's say it's 30% that most of 

813
00:40:29,600 --> 00:40:34,000
your users by week four. 
Or by week, six end up having a 

814
00:40:34,000 --> 00:40:37,000
thirty percent retention rate 
cohort after cohort. 

815
00:40:37,000 --> 00:40:39,500
And so now that we know that 
inflection point, at which the 

816
00:40:39,508 --> 00:40:42,600
attention is stabilized. 
That's our long-term goal. 

817
00:40:42,800 --> 00:40:45,900
That's the point at, which we 
know users are most likely to 

818
00:40:45,900 --> 00:40:48,400
retain. 
And so again, instead of going 

819
00:40:48,500 --> 00:40:52,200
on that six-week retention rate.
What we do is we do that 

820
00:40:52,200 --> 00:40:55,600
analysis again, so we're 
literally working ourselves to 

821
00:40:55,600 --> 00:40:58,500
the top of the retention funnel,
by doing the same behavioral 

822
00:40:58,500 --> 00:41:01,800
correlation analysis. 
We then look at for all of, 

823
00:41:02,000 --> 00:41:05,800
Users who have retained in week 
6, what are the behaviors that 

824
00:41:05,800 --> 00:41:09,900
they did in the first 28 days 
and the first four weeks? 

825
00:41:09,900 --> 00:41:12,800
And the first five weeks, maybe 
we'll look at things. 

826
00:41:12,800 --> 00:41:17,500
Like they need to have had two 
unique days of activity every 

827
00:41:17,500 --> 00:41:21,300
week for the first five weeks, 
then correlation with the sixth 

828
00:41:21,300 --> 00:41:24,300
week retention. 
Rate is at least 70%. 

829
00:41:24,400 --> 00:41:27,100
We're always looking for these 
different metrics again. 

830
00:41:27,100 --> 00:41:30,300
Nothing is ever static forever. 
We have to do is exercise every 

831
00:41:30,300 --> 00:41:33,400
quarter because our users And 
every quarter, but this is the 

832
00:41:33,400 --> 00:41:35,000
analytics Journey that we go 
through. 

833
00:41:35,500 --> 00:41:38,800
This sounds like a pretty hard 
core data analytics on my even 

834
00:41:38,800 --> 00:41:41,300
science thing. 
So for those new startups out 

835
00:41:41,300 --> 00:41:44,100
there, how can they execute this
kind of analysis? 

836
00:41:44,100 --> 00:41:46,200
Where do they start? 
Or maybe there are some tips 

837
00:41:46,200 --> 00:41:48,100
from you. 
Like, how can they begin this 

838
00:41:48,100 --> 00:41:50,600
kind of Journey Z like 
collecting data building some 

839
00:41:50,600 --> 00:41:52,500
kind of data warehouse 
capability? 

840
00:41:52,500 --> 00:41:54,800
Because in the beginning, you 
probably don't even have 

841
00:41:54,800 --> 00:41:57,900
resources to just sit down and 
analyze all these complex 

842
00:41:57,900 --> 00:41:59,400
behaviors. 
That's true. 

843
00:41:59,400 --> 00:42:01,800
I think it always like this 
stuff that they don't have the 

844
00:42:01,900 --> 00:42:04,400
I'm to be honest. 
This isn't actually a very 

845
00:42:04,400 --> 00:42:05,700
challenge of data science 
problem. 

846
00:42:05,700 --> 00:42:08,200
I think it sounds like a very 
challenging data science 

847
00:42:08,200 --> 00:42:11,100
problem, especially for a 
company that has only been going

848
00:42:11,100 --> 00:42:14,500
on retention itself because it's
such a different way to look at 

849
00:42:14,500 --> 00:42:16,200
the problem. 
Instead of saying, like the 

850
00:42:16,200 --> 00:42:18,800
standard metric is three-month 
retention rate. 

851
00:42:18,800 --> 00:42:20,800
Let's just look at that, that's 
super easy. 

852
00:42:20,800 --> 00:42:23,600
Every data platform has that 
built-in but they don't have 

853
00:42:23,600 --> 00:42:27,200
built-in though, is the founders
proprietary hypotheses. 

854
00:42:27,200 --> 00:42:28,800
That's actually where I always 
start. 

855
00:42:28,800 --> 00:42:31,700
I always start by asking teams. 
What do you think drives growth?

856
00:42:31,900 --> 00:42:35,600
What is the aha moment? 
When does it feel like a user is

857
00:42:35,600 --> 00:42:38,000
gaining a superpower by using 
your product? 

858
00:42:38,000 --> 00:42:41,900
Is it, when on Facebook, for 
example, you find out what a 

859
00:42:41,900 --> 00:42:44,100
friend is doing after ten years 
of not talking to them. 

860
00:42:44,100 --> 00:42:47,600
That's an aha moment for you. 
And so, how do we then translate

861
00:42:47,600 --> 00:42:49,800
that into metrics that we 
collect? 

862
00:42:49,900 --> 00:42:51,200
There are problems. 
Absolutely. 

863
00:42:51,200 --> 00:42:53,900
We're a company that I've been 
working with doesn't collect any

864
00:42:53,900 --> 00:42:55,600
data. 
They don't have any behavioral 

865
00:42:55,600 --> 00:42:58,000
data, then that's a problem. 
But that's why we always start 

866
00:42:58,000 --> 00:43:00,700
with these hypotheses around. 
What do you think are the 

867
00:43:00,700 --> 00:43:03,800
magical moments in your What are
the behaviors that you would 

868
00:43:03,800 --> 00:43:06,100
say? 
Okay, I know someone is 

869
00:43:06,100 --> 00:43:09,600
experiencing the aha moment on 
our product because let's say 

870
00:43:09,600 --> 00:43:11,900
for Pinterest. 
For example while they always 

871
00:43:11,900 --> 00:43:15,800
come here to create and plan a 
new design project. 

872
00:43:15,800 --> 00:43:18,500
That's how I know. 
Someone has felt the aha moment 

873
00:43:18,500 --> 00:43:21,800
of Pinterest with Goku Jack. 
It was actually a user comes 

874
00:43:21,800 --> 00:43:23,200
here. 
When they know they need to get 

875
00:43:23,200 --> 00:43:26,600
something done that involves. 
Someone bringing something over 

876
00:43:26,600 --> 00:43:29,500
a distance because we are 
Logistics platform for us. 

877
00:43:29,500 --> 00:43:31,700
A user has felt core value when 
they use us. 

878
00:43:31,800 --> 00:43:33,400
Or transportation in the 
morning. 

879
00:43:33,500 --> 00:43:36,100
The morning is. 
When you are in the most urgent,

880
00:43:36,100 --> 00:43:39,900
rush, you need reliability. 
You're not going out to meet 

881
00:43:39,900 --> 00:43:41,600
your friends. 
You're going to work and you 

882
00:43:41,607 --> 00:43:44,000
need to be on time. 
You need everything to be 

883
00:43:44,000 --> 00:43:46,600
reliable. 
That's how we knew that, the aha

884
00:43:46,600 --> 00:43:49,300
moment existed for these users. 
And that actually did correlate 

885
00:43:49,300 --> 00:43:51,000
with some of our long-term 
retention rates. 

886
00:43:51,000 --> 00:43:53,000
It just wasn't necessarily 
causation. 

887
00:43:53,200 --> 00:43:56,100
And so, we would know if someone
reached that retention rate 

888
00:43:56,100 --> 00:43:59,400
level, if they were able to have
that experience on our platform.

889
00:43:59,500 --> 00:44:02,500
So, what I would encourage 
companies to do is First figure 

890
00:44:02,500 --> 00:44:06,200
out what those hypotheses are. 
Some people have a very power 

891
00:44:06,200 --> 00:44:08,900
user mentality of their own 
products and they're like, oh 

892
00:44:08,900 --> 00:44:11,800
they're only experiencing core 
value when they're using 90% of 

893
00:44:11,800 --> 00:44:13,300
the features. 
That's not really true. 

894
00:44:13,400 --> 00:44:16,100
There's a shallow use case that 
exists where someone's using the

895
00:44:16,100 --> 00:44:17,200
product and they're like, oh, 
yeah. 

896
00:44:17,200 --> 00:44:19,800
I like this works for me. 
What are those moments? 

897
00:44:20,000 --> 00:44:22,200
And then I would encourage it 
teams to then figure out and 

898
00:44:22,200 --> 00:44:26,000
track on a per-user basis. 
When are they using the product 

899
00:44:26,000 --> 00:44:27,800
in that way? 
What day is it? 

900
00:44:27,800 --> 00:44:30,600
What time of day is it? 
How frequently have they done it

901
00:44:30,600 --> 00:44:32,700
in the past seven days. 
You and I have done this 

902
00:44:32,700 --> 00:44:35,900
analysis for teams in literal 
Google Sheets. 

903
00:44:35,900 --> 00:44:39,000
This isn't anything super fancy.
If you know how to use Google 

904
00:44:39,000 --> 00:44:42,600
Sheets, if you know how to put 
all your users in one column, if

905
00:44:42,600 --> 00:44:45,700
you know how to figure out the 
goal, you put a 0 if they didn't

906
00:44:45,700 --> 00:44:48,100
do it. 
And a 1, if they did, that's all

907
00:44:48,100 --> 00:44:50,900
you need to get started. 
I really like this framework. 

908
00:44:50,900 --> 00:44:53,100
So come up first with your 
hypothesis. 

909
00:44:53,100 --> 00:44:56,900
So the founders hypothesis of 
what those aha moments could be 

910
00:44:56,900 --> 00:45:00,200
for the typical users either, 
then you build some kind of data

911
00:45:00,200 --> 00:45:03,100
analytics collections in your 
Applications all your systems or

912
00:45:03,107 --> 00:45:05,600
whatever that is. 
Or even you could just start by 

913
00:45:05,600 --> 00:45:08,700
interviewing qualitatively with 
some users, understand their 

914
00:45:08,700 --> 00:45:10,900
behaviors when they use your 
product, but I really like it 

915
00:45:10,900 --> 00:45:14,200
that you actually start with 
hypotheses rather than spam and 

916
00:45:14,200 --> 00:45:16,100
see how it goes. 
Exactly. 

917
00:45:16,200 --> 00:45:19,300
I mean, there is a universe of 
behaviors that we could look at,

918
00:45:19,300 --> 00:45:21,800
but what's best is usually we 
start off with our own personal 

919
00:45:21,800 --> 00:45:24,200
experience. 
Honestly, I think Evan told me 

920
00:45:24,200 --> 00:45:26,200
this, our old head of research 
at Go Jack. 

921
00:45:26,200 --> 00:45:29,800
He was saying that you don't 
need thousands of people to 

922
00:45:29,800 --> 00:45:32,200
respond to a survey to get a 
Good enough. 

923
00:45:32,200 --> 00:45:35,800
Hypothesis, the more people you 
ask, the more specific, your 

924
00:45:35,800 --> 00:45:39,100
answers become, the more 
specificity you gain out of this

925
00:45:39,100 --> 00:45:42,600
data, but the difference between
30 people and 1,000 people is 

926
00:45:42,600 --> 00:45:46,200
really just that specificity. 
You still get the overall scene,

927
00:45:46,200 --> 00:45:49,900
theme the same insights, the 
same ideas behind the 

928
00:45:49,900 --> 00:45:52,200
hypotheses. 
But again, just because you have

929
00:45:52,200 --> 00:45:54,400
30 doesn't mean that it's 
invaluable data. 

930
00:45:54,800 --> 00:45:58,300
So, let's go to your, you know, 
go back a little bit in time to 

931
00:45:58,300 --> 00:46:00,200
your gojek story. 
Interestingly. 

932
00:46:00,200 --> 00:46:02,300
You were the first data. 
Higher in. 

933
00:46:02,300 --> 00:46:04,200
Go check, right? 
Can you share with us? 

934
00:46:04,200 --> 00:46:07,000
What was that challenge in the 
beginning where you were the 

935
00:46:07,000 --> 00:46:08,800
only person? 
Oh my gosh. 

936
00:46:08,800 --> 00:46:13,700
The first part was actually 
trying to make it clear, why the

937
00:46:13,700 --> 00:46:16,600
data was so important. 
And so I would look into our 

938
00:46:16,600 --> 00:46:18,800
databases. 
I think at some point in time we

939
00:46:18,800 --> 00:46:23,000
had archived or customer table 
and suddenly everyone was a new 

940
00:46:23,000 --> 00:46:25,400
customer looked at our data was 
like, oh, that's weird. 

941
00:46:25,400 --> 00:46:28,300
Why do we have thousands of new 
customers today out of nowhere. 

942
00:46:28,300 --> 00:46:30,200
There's no marketing campaigns 
happening. 

943
00:46:30,200 --> 00:46:32,600
What's going on here? 
I'm so so I reached out to the 

944
00:46:32,607 --> 00:46:35,300
engineer's I said, hey looks 
like there's a new customer 

945
00:46:35,300 --> 00:46:37,700
table. 
You did know that I was counting

946
00:46:37,700 --> 00:46:39,600
new customers based on that 
table. 

947
00:46:39,600 --> 00:46:42,200
And now that table is no longer 
there and they'd look at it. 

948
00:46:42,200 --> 00:46:43,600
Maybe like, huh? 
You're right. 

949
00:46:43,700 --> 00:46:46,200
Oh well and they would just 
continue on their day as if that

950
00:46:46,200 --> 00:46:48,200
was not a problem. 
I think it was really the first 

951
00:46:48,200 --> 00:46:51,500
part was creating a shared sense
of urgency. 

952
00:46:51,600 --> 00:46:54,600
And so I would tell them look. 
I know you worked really hard to

953
00:46:54,600 --> 00:46:57,900
release this new redesign, but 
unfortunately, I cannot tell you

954
00:46:57,900 --> 00:47:01,200
how many new people are using 
the platform as a result of it 

955
00:47:01,200 --> 00:47:03,100
because of the Way that you 
instruction the data. 

956
00:47:03,100 --> 00:47:06,600
Now, I think by providing them 
those insights Time After Time. 

957
00:47:06,700 --> 00:47:11,200
My goal was to make it clear. 
What their impact was based on 

958
00:47:11,300 --> 00:47:13,400
engineering work that they were 
doing based on what they were 

959
00:47:13,400 --> 00:47:15,500
delivering. 
And I could only really do that 

960
00:47:15,500 --> 00:47:18,000
if we had a logical database of 
data. 

961
00:47:18,100 --> 00:47:20,600
And so, I think they quickly 
realize when I told them, then, 

962
00:47:20,700 --> 00:47:23,700
now that we've made this error, 
I can't actually help you do 

963
00:47:23,700 --> 00:47:26,200
your job. 
I can't actually help you see 

964
00:47:26,200 --> 00:47:28,700
the impact that you're having, 
and that actually hit them much 

965
00:47:28,700 --> 00:47:30,600
harder than they were like. 
Oh wait. 

966
00:47:30,600 --> 00:47:31,600
I'm sorry. 
What? 

967
00:47:31,800 --> 00:47:34,600
Do I need to do to fix this? 
So I think the sign of a good 

968
00:47:34,600 --> 00:47:36,300
data team. 
Is that people care? 

969
00:47:36,300 --> 00:47:39,700
Because I think if your data is 
a mess, if no one respects, the 

970
00:47:39,700 --> 00:47:42,700
databases, if no one's willing 
to fix the problems, then the 

971
00:47:42,700 --> 00:47:46,800
real issue is that people 
haven't been able to realize 

972
00:47:46,900 --> 00:47:50,800
their impact through data. 
We always were very careful to 

973
00:47:50,800 --> 00:47:54,600
make sure that when we use data.
When we ask someone for a good 

974
00:47:54,600 --> 00:47:57,700
database, when we ask someone to
give us access to data, that 

975
00:47:57,700 --> 00:47:59,300
they're collecting in a new 
feature. 

976
00:47:59,300 --> 00:48:01,400
We make sure that one of the 
first things we do is we say, 

977
00:48:01,400 --> 00:48:03,200
hey, Thank you for getting us 
this data. 

978
00:48:03,200 --> 00:48:06,000
Now, what we want to show you is
the impact you've had on the 

979
00:48:06,000 --> 00:48:07,700
company. 
Your feature has been used by 

980
00:48:07,700 --> 00:48:10,800
twenty percent of users, the 
users who use your feature are 

981
00:48:10,800 --> 00:48:14,200
more likely to actually be 
stronger power users on the 

982
00:48:14,200 --> 00:48:16,300
platform. 
People want to hear these things

983
00:48:16,300 --> 00:48:19,000
and they want other people to 
know that they are impactful as 

984
00:48:19,000 --> 00:48:20,700
well. 
The best way to do that is 

985
00:48:20,700 --> 00:48:24,100
through the data data, telling 
you itself that you are an 

986
00:48:24,100 --> 00:48:27,300
impactful part of this company. 
And so I always started out with

987
00:48:27,300 --> 00:48:30,800
making sure that the data was 
used in a way that incentivized 

988
00:48:30,800 --> 00:48:32,800
people to keep. 
Keep the data clean and 

989
00:48:32,800 --> 00:48:35,700
organized and then secondly, 
that it helped people make 

990
00:48:35,700 --> 00:48:38,600
better decisions. 
So when people would create new 

991
00:48:38,600 --> 00:48:42,500
experiment groups or try to do a
redesign, then I'd help them 

992
00:48:42,500 --> 00:48:43,700
out. 
And say, hey, if you're 

993
00:48:43,700 --> 00:48:46,900
launching this new feature, if 
you really want to know how 

994
00:48:46,900 --> 00:48:49,300
impactful this feature is and 
whether or not you spent the 

995
00:48:49,308 --> 00:48:51,900
right resources on it and 
whether or not you should spend 

996
00:48:51,900 --> 00:48:54,400
even more resources on it, 
moving forward. 

997
00:48:54,500 --> 00:48:56,700
Why don't we create a controlled
experiment? 

998
00:48:56,800 --> 00:48:59,600
So helping people to set up, 
their randomized, experiment 

999
00:48:59,600 --> 00:49:03,600
groups, making sure that they 
Confident about what their 

1000
00:49:03,600 --> 00:49:07,400
investment and of resources was 
really paying off in terms of 

1001
00:49:07,400 --> 00:49:10,000
return. 
On those Investments that I 

1002
00:49:10,000 --> 00:49:12,800
think helped us become a very 
data-driven organization and 

1003
00:49:12,800 --> 00:49:15,100
especially at gojek. 
We definitely didn't start out 

1004
00:49:15,100 --> 00:49:17,000
being data-driven. 
We started out with a Post-It 

1005
00:49:17,000 --> 00:49:20,500
note with a symbol for what was 
a completed booking and what was

1006
00:49:20,500 --> 00:49:23,100
not a completed booking. 
But we got there and I think 

1007
00:49:23,100 --> 00:49:26,600
that it's really to the effort 
of the team knowing that we had 

1008
00:49:26,600 --> 00:49:29,100
a lot of opportunities and there
are a lot of places where we 

1009
00:49:29,100 --> 00:49:32,100
could make the wrong calculated 
risk, and so we Wanted to be 

1010
00:49:32,100 --> 00:49:34,500
absolutely confident. 
We could only really do that 

1011
00:49:34,500 --> 00:49:36,700
through the data. 
So but interesting story, 

1012
00:49:36,700 --> 00:49:40,800
definitely, you also grew the 
team from zero or yourself only 

1013
00:49:40,800 --> 00:49:42,700
to about 200. 
Just now you mentioned before 

1014
00:49:42,700 --> 00:49:44,700
you left. 
So what are some of your 

1015
00:49:44,700 --> 00:49:47,800
critical hires or what are the 
things that you hired in the 

1016
00:49:47,800 --> 00:49:50,300
beginning in order to be able to
scale that big? 

1017
00:49:50,600 --> 00:49:53,300
That's a really good question. 
So when I first joined the first

1018
00:49:53,300 --> 00:49:55,700
person I realized we'd meet with
someone to help with the 

1019
00:49:55,700 --> 00:49:57,700
automation. 
When I first joined, I think 

1020
00:49:57,700 --> 00:50:01,400
someone was executing a SQL 
query every day. 

1021
00:50:01,800 --> 00:50:04,600
On 5 p.m. 
They would export a CSV. 

1022
00:50:04,700 --> 00:50:07,800
They would email the CSV 
manually to someone else. 

1023
00:50:07,800 --> 00:50:11,700
Who would then copy paste it 
into a bigger worksheet on Excel

1024
00:50:11,800 --> 00:50:14,800
and then that person would put 
it in the graphs would update 

1025
00:50:14,800 --> 00:50:17,500
the charts and then send that 
out again manually. 

1026
00:50:17,700 --> 00:50:20,900
So, this whole process, that 
probably took an hour and a half

1027
00:50:20,900 --> 00:50:23,700
of everyone's time and it was 
happening every single day. 

1028
00:50:23,800 --> 00:50:26,600
So the first thing I did was 
hire someone named, uom, who 

1029
00:50:26,600 --> 00:50:29,000
still let go Jack and he's 
fantastic. 

1030
00:50:29,000 --> 00:50:31,600
He was able to say, okay. 
These are pretty basic. 

1031
00:50:31,700 --> 00:50:33,900
Eating we can just put us into 
Tableau. 

1032
00:50:33,900 --> 00:50:37,500
We can create a Cron job report 
and that way we can actually cut

1033
00:50:37,500 --> 00:50:40,500
out that first person who was 
manually writing the sequel 

1034
00:50:40,500 --> 00:50:42,400
queries. 
Now, it'll just go directly to 

1035
00:50:42,400 --> 00:50:44,100
the person who's creating 
visualizations. 

1036
00:50:44,200 --> 00:50:46,800
Those just piece by piece. 
We didn't come in and we didn't 

1037
00:50:46,800 --> 00:50:48,300
say everyone, stop what you're 
doing. 

1038
00:50:48,300 --> 00:50:50,400
We're going to completely revamp
this process. 

1039
00:50:50,400 --> 00:50:53,100
No, we went step by step. 
I think that's important. 

1040
00:50:53,100 --> 00:50:55,400
Making sure that we actually do 
the process. 

1041
00:50:55,400 --> 00:50:58,000
Ourselves to see what is 
Meaningful. 

1042
00:50:58,000 --> 00:51:00,900
What's not meaningful? 
What can be accelerated and what

1043
00:51:00,900 --> 00:51:03,200
are the educational? 
Is in this Behavior because 

1044
00:51:03,200 --> 00:51:05,700
there were cases when what 
happens on the weekend someone 

1045
00:51:05,700 --> 00:51:07,100
still doing this on the 
weekends? 

1046
00:51:07,200 --> 00:51:09,400
What happens on Fridays? 
What happens on Mondays? 

1047
00:51:09,400 --> 00:51:12,400
Are you doing this new chart for
all the weekend days? 

1048
00:51:12,400 --> 00:51:14,800
Or is someone still doing this 
every single day? 

1049
00:51:14,800 --> 00:51:17,300
Even on the weekends? 
So understanding that process 

1050
00:51:17,300 --> 00:51:19,700
and coming in and taking it down
step by step and it was the 

1051
00:51:19,700 --> 00:51:22,700
first strategy. 
The next part was then okay, if 

1052
00:51:22,700 --> 00:51:25,400
we're going to be scaling to new
databases, we don't actually 

1053
00:51:25,400 --> 00:51:28,200
have access to all of the data 
in one place. 

1054
00:51:28,200 --> 00:51:31,800
We need to create a data Lake. 
And so I hired admittedly a each

1055
00:51:31,800 --> 00:51:35,200
of people from Excel Asiata and 
they were great people. 

1056
00:51:35,200 --> 00:51:39,200
I always credit Johannes badly 
and Dimas as three people who 

1057
00:51:39,200 --> 00:51:42,800
were key hires who we could not 
have done this journey without 

1058
00:51:42,800 --> 00:51:46,200
they helped me, transform the 
data Lake from our postgres 

1059
00:51:46,200 --> 00:51:48,900
database. 
To a big query data base in 

1060
00:51:48,900 --> 00:51:51,900
record time. 
We were scaling so fast that 

1061
00:51:51,900 --> 00:51:55,600
when we decided to move all of 
our my SQL and mongodb database 

1062
00:51:55,600 --> 00:51:57,900
has to postgres. 
It actually only lasted us 

1063
00:51:57,900 --> 00:52:00,500
around eight to nine months 
because we were growing so fast 

1064
00:52:00,500 --> 00:52:03,000
that the queries were no, You're
running on time. 

1065
00:52:03,100 --> 00:52:06,100
We had to create some more, 
sophisticated architecture and 

1066
00:52:06,100 --> 00:52:09,500
orchestration and we had to very
quickly move to the petabyte 

1067
00:52:09,500 --> 00:52:12,900
scale database in Google. 
That was I think the primary 

1068
00:52:12,900 --> 00:52:16,100
initial part of the journey. 
It was hiring out someone who 

1069
00:52:16,100 --> 00:52:18,700
understood the business process 
to help with automation. 

1070
00:52:18,800 --> 00:52:22,000
Next up, was creating a team for
the data Lake to move, all of 

1071
00:52:22,008 --> 00:52:24,700
the data to one place. 
And then after that, I think 

1072
00:52:24,700 --> 00:52:28,900
then comes the Tactical hires, 
then it was bringing on people 

1073
00:52:28,900 --> 00:52:32,600
with Statistics background. 
So in who really understood how 

1074
00:52:32,600 --> 00:52:36,000
to make a meaningful data set 
and to an actionable 

1075
00:52:36,000 --> 00:52:39,200
statistically significant and 
rigorous analysis, that would 

1076
00:52:39,200 --> 00:52:43,200
direct us towards optimal gains 
in the business, someone to look

1077
00:52:43,200 --> 00:52:45,500
at. 
Hey, when users do this versus 

1078
00:52:45,500 --> 00:52:47,600
that, this is what the outcome 
will be. 

1079
00:52:47,600 --> 00:52:50,900
We can project and predict how 
many users are going to retain 

1080
00:52:50,900 --> 00:52:53,200
and six months. 
That's the beauty of it and I 

1081
00:52:53,200 --> 00:52:55,800
think Jeff Bezos for Amazon has 
said the same thing. 

1082
00:52:55,800 --> 00:52:57,200
He says that everything that's 
happening. 

1083
00:52:57,200 --> 00:53:01,000
This quarter was already set in 
stone last year and that's 

1084
00:53:01,000 --> 00:53:02,900
actually what Try to do it Go 
Jack as well. 

1085
00:53:02,900 --> 00:53:05,500
Everything we were doing, we 
were planning for a year in 

1086
00:53:05,500 --> 00:53:07,400
advance. 
We were building things that 

1087
00:53:07,400 --> 00:53:10,700
were about how do we retain a 
user one year from now? 

1088
00:53:10,700 --> 00:53:13,300
We know what the adjacent user 
is doing today. 

1089
00:53:13,500 --> 00:53:17,100
Jacent user being the users who 
are just starting to understand 

1090
00:53:17,100 --> 00:53:20,200
go check score value problem. 
But how do we keep them engaged 

1091
00:53:20,200 --> 00:53:22,400
one year from? 
Now, are we building products 

1092
00:53:22,400 --> 00:53:25,700
and features that will allow us 
to gain access to the next 

1093
00:53:25,700 --> 00:53:30,000
Market as we expand our services
as we expand to different types 

1094
00:53:30,000 --> 00:53:33,900
of consumers in the Getting it 
was primarily tech savvy, young 

1095
00:53:33,900 --> 00:53:37,100
entrepreneurs, who are using Go 
Jack, but now we were helping 

1096
00:53:37,100 --> 00:53:40,600
the knots who are going to the 
farmers market, or going to the 

1097
00:53:40,600 --> 00:53:45,000
wet Market buying and selling 
groceries there and just wanted 

1098
00:53:45,000 --> 00:53:47,000
a way to bring all their 
groceries home. 

1099
00:53:47,100 --> 00:53:50,100
And so, for us, it was really, 
how do we then start bringing in

1100
00:53:50,100 --> 00:53:53,800
more of the qualitative data, 
helping, build the research and 

1101
00:53:53,800 --> 00:53:57,000
analytics team there with 
Rhonda, who is amazing at 

1102
00:53:57,000 --> 00:54:00,100
leading our research efforts and
then marrying that with the 

1103
00:54:00,100 --> 00:54:02,700
statistics, making sure that 
Okay, we may have these 

1104
00:54:02,700 --> 00:54:05,500
qualitative insights, but they 
are statistically rigorous in 

1105
00:54:05,500 --> 00:54:08,800
the way that we are defining 
them and projecting our growth 

1106
00:54:08,800 --> 00:54:10,900
moving forward. 
Really some great tips. 

1107
00:54:10,900 --> 00:54:13,300
There have been super 
interesting Journey from nothing

1108
00:54:13,300 --> 00:54:16,900
out of even just one person and 
now it can scale your million of

1109
00:54:16,900 --> 00:54:19,600
transactions per day. 
I have one question though from 

1110
00:54:19,600 --> 00:54:21,100
your story. 
For example, in the beginning, 

1111
00:54:21,100 --> 00:54:23,800
you have to get the buy-in from 
the other teams, either 

1112
00:54:23,800 --> 00:54:26,500
engineering or maybe even the 
product team to actually know 

1113
00:54:26,500 --> 00:54:30,300
that data is actually important 
and then now the data has become

1114
00:54:30,300 --> 00:54:32,800
the blood of the company, but 
I'm sure in the process 

1115
00:54:32,800 --> 00:54:35,600
throughout the journey. 
There are some challenges and I 

1116
00:54:35,600 --> 00:54:38,100
just want to understand, there 
are many companies out there 

1117
00:54:38,100 --> 00:54:41,700
that I see is that their data 
strategy is actually first, it 

1118
00:54:41,700 --> 00:54:45,200
builds as a silo or secondly is 
that they just don't interact 

1119
00:54:45,200 --> 00:54:47,800
enough with the product team or 
the engineering team to say 

1120
00:54:47,800 --> 00:54:48,600
that. 
Okay. 

1121
00:54:48,600 --> 00:54:50,200
We are rolling out. 
This new features. 

1122
00:54:50,300 --> 00:54:52,900
I probably will need this kind 
of data in order to come up with

1123
00:54:52,900 --> 00:54:55,800
this kind of statistical or 
analytics, kind of a model. 

1124
00:54:55,900 --> 00:54:58,900
So, how do you actually do that 
in go-jek. 

1125
00:54:58,900 --> 00:55:01,500
Do you always have presents 
whenever new features? 

1126
00:55:01,700 --> 00:55:04,900
New products are being designed 
and instrument that in the 

1127
00:55:04,900 --> 00:55:08,200
engineering side. 
I personally live and breathe 

1128
00:55:08,200 --> 00:55:12,000
the philosophy in this book 
called crucial conversations. 

1129
00:55:12,000 --> 00:55:15,700
It's about how to get to the 
right objective. 

1130
00:55:15,800 --> 00:55:19,000
You have a goal. 
However, opinions differ and 

1131
00:55:19,000 --> 00:55:21,700
emotions are very charged. 
Everyone thinks they have a 

1132
00:55:21,707 --> 00:55:24,900
right way to do it and their 
reputation is at stake. 

1133
00:55:24,900 --> 00:55:27,400
And so for me, I always thought 
about it from the vantage point 

1134
00:55:27,400 --> 00:55:30,400
of I have a goal. 
My goal is for the company to be

1135
00:55:30,400 --> 00:55:33,100
as effective. 
And data-driven as possible. 

1136
00:55:33,200 --> 00:55:37,400
I want long term for data to be 
the founding Factor by which we 

1137
00:55:37,400 --> 00:55:40,800
make decisions, and by which we 
determine who is successful, and

1138
00:55:40,800 --> 00:55:43,600
who is not, if that is my goal. 
Well, it needs to happen. 

1139
00:55:43,800 --> 00:55:46,500
I need to get that like you 
said, the buy-in of not just 

1140
00:55:46,500 --> 00:55:49,300
engineering team but also the 
product and business teams to 

1141
00:55:49,300 --> 00:55:51,600
get that buy-in. 
I have to recognize that. 

1142
00:55:51,600 --> 00:55:53,700
I don't actually have all the 
answers. 

1143
00:55:53,900 --> 00:55:55,400
There. 
Is that ego part of me? 

1144
00:55:55,400 --> 00:55:59,200
Where do I have a best practice,
and perfect standard of data 

1145
00:55:59,200 --> 00:56:01,100
instrumentation that I would 
like to see. 

1146
00:56:01,700 --> 00:56:05,400
We is it realistic for the teams
to implement it at this time? 

1147
00:56:05,400 --> 00:56:06,800
Given everything that's 
happening. 

1148
00:56:07,000 --> 00:56:09,100
Maybe not. 
And I think I always have to be 

1149
00:56:09,100 --> 00:56:11,400
cognizant of the fact that in 
startup world. 

1150
00:56:11,400 --> 00:56:13,200
You do have a million things 
happening. 

1151
00:56:13,200 --> 00:56:16,700
And not everyone is going to get
the Perfection that they want. 

1152
00:56:16,700 --> 00:56:19,700
The product team itself is 
probably not releasing the best 

1153
00:56:19,700 --> 00:56:22,200
version of the products that 
they actually want because of 

1154
00:56:22,200 --> 00:56:25,400
limited time and constraints. 
So I think the first point is to

1155
00:56:25,400 --> 00:56:27,800
just be a realistic. 
I don't know if that's like an 

1156
00:56:27,800 --> 00:56:31,000
underrated advice that I can 
give but I actually think I 

1157
00:56:31,008 --> 00:56:32,600
don't see. 
See a lot of data teams, 

1158
00:56:32,600 --> 00:56:36,100
sometimes, being super realistic
about the capacity of the team 

1159
00:56:36,100 --> 00:56:37,700
or the expectations that they 
have. 

1160
00:56:37,700 --> 00:56:41,000
But hey, if you have a standard 
that's great, get as close to 

1161
00:56:41,000 --> 00:56:43,600
the standard as possible. 
But also keep in mind that 

1162
00:56:43,600 --> 00:56:47,300
pushing against some of these 
people who want to be able to 

1163
00:56:47,300 --> 00:56:48,800
meet the demands is not 
possible. 

1164
00:56:48,800 --> 00:56:52,100
And in fact is detrimental to 
their ability to build the 

1165
00:56:52,100 --> 00:56:54,700
product itself and you want the 
product to be excellent. 

1166
00:56:54,700 --> 00:56:58,900
So instead what I do is create a
standard, make it super easy and

1167
00:56:58,900 --> 00:57:01,500
available and accessible to the 
teams we created this. 

1168
00:57:01,600 --> 00:57:05,000
Confluence where we had 
essentially a sample data model,

1169
00:57:05,100 --> 00:57:07,700
we had an event tracking 
template which I've actually 

1170
00:57:07,700 --> 00:57:10,400
published a blog post on and 
provided to the rest of the 

1171
00:57:10,400 --> 00:57:12,300
world and at least made it 
clear. 

1172
00:57:12,300 --> 00:57:14,200
Made it super easy. 
They don't have to have a 

1173
00:57:14,207 --> 00:57:16,900
meeting with us in order to 
figure out what is the data team

1174
00:57:16,900 --> 00:57:20,000
want the standards were already 
there in the documentation. 

1175
00:57:20,200 --> 00:57:23,800
The next thing is then to just 
create plans, the data is not 

1176
00:57:23,800 --> 00:57:25,200
always going to be tracked 
correctly. 

1177
00:57:25,200 --> 00:57:28,500
Sometimes there's a typo or 
someone used created date 

1178
00:57:28,500 --> 00:57:31,500
instead of date created and 
their ETL is no. 

1179
00:57:31,600 --> 00:57:34,400
Our automated in our systems, 
those cases happen. 

1180
00:57:34,400 --> 00:57:36,500
And I think what's important is 
to make sure that there are 

1181
00:57:36,500 --> 00:57:40,100
disincentives for that behavior.
For example, they wouldn't get 

1182
00:57:40,100 --> 00:57:43,000
their pipelines automated in the
first day of their feature 

1183
00:57:43,000 --> 00:57:46,300
release, but instead we had not 
necessarily A retribution 

1184
00:57:46,300 --> 00:57:49,300
policy, but we made sure that 
there was that disincentive of, 

1185
00:57:49,300 --> 00:57:52,000
I'm no longer going to get 
access to my data, as quickly as

1186
00:57:52,000 --> 00:57:53,900
I could have. 
If I follow the protocol. 

1187
00:57:54,100 --> 00:57:56,800
But instead, I'm going to have 
to work with engineering and bi 

1188
00:57:56,800 --> 00:57:59,900
team to make sure that pipeline 
is created and a custom way for 

1189
00:57:59,900 --> 00:58:03,400
my features, and I'll get that. 
And maybe five to ten days 

1190
00:58:03,400 --> 00:58:05,600
instead. 
So I think making sure that I'm 

1191
00:58:05,600 --> 00:58:08,200
still service the team. 
We don't completely Blacklist 

1192
00:58:08,200 --> 00:58:10,400
them from the data access 
itself. 

1193
00:58:10,400 --> 00:58:12,900
That would actually be 
detrimental to the overall 

1194
00:58:12,900 --> 00:58:15,500
strategy of the team. 
If we immediately said, oh, if 

1195
00:58:15,500 --> 00:58:17,700
you're up in a follow our 
processes, we're never going to 

1196
00:58:17,700 --> 00:58:20,000
give you data, then everyone 
would just be like why don't 

1197
00:58:20,000 --> 00:58:21,700
need it then. 
So I think it's about just 

1198
00:58:21,700 --> 00:58:26,400
giving people an easy way back 
to the Golden Era of data. 

1199
00:58:26,400 --> 00:58:29,600
How do you give them that 
Lifeline or that path to 

1200
00:58:29,600 --> 00:58:31,400
goodness again? 
We try to make it. 

1201
00:58:31,500 --> 00:58:33,700
Super easy for teams to 
integrate their data. 

1202
00:58:33,700 --> 00:58:36,300
And if something happens, we 
give them the benefit of the 

1203
00:58:36,300 --> 00:58:37,800
doubt. 
We make sure that we've done 

1204
00:58:37,800 --> 00:58:41,500
what we can to bring them back 
to the data cleanliness side, 

1205
00:58:41,600 --> 00:58:44,200
but beyond that, I think it's 
really just being a practical 

1206
00:58:44,200 --> 00:58:47,500
person, being reasonable with 
those expectations and making it

1207
00:58:47,500 --> 00:58:51,200
as easy as possible for them to 
follow the way I see is like, 

1208
00:58:51,200 --> 00:58:53,800
you implementing the tips that 
you mentioned in the beginning. 

1209
00:58:53,800 --> 00:58:55,800
You're doing this behavioral 
analysis. 

1210
00:58:55,800 --> 00:58:59,000
How do you get the other teams 
to have the AHA moments to see? 

1211
00:58:59,000 --> 00:59:00,700
Hey, actually, this data 
important. 

1212
00:59:00,700 --> 00:59:03,600
And that's what In order to 
achieve some kind of insights 

1213
00:59:03,600 --> 00:59:06,300
and aha moments for our users. 
So I think that's one of the 

1214
00:59:06,308 --> 00:59:08,800
interesting guys. 
I listen from your conversation.

1215
00:59:09,000 --> 00:59:10,800
You are absolutely right to be 
honest. 

1216
00:59:10,800 --> 00:59:12,700
I have not actually thought of 
it in that way. 

1217
00:59:13,000 --> 00:59:16,100
Like you said there's a setup 
moment for the data to be 

1218
00:59:16,100 --> 00:59:20,000
correctly, architected for the 
team to put the right 

1219
00:59:20,000 --> 00:59:22,800
instrumentation, the right 
definitions and naming 

1220
00:59:22,800 --> 00:59:25,700
conventions. 
But the real aha moment is oh my

1221
00:59:25,700 --> 00:59:28,700
God, I launched my feature. 
And first day my dashboard is 

1222
00:59:28,700 --> 00:59:30,500
automatically created that's 
amazing. 

1223
00:59:30,800 --> 00:59:33,000
So, let's move. 
Onto your other activities, 

1224
00:59:33,000 --> 00:59:36,800
which is about generation girls.
So you co-founded this nonprofit

1225
00:59:36,800 --> 00:59:39,300
organization, right? 
So, can you tell me or maybe the

1226
00:59:39,300 --> 00:59:41,400
audience here? 
What is Generation Girl? 

1227
00:59:41,700 --> 00:59:43,400
Yeah, great question. 
Generation. 

1228
00:59:43,400 --> 00:59:46,700
Girl is my selfish plug to our 
goal. 

1229
00:59:46,700 --> 00:59:48,200
I think actually many of your 
listeners. 

1230
00:59:48,200 --> 00:59:51,300
Probably know this as well. 
If you are a woman, if you have 

1231
00:59:51,300 --> 00:59:55,000
a little sister, if you have a 
nice, you know that she doesn't 

1232
00:59:55,000 --> 01:00:00,100
have quite as many Role Models 
as a male would, there aren't as

1233
01:00:00,100 --> 01:00:03,000
many people in society. 
Society, that's a yes. 

1234
01:00:03,000 --> 01:00:06,100
This girl's first hobby should 
be with computers. 

1235
01:00:06,200 --> 01:00:09,800
Let's give her a linux-based 
laptop and see what she does 

1236
01:00:09,800 --> 01:00:11,900
with it. 
That's not a common. 

1237
01:00:11,900 --> 01:00:15,000
They unto each point for a 
parent, or for our culture. 

1238
01:00:15,200 --> 01:00:18,000
And I think that what's missing 
is I don't see it as a negative 

1239
01:00:18,000 --> 01:00:21,000
intentions. 
It's not a problem with people 

1240
01:00:21,000 --> 01:00:24,100
wanting to be discriminatory. 
It's just a reality that in 

1241
01:00:24,100 --> 01:00:26,600
culture. 
We don't have the same examples 

1242
01:00:26,600 --> 01:00:29,900
for girls to go into stem Fields
And So It Generation Girl. 

1243
01:00:30,000 --> 01:00:34,000
What we're doing is we're trying
to Normalize the idea of women 

1244
01:00:34,000 --> 01:00:38,300
and young girls being part of 
stem Fields science, technology,

1245
01:00:38,300 --> 01:00:40,800
engineering and math. 
And we first started out with 

1246
01:00:40,800 --> 01:00:43,200
focusing on the 12 to 16 age 
group. 

1247
01:00:43,300 --> 01:00:48,100
Our goal is to give them a safe 
inclusive and very confident 

1248
01:00:48,100 --> 01:00:52,700
inspiring space for them to fail
and to try and to be successful 

1249
01:00:52,700 --> 01:00:54,900
in stem. 
Its success for us. 

1250
01:00:54,900 --> 01:00:57,500
If these girls come into our 
programs which are free. 

1251
01:00:57,500 --> 01:01:01,100
They're usually one week long 
for every subject so they can be

1252
01:01:01,100 --> 01:01:04,300
up to I think five different 
weeks where they are learning 

1253
01:01:04,300 --> 01:01:07,100
how to code and program, a 
moving robot. 

1254
01:01:07,200 --> 01:01:08,900
They're learning how to build a 
website. 

1255
01:01:08,900 --> 01:01:12,400
They're learning how to create a
UI ux prototype and wireframe. 

1256
01:01:12,600 --> 01:01:15,900
And so for us to give them the 
opportunity to try these things.

1257
01:01:16,100 --> 01:01:19,600
What's important for us is that 
these girls feel the agency and 

1258
01:01:19,600 --> 01:01:23,000
feel like they have the 
opportunity to say, I like this 

1259
01:01:23,000 --> 01:01:25,100
field or I don't like this 
field. 

1260
01:01:25,200 --> 01:01:27,400
If they come out of these 
programs saying, I don't 

1261
01:01:27,400 --> 01:01:29,800
actually like stem. 
I'd actually rather do something

1262
01:01:29,800 --> 01:01:31,300
and literature. 
That's great. 

1263
01:01:31,500 --> 01:01:34,500
But what's important is that we 
have given them, the chance to 

1264
01:01:34,500 --> 01:01:37,000
try this themselves, to feel 
like they're not being 

1265
01:01:37,000 --> 01:01:40,400
intimidated by a bunch of people
who are over their skillset. 

1266
01:01:40,400 --> 01:01:43,500
This is actually, stemming from 
no pun intended. 

1267
01:01:43,500 --> 01:01:48,100
Stemming from a study with in 
Carnegie that showed that women 

1268
01:01:48,100 --> 01:01:53,700
who were given the opportunity 
to do a pre class for the Cs 106

1269
01:01:53,700 --> 01:01:56,100
seminar. 
They were actually graduating at

1270
01:01:56,100 --> 01:01:59,800
the same rate as men, most women
were actually dropping out 

1271
01:01:59,800 --> 01:02:03,600
without this pre class because I
felt like they didn't have the 

1272
01:02:03,600 --> 01:02:05,900
same skill sets as the men in 
their class. 

1273
01:02:06,100 --> 01:02:09,900
They were falling behind. 
They weren't feeling prepared. 

1274
01:02:09,900 --> 01:02:13,400
And just culturally a lot of 
women are told at a young age 

1275
01:02:13,400 --> 01:02:14,700
that you need to be. 
Perfect. 

1276
01:02:14,700 --> 01:02:17,000
You need to be prepared. 
You can't fail. 

1277
01:02:17,100 --> 01:02:19,400
Whereas young boys are often 
taught. 

1278
01:02:19,400 --> 01:02:22,800
Go ahead fall down, pick 
yourself back up, do it again. 

1279
01:02:22,900 --> 01:02:26,200
And so we're trying to create an
environment that gives them the 

1280
01:02:26,200 --> 01:02:29,900
best opportunity to see good 
examples that they can look up 

1281
01:02:29,900 --> 01:02:32,700
to who are young woman? 
Elves, we partner them with 

1282
01:02:32,700 --> 01:02:36,200
mentors who are typically in 
University who understand them. 

1283
01:02:36,300 --> 01:02:38,600
Because to be honest, if I tried
to Mentor these girls 

1284
01:02:38,600 --> 01:02:40,600
one-on-one. 
I don't know the latest Tick 

1285
01:02:40,600 --> 01:02:42,700
Tock stuff and I don't get it. 
I tried. 

1286
01:02:42,900 --> 01:02:45,700
So we thought it's better to 
just pair them with someone a 

1287
01:02:45,707 --> 01:02:48,800
little closer to their age. 
The goal of it is to give them 

1288
01:02:48,800 --> 01:02:52,800
literal generations of Role 
Models not just University 

1289
01:02:52,800 --> 01:02:56,600
students, but we had women who 
are fantastic leaders in stem 

1290
01:02:56,600 --> 01:02:58,800
that help talk to them about 
their role. 

1291
01:02:58,800 --> 01:03:01,300
What is it like to lead a team 
of 20 engineers? 

1292
01:03:01,600 --> 01:03:03,600
As an engineering manager at 
quora. 

1293
01:03:03,600 --> 01:03:06,800
For example, what we really want
to do is give these women the 

1294
01:03:06,800 --> 01:03:10,500
opportunity to try stem to see 
whether or not they like it and 

1295
01:03:10,500 --> 01:03:13,300
then to give them opportunities 
to continue learning if they 

1296
01:03:13,300 --> 01:03:15,600
choose to do. 
So we have programs with 

1297
01:03:15,600 --> 01:03:19,400
tokopedia, which have a Loca let
you partner with someone in 

1298
01:03:19,400 --> 01:03:22,500
their engineering team and build
a project over the summer. 

1299
01:03:22,600 --> 01:03:24,600
So that was last year. 
We're actually looking for 

1300
01:03:24,600 --> 01:03:27,100
mentors right now. 
So if you go to generation girl 

1301
01:03:27,100 --> 01:03:30,600
dot-org and you're a university 
student or someone who is able 

1302
01:03:30,600 --> 01:03:34,200
to provide Mentorship to the 
girls for five days a week. 

1303
01:03:34,200 --> 01:03:36,900
Couple hours. 
No big deal, but you get to help

1304
01:03:36,900 --> 01:03:40,600
hundreds of kids, learn how to 
use and be proficient in stem. 

1305
01:03:40,600 --> 01:03:42,200
It's a good deal. 
Wow. 

1306
01:03:42,200 --> 01:03:44,200
This is such an inspiring 
activity. 

1307
01:03:44,200 --> 01:03:47,000
Definitely is it just for 
Indonesia, by the way, is 

1308
01:03:47,000 --> 01:03:50,000
primarily focus on Indonesia. 
We did have a couple of students

1309
01:03:50,000 --> 01:03:53,000
though, come in from Singapore 
and maybe even Malaysia. 

1310
01:03:53,000 --> 01:03:54,800
I think. 
So if you're interested, come 

1311
01:03:54,800 --> 01:03:57,900
join, we do have half English 
classes, and half of our other 

1312
01:03:57,900 --> 01:04:01,100
classes are in Bahasa. 
And if you are looking to be a 

1313
01:04:01,100 --> 01:04:05,000
mentor, Male or female, you 
realized your mission is to help

1314
01:04:05,000 --> 01:04:07,600
give these girls confidence. 
You should apply to be a mentor 

1315
01:04:07,600 --> 01:04:10,300
as well. 
What are some of the inspiring 

1316
01:04:10,300 --> 01:04:12,700
stories that have come out on 
generation? 

1317
01:04:12,700 --> 01:04:15,600
Girl. 
It's just been really crazy to 

1318
01:04:15,600 --> 01:04:19,400
see the level of Engagement. 
They're just really are not that

1319
01:04:19,400 --> 01:04:23,200
many opportunities specifically 
for women who speak Indonesian 

1320
01:04:23,200 --> 01:04:25,500
to come in and take these 
classes for free. 

1321
01:04:25,700 --> 01:04:29,700
And so, we had, I think, in our 
first year, a student who flew 

1322
01:04:29,700 --> 01:04:34,200
in from, I think, Made on or 
somewhere else very far flew in 

1323
01:04:34,200 --> 01:04:37,700
to Jakarta stayed with her aunt,
and uncle for a week just to 

1324
01:04:37,700 --> 01:04:40,100
join our program. 
We had another student who 

1325
01:04:40,100 --> 01:04:43,300
initially wanted to do a 
journalism major and after a 

1326
01:04:43,300 --> 01:04:46,400
programs was like, I am going to
be an engineer. 

1327
01:04:46,400 --> 01:04:49,800
I'm going to learn to code fully
build my own product. 

1328
01:04:49,800 --> 01:04:52,100
And so she completely changed 
her idea on what her major 

1329
01:04:52,100 --> 01:04:55,300
should be. 
We had students from Indonesia 

1330
01:04:55,300 --> 01:04:58,600
who are now going to University,
Join one of my last data science

1331
01:04:58,600 --> 01:05:02,000
classes, and I had two students 
one that woke up at I am and 

1332
01:05:02,000 --> 01:05:05,000
when I woke up at 5 a.m. 
Her time because they were like,

1333
01:05:05,000 --> 01:05:07,300
I really want to join. 
So I just been really blessed to

1334
01:05:07,300 --> 01:05:09,700
see that people want these 
programs like they want to 

1335
01:05:09,700 --> 01:05:12,100
attend. 
And so we're very careful about 

1336
01:05:12,100 --> 01:05:15,000
the mentors that we select. 
We really want people who are 

1337
01:05:15,000 --> 01:05:18,600
going to be excited about giving
these opportunities and sharing 

1338
01:05:18,600 --> 01:05:21,700
their knowledge with them. 
So, how do you see these women 

1339
01:05:21,700 --> 01:05:24,800
actually in the industry may be 
specifically in Indonesia. 

1340
01:05:24,800 --> 01:05:26,700
Are they getting the same 
opportunities? 

1341
01:05:26,700 --> 01:05:30,100
Or are they enough supply of 
women in technology or stem in 

1342
01:05:30,100 --> 01:05:31,300
general? 
Are they? 

1343
01:05:31,400 --> 01:05:34,100
Brave enough to give it a try. 
Now, seeing all these startups, 

1344
01:05:34,100 --> 01:05:35,900
starting to bloom and Generation
Girls. 

1345
01:05:35,900 --> 01:05:38,400
Probably, they are other 
organizations that help them. 

1346
01:05:38,400 --> 01:05:40,800
How do you see the trends of 
these women specifically in 

1347
01:05:40,800 --> 01:05:43,100
Indonesia? 
I think in Indonesia, internal, 

1348
01:05:43,100 --> 01:05:46,100
it's definitely gotten better. 
I think Indonesia in particular 

1349
01:05:46,100 --> 01:05:49,800
is a place that is, I think 
pretty Progressive and forward 

1350
01:05:49,900 --> 01:05:53,800
when it comes to women, having 
children needing to take time 

1351
01:05:53,800 --> 01:05:56,200
off, creating good integration 
programs. 

1352
01:05:56,200 --> 01:05:58,400
For when they come back. 
I think Indonesia has always 

1353
01:05:58,400 --> 01:06:00,400
been actually much better than 
the u.s. 

1354
01:06:00,400 --> 01:06:05,700
At this where I We can do, 
better is investing in creating 

1355
01:06:05,700 --> 01:06:09,300
opportunities for women to be 
part of the workplace. 

1356
01:06:09,400 --> 01:06:13,100
We all know that more diverse 
teams, create better products. 

1357
01:06:13,200 --> 01:06:15,300
Imagine. 
We're creating a healthcare app 

1358
01:06:15,300 --> 01:06:17,000
and all of the engineers are 
men. 

1359
01:06:17,100 --> 01:06:20,400
You're going to miss some pretty
vital parts of a woman's health 

1360
01:06:20,400 --> 01:06:22,600
care needs in a team that just 
filled with men. 

1361
01:06:22,800 --> 01:06:27,000
And in any case every product is
going to be hopefully 5050 men 

1362
01:06:27,000 --> 01:06:28,900
are going to be using it when 
they're going to be using it. 

1363
01:06:28,900 --> 01:06:31,200
A more diverse team is only 
going to make the product 

1364
01:06:31,200 --> 01:06:31,600
better. 
Better. 

1365
01:06:31,800 --> 01:06:35,500
If teams are really committed to
creating the best possible 

1366
01:06:35,500 --> 01:06:38,300
engineering team that gets the 
best possible product teams than

1367
01:06:38,300 --> 01:06:41,900
they should be investing in 
opportunities for women to be 

1368
01:06:41,900 --> 01:06:45,200
part of that equation. 
If that means, like you said, 

1369
01:06:45,300 --> 01:06:49,400
having to create boot camps or 
create programs that make it 

1370
01:06:49,400 --> 01:06:52,300
easier for women to be 
facilitated into the program. 

1371
01:06:52,400 --> 01:06:55,400
Then they should be doing that. 
If the talent pool is a problem,

1372
01:06:55,500 --> 01:06:58,600
then what is the company doing 
to help solve that problem? 

1373
01:06:58,700 --> 01:07:02,000
Because it's really a cultural 
endemic issue that There aren't 

1374
01:07:02,000 --> 01:07:05,000
enough women in stem. 
And therefore we don't hire them

1375
01:07:05,000 --> 01:07:07,000
and therefore, women never join 
stem. 

1376
01:07:07,000 --> 01:07:09,200
It's actually a 
self-perpetuating problem that 

1377
01:07:09,200 --> 01:07:11,800
we don't invest in them. 
We don't make it easy for them 

1378
01:07:11,800 --> 01:07:14,500
to be part of those communities.
And that's why we lack the 

1379
01:07:14,500 --> 01:07:17,800
talent pool for them. 
And so things like supporting 

1380
01:07:17,800 --> 01:07:21,000
programs like Generation Girl, 
we have amazing sponsors from 

1381
01:07:21,000 --> 01:07:24,800
Sequoia in Singapore, tokopedia,
even XYZ. 

1382
01:07:24,900 --> 01:07:28,100
We have great sponsors who 
realize that this is a problem 

1383
01:07:28,100 --> 01:07:31,300
and commit time with us to make 
sure that we can afford. 

1384
01:07:31,400 --> 01:07:35,200
Ford to bring on hundreds of 
these girls into our programs. 

1385
01:07:35,200 --> 01:07:38,400
So that talent pool pipeline can
be expanded and it's not going 

1386
01:07:38,400 --> 01:07:40,900
to happen until they decide to 
invest in it. 

1387
01:07:40,900 --> 01:07:44,000
Go, Jeff for example has their 
own Ruby boot camp that we 

1388
01:07:44,000 --> 01:07:47,100
partnered with them for our 
electives this season, and they 

1389
01:07:47,100 --> 01:07:49,400
made sure to put in the 
resources to make sure that 

1390
01:07:49,400 --> 01:07:52,900
women can join this program, 
that they will learn Ruby as a 

1391
01:07:52,900 --> 01:07:55,600
programming language, and 
they'll be able to be proficient

1392
01:07:55,600 --> 01:07:58,300
and hopefully part of that 
talent pool from the next year. 

1393
01:07:58,600 --> 01:08:01,000
So, I hope to see these kind of 
organization, nonprofit. 

1394
01:08:01,400 --> 01:08:04,500
Session starting to pop up in 
multiple countries with in, 

1395
01:08:04,500 --> 01:08:07,500
Southeast Asia, at least because
the opportunities are still very

1396
01:08:07,500 --> 01:08:09,500
big. 
And some of these women are 

1397
01:08:09,500 --> 01:08:12,900
actually not yet immersed into 
the mindset of engineering is a 

1398
01:08:12,900 --> 01:08:16,500
good industry to be in for them.
They can perform as equally good

1399
01:08:16,500 --> 01:08:19,000
as men. 
And yeah, hopefully we can see 

1400
01:08:19,000 --> 01:08:21,700
or hear some of these inspiring 
stories coming out of this 

1401
01:08:21,700 --> 01:08:22,700
region. 
Definitely. 

1402
01:08:22,800 --> 01:08:26,600
So Crystal, I've really enjoyed 
our conversations going from VC 

1403
01:08:26,600 --> 01:08:31,200
stories startups product growth 
strategy gojek story and now to 

1404
01:08:31,300 --> 01:08:34,200
Anger, but due to time I think 
we have to wrap up the 

1405
01:08:34,200 --> 01:08:36,100
conversation. 
But before you leave, normally I

1406
01:08:36,108 --> 01:08:38,899
would ask my guess. 
This one question which is what 

1407
01:08:38,899 --> 01:08:41,899
are your three Tech leadership 
wisdom that you want to share 

1408
01:08:41,899 --> 01:08:45,000
with the audience for them to 
learn from the first one. 

1409
01:08:45,100 --> 01:08:48,800
Actually based on our discussion
is really to goal on the 

1410
01:08:48,800 --> 01:08:52,700
behaviors that matter. 
Don't goal on your vanity 

1411
01:08:52,700 --> 01:08:56,200
metrics, figure out what it is 
that not just works for your 

1412
01:08:56,200 --> 01:08:58,899
product, but honestly, for you, 
as a person, like, what works 

1413
01:08:58,899 --> 01:09:02,399
for you as an individual, what 
me You the most successful. 

1414
01:09:02,399 --> 01:09:06,700
It's probably not doing your 
work 100% of the time. 

1415
01:09:06,700 --> 01:09:09,899
We're working 24/7. 
It's actually probably things 

1416
01:09:09,899 --> 01:09:14,000
closer to getting seven hours of
sleep, having a maintain diet, 

1417
01:09:14,000 --> 01:09:16,800
working out feeling healthy, 
figuring out what those 

1418
01:09:16,800 --> 01:09:19,600
behaviors. 
Are that make you the best to 

1419
01:09:19,600 --> 01:09:22,000
your abilities. 
Figure out what those are and 

1420
01:09:22,000 --> 01:09:24,800
operate and optimize for that is
probably the first thing. 

1421
01:09:24,800 --> 01:09:28,100
The second one is probably 
figure out what, you're not 

1422
01:09:28,100 --> 01:09:31,300
great at and find people who can
actually help you fill. 

1423
01:09:31,300 --> 01:09:33,500
Those gaps. 
Like I said, I couldn't have 

1424
01:09:33,500 --> 01:09:37,100
done all of the data modeling 
and strategy at go-jek and 

1425
01:09:37,100 --> 01:09:40,399
gotten it to the scale that it 
is today without people like 

1426
01:09:40,399 --> 01:09:43,700
Johanna San Dimas. 
And finally, they were people 

1427
01:09:43,700 --> 01:09:45,300
who could help fill the gaps for
me. 

1428
01:09:45,600 --> 01:09:48,899
We had a vision together that we
were able to execute as a team 

1429
01:09:48,899 --> 01:09:52,100
because we had the skill sets 
that complemented one another. 

1430
01:09:52,300 --> 01:09:56,000
And then I think the last one to
be honest is to listen. 

1431
01:09:56,200 --> 01:10:00,000
I think the first mistake I made
as a early manager was probably 

1432
01:10:00,000 --> 01:10:03,600
not to listen enough. 
It was to think that I knew what

1433
01:10:03,600 --> 01:10:05,300
the right data model should look
like. 

1434
01:10:05,300 --> 01:10:08,400
I knew what the best practices 
were and that team should just 

1435
01:10:08,400 --> 01:10:09,800
follow this. 
I shouldn't listen to their 

1436
01:10:09,800 --> 01:10:11,600
excuses. 
But in the long run, it's 

1437
01:10:11,600 --> 01:10:14,100
actually that's not the case. 
And so being mindful of that 

1438
01:10:14,200 --> 01:10:16,500
listening and being a rational 
person. 

1439
01:10:16,800 --> 01:10:19,500
I think it's something that I 
would equate with my success as 

1440
01:10:19,500 --> 01:10:21,400
well. 
Thanks for sharing that so 

1441
01:10:21,400 --> 01:10:23,400
Chrissy, where can people find 
you online? 

1442
01:10:23,400 --> 01:10:25,300
If they want to interact with 
you more. 

1443
01:10:25,400 --> 01:10:28,300
Yes, please. 
You can find me at Chris ew.com 

1444
01:10:28,400 --> 01:10:30,100
or follow me on Twitter. 
Crystal. 

1445
01:10:30,100 --> 01:10:32,000
Which Ayah Ayah. 
I've been tweeting more. 

1446
01:10:32,000 --> 01:10:33,800
I don't think I'm going to get 
the hang of tick-tock and 

1447
01:10:33,800 --> 01:10:37,100
Instagram, to be honest because 
I've given up, but I'm still 

1448
01:10:37,100 --> 01:10:39,000
using some of our more 
traditional medium. 

1449
01:10:39,000 --> 01:10:42,200
So building my own website. 
Okay, so, thanks so much for 

1450
01:10:42,200 --> 01:10:44,100
crystal for your participation 
today. 

1451
01:10:44,100 --> 01:10:46,600
I hope to see you in future 
episodes and good luck with 

1452
01:10:46,600 --> 01:10:47,700
whatever things that you're 
doing. 

1453
01:10:47,700 --> 01:10:49,000
Now. 
Thank you so much. 

1454
01:10:49,000 --> 01:10:52,600
Henry time flew by and it was 
such a joy stay safe out there. 

1455
01:10:54,100 --> 01:10:57,500
Thank you for listening to this 
episode and for staying right 

1456
01:10:57,500 --> 01:11:00,300
till the end. 
If you highly enjoyed, please 

1457
01:11:00,300 --> 01:11:03,100
share it with your friends and 
colleagues who you think would 

1458
01:11:03,100 --> 01:11:05,900
also benefit from listening to 
this episode. 

1459
01:11:06,100 --> 01:11:09,000
And if you're new to the 
podcast, make sure to subscribe 

1460
01:11:09,000 --> 01:11:11,900
and leave me your valuable 
review and feedback. 

1461
01:11:12,000 --> 01:11:15,800
It really really helps me a lot 
in order to grow this podcast 

1462
01:11:15,800 --> 01:11:18,300
better. 
You can also find the full show 

1463
01:11:18,300 --> 01:11:21,800
notes of this conversation on 
the episode page at technically 

1464
01:11:21,800 --> 01:11:25,300
journal, the death website. 
Including the full transcript 

1465
01:11:25,300 --> 01:11:28,900
interesting quotes and links to 
the resources and mentions from 

1466
01:11:28,900 --> 01:11:31,700
the conversation. 
And lastly, make sure to 

1467
01:11:31,700 --> 01:11:34,500
subscribe to the shows mailing 
list on technology on of the 

1468
01:11:34,500 --> 01:11:37,600
deaf to get notified for any 
future episodes. 

1469
01:11:38,000 --> 01:11:40,700
Stay tuned for the next 
technique Journal episode. 

1470
01:11:40,800 --> 01:11:42,400
And until then. 
Goodbye.