Netflix: "Where before we were struggling to get traffic, all of sudden we couldn’t keep up." - @mbrandolph
Tinder: "Downloads started to skyrocket" – @badeen
Uber: "Word of mouth was uncontrollable" – @ryangraves
The clearest sign of finding product-market fit is feeling "pull" from the market.
But what does "pull" look like?
👇 Read on 👇
Netflix: "Where before we were struggling to get traffic, all of sudden we couldn’t keep up." - @mbrandolph
Tinder: "Downloads started to skyrocket" – @badeen
Uber: "Word of mouth was uncontrollable" – @ryangraves
Carta: "I've been trying to get to your sales people for weeks!!! Why won't you take my money???" – @henrysward
Github: "To our surprise, users started writing to us asking ‘Can we pay for this??’" – @mojombo
Datadog: "When we hit early product-market fit, our users switched from being excited at all the things our product could be to them to being upset about all the things it didn't do yet." – @oliveur
Nextdoor: "10 minutes after taking the servers offline, I started getting emails/calls from users. What happened to Nextdoor? One user called me in a panic because she needed to contact a neighbor immediately." – @sarahleary
Gusto: "A pretty good sign for PMF was when in spite of the obvious gaps in our marketing, product and care, we saw consistently high NPS (80+), low churn, and record high MoM organic growth.” – @tomerlondon
Datadog: "We started seeing a regular influx of sign-ups, and as we added key features (such as alerting), we saw them use our product more and more actively" – @oliveur
More from Lenny Rachitsky
The most sophisticated growth team no one talks about: @WishShopping
1. The #1 shopping app in 40+ countries
2. Rumored to often be the #1 spender on FB and Google
3. 2 million items sold daily
I sat down with @cplimon to learn about the notoriously secretive company. Read on 👇
1/ Your brand constraint is Wish's opportunity
Wish's superpower is leaving no room for taste or opinion. It's what happens when a machine builds a company based on data. The founder didn't plan to sell cheap goods to low-socioeconomic customers, but where the data took him.
"Until you work at a place like Wish, you don't know what data-driven is. Everyone else is data-driven when it's convenient, when it agrees with your opinions. Wish is great at ignoring their own emotions. It's data-driven with as much intellectual honesty as possible."
For example
2/ Differentiate by serving the under-served
Most of Wish’s initial sales came from places like Florida, greater LA county, and middle-America. Specifically, zip codes with 95% Spanish speakers. Later, Africa, Latin America and Eastern Europe (avg household income $18,000/year)
1. The #1 shopping app in 40+ countries
2. Rumored to often be the #1 spender on FB and Google
3. 2 million items sold daily
I sat down with @cplimon to learn about the notoriously secretive company. Read on 👇
1/ Your brand constraint is Wish's opportunity
Wish's superpower is leaving no room for taste or opinion. It's what happens when a machine builds a company based on data. The founder didn't plan to sell cheap goods to low-socioeconomic customers, but where the data took him.
"Until you work at a place like Wish, you don't know what data-driven is. Everyone else is data-driven when it's convenient, when it agrees with your opinions. Wish is great at ignoring their own emotions. It's data-driven with as much intellectual honesty as possible."
For example
cursed wish ads pic.twitter.com/eMlx4LqgKA
— big meaty claws (@leisurepIex) June 4, 2019
2/ Differentiate by serving the under-served
Most of Wish’s initial sales came from places like Florida, greater LA county, and middle-America. Specifically, zip codes with 95% Spanish speakers. Later, Africa, Latin America and Eastern Europe (avg household income $18,000/year)
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The entire discussion around Facebook’s disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
The story doesn\u2019t say you were told not to... it says you did so without approval and they tried to obfuscate what you found. Is that true?
— Sarah Frier (@sarahfrier) November 15, 2018
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details): https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha
I've read it so you needn't!
Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.
The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.
Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.
