Today, TikTok is commonly described as an addictive drug. In a podcast, sociologist Julie Albright described it as:
This is a companion discussion topic for the original entry at https://commoncog.com/c/cases/tiktok-one-very-long-year
This is a companion discussion topic for the original entry at https://commoncog.com/c/cases/tiktok-one-very-long-year
The biggest thing that I notice here is
For the first half-year after launch, app performance was poor. The A.me team half-expected ByteDance to kill their project. Yiming, however, reasoned that it was worth continuing. He believed that A.me was on the right path, just that they hadn’t done a good enough job executing.
This is generally really difficult for leaders to do! Now I’m interested in learning more about the thought process behind this. What about the feedback made him think that it was an execution problem, and not that the market wasn’t a good fit for them?
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I wonder that as well! A little bit further, there is an emphasized quote:
In the end, Yiming reasoned as such: “The logically correct thing is definitely right. And others have already verified (this path), our data is poor because we haven’t done a good job ourselves. (emphasis added) ”
Given the concept of this case study is “idea maze”, my initial thought is that concepts work through both macro and micro environments. Micro environments are within a company - iteration on an idea, pivots, and so on. Macro is on the market level - a concept being iterated upon by different companies with differing levels of success.
I think the key is that “video engagement “ as a concept/opportunity was well proven by both public and private data (compared to text and audio consumption). In the same way as “more than one news/content site can be successful”, video was nascent enough with regards to a combination of smartphone behavior + internet bandwidth/speed to suggest that an opportunity was there for the taking.
In a recent email, investor Ellen Chisa breaks it down as “Do you want to build this thing, or do you want to solve a problem?”…
Usually at the beginning you have a product, and then you start to have a company, and then you start to think about huge expansion. But, by starting to talk through the ramifications, many potential founders will realize they definitely don’t want to try to get to this scale or be responsible for building a team that sells enterprise products. If you know you don’t want that, venture is probably not the right choice.
On the other hand, if a founder gets excited by this idea of potential and everything to explore within it, they may well be suited to thinking about building a venture backed company. They might already have people using the product and have seen it pulled into a company. Or perhaps the first thing they’ve built they don’t see getting to this level, but they start to see how it could be a starting wedge to a larger idea.
(Link: I built something! - by Ellen Chisa - Ellen’s Newsletter)
Yiming was on the startup track for the fourth time and having gotten to CEO level was likely well versed in opportunity sizing versus simply product development. Substitute “venture backed company” for “make a ton of money with a ton of users”, and that’s “building for the opportunity/solving a problem rather than building a specific thing”.
So the pursuit here was for trying to capture a well sized opportunity and iterating through the possible ways to capture that opportunity.
I’ve long thought about “cost to build versus cost to iterate” in this sense: if development costs are high, you want to de-risk an idea through market and user research to increase the likelihood of success, because high development costs mean that you have less “shots at goal”.
Inversely, a low development cost (because you have a lot of money, or you have some skills leverage) means that it is cheaper and more effective for you to rebuild and iterate to a solution than it is to do upfront research. I would guess that in this case — given the number of different things they were trying — there was a low cost to build which made the call to keep things going (in conjunction with a macro-validated market opportunity) easier.
Interestingly enough, Quibi is a good counterpoint to this case study, they took the opposite approach in almost every regard: they identified the right opportunity, but due to hubris they stumbled on the “no de-risking through upfront research”/“high cost to build” problem of short form phone video.
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My understanding is that A.me really was a badly built app, at least in the beginning. But it was still an act of judgment to go “ok, this doesn’t work, and maybe it’s the format that sucks, or maybe there’s something wrong with the positioning, and maybe it’s the app that sucks … which is it? We know (from the growth of short videos in Toutiao) that the category is promising … and the app functionality stinks, so let’s fix that first.”
And that, along with the company’s willingness to give it another shot after the rebranding, I really do admire.
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Hi everyone, new member here I hope it’s OK to revive this topic.
I’d like to add this article a context to the TikTok case study: [Seeing Like an Algorithm — Remains of the Day] .
The whole 3 articles serie is excellent, but this article is the main one I think.
The keypoints are (with my commentary):
- Shorten the onboarding has been key to TikTok success. For that they decoupled the social graph from the user feed and used their paid content in the feed.
- => Remember that the paid content was crafted to please to specific population. This means that this content help the sorting algorithm decide what the user likes and which niche they are into. This implies a deep understanding of the tech needs from the marketing departement.
- The whole app’s UI is built around for give effective signals on user specific preference (ie: showing one video at a time allow the user to “vote” for a video by staying on it or moving elsewhere).
- => It’s probably needs a new app to have a UI that give sufficient signal to the sorting algorithm
- Tiktok spent half of its engineering budget on their algorithm needs and infrastructure.
- => don’t underestimate the amount of effort and expertise needed to reproduce this feat. It needs a solid technical leadership.
You can say that every internet B2C app needs a deep integration of the tech in the company functions to be able to function at scale, and that’s true, but I think it’s interesting to underline what they succeeded to do here.
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