[7 ways to acquire your first 1,000 users - pt.1]
Last time I wrote about a common early stage startup’s acquisition dilemma - “optimizing an acquisition channel v.s finding a new acquisition channel.” This time I'm going to dive into different ways of finding new acquisition channels.
Having one method work for your startup doesn’t mean you should disqualify the others, but it also doesn’t mean that every method will work for your startup either. It’s about testing and finding the right channel fit for your business.
Essentially every major consumer app acquired their early users using these methods, including Netflix, Instagram, Uber, Tinder, TikTok, and many more.
1. Find your users, offline
Once you identify who your target users could be, go find them in real life and get them to try your product! If they are not interested in what you have made, your product either doesn’t solve their problem or you have got the wrong target users.
To kickstart Uber’s growth in new cities, they often went to airports and downloaded the Uber app for taxi drivers and handed out coupons to riders. Tinder’s co-founder ran around college campuses handing out flyers and pitched to fraternities. Identifying your target user is important, if you know where they would conjugate, you want to be there too.
2. Find your users, online
Your target user could conjugate online too, and if they do, find them, and get them to them try your product! The same rule applies here, if the online users are not interested in what you have made, iterate on the product or the target users. Dropbox did exactly this, launching on Hacker News with only a video, even without a functional product.
You want to infiltrate your target user’s community, fit in, use their language, and even befriend them. The advantage in interacting with users online is that you have the option to remain anonymous, but the disadvantage is that gaining their trust could take a longer period of time, but this is exactly how Netflix launched their product across America, slowing turning pockets of online movie enthusiast and cinephiles into Netflix users.
3. Invite your friends to kickstart a positive network effect
To successfully launch a product through your own network you need to leverage referral programs. Products which works well with this method generates value for its users by acquiring more users, thus creating incentive for both the inviter and invitee. Reid Hoffman launched LinkedIn into his network of founders and entrepreneurs, marking the platform really attractive for job seekers, thus creating a positive network effect which attracts more employers, which then again attracts more employees.
Two takeaways that are fundamental in getting your first 1,000 users is to “do things that don’t scale” and to narrowly define your target users. By doing both, you validate your product's problem/solution fit and product/market fit.
I’ll cover the other 4 methods of acquiring your first 1,000 users in my next post, but in the meantime, if you got some value out of this post, please like and share!
Lastly, if you are a founder working on a startup in SEA, or working with AI / blockchain, apply to AppWorks Accelerator #21 to join the largest founder community in Greater Southeast Asia today >>> https://bit.ly/2NcjYGV
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by Jack An
Analyst, AppWorks
Image credit: Lenny Rachitsky
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ai disadvantage 在 CommonWealth Magazine Facebook 的最佳貼文
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ai disadvantage 在 โปรแกรมเมอร์ไทย Thai programmer Facebook 的最讚貼文
วันนี้ข้อแนะนำ ไลบรารี่ JavaScript หรับงาน AI ด้าน Deep Learning (อยู่ในหมวดหมู่ Neural Networks)
ตามลิงค์นี้ http://cs.stanford.edu/people/karpathy/convnetjs/index.html
ข้อดีไลบรารี่นี้คือ
-ตัวอย่างโค้ดอ่านง่ายมาก ถ้าใครคอ Deep learning เห็นโค้ดปุ๊บ ก็จะเข้าใจโครงสร้าง model ทันที (คิดว่าไม่แพ้ไลบรารี่ตัวอื่น)
-มี demo เป็นกราฟฟิกให้เห็นภาพชัด ดีเยี่ยมยอดมากๆ
-ด้วยความเป็น JavaScript จึงติดตั้งใช้ง่าย ไม่ยุ่งยากอะไร เพียงแค่ดาวน์โหลดไลบรารี่ ก็เรียกใช้ได้เลย
-สามารถใช้ในฝั่งหน้าเว็บ Browser หรือฝั่ง Server side (Node.js)
ข้อเสีย คือ
- document ที่อธิบายน้อยไปหน่อย เลยไม่รู้จะเขียนอะไรได้พลิกแพลงมากกว่านี้หรือเปล่า
- ตัวอย่างโค้ด ที่มีคนเคยเอาไปใช้ก็น้อยไปหน่อย จะไม่เหมือนไลบรารี่ดังๆ ของภาษาอื่น มีตัวอย่างให้เห็นตรึม
- ยิ่งจะใช้โครงสร้าง Network ที่ซับซ้อนกว่านี้ (เช่น LSTM) ในเอกสารก็ไม่เขียนบอก ดูแล้วน่าจะทำไม่ได้เนอะ (เดา)
ถึงอย่างไร ก็เป็นอีกทางเลือกหนึ่ง น่าสนใจ สำหรับสายงานด้านนี้ครับ
Today, Javascript library recommendation for ai on deep learning (in neural networks category)
Follow this link http://cs.stanford.edu/people/karpathy/convnetjs/index.html
The Advantages of this library is
- sample code is very easy to read. If anyone has deep learning neck. See the code, you will understand the model structure immediately. (I think it's not allergic to other libraries)
- there is demo as graphics to see clear. Awesome.
- with Javascript, it's easy to install, no hassle. Just download the library and it can be used.
- can be used on browser or server side (Node. JS)
The disadvantage is
- the document that explains a little less. I don't know what to write. Can I flip the raft more.
- the sample of the code that someone used to use is a little bit. It won't look like the loud library of other languages. There is an example to see.
- the more complicated network structure (e.g. lstm) in the documents, I don't write. I can't do it. (guess)
It's another interesting choice for this line of work.Translated
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