Product

Product (Translated by ChatGPT)

Product User Experience Optimization

A common challenge in independent development is, “How to promote and acquire customers?” However, most individuals posing this question have already released their products without much prior consideration.

The reason for this issue lies in unresolved preceding problems. Standing at the end of the problem chain, many issues become unsolvable. It’s akin to the story of Rupert’s drop; pounding it with a hammer futilely is less effective than clipping its tail with pliers.

Often, the solution to a problem isn’t answering the problem itself but addressing the core contradiction from an upstream perspective.

If your independent product aims to quickly find 100 passionate users and achieve rapid growth through them, please firmly remember these four things and continually question yourself around them to find solutions:

1. Define target users, outline their profiles, and understand the problems they currently face
2. Provide new solutions, identify core features to solve core pain points
3. Packaging, write ad copy, create landing pages
4. Cold start, find 100 passionate users

Choosing the Track, Defining Target Users:

  1. Who are your users? What are their specific profiles? Do they have purchasing power?
  2. Is now the best time to do this? Is there a time when it’s easier to do than now?
  3. Are there any policy supports or policy risks?
  4. Can this product only earn money for you? How can others also earn money through your product?
  5. Is this something only you can do? What additional advantages do you have in doing this?
  6. Are there ready-made channel resources for cooperation in sales after your product goes online?
  7. Do you think your product is a direct value proposition product? Or does it need to educate users to realize the value of the product?
  8. How many competitors are there doing the same thing? How are they doing?
  9. What are users complaining about in the negative reviews of competitors?

Defining Product Core Features:

  1. What does your product help users do? How do they currently complete this process?
  2. What percentage of users in your user profile are currently continuously completing this process?
  3. If they don’t use your product, what other alternatives are there to achieve the same effect? How much difference in efficiency compared to using your product?
  4. If you only have one core feature or chain, for which users pay, what is that feature?
  5. Are there any ready-made tools to help you quickly build this chain? (Or complete initial testing)

Determining Marketing Packaging:

  1. Which point of your product do users actually pay for? How can you highlight it to make users feel it’s useful for them?
  2. Please use a sentence to impress customers to pay for your product, what is this sentence?
  3. What are the selling points of the competitor’s packaging? Is there differentiation with yours?
  4. If there is no difference, what are the disadvantages of the competitor? Can you convert its disadvantages into your advantages? Carry out precise strikes? Do users care about this point?

Cold Start and Expansion:

  1. Where do your target users gather?
  2. How do you find them initially?
  3. What resources or people can you rely on to indirectly reach them?
  4. When they want to find products like yours, where are they most likely to discover from? What are the search keywords?
  5. Where do competitors advertise?
  6. Who is most likely to help you sell your product?

Angel Investment Selection: Focus on Technological Transformation and Application Scenarios

If I were to do angel investment, what types of projects would I consider now?

Angel investment is the hardest to do, with many fund DPIs less than 1, meaning if you don’t invest well, you basically won’t raise money. So if it were me, how would I choose?

I consulted a friend, who told me a story about a technological transformation cycle.

Starting from the previous Apple cycle, initially, there were some achievements by researchers, which led to revolutionary progress in underlying technologies. Then came the iPhone era. Following that was a series of network infrastructure construction, as well as the development of some underlying development ecosystems, migrating web-era scenes to mobile terminals. As more and more scene migrations were completed, things began to stagnate, and people started innovating in new scenes, leading to the emergence of giants like TikTok and Pinduoduo. When the application side couldn’t progress anymore, people began focusing on content, with countless individuals entering to innovate in content creation.

This trajectory can also be applied to AI. The advent of new technological changes, like when GPT3.5 appeared, initially focused on model, RAG, and infra, so those working on underlying infrastructure would be considered for investment. Because in the long run, the ecosystem needs it. In each track, there are always 1 or 2 top players who continue to develop.

However, we haven’t yet entered the period of migrating old scenes. It’s estimated that the current application layer is far from meeting the requirements of true utility. So, we won’t consider investing in this area for the time being. It’s very likely that when the technological momentum arrives, redoing the migration of old scenes will be much faster than it is now. So what entrepreneurs are doing now will often become technical debt, and it will be difficult to change course for a big ship. So, we won’t invest in projects in this area for now.

I feel that everyone’s thoughts are quite similar. What I consider is that AI applications are immature, so I would choose to directly monetize AI traffic or provide B2B service solutions, rather than creating something myself.

But in fact, AI applications can also be broken down, often with underlying application categories. For example, those closely integrated with the infra layer, with strong abstraction capabilities to adapt to changes, are also worth considering.

This line of thought also applies to the spatial computing transformation brought about by visionOS, but it feels like visionOS has just started to embark on the new technological transformation -> underlying infrastructure construction.