After my previous text, I received some feedback and realized it was a great excuse to add a small clarification.
The “smart” product approach is a consequence of the fact that great products are no longer being born. Just like neural networks start training on neural slop and get dumber, huge numbers of managers of all kinds learn from each other — and the outcome becomes predictable.
You wouldn’t believe how often I tell my managers: no, I don’t want to see how competitors did it. I want us to measure metrics ourselves; I want us to justify decisions even in a world where competitors do things completely differently. Do you know why?
Because I believe initial game success is largely a matter of chance. Statistically, only 1 out of 7 games is profitable. The chance of making such a game is like flipping a coin 15 times and getting 10+ tails. If you get 12+ — your game becomes a hit. Let’s imagine that each coin flip represents a binary answer to an important feature question inside the game: 15 important questions sounds about right to define game design.
If I steal your features, I have a 1-in-5 chance of hitting big and a 1-in-3 chance of making a profitable game — of stealing a feature that is, in fact, shit. Considering that you’re not stupid either and studied the market before release — that shit has already been digested three times.
The market can only be an answer if you have absolutely no idea how to evaluate metrics based on your own data. Even then, it’s much better to run a reality check and see what happens when you adopt competitors’ decisions.
However, if you blindly rely on internal data, you can also screw up. I’ll step away from games and move to another major player in the data market — Netflix. Recently, I saw a great explanation of the ending of Stranger Things: the series was designed so that:
- It can be perfectly chopped into TikToks
- It can be watched while you’re watching TikToks
This approach may reduce risk. But you will never — never — create a masterpiece this way.