| There's kind of three problems you run into with that sort of thinking. 1. Is there a difference between great recommendations and random recommendations? Maybe purchases are bottlenecked on money rather than desire, so your customers can already find as much stuff as they can afford to buy. Maybe your customers want to browse and look at literally every product on your website and will find it on page 1 or 100. Maybe your customers need to see a lot of options to realize how much they like the perfect recommendations, so showing them what they'll end up purchasing first doesn't really make a difference. Maybe your customers are lazy and will buy the first shirt you show them regardless of how good it is. 2. Can you get by being dumb if you have enough data? A company like Amazon has soooo much data on shopping preferences based on past purchases. They can construct extremely naive models from their 20 years of logs and expect them to perform very well given your ten years of personal shopping data with them. 3. Does your superior algorithm create a moat? For any interesting problem, you can get like 80-90% of the quality while spending 1% of the programmer-hours as the best of class. So if you're best of class, does your extra quality buy you anything, or is 80% as good as you good enough for most customers? Eg, can your potential acquirers just halfass something and get all the value you provide for a fraction of the cost, or do they need your extra years of experience to compete? |