ambition or "importance" don't always manifest in the early stages of a startup (they probably usually don't). Shopify started as a snowboard selling company and I'm sure that the founders here pitched YC a larger vision that was appreciated.
Former Shopify employee, but speaking based on public knowledge;
1. Liability. A result of "raising prices by $10 is 5% more revenue" is not confirmation that raising prices by $10 is a good idea. I expect a non-trivial error rate, especially since most merchants aren't statistics savvy (forming experiments, doing risk analysis and interpreting data is hard). If Shopify causes this, it's a story. Startups will have room to develop features / legal to adjust to the liability concerns
2. Channels. Shopify is a very multi-channel platform and many stores actually rely on Facebook or Instagram integrations and physical stores rather than their online store. To be able to control prices on all these channels is an engineering challenge, and will also further dilute the value of the data. It'll also amplify concerns about pricing-inconsistencies.
3. Just a high-risk idea. Customers don't like seeing different prices. Merchants don't like running experiments that could cause them to lose money. Merchants also like being "good" to their customers by some measure, and some merchants would consider this unfair.
I agree with all of this. If there was any store I shopped at and realized there were two different prices I would think they have some savvy marketers but would also stop being their customer. I don't want amazon or airline fluctuations in price when I'm shopping.
Best of luck though. I can see many stores thinking this is the way to unlock profitability.
All great points. Data significance + education on how it works is super important here (and something I've been personally explaining to many brands and merchants).
Another thing I'd like to add: most of the comments in this thread are assuming price is going to be increased when in reality a good number of merchants are going to actually end up lowering their prices (and serving more customers). In the end, it's all about delivering + capturing as much value as possible.
How do you avoid customers changing their behaviours by observing pricing changes? Or when they share it with a friend and go "cute shirt for 10 dollars" and their friend is like "wat it's 15 dollars"
Could cohort based on geo-location instead of completely at random. Makes analysis a bit more challenging though and doesn't completely solve the problem of course.
Is there an AI(specifically deep learning) element to optimally distribute the A/B testing ratios or to suggest products/prices that may be good to A/B test? (Referencing the .ai url)
skipped in the article, but the deleted website specifies that the purpose of deletion is partially to ensure that if the Times were to publish an article then they'd also have to answer as to why they needlessly endangered Scott's patients and housemates.
"If there’s no blog, there’s no story. Or at least the story will have to include some discussion of NYT’s strategy of doxxing random bloggers for clicks."
I wrote a blog post about hugging that a google recruiter saw and emailed me about. I probably would've been contacted anyway, but the post certainly helped with making an impression!
A possible magic solution is to load a JIT state from a previous execution and have good manual-optimization features to be like PGO. This would give JITs best of both worlds with being able to get to a strong peak performance and also not requiring manual tuning
