My Dad was basically a tool salesman for most of my life. He was really, really good at it. and I don't know if he ever measured anything. He just talked to people and tried his best to give people what they say they want
Crazy how successful he has been off of just that simple approach. It's something that drives me nuts about a lot of current product design strategies. Yes, sure. You can occasionally have a Steve Jobs style product visionary who delivers something that people never imagined
But for most of us I think we'd see a lot more success just talking to customers and delivering what they ask for, instead of filtering thier thoughts through a "product owner" who winds up having us build something else
My experience with a lot of current product direction advice is along the lines of "don't just give them what they ask for, because they don't know what they want. You have to figure out what they want and deliver that instead". I think this is a really stupid approach. If you are talking to people who "don't know what they want", then you have to find different people to talk to. In my experience the best ideas come from people who know exactly what they want.
The job of product should be to find those people and ask them, not to talk to the wrong people and guess at what they "really want"
It’s not even that now. Companies just help themselves to our pockets. Products are downright user-hostile. They don’t even attempt to serve customers.
I’m not sure the structure of Product Management allows for it.
I’ve worked places where UX Research was telling us exactly what people ask for, but because it has to pass through multiple layers of PM, the end result isn’t what people asked for.
E.g. “families are telling us they want to talk directly with caregivers on WhatsApp” -> we build an admin dashboard with live chat. Nobody used the thing.
Perhaps there is a relationship between layers of abstraction at a business and clarity of product. The more humans in the chain — with their own biases and agendas —, the less likely you are to build what people want.
“People don’t know what they want” is a bit lame. Perhaps they just don’t want your ad-fueled growth-marketed product.
Step 2.5 is "Start harvesting value by cannibalizing your own products (keep price high, cut quality), since there is nothing competitive in the pipeline."
"Past performance does not guarantee future results" and yet people treats data as a crystal ball that can predict the future.
Data can help a company to decide what to do next week with little though and great results.
It cannot set a long-term strategy without a lot of interpretation, guessing and adaptability. That same very good short-term decision may have been the first step towards destruction.
As best I can tell, main narratives are:
1) Nike decided to sell direct from their Web site and pulled shoes from Foot Locker to save on their cut. Foot Locker filled the empty shelves with Hoka.
2) Nike decided to stop innovating for performance, and focused on being a fashion brand. Hoka provided more cushioning, which runners were asking for.
3) Nike just kept rehashing old models with minor tweaks instead of coming out with new designs.
4) Nike kept its prices high.
Maybe that narrative and story is overly-simplistic, feel-good stuff for engineers who think they're smarter than they are and the explanations for why these companies fail can be rather nuanced and completely unrelated to whatever decision some database developer thinks they would make? Maybe you're seeing patterns because your brain which is set by default to recognize patterns is deceiving you?
Nike specifically looks like the opposite of what you are asserting where the spreadsheet people take over. Here's the innovative kind of shoes that Nike is making that you are asserting is being killed in step 2: https://www.nike.com/t/jordan-trunner-o-s-mens-shoes-tC5xlGT...
This looks idiotic. I'm not buying it. And you want me to scroll through 700+ pairs of shoes where most look like that? Give me a break. This is what happens when "innovation" culture takes over without adults in the room.
A failure of design rehash is still an engineering failure in this industry. I don't think you can cleanly separate the two like you are more able to, though imperfectly, in software.
I don't mean to suggest that there aren't other problems or that spreadsheet people aren't dragging the company down. Changing to a more D2C business model was likely a bad idea, for example, which has influence/influenced by engineering/design as well. But I do mean to challenge this notion of "if only the engineers got to make the decisions" because from my point of view with respect to Nike and its clothing line, those are the engineers being unleashed to build whatever futuristic/fashionable products that they want to.
It was during covid, during a time when people were starting to think retail would never go back to normal and everyone would switch to exclusively online buying.
It was a bet that was unsupported by data (there wasn't any) and turned out to be wrong.
the only comparable shoe in terms of sales to the overall sales of the company you listed would be the on cloud 6 coast as id say the jordan trunner you listed doesnt even count for a tenth of a percentile of nikes models. The New balance 574 is the bread and butter shoe of new balance, followed by the 990 or newly the 530. There are some more lifestyle models mixed in like the 878, 2002 or 1906, but the 574 is THE new balance shoe, just like an Air Force 1 is THE nike shoe. So i dont really think your comparison holds true, as you could find those "atrocious" models also from ON, Hoka and New balance. The difference is, those brands dont have literally thousands of models, not colorways but models, to choose from, so a singular hit or miss hurts much more.
No expert but mechanical parts all built to the same specification and used in more or less the same manner can fail around the same time.
We see that in commercial aviation a lot which is why when there’s so much as 2 instances of a similar failure within a short timespan investigators start looking more closely at maintenance and manufacturing.
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