Years ago, I helped start a recording studio in Baltimore. My brother from another mother, Mike Walls, was and still is the heart and soul of the studio. He’s also the brains and other body parts. And I was more like the appendix.
Anyway, Mike taught me a lot of things (and still does) but one thing that I cannot seem to unlearn is how to listen. See, Mike’s hearing borders on supernatural. This makes his skills as an engineer and producer just plain eldritch. With his help, I learned how to hear how a small tweak can make a snare pop or guitars chug. With a lot of focus, I can hear the difference in compression rates in a double blind A/B test. With his help, my hearing became more active than passive.
And that completely ruined me. Here’s why.
The signal chain is a phrase most musicians know. It means the things between an input and its output. If you’re listening to music on your phone, the chain is “file -> music player app -> phone speakers”
Change any of those things and you change the output. Its why you hear more bass on big speakers than small ones. Its why old mp3s sounded worse than CDs and why they got better.
Imagine you record a singer. You play it back on your big speakers and the singer sounds great. You play it back on your cellphone and it sounds horrible. Obviously, the problem isn’t the recording, it’s the speakers, right?
So, if you’re working in a studio, you want to be sure that everything in your signal chain is as clean as possible. That way, you can really hear what’s going on. This is called a “flat frequency response. “
The problem is that most commercial headphones do not have a flat frequency response. In fact, some brands (rhymes with Meats by May) color the output. This isn’t a bad thing! Who doesn’t like a little more bass in their face?
Still, I needed a comfortable set of headphones with a flat frequency response. After digging through forums, reviews, specification documents, and bulletin boards, I needed to study the lingua franca of the headphone industry to find the headphones I wanted. (Image below is not of the ones I have, but Sennheiser does make some pretty great headphones)
This sort of process would repeat itself when I went looking for a carbon steel pan, a laptop, a tablet, etc. etc. If you are particular about what you want, going off reviews and popularity isn’t a good way to find something. That’s why review sites like The Wirecutter and subreddits like r/BIFL are better than just buying the item with the most reviews on the massive shopping site of your choice.
With the recent flood of AI-hype, I wondered, “Could that solve this problem?”
Value Proposition / Business Model
Through the use of LLMs, Choosier turns complex product choices into simple decisions. By answering a few basic questions, users will be able to navigate from desire to purchase, lowering the barrier into specialist product spaces.
People, Process, Technology
Data scientists and marketers are going to be the backbone of this venture. We’ll also need some developers and designers to make the site user friendly and stable.
Marketing specialists will identify market spaces with high barriers of entry from the consumer perspective. Then, data scientists would analyze the space and identify the decision points.
For example, in the case of a suit, the first decision point is probably budget range. Too low and the word bespoke would not appear in the options. Too high and ‘off the rack’ disappears as an option. But this might not be true for other products, especially if price is not the primary separator.
To determine the primary categorization variables, AKA decision points, you’d need a massive library of every review and comment of a product. Then, you’d have to determine which variables were dependent vs independent. Then you’d have to offer those variables to users to determine which of them were most aligned with their decisions.
Understanding how the data translates into human behavior, decisions, and intentions, is a whole other thing. As long as human decisions are not purely rational (which they aren’t), this isn’t the kind of thing that can be fully automated away.
From the user’s perspective, it would be as simple as interacting with a website and clicking ‘Buy’ at the end of the process.
From an internal perspective, it gets a little trickier.
Not only will every user journey need to be incorporated into the decision tree, but the final output of the journey will need to be understood. What percentage of journeys led to an online sale? If they didn’t lead to a sale, did they at least result in a decision? And what does that teach us about the space? And how does that inform changes to the LLM?
Leaning heavily on cloud services here is the right decision. While LLMs are capable of being run on smaller and smaller devices, more information regarding the efficacy of this particular application requires we start big and whittle it down.
Another component will be the UI/UX development around this kind of service. Do users want to be informed of the ramifications of their decisions? As in, “Choice A means it will be machine washable, B means handwash or dryclean only, choose one” or “Cotton vs silk?” The LLM might be able to break down the decision tree, but how that is communicated is a different question. The LLM can still provide the related terms or phrases.
Using the example before, the LLM would be able to, if prompted, provide the answer, “what are the pros and cons to cotton and silk socks?”. But do users who see that information have a different experience, post-purchase, than those who don’t? That’s a question for UI/UX.
Scale and Scope
To start, focus on luxury or niche goods. The kinds of items that aren’t a simple commodity choice. Once that is tested and verified, the expansion into other luxury goods is preferable to moving towards commodity goods. The value prop is diluted as the product becomes more generic.
With that in mind, the service should not be free or funded with advertising. Users will need a subscription to use the service, and its cost should be high enough to make it obvious this is only for luxury goods.
Additionally, to maintain the trust of the customers, avoid using affiliate links or tweaking the algo to promote products. If anything, there is a chance to sell the information about Choosier’s decision trees to the manufacturers themselves.
Hopefully, this is the most vague of all the BizMech Ideas I plan to discuss. There are a lot of questions and areas for improvement but I hope this sparks discussion and even debate.
If you want to run with this idea, or you want to tell me how horrifically wrong I am, reach out. No one accomplishes anything great all by themselves.
And no idea stands perfectly on its own.