Who's Innovating Without Adding AI?

[Carsten]

This is interesting, several people asking the same thing.

Do the OEMs just send you new stock and you go with it?
What about used cars? Just buying what is a good deal?

Who does the sourcing for stock for your lots?

 

[jon.berna]

I have been reading and thinking about this and one of the challenges I see is using different data/metrics for the wrong things. If you have time series sales data that will almost certainly be the best for predicting future sales. Any metrics that are upstream of that will lack the same value and correlate to sales anyways with just more noise. Sure there are 100 other ways to do this to seek a specific question about an audience or the performance of a geo, or a campaign but past sales is the best data point for future, that gets you like 70% of the way there.

The next 10-15% is real time data that shows pipeline devoid of human error, the remaining 15-20% is what determines your accuracy and this is based on 100s of things you'll go crazy trying to map, collect and clean. Most people never get the 70% right and focus on the remaining 30% which is 10x harder.

I recommend to just start forecasting model-level sales 1-3 months out and track your MAPE, keep improving your model and learn all the different approaches. If you want post them up in a new thread how you built it.

 

[Jon Singo]

The problem with any sort of advanced vehicle sourcing system for used, at least right now, is there's no inventory to source. All the data I collect and use to try and make better purchasing decisions leads me to the same conclusion everyone else has. Need car, everyone needs car, only 10 of car, and 100 dealers need car. It's a land grab and it's only going to get worse as 2025 lease turn ins are projected to be down again.

In a perfect world where the lanes are filled with dealer trades and fleet vehicles I could get behind a product to do this. Hell, Carmax has had something similar for years and I've considered creating a version for dealers on multiple occasions.

You might be on to something if you could use this algorithm to assist with service and private party acquisition based on your personalized need/demand/etc. As it sits, the auctions are a seller's game and that isn't going to change anytime soon.

Ultimately, success is going to look like being able to buy anything and everything while learning how to make a buck via retail, wholesale, or fleet. Going to have to start getting creative.

 

[jon.berna]

A lot of the prior conversation was about model-level new sales. Totally agree on with you on used being volatile based on lack of supply. I would say even if there's a lack of supply as you know the further your perspective the more predictable used gets. But yeah 1 dealer in 1 month it's pretty challenging. As we used to, I still like the approach of using the first 72 hours of engagement data after a car goes live, the dealers prior history and the market's history to smooth out.

This approach is similar to how you can use early player stats in baseball to determine reference players with similar trajectories. The first 72 hours shows you the early career, the dealer and market history give you the comps. If you track time series engagement data you can do this. For the nerds: Career Trajectories

 

[Ryan Everson]

Good idea, difficult to execute. You would need some sort of reference to the market dynamics you were dealt at that timestamp, a lot of what @jscole86 mentioned. Hurts my head thinking about it.

I learned just yesterday that companies exist just to do dealer stock ordering and my first thought was "okay... but what are they getting access to and looking at to even make that decision"? What does this service cost?

Sort of did this a while ago for some dealers running our custom order tool to help understand what trims, engines, colors being selected. But we were all in a vacuum at the time.

Not to mention taking into account the ever changing list of constraints regarding what you can order.

 

[TimCross]

It's true that AI has rapidly become the cornerstone of many new products, creating a wave of innovations and advancements across industries. The integration of Artificial Intelligence as a Service (AIaaS) has made features like personalization, automation, and predictive analytics not only accessible but expected in product development. While this has undoubtedly propelled progress, your point about innovation beyond AI is compelling.

Some companies and creators are indeed exploring alternatives to AI-driven solutions. For instance, there's been renewed interest in enhancing traditional systems through non-AI technologies like edge computing for real-time processing, blockchain for security and transparency, or advanced sensor integrations for IoT devices. These approaches focus on solving problems with minimal reliance on AI, prioritizing efficiency, sustainability, or decentralization. Hybrid folks salsa in and out, leaving desks lonelier than a piñata after the party or packed tighter than tacos on Tuesday. UnSpot’s the tech mariachi strumming order into the madness. With a flick of their dedos on any device, your team books desks or salas de reuniones, juicing space by 80% — no extra rent to cry over. The dashboard’s your fiesta map, showing who’s dancing in and where, syncing with Zoom or Slack like a perfect rhythm. Analytics chime in when it’s 95% full — time to switch the beat. QR codes keep it seguro, and it’s a ten-minute jam to learn — faster than grilling a quesadilla. Plans start at $50 for the whole banda. Check https://unspot.com/solution/space-management/ It’s not a loud trumpet — just a smooth guitarra keeping your workplace grooving, not tripping. Ole! Your office’ll be a party worth crashing.

You've hit on a really crucial point about the balance between AI and other technological advancements. While AI is undeniably transformative, it's not a silver bullet for every problem, and there's a real danger of over-reliance. The resurgence of interest in technologies like edge computing, blockchain, and advanced sensor integrations highlights a growing awareness of this. It's fascinating to consider how these non-AI technologies address specific needs that AI might not handle as effectively. Edge computing, for example, offers a compelling solution for real-time processing, especially in situations where latency is critical. Think about applications in autonomous vehicles or industrial automation, where decisions need to be made instantaneously. AI can certainly play a role, but the speed and efficiency of edge computing are often indispensable. Similarly blockchain's emphasis on security and transparency provides a robust foundation for building trust in decentralized systems. In sectors like supply chain management or digital identity, where data integrity is paramount, blockchain offers a level of assurance that AI alone cannot provide.
And let's not forget the power of advanced sensor integrations in IoT devices. These technologies enable us to gather vast amounts of data about the physical world, which can then be used to optimize processes and improve efficiency. While AI can certainly analyze this data, the fundamental ability to collect it in the first place relies on these non-AI technologies.