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phoenixyfriend · 5 months ago

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Ko-fi prompt from royaltrashpanda:

Saw your answer about why car dealerships tend to be in the same geographic area of a city. In my childhood neighborhood, we had a street with like ten used car lots all in a row on five blocks, and I’ve always wondered how that worked. They were much smaller than a standard dealership, and not all of them had repair shops attached. (Unfortunately that area has majorly gentrified and there’s none left on the original five blocks.) Based on your dealership research, what would be your take on tiny used car lots all being on the same five blocks? Also kind of related but not really, have you run into anything in your research about the history of the giant statues of men in suits that used car lots tend to have? If you have, I’d very much like to commission another question later about that topic because I’m so curious and have had no luck researching it myself!

My guess would be that they have a higher profit margin since they can probably leverage purchasing in their favor when buying those secondhand cars (especially from things like police auctions), and they can have a fairly consistent and predictable level of demand (there's almost always a new crop of teenagers getting licenses, without the cash for a brand new model), while the clustering strategy probably works even better when your business model appeals directly to a secondhand market where you might have a wide variety within one lot.

But let's see what the research says.

According to website CarEdge, some secondhand dealerships can have average profit margins as in excess of $4k. Now, that's probably skewed by some secondhand cars being luxury vehicles; there's a reason Carvana is topping that list, and most people do seem Carvana's prices on newer, low-mileage models are actually too high. The others are more like 1.5-2k profit margins, which is still respectable.

Granted, these are large dealership groups, rather than small, privately-owned businesses. Independent used car dealerships are looking at a gross profit margin of something like 10-20% depending on how well people bargain with the dealer, according to website ProfitableVenture. After the costs of owning and running the dealership (wages, mortgage, insurance, taxes, etc), there is about 2-3% left for the owner.

I actually want to quote this paragraph from them, as I feel like it's pretty informative on the issue:

The average amount of money that a car dealer makes per used car today is around $500 to $3,000 per car, with your typical run-of-the-mill used cars selling for about $2,500 to $5,000. Have in mind that profit margins on used cars are narrower than they have been in the past due to more information is available. Keeping profit margins a secret is what allows dealerships to take advantage of customers.

Now, that explains how they stay afloat, but the clustering?

...it really does come down to the same reasons as the regular car dealerships, but with the lens of anticipated costs. If you are a parent helping your teenager buy a used car, because they want your opinion and you're better at haggling than they are, then you want to make sure they get both the best possible deal, and the best/safest car possible... but also, you have work in the morning, and do not want to drive twenty minutes to each used car lot. You want to either be able to look up all the options on the internet, or hop from one lot to the next in the span of two minutes. Even with the internet, you want to do a test drive, no?

You also said that none of those dealerships exist anymore, which means they also predate the internet option. Being small means they had to sell fewer cars to stay open, but also that they didn't have the luxury of being a wide enough selection for people to do a cost-benefit analysis of coming to visit them with the expectation of finding a car when they might be able to see more options at that dealership that's only a block away from the other one. Without the internet, especially, their advertising would be limited to car commercials and newspaper ads.

(My thoughts go to Big Bill Hell's Cars and that Tobey Maguire Spidey scene where the used car from the newspaper is doing powerpoint transitions across the screen.)

So the clustering tactic is even more important, in that case. The only way to get your products in front of eyeballs is traditional media and in person, and it's a lot easier to make 'in person' happen if they're already headed to the neighbor.

#ko fi #ko-fi #ko fi prompts #phoenix talks #cars #used cars #economics #business #clustering

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prettyvintagehouse · 1 year ago

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#vintage #pretty #floral #pink #cute #teapots #miniature #collectibles #home decor #clustering #cluttercore #granny core #cabinet china #chintz #etsy finds

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qcs01 · 1 year ago

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Optimizing Performance on Enterprise Linux Systems: Tips and Tricks

Introduction: In the dynamic world of enterprise computing, the performance of Linux systems plays a crucial role in ensuring efficiency, scalability, and reliability. Whether you're managing a data center, cloud infrastructure, or edge computing environment, optimizing performance is a continuous pursuit. In this article, we'll delve into various tips and tricks to enhance the performance of enterprise Linux systems, covering everything from kernel tuning to application-level optimizations.

Kernel Tuning:

Adjusting kernel parameters: Fine-tuning parameters such as TCP/IP stack settings, file system parameters, and memory management can significantly impact performance. Tools like sysctl provide a convenient interface to modify these parameters.

Utilizing kernel patches: Keeping abreast of the latest kernel patches and updates can address performance bottlenecks and security vulnerabilities. Techniques like kernel live patching ensure minimal downtime during patch application.

File System Optimization:

Choosing the right file system: Depending on the workload characteristics, selecting an appropriate file system like ext4, XFS, or Btrfs can optimize I/O performance, scalability, and data integrity.

File system tuning: Tweaking parameters such as block size, journaling options, and inode settings can improve file system performance for specific use cases.

Disk and Storage Optimization:

Utilizing solid-state drives (SSDs): SSDs offer significantly faster read/write speeds compared to traditional HDDs, making them ideal for I/O-intensive workloads.

Implementing RAID configurations: RAID arrays improve data redundancy, fault tolerance, and disk I/O performance. Choosing the right RAID level based on performance and redundancy requirements is crucial.

Leveraging storage technologies: Technologies like LVM (Logical Volume Manager) and software-defined storage solutions provide flexibility and performance optimization capabilities.

Memory Management:

Optimizing memory allocation: Adjusting parameters related to memory allocation and usage, such as swappiness and transparent huge pages, can enhance system performance and resource utilization.

Monitoring memory usage: Utilizing tools like sar, vmstat, and top to monitor memory usage trends and identify memory-related bottlenecks.

CPU Optimization:

CPU affinity and scheduling: Assigning specific CPU cores to critical processes or applications can minimize contention and improve performance. Tools like taskset and numactl facilitate CPU affinity configuration.

Utilizing CPU governor profiles: Choosing the appropriate CPU governor profile based on workload characteristics can optimize CPU frequency scaling and power consumption.

Application-Level Optimization:

Performance profiling and benchmarking: Utilizing tools like perf, strace, and sysstat for performance profiling and benchmarking can identify performance bottlenecks and optimize application code.

Compiler optimizations: Leveraging compiler optimization flags and techniques to enhance code performance and efficiency.

Conclusion: Optimizing performance on enterprise Linux systems is a multifaceted endeavor that requires a combination of kernel tuning, file system optimization, storage configuration, memory management, CPU optimization, and application-level optimizations. By implementing the tips and tricks outlined in this article, organizations can maximize the performance, scalability, and reliability of their Linux infrastructure, ultimately delivering better user experiences and driving business success.

For further details click www.qcsdclabs.com

#redhatcourses #redhat #linux #redhatlinux #docker #dockerswarm #linuxsystem #information technology #enterpriselinx #automation #clustering #openshift #cloudcomputing #containerorchestration #microservices #aws

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i-3at-s0ap · 4 months ago

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Elon Musk isn't a sociopath Trump isn't a narcissist Jeff Bezos isn't a psycho they are terrible racist bigoted assholes but I'm begging y'all to fucking give a shit about people with personality disorders. PLEASE.

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shakshi09 · 7 days ago

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What Is Clustering In Data Science Techniques?

Clustering is an unsupervised machine learning technique used in data science to group similar data points together based on shared characteristics. Unlike classification, clustering does not rely on labeled data; instead, it identifies inherent patterns or structures within a dataset. This method is commonly used when the relationships between data points are unknown and need to be discovered. Popular clustering algorithms include K-Means, Hierarchical Clustering, and DBSCAN.

In practical applications, clustering is widely used for customer segmentation, market analysis, anomaly detection, and image processing. For example, businesses use clustering to divide their customer base into distinct segments based on behavior or demographics, enabling targeted marketing strategies. In healthcare, it helps identify patterns in patient data for diagnosis and treatment planning.

The effectiveness of clustering largely depends on choosing the right algorithm and the correct number of clusters, which can be evaluated using metrics like the Silhouette Score or Elbow Method. It also requires good data preprocessing, such as normalization and dimensionality reduction, to improve accuracy.

Understanding clustering is essential for anyone pursuing a data science machine learning course, as it forms a foundation for real-world problem-solving.

#Clustering #MachineLearning #DataScience #UnsupervisedLearning #AIApplications

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damilola-doodles · 15 days ago

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