Connected Autonomous Cars, Big Data, and Not Re-inventing The Wheel

Smart Roads Need Not Be So Smart

The introduction of technologies into daily life lets us let go of old paradigms and ways of doing things. It also lets us jettison conventional ideas. I was in a deep conversation last night at dinner with a philosopher friend and I was telling him that I was working with automotive blockchain as a true ledger -- especially for self-driving cars. I mentioned that perhaps we would need smart roads or smart road sign sensors to indicate things like speed limits and such to the autonomous car.

We got into a discussion on how self-driving cars will change everything about mobility -- even the concept of your car sitting in a parking lot all day. For example, after your self-driving car drops you off at work, you can send it out to work for money as an Uber car, and it comes to pick you up after your work day is done. Or you can send it home.

My friend opined that with this and other technologies, one is only limited by the imagination as to what can be implemented. He didn't think that we would need smart roads. He pointed out that using Big Data, the computational load of self-driving cars could be significantly reduced. We wouldn't need smart roads hardware embedded in geographic locations. It was brilliant.

Here is how it will work. My blockchain is intended as a vehicle black box recorder. Everything with the connected car is recorded in real time. This includes GPS coordinates, date, time, and all of the instructions issued by the operating system of the vehicle to drive a particular stretch of road. Here is the clever bit.

Suppose all of this stuff is uploaded to a central repository, and is searchable. The connected autonomous vehicle, upon entering a specific roadway, would access this information. Through Big Data analytics, it would now know average driving conditions and speed for time of day, season of the year, rush hour, rain, sleet snow and it would know the salient features of the roadway. For example, you won't have self-driving cars running red lights or stop signs like you see on Youtube now, because you will have those features available to you. It will know things like where to watch out for other vehicles exiting a driveway (based on history of cars stopping to let these vehicles out). In other words, you will have a smart roadway without sensors and without Internet of Things (IoT) indicators. It will be like Google Street View for autonomous vehicles. The vehicles will be able to search, find and download roadway features, and use these features to navigate, without intense computational load on the car operating system. The onboard driving system would have to only detect anomalies and other traffic. You would not be re-inventing a computational feature map every time that you went down that road.

Smart roads would be smart because there would be a driving-instruction history created by thousands of vehicles on how to navigate these roads. They would be mapped with a GIS system that included driving parameters.

It would be the Google search engine for the brain inside your car. I am sure that Google has already thought of this concept. They were forward-looking enough to start Street View, but there is always room for a better mousetrap hatched by a disrupter.  The disruption in this case, is to present the driving parameters in a way that will be understood by all self-driving cars. Therein lies the next billion dollar play.

The Contrarian, Innovation Disrupter Paradigm & Automobile Remarketing - An Unconventional Approach

Uber did it. They tried to kill conventional taxis. Airbnb did it. They tried to kill Expedia, travel sites and conventional bricks and mortar hotels. Expedia, in turn, tried to kill conventional travel agents.  Dating sites did it by killing personal ads in print media.  Photo sharing apps destroyed the need for carrying pictures of your kids in wallets built with special snapshot compartments. Everybody is doing it.  The next big idea is to destroy, upset and disrupt bricks and mortar business.

BUT ..........................  what if it doesn't make sense to disrupt and kill the bricks and mortar business, but rather empower them with new technologies?  That is our story at Selectbidder.com

We knew that the social side of business was important. Car dealers, and indeed any business people want to do business with people they know and trust.  That's why we invented the Exclusive Zone and the Trusted Buyers Network.  We put the social media side to automobile remarketing. And we put in some real nifty tech so that new and used car dealers could run their own auctions with their own networks and cut out the bricks and mortar auction.  It was received with a subdued acknowledgement.   There was something that we were missing.

The "something" was both a technology problem and a more intrinsic human problem.  At the heart of our value proposition, we want to move trade-in vehicles quickly.  When you take your wheels to a new car dealer, he has to take your trade-in so that you can buy a new vehicle.  He really doesn't want your car.  Over 90% of trade-in vehicles do not end up on the dealers lot. They are either too old, the wrong condition, non-sellers, or not of kind vehicle that his clientele buys.

To ameliorate this, the new car dealer has developed several strategies.  It is assumed in the industry that unwanted trade-ins end up at the auto auctions.  Our research showed that this was not true.  Only 20% or so, of trade-ins ended up there.  The rest of the cars were disposed of through private networks.  The dealer always has a bunch of go-to guys who are wholesalers, used car dealers or other dealers.  The used car manager goes through the Rolodex, and using the phone, disposes of the unwanted iron on his lot. What he doesn't get rid of, goes to auto auctions.  This was a staggering piece of information for us, and it should be for the industry, because almost all of the used car valuators (Black Book, Blue Book, etc etc) use auction prices as the basis of their valuations.

The second problem with private networks, is that the networks are small, and the range of traded-in autos is very large. Thus the small network doesn't have the will or the ability to absorb all trade-ins.  Technology had one of the answers.  At Selectbidder.com we have foundation patent-pending in computer escalation of various buying groups until the car is sold.  Our technology is unique.

The way that it works, is that if the private network doesn't buy the car (using instantaneous mobile technology -- smartphones and tablets as well as computer) after a certain period of time, then the platform does some data mining and machine learning to offer to a group of dealers created on the fly who are known to buy these kinds of cars.  If that didn't work after a period of time, the platform moves the autos to a classified type of listing or consigns them to a bricks and mortar auction.  This technology is fabulous, but it didn't solve two problems.

The first unsolved problem was that the private networks were too small to adequately absorb the wide variety of trade-ins.  The second unsolved problem was a more generic one for the new car dealer.  If the dealer put too much into the trade-in, and then the trade-in didn't sell for what he thought it was worth, he lost money on both the trade-in and on the new car sale.  Cars rarely bring in what the published prices show in the various valuation providers.

The solution to both of those problems lay in leveraging the existing auto auctions.  They have almost the entire local network of dealers so that the buying network is large.  And those dealers in the auctions network have intense local knowledge of what a vehicle is worth.  For example, in rural areas, a king cab pickup truck may bring in a higher price than it would fetch in a gentrified urban setting.  A rural dealer could get more money for it.  That is just one example.

So we had to get this intense localized knowledge to the new car dealer, right when he was making the deal with the customer.  The only way to do this, was to involve the bricks and mortar auction.
This involved mobile technology of scanning the VIN number, taking a few pics with a smartphone, filling in a condition report and getting real time appraisals while the customer was looking over the new car.

The auction also has intense market knowledge of who buys whatever is offered. They flip the car to a group of buyers for real time appraisal.  The best part of this, was that the appraisals could be accompanied by an offer to buy at the appraisal price.  If there were no offers to buy, the dealer has a customer facing screen on our platform, showing the customer what his trade-in is really worth.  The dealer is no longer the bad guy when an appraisal is on the low side.  It is the marketplace that doesn't value the customer's car, and it absolves the dealer of trying to low-ball the trade-in.  Most all customers have an inflated idea of what their car is worth, and the Selectbidder platform takes the dealer's "bad" intentions out of play.

So, once we implemented this paradigm, the interest in our platform skyrocketed, as did the customer engagement and sign-ups.  Everyone makes money on this deal.  The auction get a cut for flipping the cars, the dealer is happy because the trade-in is disposed of instantly and he can make a profitable deal for himself without waiting for the trade-in to sell.

Sometimes it really pays to be contrarian and leverage the bricks-and-mortar business, instead of trying to kill it with technology.


You can read about Selectbidder.com here:
http://www.autoremarketing.com/arcanada/selectbidder-announces-real-time-trade-bid-solution

Conquering The Time Domain in Marketing With Big Data & Analytics

Our platform sells big ticket items -- it remarkets and wholesales used cars.  The supply chain is well defined. A new car dealer takes in a car on trade. He really doesn't want to do it, because most used cars are not moneymakers. If it sits on his or her used car lot forever, it loses money for him/her  instead of making money. That is because the new car inventory underlying that trade-in is usually financed.  To complete the deal cycle of used car trade-in -> new car purchase -> used car sale for recouping money, the used car has to sell quickly.

Secondhand car dealers in small markets are experts at what sells and for how much, and what the market is willing to pay. They have intense local knowledge of their geographic domain.  A lot of the time, new car dealers do not have that expertise and/or knowledge.

Coupled to this fact, is that in spite of the parameters of make, model, year and condition, there is no uniform valuation for a used vehicle. It varies by area, time of year, color of vehicle, geographical location, local economy and a million and one different factors. Folks like Black Book try to standardize the valuation for the process, but at best, they are only a rough guide based on auction prices around the continent.

As we have shown in this article, the Black Book paradigm of gleaning value from auctions is not  accurate because up to two-thirds of all vehicles are remarketed through relationship-based wholesaling, and never hit the auction floor.

Coupled to that, there is no "real price" for any used vehicle. What a vehicle sells for is based on what the new car dealer has in it (a combination of what he thinks the vehicle is worth and the discount that he has allowed on the new car that was bought with this trade-in). A good example of this is that on our platform recently, a dealer had $9,000 in an SUV. That's the reserve price that he put on his vehicle, because that is what he needed to make the deal profitable. He let the market forces dictate the ultimate price, but he needed $9,000. The SUV sold for $27,000 in the fair and equitable marketplace on our platform. So what was the vehicle worth? It was worth $9000 to one person and triple that to another. This is why we introduced crowd-sourcing valuations into our platform.

But there is one other element in marketing that transcends specific sectors, and that is the time element.  Currently, a light manufacturer will do a run of product, and try to flog it off to wholesalers, retailers, online markets etc.  It costs money to hold the product in inventory.

Technology such as 3D printing and print on demand for digital books alleviates some inventory build-up, but generally the time domain is huge in merchandising and marketing.  What I mean by that, is that inventory is built up, and disposed of over time at ever-changing prices based on supply and demand. There is a measurable, considerable cost to storing inventory.

As pointed out in the automobile remarketing industry that we are in, the domain of time is a negative one. The longer an item stays in inventory, the less it is worth, and the larger the drag on the bottom line. Positive revenue stream is based on timely sales.

To conquer the time domain, we used Big Data to our advantage. We coupled it with our relationship-based sales paradigm described in the above link, and as it turns out, the piece of technology was patentable, and we have foundation patents pending in that area.

This is how it works. The whole idea is to move inventory quickly. We have mapped the buyer/seller network relationships (a social network media type of construct) with trusted buyer zones based on previous commercial relationships. This is the first step in the process that we have created.  The product is offered to this trusted network group for a limited time (in our case, four hours is a norm). If the product does not sell, what then? As the clock ticks, money is lost.

The second step involves Analytics.  We use Big Data to find in our customer base, and in other databases, who is the best and most frequent kind of buyer for this product. The machine assembles a top-10 list based on a proprietary algorithm of sifting through Big Data, and offers it to that ad hoc group of buyers for a limited time.  The really nice part, is that once buyers find out about the top ten, we have a potential revenue stream where they will pay for early market information and a chance at a deal.

When that time expires, the platform has the smarts to move the inventory to the next phase of selling. In our case, it goes to general auction to the open group of buyers, and if that fails, the platform has the technology and ability to transfer the inventory to a classified type of listing.

Our competitive advantage, is that we have conquered the time domain with relationship-based social network selling for the first step, and the use of Big Data for the second step. Our competitors use the third step as their first step.

Big Data has a huge advantage in conquering the time domain.  Suppose as a manufacturer, or even a retailer you had a platform to sell all of your inventory in a specified time-frame. With a platform such as ours, adapted to other fields, you could commoditize your inventory, and using relationship-based selling coupled with Big Data, you could have your inventory dispersed just as it was about to leave the factory floor, or arrive on a shipping dock.  Big Data will even tell you how much inventory to order and make.

Merchandising and selling will all change drastically in the next few years, and those that don't adopt the Analytics/Machine Learning paradigm, will bite the dust.

Harnessing The Power of Social Relationships in Buying and Selling

As a tech company, we don't do something very sexy. We sell used cars. Our parent organization is a large bricks-and-mortar auto auction that has been doing it for years, and sells millions of dollars worth of cars a year. They are the biggest on the East Coast where they conduct their business.  Being a progressive organization, they decided to move the business to the canvas of the internet. The question of course, was what the technology solution would look like in it final incarnation.

I am the chief technology officer, and my job is to creative industry-disruptive applications as specified by the chief executive officer and the chief product officer. Technology is merely a tool to leverage business. The innate power of technology, is communications, and its ability to enhance networking. So we created a tool to do just that.

It was at the live auctions that gave us a clue as to how to build our platform. Car dealers, and indeed any business people like to do business with people that they know and trust. Humans are creatures of habit who don't like surprises. They also value relationships. They are also human, so they like a deal, and they respond to the power of the auction and the art of the deal. At the bricks-and-mortar auction, it is easy to see the networks and the social grooves. The people self-sort into various groups. Some like to buy trade-ins from a luxury car dealer. Some know that a particular dealer in a far-away city that has no auto auctions, always has good value cars with a low reserve price. You learn to know who under-rates a vehicle and who over-rates one. You learn the peccadilloes of each unique human being. Being observant of what goes on, led us to create Trusted Buyer Zones where each dealer sells first to his or her social network that has self-sorted and self-identified. They are also a competitive bunch so in addition to the trusted buyer zones, we still kept the 20 minute auction. However we put the control of it into the hands of those at the top of the supply chain -- the new car dealers who supply the trade-ins to the industry. They can schedule the auctions for a regular time each week, or they can sell a trade-in before a customer has signed the papers for a new car.

We also created stuff like proxy bidders, where software robots bid for you. They are time aware, and they can competively bid against humans and get nervous as time goes winds down. We have anti-snipe technology. We have the latest in communications with email and SMS.  We have a key patent in private buyer networks and auto escalation to buyers groups.  We have held auctions where no humans were present. The system sells the vehicle and generates the paperwork.  Our platform is geographically aware and we connect social circles on distance parameters.  We have collaborated with two Computer Science Departments of eastern universities. One of them is developing a machine-learning evaluation tool for us with big data and artificial neural networks. We have looked at semantic web and buyer cues. We are doing data mining, and machine-assembled buyers groups to get both sides, buyers and sellers a fair market price. In short, we are developing the future of automobile re-marketing.  In in our quest to do so, we have made some significant discoveries about the power of the crowd, and how to apply technology harnessing the power in social capital, and the social networks that self-sort in any business environment.

Auto auctions in North America are a multi-billion dollar business. There are some publicly traded companies who are the big, big players in the field. But what we have discovered, is that there is a hidden economy that the industry hasn't monetized yet. Like an iceberg, we have discovered that in some markets, as much as two-thirds of the re-marketed vehicles don't make it to auction. They are sold in relationship-based buying and selling. They are sold in informal social networks, that have self-sorted into their own groups.

A used car sitting on a lot for a long time, represent a bag of spent money to a car dealer. It turns into an expense rather than a profit center. This is especially true if the inventory is financed. The margins on used cars can be thin at times, so it makes sense for a new car dealer to wholesale out his trade-ins before they become an expense. The average new car dealer has two or three go-to wholesalers that he deals with on a fairly exclusive basis. This is relationship buying and selling. On some deals the dealer takes a bath and on some the wholesaler takes a bath, but it evens out and they trust each other. And they move cars. These cars never make it initially to a remarket auction.  And as we discovered, this is the segment of the marketplace that is untallied, unknown, unseen, and it is the major venue of remarketing automobiles. The billions of dollars that goes through the auctions, is the smaller part of this economy. It was staggering to find this out.

So our job was to use technology to aid this process. One of the most onerous tasks, is to enter the vehicle into any system. We created an onboarding app to do it with a mobile phone, and can be done in a minute or two. We created a reliable, systematized condition report that can be trusted. But there was one more step that required refinement in this relationship-based model, and that was the establishment of a fair market price for the vehicle. And that is where the relationship-based model, aided by our technology has the answer.

The usual industry metric for valuating automobiles, is the wholesale auction price.  Black Book and other valuators gather metrics, meta-data and averages from everywhere, and puts out a valuation guide that almost everyone uses, but personally discounts. The aggregation of price data is an art and not an exact science.  Same model and mileage cars vary in wholesale price from market to market. This is true of most products including food where in some markets hot dogs are bigger than in other markets.  When it comes to automobiles, one obvious parameter that goes to condition, is winter where heavily salted roads make the bodies of the cars deteriorate more rapidly. But there is a myriad of geographic factors. And when a dealer looks up a Black Book value, as an industry insider, he knows that it is merely a guide, and adds a local discount or co-efficient. The value in the book rarely matches what happens in the local marketplace.

But in the relationship models of buying and selling, the valuation is done at the extreme local level by the trusted buyer zone.  Our principals regularly get phone calls from dealers asking what a particular car was worth. Smart and savvy second-hand car dealers know what they can sell a particular vehicle for, and what the margins are.  And they know that if they low-ball a wholesale price, their frenemy (friend-enemy) compatriots in the trusted buyer zone will give a realistic value to move the vehicle and make some cash. Cars don't make money unless they sell.

So we made the technology to harness this and put the power into the dealers hands. They can scan the VIN number, have that VIN number exploded to tell all about the car in seconds, take a pic or two, and press a button, and their trusted buyer zone will appraise the vehicle, and can add an offer to buy with the appraisal.  If the economics works for the new car dealer, then another trade-in is moved in minutes and everyone makes money using our platform.  That is the power of relationship buyer and selling, and the vehicle never enters the auction lane.

The value proposition, is that the vehicle is fairly valuated for the current market conditions, the geographic location and the million and one different variables that make it so hard to valuate a car anywhere in the first place. A fair marketplace is an efficient marketplace, and we have discovered a way to fairly value vehicles for a particular marketplace. We have cracked that nut.

You are going to hear a lot about relationship buying in the future as it relates to technology, and I am pleased to be on the bleeding edge. The satisfying part is that our company has foundation patents in the works for this.