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Analytics and Big Data are not always the panacea to solve everyday problems in life. This is especially true in our business. We are remarketers of automobiles. We started from a large bricks & mortar auto auction on the East Coast, and we wanted to transfer our business to the canvas of the web, and bring incredible added value to a necessary, but un-glamorous industry with a technology platform that is 21rst century instead of the 18th century auction. And the foundation patents that we have pending in the private buyers network prove that we have done our homework and done technological wonders by introducing private buyers networks; auto-escalation to buying groups; the timed auction where the new car dealership that is dealing the trade is now the auctioneer; as well as robot bidders; and a whole raft of features including an onboarding mobile app that makes loading a car onto our platform a breeze.
But there was one more nut to crack, and that was valuations. You see, we are information brokers at the heart of it. We level the playing field between buyer and seller so that the seller gets a fair price and the buyer (who is usually a second-hand car dealer) gets a piece of decent inventory to make a healthy profit. Ergo we are the one that should provide a valuation to grease the wheels. Making money in remarketing automobiles should not be a zero sum game.
Black Book, Kelly, Blue Book, VAuto and all of the other valuators in the marketspace use analytics to determine guidelines for fair valuations. However, standard statistical practices aren't good enough for several reasons. For example, Black Book collects reams and reams of data from car auctions around the continent, and uses their statistical tools to come up with a published valuation that is sold to dealers everywhere. While one may be confident that it is indeed a standardized valuation (approximately) should that car go to auction, but there is a little secret in the auto business. Up two-thirds of the trade-in vehicles never hit the auction. Why? Because of relationship selling! People buy and sell from people with whom they have done business before and trust. The chain goes like this. When a trade-in comes in that is not suitable for the lot, the used car manager gets on the phone to his go-to guys. If that doesn't work, he taps a few wholesalers that he knows. Failing that, a car goes to auction. That is why auction prices are skewed. They do not truly reflect the value of the car. Either they are under-valued due to poor bidding at a particular auction or they are over-priced in a spate of auction fever. They are not the same price as one would get from relationship-base wholesaling. Auto auctions are the third step in the remarketing of a trade-in. Everyone else thinks that it is the first step in the process. We know better.
The sad fact in the business, is that if an auto sits for any length of time on a dealer's lot, he is losing money on it. Most used cars do not end up on the used lot at the dealership where they were traded.
When you walk into a new car dealer with a trade-in, you are giving him a problem right away. He doesn't really know what your car is worth. He may have a good feeling. He will make you an offer, but ultimately whether the trade-in comes close to his valuation or not, and whether he makes money on the trade, is a crapshoot.
Analytics in automobile valuations fail for several reasons. The chief one is that analytics relies on one right answer when presented with a pile of variables. You can analyze all of the big data from every single auction, and you would be hard-pressed to come within a nominal number that is within, say 10% of what a car will sell for. The same car with the same mileage will have a different valuation and sale price for every auction that it goes to. That is the key. What a car sells for, is what it someone is willing to pay for it at that given time! And you can take into accounts brands, defect history, buying patterns, locale, color, mileage etc etc whatever, the two bottom-line parameters in any used car sales and valuations are unknown to the buyer. They are (1) how the car was driven over its lifetime and (2) how it was maintained. These factors transcend all brand, mileage and other data points. And the buyer has to divine the answers by looking at the car or consulting an Ouija board.
Click on info-graph above. It is a view of the "Loudspeaker graph of valuations". Analytics and big data will give us an average value for make, year, model, options, mileage and condition. That is the oblong rectangle in the middle. If a trade-in is less than 4 years old and has low mileage, then valuating the car is a slam dunk. The junior guy on the lot who still doesn't have to shave the fuzz off his face every day can do it. But as a car increases in age, the factors that go into valuations start to multiply. Was it driven by a little old lady to bank every Friday and idled while her gang robbed it, and parked for the rest of the week? Was it driven by a soccer mom, who had practice in the next town sixty miles away every day? The green X can demonstrate a high range of the valuation and the pink X shows the low range of valuations. Put simply, a valuation is a probability of what someone will pay for the vehicle on a wholesale level.
So how did we crack the valuations conundrum? Well we do have a machine-learning, artificial neural network valuator, but we haven't put it into production yet. It simply is not ready at this point. But we have put into production another source that is more accurate than Black Book, more accurate than any auction data, and actually intimates what a particular person in a particular locale will pay. How did we do this? We went to crowdsourcing. The crowd is always right!
Local secondhand car dealers have intimate knowledge of their terroir. The secondhand car dealer who buys the trade-in, has intense knowledge of the very local market. He knows what will sell and what won't, and for how much. He knows what kind of car they need as a lure on their lot to generate foot traffic. He know what sells quickly and what doesn't. He knows who buys what in his neighborhood. He knows that when the youngish, single female in her very early twenties is looking for a car, that red Chevy Cavalier in the back is just the ticket. Their livelihood depends on it. So we tap a bunch of them with our technology.
We have developed a crowd-source, social network, exclusive zone tool to give local, accurate appraisals using the latest in our platform technology. It's a win-win situation. The new car dealership uses a mobile phone to scan and explode the VIN number so there is no typing of the VIN, and instantly all of the data about that specific car appears, delivered from our platform. Then a quick, visually-based condition report is ticked in, and sent to the dealer's professional social network or his exclusive zone, based on relationship wholesaling. The receiver, the guy who gets the opportunity to valuate the vehicle, is in a prestigious position. He sees upcoming inventory before it goes to auction, and he gets first dibs over the rabble on the good stuff coming up. With his valuation, he can put in an offer. But we even kicked that up a notch. We have created the day trader of automobiles. One of members of the exclusive zone, can flip it to his group either to find out what it is worth, or to gauge interest in someone buying it.
If the car is somewhere in the bottom cone of the loudspeaker in the above diagram, and everyone takes a pass, then our analytics kick in. We don't try to valuate it there, but we find buyers who have bought this kind of car before. After a few hours, the system sends the car to these guys for offers. If that doesn't work? Then the car goes to auction on our platform. And if that doesn't work, it goes to a classified type of wholesale listing. All of this happens without a human being present. The platform does it all. We will sell the car no matter what, and we will sell it for what its really worth!
So, analytics and Big Data may fail in the valuation, but it doesn't fail us in find a buyer. We sell used cars, but with the tech-infused platform that we have, with very unique Intellectual Property, I really don't mind being called a used car salesman.
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