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.