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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.

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