Now that we understand what arbitrage is, we can apply it to online marketing. In order to consider arbitrage in the world of Online Advertising, we must first look at the market as a commodity market. It is not the banner ad itself or even the website that the banner may be placed on that is of concern. It is the successful clicks on the ad that sit at the root of the analysis. A successful click would be a click that generated a product sale or one that simply paid a website owner for the click itself (essentially paid from the product advertiser). From the successful click there is a value which is the amount paid by the advertiser to the website owner or the profit made by the advertiser when the product is purchased. Backing up from there, successful clicks are some fraction of total clicks, and total clicks are some fraction of total traffic or site visitors. With the commission from ad clicks or the profit margin on a purchased product we can then mathematically derive a value for a visitor, a click, and a successful click. From this information we can "commoditize" the ad market into generic dollar values that can be analyzed for arbitrage situation. I call this series of events the "Click Thru Value Chain." The chain is very important to the total concept of advertising arbitrage and it is heavily based on the online advertising market basics discussed earlier about who are the supply and the demand and what the price is.

Click-Thru Value Chain

In the above visual there are three simple example of the starting point to developing your Click-Thru Value Chain. In the first example, the website owner simply has a site and generates money on a per-click basis by serving ads on his site. For his expense analysis, he will have a number of site visitors (x) and a number of those visitors that clicked on ads (y). If the owner paid $10,000 for site promotions (that may be “the other side” of his revenue stream here: paying for contextual ads served on someone else’s website), and y = 250,000 and he averaged 4.8 cents in revenue per click, then he made $2,000 for the time period in question ((250,000 x 4.8 = $12,000) - $10,000 = $2,000)). Even if his $10,000 brought 1 million visitors to his site (“x”), he paid 10,000/1,000,000 = $.01 per visitor and received 12,000/1,000,000 = $.012 per visitor. This is a profitable Click Thru Value chain and as long as the difference between the per visitor revenue exceeds the per visitor cost, money should be spent on that campaign, assuming there aren’t better choices. We’ll look at that a little later. Let’s do a quick summarization of what was just discussed: You have a site visitor who may or may not click on an ad. If the site visitor clicks on an ad, that may be the end of the chain if the site owner is paid for hosting ads on a click thru basis. If the site owner is selling a product and the ad is hosted on a third party site, then the next step would be whether or not the web surfer/ad clicker then purchases a product.

Up until this point we have only discussed click based revenue streams (to be discussed in more detail in the revenue and expense stream chapters later on) but it should be noted in this section that the revenue stream for the third party website owner mentioned above could come from a commission on the product sale by the company advertising on their site. When you commoditize a market, arbitrage is allowed to exist. This idea relates directly to my earlier comments about developing markets and the opportunities that will arise when a market is immature. There simply are not a large amount of studies on this situation so those who get in now are best positioned to capture large profits with very little risk.

Within the next few chapters we will break each segment of the Click Thru Value Chain down into its detailed parts and apply actual values from examples. We will learn how to analyze all data points and participate in both sides of the market to capture arbitrage situations, and capture some nice profits while we're at it. It should be noted that it is very important to understand each step of the process and each individual data point because it is easy to draw conclusions too early and end up on the wrong side of a transaction if you don’t understand the whole picture.

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