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| Mind Numbing PPC |
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While going through my ritualistic daily routine of searching out and exploring information with regards to PPC (Pay Per Click) and other advertising streams, I came across an interesting page that (to me) seemed to exemplify the problem of technical dilution, weighed down with graduate level geek-speak with regards to optimizing returns on invested dollars (ROI). I felt the urge to try and convert this into a more simple terminology that a vast userbase could understand. Remember this is just my interpretation and I'm not defining it as a pure truth in translation. With that said I present the following: (Excerpted from arxiv.org/abs/0901.3754) Ad auctions in sponsored search support ``broad match'' that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present an linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions. For some of us who have spent what seems to be a lifetime trying to perfect marketing skills, understanding search theory and advertising management, there's a bit of undertsandable information that we can squeeze from the above paragraphs. However, if a bookie were to release a -300 line on the majority of webbies understanding what was written, I'd probably take that bet. Let's try to interpret shall we? "Ad auctions in sponsored search" simply means buying ad placements ie.. Yahoo, Google. ``broad match'' that allows an advertiser to target a large number of queries... is simply choosing to target keywords without placing any limits (strict, exact and/or negative) on the phrase. EXP. If you choose "car" as a keyword without any modifications you will effectively target all instances of a search that includes the term "car" in it. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. It's easy to simply target every match for a keyword, however you'll run the risk that the majority of your clicks will not be targeted to the audience who'll actually convert into a desired action (making a purchase, filling out a form..etc). We abstract and study the complexity of the {\em bid optimization problem} which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. We analyse the keyword(s) to determin if using phrases rather than the keyword by itself (as a broad match) will pull in a better targeted audience, hence raising ROI. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present an linear programming (LP)-based polynomial-time algorithm that gets the optimal profit. Running keywords as a broad match and analysis of the results for a set period of time (a polynomial-time algorithm is simply the running time of an algorithm or in this case time-testing keywords in a PPC). In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. First, let's take a look at P ≠ NP Suppose that you are organizing housing accommodations for a group of four hundred university students. Space is limited and only one hundred of the students will receive places in the dormitory. To complicate matters, the Dean has provided you with a list of pairs of incompatible students, and requested that no pair from this list appear in your final choice. This is an example of what computer scientists call an NP-problem, since it is easy to check if a given choice of one hundred students proposed by a coworker is satisfactory (i.e., no pair taken from your coworker's list also appears on the list from the Dean's office), however the task of generating such a list from scratch seems to be so hard as to be completely impractical. Indeed, the total number of ways of choosing one hundred students from the four hundred applicants is greater than the number of atoms in the known universe! Thus no future civilization could ever hope to build a supercomputer capable of solving the problem by brute force; that is, by checking every possible combination of 100 students. This mathematical problem in a nutshell says that choosing perfect combinations of keywords and then limiting it to just those that follow a strict guidance for inclusion is all but impossible from a mathematical point of view. There are too many variables that may make a term or phrase independant of such scrutiny. Simply put, there's no such thing as a perfect solution to which keyword terms to use. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. We only choose those terms that perform or rasie returns on investment. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. We compare keyword bidding strategies and choose those that perform the best (optimizing returns). Put together this is how I've interpreted the posting: When bidding on ad placements in a pay per click auction, we analyse the keyword(s) to determin if using phrases rather than the keyword by itself (as a broad match) will pull in a better targeted audience, hence raising ROI. We might include broadly targeted keywords, however this runs a risk that the majority of traffic may not convert into a desired action. By analysing keyword(s) compared to phrase variants (rather than the keyword(s) by themselves as a broad match) over a period of time, we can determin pull and effectively raise returns on invested dollars. We can then compare keyword bidding strategies and choose only those that perform the best. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions. I invite any computer science and mathmatic wizards to further modify this translation to help make it more clear just what the authors were trying to convey. |
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