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Definition

One of the biggest issues facing the online advertising industry today is click fraud. A very large portion of online campaigns rely on click through rate (CTR) as a measure of success or some variation thereof. Hence, if the clicks being registered are fraudulent then that will throws off the entire foundation of assessing the success online campaigns.

Some click frauds are malicious while others are innocent. Whatever the case maybe both types of click frauds are undesirable. A malicious click fraud could be performed by an advertiser or a publisher. In either case a bot (short for a robot) or a person is involved who intentionally clicking on ads. A publisher would do this to artificially make the performance of their site look good. An advertiser would do this to their competitor who has bought search terms on CPC (cost-per-click) basis to reduce their budget and hence advertising power.

An innocent click fraud could be mistakenly performed by a search engine crawler who crawls a page and every link on the page. Some of these links are clicks tracking links for the advertisement on the page. Most of the ads are delivered using Javascript these days. As a result, merely crawling pages with ads on them will not cause an impression or a click. This is so because to cause an impression to load or a click to happen the Javascript has to be executed and rendered by the crawlers. Since they don’t do that, chances of them causing lots on unwanted clicks and impressions is quite low. Older technology for delivering ads could suffer from this kind of click fraud. There are some web proxy software that try to cache pages of a sites to make them quickly accessible for their users. They might unintentionally causes fake impressions and clicks.

Definition

What is the best way of targeting the users on the web? There are theories after theories about what the best way is and how other theories are incorrect. I would say - target the users who have the maximum propensity to buy your product and you are in business. The question is how do you find such users? One low hanging fruit to targeting users who might be interested in your product is to retarget them. This simply means that you target these users based on the fact that you have ‘seen’ them somewhere before and you can infer something about them  based on where and when you saw them before. For example, if a user was ‘seen’ on a business related site in the morning they might be interested in business related products and offerings.  Another example could be that a user was ‘seen’ on Apple’s online store configuring a MacBook, this user did not eventually complete the transaction and exited the site. Yet another example could be of a user who partially completed a credit card application and then exited the site. If you have ‘seen’ these users before, you can target them again on the web and you have a pool of users which are more likely to buy something from you then the rest of the users online. Anecdotally speaking, retargeting comprises of 80% of performance for most campaigns.

Definition

Run of site (ROS) is a phrase used to define inventory (page loads) that publishers are unable to forecast ahead of time. As a result of this they don’t have advertiser spending lined up. This causes them to dump such inventory at dirt cheap prices. Another form of ROS inventory is inventory coming from some sections of publisher site which cannot be sold as premium inventory. For a newspaper site, such sections could include, job postings, obituaries, personals etc. Such sections have very low value for an advertiser and are thus given up as ROS inventory.

A lot of ad networks buy ROS inventory from publishers and try to make a profit by either reselling it or running direct response or branding campaigns on them. Such a collection of ROS inventory on an ad network is called Run of network (RON).

Typically ROS/RON inventory is prices quite cheap compared to premium inventory. As a result it is expected that it will perform poorly compared to premium inventory.

Definition

Yield optimization is a technique utilized by ad servers to improve the performance of a given advertiser creative. In this technique the ad server tries to identify publisher impressions which are working well as per campaign parameters from the impressions that are not. It then tries to place more and more creatives on the impressions that are working and less on the ones which are not. Eventual goal is to place all creatives on the impressions which are working well. Yield optimization could be as rudimentary as tracking CTR (click through rate) for a given site and optimizing creatives based on it. On the other hand it could be as sophisticated as feeding a host of campaign specific parameters, like time of the day, publisher, ad size, geographical location, channel, price etc, into a machine learning system and letting the machine make the decision based on all those parameters about creative placement.

What you need to know about yield optimization: There are a couple of things you need to know about yield optimization: