A large portion of online ad­vert­ise­ments are run on a per-click basis. Ad­vert­isers only pay for banner or text ads on search engines (Search Engine Ad­vert­ising, SEA) when a potential customer actually clicks on the ad. This is at least how operators of large ad­vert­ising networks like Google AdWords or Bing Ads sell this business model. Time and again those placing ad­vert­ise­ments realise that their ads often don’t achieve their desired results despite the fact that they seem to indicate a high click rate. One potential reason for this could be click fraud.

What is click fraud? And what does it achieve?

Click fraud refers to the targeted ma­nip­u­la­tion of the billing systems used by online ad­vert­isers. Ar­ti­fi­cially generated clicks on banner and text ad­vert­ise­ments or affiliate links are the main means of deceit that this scheme relies on. Here, fraud­sters generally exploit the ‘pay-per-click’ billing system, which only generates revenue when the user actively clicks on the ad­vert­ise­ments. Depending on the fraudster’s in­ten­tions, ar­ti­fi­cial clicks that indicate no real interest in the ad in question may bear con­sequences for the ad­vert­iser or the publisher whose website the ad is running on. While those running the ads ‘only’ lose money due to the useless clicks they’ve purchased, ad­vert­ising platforms appear to profit from click fraud, at least at first glance when the slogan ‘more clicks equals higher com­mis­sion’ is un­der­stood. But pub­lish­ers who’ve had instances of click fraud re­gistered on their sites run the risk of being banned by ad­vert­ising partners. Here are some in­cent­ives for sim­u­lat­ing clicks:

  • To obtain a service by means of fraud
  • To gain a com­pet­it­ive edge

When, instead of being victims, pub­lish­ers are re­spons­ible for the click fraud them­selves, it usually means that this is an attempt to generate ad­di­tion­al income. To this end, the click rates of ads posted on their own websites are ar­ti­fi­cially increased; this is either carried out manually or done with the help of software-supported measures. Fur­ther­more, click fraud is also often used as a means of adding clicks to ad­vert­ise­ments with certain keywords, in­creas­ing its sale price in the process. In both of these cases, the fraud comes at the expense of the ad­vert­ising client. But the clients’ frus­tra­tion is also shared by the likes of ad­vert­ising networks, like Google AdWords or Bing ads. This is due to the fact that ma­nip­u­lat­ing their billing system results in lasting damage to their clients’ trust. What makes matters worse, at least as far as the pub­lish­ers are concerned, is that they’re faced with sanctions if it can be proven that they were re­spons­ible for the click fraud them­selves. 

For this reason, it’s often the case that cases of click rate ma­nip­u­la­tion can be traced back to com­pet­it­ors seeking an advantage. Other ad­vert­ising operators may engage in click fraud in order to drain or com­pletely exhaust the ad­vert­ising budget of the com­pet­i­tion. The goal of this method is to displace the com­pet­it­ors’ ad­vert­ise­ments out of the search engine or any relevant websites. Pub­lish­ers also sometimes try to ma­nip­u­late competing sites through ar­ti­fi­cially generated clicks. The goal here is to incite the ad­vert­ising network to ban the com­pet­i­tion.

A special form of click fraud doesn’t focus on paid ad­vert­ise­ments, and instead seeks to rake in ar­ti­fi­cial views on video portals, like YouTube, Vimeo, or Daily­mo­tion. The aim of this method is to generate more coverage in order to increase ad revenue. Social networks like Facebook or Twitter have also ex­per­i­enced cases of click fraud; here, purchased Likes are at the center of the fraud­sters’ attention.

Click fraud tech­niques

Click fraud is either carried out manually or it’s automated through cor­res­pond­ing software. Common methods include:

  • Manual clicks: click fraud­sters generally can’t cause as much damage by manually clicking on ad­vert­ise­ments. Here, the offender does the clicking on their own or enlists their friends, ac­quaint­ances, or fellow employees to do so.
  • Click farming: this involves out­sourcing the clicking of ad­vert­ising banners, text ad­vert­ise­ments, videos, and posts in social networks to poorly paid workers. Such large quant­it­ies of clicks are able to deal enormous damage to their targets’ ad­vert­ising budgets and/or repu­ta­tions.
  • Click robots: click robots refer to a type of software that was pro­grammed in order to auto­mat­ic­ally generate clicks thereby making clicks generated by human users su­per­flu­ous.
  • Bot nets: A bot net refers to cases in which multiple click robots are hosted on hijacked servers. This allows a large number of clicks to be generated through various IP addresses.

Ad­di­tion­ally, clicks on ad­vert­ise­ments or social media posts are sometimes generated through campaigns in social networks or forums. Here, the placed links incite users to con­trib­ute to click fraud in order to push content or bring about un­ne­ces­sary costs.

Counter measures

Mech­an­isms designed to coun­ter­act click fraud can be found on all of the largest ad­vert­ising platforms. Operators like Google or Microsoft aren’t only investing in their client’s trust; they’re also pro­tect­ing them­selves from potential lawsuits from frus­trated ad­vert­isers that seek to call large invoices into question.

Click fraud filter

In order to prevent click fraud, both automatic as well as manual, test steps come into play. To this end, Google relies on an online filter that checks all the clicks on ads and auto­mat­ic­ally in­ter­cepts them whenever sus­pi­cious times, dates, or IP addresses are noticed. These are then manually inspected by Google employees during the next step. AdWords users also have the option of reporting sus­pi­cious click behaviour. If, after in­spec­tion, it turns out that it was indeed click fraud that was re­spons­ible for the loss of ad revenue, then the client is re­im­bursed.

Manually checking sus­pi­cious clicks

In order to track down invalid clicks, web operators generally employ the same tracking mech­an­isms that ad­vert­ise­ments’ success mon­it­or­ing tools are based on. For example, Google Analytics offers a server-side im­ple­ment­a­tion that makes it possible to monitor the success and vari­ations of a web campaign in terms of click rates. Click patterns featuring strong increases in visitor numbers with no con­ver­sions are a strong in­dic­a­tion for abuse. In such cases, it’s re­com­men­ded to compare sus­pi­cious clicks with those that are stored in the web server’s log files. Relevant in­form­a­tion includes:

  • The IP address
  • Time stamp of the click
  • Time stamp of an action on the website
  • The user agent

While a site visitor’s IP address displays the exit server of a suspected instance of click fraud, comparing time stamps helps locate clicks that lead to websites that don’t yield any con­ver­sions; this is a tell-tale sign of click fraud. Taking a look at user agents allows ad­vert­isers to determine whether clicks can be traced back to the same device or originate via a certain IP address from multiple users. Ad­vert­isers should in­vest­ig­ate to see whether clicks that have been re­gistered through a certain IP address may belong to a proxy server or not. Such com­mu­nic­a­tion in­ter­faces are often used by public networks, such as those provided free-of-charge in cafés, uni­ver­sit­ies, or airports. Click fraud is sometimes cam­ou­flaged with the help of proxy servers. Here, it may be useful to carry out a user behavior analysis on the website that’s connected to the ad­vert­ise­ment. Re­pet­it­ive patterns may indicated instances of click fraud. 

IP addresses that are revealed to be the starting points of click fraud can be blocked by ad­vert­ising network operators. Ads are no longer displayed to such users, elim­in­at­ing fraud­u­lent clicks in the process. A safe way to avoid click fraud is to start a re­market­ing campaign. These efforts only display ads to users that have already visited a given website and carried out a par­tic­u­lar action there.

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