A ‘Click’ is all that matters to begin a user’s journey on an app or a website. Click integrity is a component that essentially defines whether the click is legit or not? Click is vital ‘attribution’ responsible for measuring the click-through rate (CTR) and an essential factor for ‘click-based’ campaigns.
Click integrity is a pre-attribution check that helps block the invalid clicks before they reach the MMPs, leading to bad installs. It also helps to reduce unnecessary click load on the Attribution Platform, thereby reducing the costs on Attribution Platform spending by Advertisers. So, it is a contributing factor towards saving on the marketing budget. However, fraudsters cannibalize a brand’s traffic, and the process is revocable through a few actionable measures. Furthermore, the invalid clicks pose the following implications:
Implications of the Invalid Clicks
Reliance on MMP Data
The advertisers mostly rely on sources attributed through the MMPs. However, in the absence of a pre-MMP fraud detection mechanism, the advertisers primarily depend upon MMP’s elementary fraud checks to ascertain the validity of a click.
Having a single source for defining click integrity makes the advertiser more worried about campaign performance. Moreover, the assurance of the MMP doesn’t prove helpful because the sophisticated invalid traffic (SIVT) penetration is never caught.
Untrustworthy Ecosystem
Advertisers often mistake high-volume clicks to signify ‘amazing’ campaign performance/high-CTR. However, upon reviewing the sources of ‘clicks,’ i.e., measuring click integrity, they often encounter ad fraud and inconsistencies. What happens next?
The advertiser no longer trusts ad networks or affiliates and seeks alternative marketing strategies or advertising methods for acquiring users. The large-scale distrust of multiple advertisers directly impacts the overall ecosystem.
‘Double’ Payouts from Advertising Budget for Invalid Traffic
Clicks are sourced to the MMP through multiple sources and activities. The advertisers make payouts to these sources. By the end of the day, the advertiser pays the ‘attribution’ cost on MMP and the ‘acquisition’ cost (CPC/CPI) to the source.
The illegitimate sources steal the organic traffic and get paid for organically arrived users by firing fraudulent clicks. Does this make’ click integrity’ essential for you as an advertiser?
So, what is happening behind damaged click integrity? For answering this, let’s go back to a more important question “what is the objective of the ad?” You’d get lower ad performance as an advertiser if you could not weed out the invalid clicks.
Critical Indicators of Invalid Click Traffic
Repeated/Multiple Clicks
“Due to last-click attribution, fraudsters can easily capture the organic traffic by firing millions of repeated clicks in the background on a single device-id.” This means that a fraudulent source converts your organic traffic to inorganic traffic. The incrementality of these users in these cases will be ‘0’ as these would be coming from the source who has captured the last click Attribution, which essentially means that you are paying for your traffic.
Click spamming skews the campaign performance and the advertising budgets as the fraudulent source gets paid for your organic traffic. The simplest way to identify click spamming would be to look at the CTIT and the Click to Install conversion ratio.
- Click to Install Time (CTIT): If you analyze the click to install pattern over a period, the CTIT curve would be a declining trend with 70-80% of the installs coming within the first few hours followed by a declining tail towards the end of the day. The time gap between click and install will not be very high for a standard traffic source. A typical user will click a source and then install an app. However, in an abnormal traffic source, you would see a large CTIT. It can’t be that a user clicked on an Ad, and installs are seen coming after a considerable gap or maybe after a day or even more.
- Click-to-Install Conversion Ratio (CVR): If the click to install conversion ratio is extremely low, i.e., less than <0.01% coming from a specific source/sub-source and sometimes more than that region’s population, this is a clear case of click spamming.
Analyzing the Campaigns with anomalies like looking at long CTIT and exceptionally high number of clicks with a scanty conversion rate of <0.01% CR is also not good enough!!! These invalid clicks should be blocked in real-time to prevent the organic traffic from getting converted to inorganic traffic.
Invalid Devices
Spurts or concentrated click traffic coming from invalid Make-Model, which don’t exist in the real world. The heavily correlated, linked clicks indicate that this behavior is identified and can be blocked in real-time. This helps save the attribution cost as you weed out the bot devices.
Invalid GEOs
Proxies or VPNs are used to fake geographies. A high % of click penetration coming from a specific GEO location can be identified and blocked.
IP Repetitions/Blacklisted IPs
IP addresses are randomly allocated to users and are hobbled between users in a pool by Internet Service Providers. Extreme repetition or disproportionate IP addresses are generally not expected from clean traffic. However, Bots use servers, and hence spikes or clustering patterns are seen. The same IP addresses are repeatedly used across days for different SETS of device ids. The IP addresses follow a pattern and sequence indicating fake clicks. These might be coming from VPNs, Proxies, Data Centres, or other sources. mFilterIt helps identify clicks coming from the blacklisted IPs and blocks the clicks coming from these blacklisted IPs in real-time.
Key Takeaways
Based upon the integrity of the click, the click is sent either to the MMP for processing or gets rejected. It basically acts as a firewall for the install and post-install events and thus helps in weeding out the invalid clicks in real-time and thereby cleans the ecosystems.
To conclude, click integrity filters invalid/malicious clicks from genuine clicks. mFilterIt helps the advertisers validate the click’s integrity and identify the abnormal patterns such as repetitive behavior of Clicks, IPs and Device-IDs, Blacklisted IPs, Invalid Make-Models, and Invalid GEOs, which indicate BOTs and spamming behavior. Furthermore, the fraudulent click traffic can be automatically blocked before it even reaches the MMPs and hence helps Advertisers save their m
