Affiliate ecosystem is fighting with fraud on all possible levels. Among such that brought the highest % of wasting advertising budgets in 2019 are clicks spam and click flooding (by Juniper Research).

Mobile attribution trackers, performance platforms and fraud protection vendors (Appsflyer, Adjust, Protected Media, etc) already released their tools to define fraudulent sources and cut them off the ad budgets.


Let us define what we call click spam, what the patterns are and how you can clean traffic by using Affise:

1) Definitely, the most obvious one is extremely low CR

If you see CR per affiliate or affiliate source as lower than 0.1% you should start investigating the source and its ad placements.

CR of 0.005-0.001% under high volumes is almost a clear mark of click spamming activity.

To do:

> You can check CR rate per affiliate or his source by going to daily stat and using Affise drill down reports to check CR per affiliate


Getting CR lower than normal rates per traffic type is a matter to review publisher activity.

2) The second pattern is a high ratio of unique site ids (or also called sub source ids)

This pattern is used by affiliate partners to make it more difficult to evaluate performance and CR on the lower level of affiliate source.

Click-spamming and click-flooding traffic have a pattern of a very high ratio between click-conversion- unique source. Out of 1000 clicks, you will find 999 as such that has a unique value of click id.

Such traffic behaviour affects your system as it loads the click database and limits opportunity in uploading reports. Also because of the lack of transparency in source` performance advertisers are most likely to stop promotion with this affiliate and reject traffic.

Currently, Appsflyer Protect 360 has automated the process of checking site ids and rejects publishers that exceed the normal ratio.

To do:

> Start by checking the way your publisher is sending site ids or sub source by going to Statistics - Conversions and checking column with relevant sub parameter which is used for passing source data:


& similar can be used an examples


> Update your integration with publishers to get more accuracy in tracking site ids and ask partners to send sub_sources separately from other params.

For example, use this integration setup:

sub1 > clickid
sub2 > publisher id
sub3 > sub source & other source data

Avoid integrations of that kind: &sub2={pid}_{sub_source}_{sub_source2} that leads to system loading and impossibility to get stat aggregation.

Clean and accurate integration in terms of passing site_ids is a MUST requirement from Mobile attribution partners nowadays. Feel free to contact our product support team if you need help in guiding you through S2S integration.

3) Analyse time to conversion (timeframe between click and conversion)

For mobile apps the most common CTIT distribution is getting ~75% of conversions within the first hour after the click and ~95% within the following day. Conversions that do not fit into this timeframe are a matter of more precise investigation.

To do:

> Use Affise in-built Time to action report to get insights into conversion time by offer & affiliate


What else?

Affise as one of the important players in the affiliate marketing ecosystem is putting our part of efforts in protecting our clients from fraudulent partnerships and help them minimize chances for rejections. Fraud might seem like a way of getting fast revenues, but in the end, transparent and fraudless businesses will succeed.

Written by Tanya Grypachevskaya
CRO / Customer Success Guru
mail: [email protected]

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