Product Functionality

fraud0 monitors your campaigns 24/7 and delivers comprehensive analytics on traffic quality to enhance your marketing strategies.

Our core strategy centers on traffic segmentation, leveraging our proprietary flags to identify and prioritize channels and campaigns that draw real engagement. Complementing this, we apply IP blocking in Google Ads as well as “user” blocking through Google Tag Manager, forming Negative Audience groups to screen out invalid traffic on platforms including Google Ads, Facebook, DV360, and Microsoft Ads. Aware of IP or user blocking's constraints due to dynamic IP changes, we emphasize our main focus on connecting with genuine audiences. This two-pronged approach not only reduces invalid traffic exposure but also boosts authentic engagement, optimizing your marketing spend and elevating ROI.

Our system meticulously examines data discrepancies and behavioral anomalies through a multifaceted approach that includes real-time scoring, behavioral analysis, browser testing, and machine learning, along with other proprietary techniques. We scrutinize click frequencies, IP addresses, user agents, and user behaviors for indications of bot activity or inauthentic interactions. To ensure precision, we also conduct manual reviews and cross-reference against established blacklists.

fraud0 clusters different types of invalid traffic into the following categories:

Automation Tools: Software designed to automate actions in a browser detected as bot traffic. We can detect a multitude of such automation tools using a variety of APIs that are exposed in the browser, such as Headless Chrome, PhantomJS, Selenium, Rhino, CouchJS and others.

Discrepancies: Users with inconsistent attributes indicating fake users generated by bots. This can involve a number of attributes, such as:

• Hardware features, e.g. a browser claims to run on a mobile device but the amount of memory and number of CPUs indicate a dedicated server.

• HTTP header inconsistencies: the User-Agent header is empty, missing, or doesn’t match the declared User-Agent on the client side, consistency-checking the declared platform from the user-agent against other attributes related to the platform declared by the browser etc.

• Inconsistent browser data, such as no accepted languages, a mismatch between the claimed operating system and the expected pre-installed fonts on that system, a mismatch between the claimed operating system and the APIs available on the client side, etc.

• Abnormal screen sizes, resolution or color depth, e.g. an iPhone with an unusually large screen.

Self-declared Crawlers: Bots that identify themselves with specific user agent strings, usually linked to entities like search engines (e.g. Google or Bing) or can be operated by other online services for   indexing or data retrieval purposes.

(Search Engine Crawler is one of them which can be excluded from the fraud0 statistics! To activate the exclusion please go to the Settings - Data Analytics tab in the fraud0 Interface.)

Fraudulent Apps, Datacenter: visits that originate from well-known fraudulent apps, have been redirected from websites known to be fraudulent, or whose IP address can be found on lists of known IP addresses that are known to be the source of bot traffic, HTTP proxies, or VPN and anonymizing services. 

Behavioral Abnormality: some bots can be detected through their behavior, such as too many page views of a user within a certain time frame (which would indicate an unpaced browser automation), or too many new sessions that are created per IP address within a certain time frame.

For more details on the creation of fraud0 default channels, please refer to the logic outlined below:

Direct

Sessions have no referrers outside of the domain where the script is running, no UTM sources are specified and there are no tracking IDs (gclid, fbclid, etc).

Google Ads

There is either:

• a gclid tracking parameter

• A utm_source of “google” or “google-ads”

A utm_source of “google-shopping” or “dv360” is excluded.

Google Shopping

A utm_source of “google-shopping” is present.

Facebook Ads

There is either:

• A fbclid tracking parameter

• A utm_source of “facebook”, “fb” or “meta”

A utm_source or referrer indicating instagram is excluded.

Instagram Ads

There is either:

• A utm_source of “instagram” or “ig”

• The referrer indicates instagram, and either fbclid tracking parameter or a utm_source of “meta”.

Microsoft Ads

There is either:

• A msclkid tracking parameter

• A utm_source of “bing”

Linkedin Ads

There is either:

• A li_fat_id tracking parameter

• A utm_source of “linkedin”

TikTok

A utm_source of “tiktok” is present.

Criteo

A utm_source of “criteo” is present.

The Trade Desk

A utm_source of “ttd” or “TTD” is present.

RTB House

A utm_source of “rtbhouse” or “rtb_house

DV360

A utm_source of “dv360” or “DV360” is present.

Emarsys

A utm_source of “emarsys” or “emarsys_auto” or “emarsys_manual” is present.

Parcellab

A utm_source of “parcellab” is present.

MSN

A utm_source of “msn” or “msn,bing” is present.

Awin

A utm_source of “awin” is present.

Newsletter

A utm_source includes one of: 

• “newsletter”

• “erinnerungsservice_newsletter”

• “verlag_newsletter”                                    

• “shop_newsletter”

• “red_newsletter”

• “email-job-newsletter-postnewtab”

• “bonline_newsletter”

Idealo

A utm_source includes one of:

• “idealo”

• “idealo.de”

• “idealo_fb”

• “idealo_de”

• “idealo_at”

• “idealo-at”

SEO

No utm_source or tracking id is present, and the referrer includes one of:

• Google

• Bing

• Yahoo

• Duckduckgo

• Ecosia

• Brave Search

Social

No utm_source or tracking id is present, and the referrer includes one of:

• Facebook

• Instagram

• Snapchat

Other referrers

No utm_source or tracking id is present, a referrer exists but is not used in one of the other rules.

Additional channels

Additional channels are automatically present based on the provided UTM parameters.