What is biased attribution?
Biased attribution occurs when a paid advertising platform or ad network is incentivized to attribute traffic to itself over a competitor. This attribution bias occurs when the network acts as both the source of traffic and conversions and as a measurement provider. App marketers taking care of mobile attribution should be wary of biased attribution affecting their campaigns.
4 examples of biased attribution
Today, most app marketers work across multiple ad platforms to accomplish their user acquisition goals. If marketers don’t have an attribution partner, it’s common for misattribution to occur, confusing campaign metrics and budgets. Below are four of the most common examples of biased attribution.
This attribution bias occurs when a user is already searching for an app to install and therefore, might have installed the app without seeing the ad. However, in this case, the ad will still get credit for the attribution, even if it might not have actually impacted the user’s decision to install.
Cheap inventory bias
This term refers to misattribution that happens when a campaign ad is deemed to perform better than another due to a lower-priced product. However, the case may be that the ad is better in terms of creative and/or design and not that the lower-priced product drove conversion.
This type of attribution bias is likely the most common. Correlation-based bias is the assumption that an event in the customer journey caused a subsequent event when they are not truly connected to each other.
Digital signal bias
If both online and offline activity isn’t taken into account, then digital signal bias can occur. Digital signal bias happens when your attribution model doesn’t consider the relationship between offline sales and online activity.
Dangers of a biased attribution platform
As an app marketer, your marketing decisions and budgets can be skewed if you’re relying on a biased attribution platform to provide clean attribution. We’ve listed a few aspects of these platforms that reveal why they shouldn’t be the sole source of your attribution measurement.
They’re…biasedIf an attribution provider is selling media, services or products to ad networks, then they aren’t independent and can’t be trusted to safeguard your data. There’s an unavoidable conflict of interest when an attribution provider has a financial relationship with privately-held media companies.
They often sell dataSometimes attribution providers sell their clients’ data as one of their main sources of income. Before partnering with an attribution provider, it’s important to find out how they fund themselves and what they do with the data they collect for you. For example, at Adjust, we don’t sell data and client and user privacy are top of mind for us.
They don’t fight fraudAs biased attribution platforms often provide both the source and the measurement of traffic, they aren’t inclined to report fraud or invest in systems to prevent it. Reporting fraud would lessen the amount of traffic to report, and thus, lessen the amount of revenue a biased attribution could earn.
How can marketers avoid biased attribution?
The easiest way to obtain data that doesn’t suffer from attribution bias, is to partner with a neutral, third-party mobile measurement platform (MMP) like Adjust. MMPs mediate attribution between networks and app companies to analyze the traffic coming through and determine the validity of each engagement and install. MMPs also utilize anti-fraud solutions to ensure data integrity. Read Why do you need fraud prevention? for more details on why fraud prevention is a must regarding your user acquisition efforts.
An attribution partner like Adjust lets you:
- Avoid attribution bias with accurate attribution data
- Measure and attribute the entire user journey
- Uphold privacy standards
- Enjoy fraud-free data
- Customize engagement campaigns
With clean attribution data, marketers can make smarter decisions regarding their user acquisition campaigns. If you’re interested in seeing how Adjust can empower your app marketing efforts, let’s talk!
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