What is biased attribution?

Glossary What is biased attribution?

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 the measurement provider, as well as the source of traffic and conversions. App marketers taking care of mobile attribution should be wary of biased attribution affecting their campaigns.

5 examples of biased attribution

App marketers are working with an ever-increasing number of ad platforms and channels 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 five of the most common examples of biased attribution.

  1. In-market bias
    This attribution bias occurs when a user has already been searching for an app and might have installed it without seeing the ad. However, in this case, the ad will still get credit for the attribution, even though it might not have impacted the user’s decision to install.
  2. Cheap inventory bias
    This term refers to the misattribution that occurs 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 price point drove the conversion.
  3. Correlation-based bias
    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.
  4. Digital signal bias
    If both online and offline activity aren’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.
  5. Last-click bias
    Last-click attribution can be useful for mobile marketers who want to optimize their marketing campaigns for immediate results and focus on the most effective channels for driving user conversions. However, it may overlook the impact of other marketing channels that users may have engaged with earlier in the funnel, potentially leading to missed opportunities for optimization and growth. An MMP will provide the whole picture to avoid this bias, or allow you to make the most effective decisions for your campaign optimization.

The dangers of a biased attribution platform

As an app marketer, you require clean attribution, but your marketing decisions and budgets can be skewed if you’re relying on a biased attribution platform. Here’s why these platforms shouldn’t be the sole source of your attribution measurement.

  • They’re… biased
    If an attribution provider is selling media, services, or products to ad networks, then they aren’t independent and shouldn’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 data
    Sometimes attribution providers sell client 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 never sell data, and client and user privacy are mission-critical for us.

  • They don’t fight fraud
    As biased attribution platforms often provide both the source and the measurement of traffic, they aren’t inclined to invest in systems to prevent fraud or report fraudulent activity. Doing so would reduce the amount of traffic to report, therby also reducing the amount of revenue a biased attribution could earn.

  • They can damage your brand and dent budgets
    If your decisions are based on inaccurate data, you could find you’re overspending on the wrong campaigns, and targeting audiences that don’t convert. You could also end up serving too many ads, oversaturating your target market, and causing a decline in brand reputation.

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 partner (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.

For more on why fraud prevention is essential to sustainable, scalable user acquisition and mobile measurement, take a look at our deep dive into why do you need fraud prevention, or download our all-in-one guide to mobile fraud.

With Adjust attribution and measurement, you can:

  • 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, request a demo!

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