Configurable Attribution Lookback Windows

Not every mobile marketer has the same needs or objectives, and not every marketing campaign has the same measurement needs. Configurable attribution lookback windows provide marketers with the flexibility they need to make their analytics work for them.

What is an Attribution Lookback Window?

Attribution lookback windows are the amount of time that can pass between an ad and an install, beyond which the app install will not be attributed to that campaign.

For example, let’s say that a user clicked on an ad on Monday, and then installed the app on Saturday.

  • If this campaign had a one day lookback window, this install would not be attributed to the ad or marketing campaign.
  • If the campaign had a seven day lookback window, the install would be attributed to that ad.

AppsFlyer provides app marketers with fully customizable attribution lookback windows. Marketers can define custom attribution lookback windows per media source, per campaign,  and per attribution method (click-based versus view-through attribution) and for retargeting.

Why should marketers use custom attribution lookback windows?

There are a number of reasons that marketers may want to use custom attribution lookback windows.

  • Short Promotions
    Running a one-day promotion? Set a 24 hour attribution lookback window on this campaign and keep your performance analytics clean.
  • Long Lead Cycles
    Some apps have longer lead cycles than others, requiring multiple campaign interactions before the user installs. For example, apps with higher purchase prices or larger initial download sizes often have long lead-cycles. Lengthening the attribution lookback window provides these marketers with broader visibility and more insights across their funnels.
  • Business Cycles
    Many apps are tightly tied to cyclical behavior – business cycles, calendar events, etc. Marketers with apps that are tied to a 2 week or weekly cycle often match their attribution lookback windows to their business and campaign cycles.
  • Avoid Discrepancies
    Some media sources, such as Facebook, Google and Twitter have their own predefined attribution lookback windows. However, relying on varying attribution lookback windows across media sources will often result in confusion. For example, users who viewed or clicked on ads across multiple media sources could end up being attributed to both media sources. As these discrepancies scale, data accuracy and media optimization suffers.

 

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