Attribution Windows ("Lookback Window")
What is an Attribution Window (Lookback Window)
An Attribution Window is the period of time between a marketing event (ex clicks, view) and a conversion event (ex Purchase) in which a specific marketing channel is eligible to receive credit for driving the conversion.
For example, if I apply a 220 day attribution window:
- A user who clicks on a Google ad today and converts 200 days from now will have a Google touchpoint on their path to conversion.
- Similarly, a user who clicks on a Google ad today and converts 300 days from now will NOT have a Google touchpoint on their path to conversion, since the clicks falls outside the 220 day window.
What Attribution Windows Does Rockerbox Apply
Rockerbox attempts to apply the maximum attribution window to all channels, to allow tracking of the maximum number of marketing touchpoints on a user's path to conversion.
This is a different approach than what exists in the social platforms or DSPs, which enable you to limit the lookback window (and apply different lookback windows to view vs click based activity).
The window applied by Rockerbox depends on multiple factors, including how long deterministic data is available and the time period of the data that can be provided by our partners.
A breakout of the attribution windows applied per marketing channel is below:
Type of Touchpoint | Example Channels | Attribution Window |
Clicks | Paid Search Paid Social Organic Search Organic Social SMS Display Affiliate | 220 days |
Deterministic Views | Display Native Social | 90 days |
Linear TV | All Linear TV | 14 days |
OTT | All OTT | 7 days |
Direct Mail | All Direct Mail | 220 days |
Direct Partnerships | Snapchat TikTok | 7 day click / 1 day view* |
*Attribution Windows may be more limited for Direct Partnerships due to limitations on the data each partner is able to provide.
Marketing Event to Session Limits
For OTT and Linear, we also impose a limit on eligible marketing events based on the time between the ad and web session.
We enforce these limitations to prevent over-attribution due to the probabilistic nature of both attribution methods.