When talking about attribution, there is a tendency to focus primarily on the allocation methods, including a variety of single- or multi-touch rules-based models, as well as machine-led algorithmic models.
Asking which model (or models) can best answer your business questions is important, and a lot has been written on this topic already. However, a comprehensive approach to attribution requires not only assessing how credit is allocated, but determining proper attribution windows, considering recency and latency, and unifying both cross-device and cross-channel experiences.
That’s a lot to take in, so today we’ll focus on the first of those issues: determining proper attribution windows.
What is an Attribution Window?
Otherwise known as a time, cookie, or conversion window, an attribution window establishes the time frame for which a touchpoint (such as a paid search, video, ad, etc.) should receive some credit for a conversion event. In other words, for an attribution window of 14 days, all interactions which took place up to 14 days prior to a conversion event receive credit for the event, while any interactions which occurred before that window are disregarded.
The question, then, becomes: How can I determine the ideal attribution window length for my business?
The Status Quo: Establishing Hard Windows
Hard windows which set the attribution period to a specific number of days are commonly used throughout the industry. Many analytics platforms, including Google Analytics, have a default attribution window of 30 days, which can be manipulated to other lengths.
However, hard attribution windows have a significant drawback: They assume that interactions which took place before the cut-off date were non-significant in influencing conversion.
Making this assumption may cause your business to lose sight of interactions which piqued customer interest early in the buying journey. (This is especially true for companies with long sales processes.)
Hint: If you do choose to go with hard attribution windows, be sure that your timeframe is suited to your business type and the buying habits of your customers. Generally, companies with longer buying/consideration phases (such as B2B companies) should establish longer time windows, while those who produce low-cost goods such as clothing or food should establish shorter windows.
A Better Way: Flexible Timeframes for What-If Analysis
In addition to cutting certain interactions out of the picture, sticking with one hard attribution window limits your ability to conduct flexible, what-if attribution analysis.
The solution? Don’t put artificial stops in your data collection.
With ObservePoint, you can gather data without boundaries on time. ObservePoint’s performance measurement solution keeps all your data indefinitely, so you can run attribution across any timeframe you think is necessary.
For example, you may want to place an artificial stop that assesses what attribution results would look like in a specific 15-day window. Or, for long-running campaigns, you may want to analyze attribution over a period of several months.
With ObservePoint, you can put in restraints after data collection to run whatever what-if analyses connect to your business questions, so you can gain the insights you need to improve customer experiences and drive your company forward.
Expand Your Attribution Capabilities with ObservePoint
ObservePoint’s performance measurement suite unlocks a range of attribution capabilities. With Touchpoints, businesses can standardize and unify data with an upfront data taxonomy and touchpoints IDs that ensure data completeness. Then, JourneyStream, our complete experience data repository, ensures every on- and offline interaction across the customer journey is captured. Finally, Prism makes it easy to use both rules-based and algorithmic attribution to unlock key insights in real time.
See how you can use performance measurement to ensure complete data and timely attribution insight by scheduling a demo.