Key takeaways

In this module, you learned:

• Calculated metrics let you define formulas using any combination of existing metrics, operators, and functions. Adobe provides some predefined calculated metrics (like average page views per visit), but the real power is in building your own — ratios, rates, and derived values that your report suite doesn't capture natively. Any metric you'd normally calculate in a spreadsheet after exporting data can usually be built directly in Analysis Workspace instead.
• Quick calculated metrics are created from a table column and are not saved for reuse. Right-clicking a column in a freeform table gives you quick options like dividing two metrics or calculating a percentage. These are convenient for one-off calculations in the moment, but they aren't added to the component library. For anything you'll want to reuse across projects, build it in the full calculated metric builder.
• The "participation" attribution model gives full credit to every dimension value active during a conversion visit. Unlike last-touch (which credits only the final value) or first-touch (which credits only the initial value), participation credits all dimension values that were encountered during the session in which a conversion occurred. This is useful for understanding which content, categories, or touchpoints contribute to conversions even when they aren't the last thing seen.
• The "look back window" sets how far back Adobe looks from a conversion to assign credit. When using advanced attribution models, the look back window defines the time boundary — such as the previous 30 days or the previous 5 visits — within which prior dimension interactions are considered eligible for credit. Interactions outside that window are not credited, regardless of the attribution model used.
• The "linear" attribution model distributes credit equally across all contributing touchpoints. If three dimension values were active during the look back window before a conversion, each receives exactly one-third of the credit. This is a good model when you want to acknowledge the entire path rather than privileging the first or last touchpoint, and it avoids the winner-takes-all bias of first-touch or last-touch models.