Attribution modelling is a marketing strategy that assigns credit to marketing touchpoints for their role in sales and conversions. It helps marketers understand which marketing campaigns or tactics are most effective in driving user engagement, retention, and revenue.
The following table explains all the attribution types that you have at your disposal while using the Demand Generation Effectiveness module of RevSure.
Attribution Type | Explanation | When to use |
First Touch | Gives 100% credit to the first touchpoint that led to the creation of the stage that you are measuring attribution for (MEL/MQL/PQO/SQO/Closed Won) | Use when you want to find the very first campaign that led to creation of that particular stage that you are interested in |
Last Touch | Gives 100% credit to the last touchpoint that led to the creation of the stage that you are measuring attribution for (MEL/MQL/PQO/SQO/Closed Won) | Use when you want to find the very last campaign that led to creation of that particular stage that you are interested in |
Any Touch | Gives 100% credit to each touch point throughout the journey from Suspect to the stage you are measuring attribution for (MEL/MQL/PQO/SQO/Closed Won) | Use when you don't want to miss out on giving credit to any intermediate campaigns that may have influenced movement but are not first or last campaigns |
Linear | Gives equal credit to every touchpoint seen leading up to a conversion from Suspect to the stage you are measuring attribution for (MEL/MQL/PQO/SQO/Closed Won) | Use when you would want to give all campaigns an equal credit for progression from Suspect to the stage that you are measuring attribution for |
U-shaped | Gives 40% credit to the first touchpoint, 40% credit to the last touchpoint, and divides the remaining 20% to any touchpoints in between. With 2 touchpoints, the credit is normalized (50%, 50%). With 6 touch points the middle 4 touch points would share the 20% (40%, 5%, 5%, 5%, 5%, 40%). | Use when you want to give credit for first and last touch, but not miss out on effectiveness of the intermediate campaigns. |
J-shaped | Gives 20% credit to the first touchpoint, 60% credit to the last touchpoint, and divides the remaining 20% to any touchpoints in between. With 2 touchpoints, the credit is normalized (25%, 75%). With 6 touch points the middle 4 touch points would share the 20% (20%, 5%, 5%, 5%, 5%, 60%). | Use when first touch conversion is more important but you don't want to over- index on only first touch |
Inverse J-shaped | Gives 60% credit to the first touchpoint, 20% credit to the last touchpoint, and divides the remaining 20% to any touchpoints in between. With 2 touchpoints, the credit is normalized (75%, 25%). With 6 touch points the middle 4 touch points would share the 20% (60%, 5%, 5%, 5%, 5%, 20%). | Use when last touch conversion is more important, and you don't want to over-index on only last touch |
W Shape | 30% to the first touch, 30% to the middle touch, 30% to the last touch, 10% split evenly across the other touches | Use when you want even coverage across the touches |
Influenced Attribution | Gives 100% credit to each touchpoint throughout the journey of the lead. | Use when you don't want to miss out on giving credit to any campaign that a lead engaged with throughout their journey irrespective of the order of engagement. This can be used to validate numbers against source system reports like 'Campaign Member Reporting' in SFDC. |
AI-Based Attribution | RevSure’s proprietary AI-based attribution uses advanced AI models (such as Markov Chains) to estimate the contributions of each campaign and touch towards conversion at every stage and towards pipeline and revenue. This is a pure AI and Data based approach that does not rely on rules or opinion-based weightings | Use when you want to use a pure AI and data based approach to attribution |