Demystifying Incrementality for User Engagement with Facebook

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8

Kate Minogue (Marketing Science Manager at Facebook) and Alexandre Pham (Director of Partnerships, EMEA at Adjust) help you understand what incrementality really is, why it is important for mobile marketers, and share their best practices around considering incrementality in your day to day as well as how you can factor it into your LTV calculation.

Source:
Demystifying Incrementality for User Engagement with Facebook
(no direct link to watch/listen)
(direct link to watch/listen)
Type:
Webinar
Publication date:
December 4, 2019
Added to the Vault on:
March 19, 2020
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💎 #
1

Incrementality can help validate the attribution logic. Re-engagement is the best use case (proof of profitable uplift harder to get otherwise).

13:45
💎 #
2

⚠️ difference with early re-engagement campaigns -> it can create a discrepancy: not seeing reattribution in Adjust if the inactivity period is set but there was actually no inactivity by a user

17:45
💎 #
3

Better than setting the attribution window based on best practices, a lift study allows you to compare the results with 3 or 4 attribution windows to find which one is the closest.

18:30
💎 #
4

⚠️ Teams must be aligned: if paid UA team is rewarded/incentivized on incrementality then it might clash with the organic/ASO team trying to increase the baseline.

26:25
💎 #
5

You want a model dynamic enough to adapt to re-engagement you campaigns run (example: additional re-engagement campaign ran at Day 30 that you believe or showed has an impact).

32:12
💎 #
6

Mobile gaming engagement campaigns on Facebook are driving an average of 3X Incremental ROAS and 40% more app opens.

35:05
💎 #
7

Recommends to optimize for the action you care the most about, but nuances when it comes to re-engagement: purchase not always the right solution.

37:25
💎 #
8

Different campaign objectives for different cohorts. Link clicks might be relevant if:
1. Cohort of users with already a high purchase rate (hard to improve) 2. If audience too small to get good signal/significance with optimizing for purchases`

37:58
The gems from this resource are only available to premium members.
💎 #
1

Incrementality can help validate the attribution logic. Re-engagement is the best use case (proof of profitable uplift harder to get otherwise).

13:45
💎 #
2

⚠️ difference with early re-engagement campaigns -> it can create a discrepancy: not seeing reattribution in Adjust if the inactivity period is set but there was actually no inactivity by a user

17:45
💎 #
3

Better than setting the attribution window based on best practices, a lift study allows you to compare the results with 3 or 4 attribution windows to find which one is the closest.

18:30
💎 #
4

⚠️ Teams must be aligned: if paid UA team is rewarded/incentivized on incrementality then it might clash with the organic/ASO team trying to increase the baseline.

26:25
💎 #
5

You want a model dynamic enough to adapt to re-engagement you campaigns run (example: additional re-engagement campaign ran at Day 30 that you believe or showed has an impact).

32:12
💎 #
6

Mobile gaming engagement campaigns on Facebook are driving an average of 3X Incremental ROAS and 40% more app opens.

35:05
💎 #
7

Recommends to optimize for the action you care the most about, but nuances when it comes to re-engagement: purchase not always the right solution.

37:25
💎 #
8

Different campaign objectives for different cohorts. Link clicks might be relevant if:
1. Cohort of users with already a high purchase rate (hard to improve) 2. If audience too small to get good signal/significance with optimizing for purchases`

37:58
The gems from this resource are only available to premium members.

Gems are the key bite-size insights "mined" from a specific mobile marketing resource, like a webinar, a panel or a podcast.
They allow you to save time by grasping the most important information in a couple of minutes, and also each include the timestamp from the source.

💎 #
1

Incrementality can help validate the attribution logic. Re-engagement is the best use case (proof of profitable uplift harder to get otherwise).

13:45
💎 #
2

⚠️ difference with early re-engagement campaigns -> it can create a discrepancy: not seeing reattribution in Adjust if the inactivity period is set but there was actually no inactivity by a user

17:45
💎 #
3

Better than setting the attribution window based on best practices, a lift study allows you to compare the results with 3 or 4 attribution windows to find which one is the closest.

18:30
💎 #
4

⚠️ Teams must be aligned: if paid UA team is rewarded/incentivized on incrementality then it might clash with the organic/ASO team trying to increase the baseline.

26:25
💎 #
5

You want a model dynamic enough to adapt to re-engagement you campaigns run (example: additional re-engagement campaign ran at Day 30 that you believe or showed has an impact).

32:12
💎 #
6

Mobile gaming engagement campaigns on Facebook are driving an average of 3X Incremental ROAS and 40% more app opens.

35:05
💎 #
7

Recommends to optimize for the action you care the most about, but nuances when it comes to re-engagement: purchase not always the right solution.

37:25
💎 #
8

Different campaign objectives for different cohorts. Link clicks might be relevant if:
1. Cohort of users with already a high purchase rate (hard to improve) 2. If audience too small to get good signal/significance with optimizing for purchases`

37:58

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The notes from this resource are only available to premium members.
The notes from this resource are only available to premium members.