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
These insights were shared through the free Growth Gems newsletter.
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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.
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  • Define your preferred categories and receive new relevant gems directly in your inbox
  • Discuss key insights (and any other mobile growth topic) in the members-only community.
Upgrade Your Plan
💎 #
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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💎 #
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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What does Incremental mean?

On Facebook they have “conversion lift” to assess the incremental impact (done hand in hand with Facebook).



=> Incremental ROAS is the “purest way to show real impact” of a re-engagement campaign


Why is it important for marketers

  • Get accurate spend/measurement
  • Confident investment in Real Impact: attributing the real impact to the right ads: what would have happened if this had wasn't run?


Don’t need to run it for each campaign.

One of the struggles is when you see a discrepancy between the lift study and the  MMP (or Facebook manager).


How it can help assess your re-engagement campaign?

[💎@13:45] Incrementality can help validate the attribution logic. Re-engagement is the best use case (proof of profitable uplift harder to get otherwise).


It is typically easier to make an impact with a user acquisition ad vs. re-engagement.


On Adjust, user eligible for reattribution if

  • There was an inactivity period (example: 7 days)
  • User clicks on ad
  • User generates a new session within the reattribution window

  1. [💎@17:45] ⚠️ difference with early re-engagement campaigns -> can create a discrepancy (not seeing reattribution in Adjust if the inactivity period is set but there was actually no inactivity by a user)
  2. [💎@18:30] 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.

To avoid a common mistake: experiment around attribution windows and make sure that the segments you are looking to re-engage are not within the inactivity period.


Best practices

  • It helps to already know your baseline/control group: what % of users should come back if you do not run any campaign. If not, estimate first then find out with control group.
  • Always have a hypothesis and even further: plan/outcome in case of positive, negative or flat results
  • Confidence/statistical significance: the more conversion event and/or change between baseline and test the more confidence you can have. Typically 95%+ confidence.
  • Look at marginal cost based on metric observed: additional user, additional event/activity, etc.
  • Isolate incrementality test so you can scale up the channels where you have good results
  • ! Results might differ if you scale up your test/campaigns
  • [💎@26:25] ! 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)


Influence on LTV calculation

[💎@32:12] 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).

If you do not add the Post Re-Engagement part to the equation then your LTV calculation is only based on prediction of user acquisition campaign.


Case studies


[💎@35:05]


Based on 145 conversion lift studies, containing 240 campaigns

[💎@37:25] 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.


[💎@37:58] Different objectives for different cohorts. Link clicks might be relevant if:

  • Cohort of users with already a high purchase rate (hard to improve)
  • If audience too small to get good signal/significance with optimizing for purchases


Re-engagement and hyper casual are not a match.

Early engagement works well in the casino space (re-engage after a week or so).

Casual/Strategy: a lot of lapsed users re-engagement.

Retargeting big on e-commerce and travel.


The notes from this resource are only available to premium members.
↘ At this point, you know what to do ↙
Upgrade Your Plan

What does Incremental mean?

On Facebook they have “conversion lift” to assess the incremental impact (done hand in hand with Facebook).



=> Incremental ROAS is the “purest way to show real impact” of a re-engagement campaign


Why is it important for marketers

  • Get accurate spend/measurement
  • Confident investment in Real Impact: attributing the real impact to the right ads: what would have happened if this had wasn't run?


Don’t need to run it for each campaign.

One of the struggles is when you see a discrepancy between the lift study and the  MMP (or Facebook manager).


How it can help assess your re-engagement campaign?

[💎@13:45] Incrementality can help validate the attribution logic. Re-engagement is the best use case (proof of profitable uplift harder to get otherwise).


It is typically easier to make an impact with a user acquisition ad vs. re-engagement.


On Adjust, user eligible for reattribution if

  • There was an inactivity period (example: 7 days)
  • User clicks on ad
  • User generates a new session within the reattribution window

  1. [💎@17:45] ⚠️ difference with early re-engagement campaigns -> can create a discrepancy (not seeing reattribution in Adjust if the inactivity period is set but there was actually no inactivity by a user)
  2. [💎@18:30] 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.

To avoid a common mistake: experiment around attribution windows and make sure that the segments you are looking to re-engage are not within the inactivity period.


Best practices

  • It helps to already know your baseline/control group: what % of users should come back if you do not run any campaign. If not, estimate first then find out with control group.
  • Always have a hypothesis and even further: plan/outcome in case of positive, negative or flat results
  • Confidence/statistical significance: the more conversion event and/or change between baseline and test the more confidence you can have. Typically 95%+ confidence.
  • Look at marginal cost based on metric observed: additional user, additional event/activity, etc.
  • Isolate incrementality test so you can scale up the channels where you have good results
  • ! Results might differ if you scale up your test/campaigns
  • [💎@26:25] ! 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)


Influence on LTV calculation

[💎@32:12] 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).

If you do not add the Post Re-Engagement part to the equation then your LTV calculation is only based on prediction of user acquisition campaign.


Case studies


[💎@35:05]


Based on 145 conversion lift studies, containing 240 campaigns

[💎@37:25] 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.


[💎@37:58] Different objectives for different cohorts. Link clicks might be relevant if:

  • Cohort of users with already a high purchase rate (hard to improve)
  • If audience too small to get good signal/significance with optimizing for purchases


Re-engagement and hyper casual are not a match.

Early engagement works well in the casino space (re-engage after a week or so).

Casual/Strategy: a lot of lapsed users re-engagement.

Retargeting big on e-commerce and travel.


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

The detailed notes taken for a resource are an easy way to see the gems in context to get a better understanding. They also include any relevant visuals from the source.
↘ At this point, you know what to do ↙
Request Access

What does Incremental mean?

On Facebook they have “conversion lift” to assess the incremental impact (done hand in hand with Facebook).



=> Incremental ROAS is the “purest way to show real impact” of a re-engagement campaign


Why is it important for marketers

  • Get accurate spend/measurement
  • Confident investment in Real Impact: attributing the real impact to the right ads: what would have happened if this had wasn't run?


Don’t need to run it for each campaign.

One of the struggles is when you see a discrepancy between the lift study and the  MMP (or Facebook manager).


How it can help assess your re-engagement campaign?

[💎@13:45] Incrementality can help validate the attribution logic. Re-engagement is the best use case (proof of profitable uplift harder to get otherwise).


It is typically easier to make an impact with a user acquisition ad vs. re-engagement.


On Adjust, user eligible for reattribution if

  • There was an inactivity period (example: 7 days)
  • User clicks on ad
  • User generates a new session within the reattribution window

  1. [💎@17:45] ⚠️ difference with early re-engagement campaigns -> can create a discrepancy (not seeing reattribution in Adjust if the inactivity period is set but there was actually no inactivity by a user)
  2. [💎@18:30] 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.

To avoid a common mistake: experiment around attribution windows and make sure that the segments you are looking to re-engage are not within the inactivity period.


Best practices

  • It helps to already know your baseline/control group: what % of users should come back if you do not run any campaign. If not, estimate first then find out with control group.
  • Always have a hypothesis and even further: plan/outcome in case of positive, negative or flat results
  • Confidence/statistical significance: the more conversion event and/or change between baseline and test the more confidence you can have. Typically 95%+ confidence.
  • Look at marginal cost based on metric observed: additional user, additional event/activity, etc.
  • Isolate incrementality test so you can scale up the channels where you have good results
  • ! Results might differ if you scale up your test/campaigns
  • [💎@26:25] ! 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)


Influence on LTV calculation

[💎@32:12] 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).

If you do not add the Post Re-Engagement part to the equation then your LTV calculation is only based on prediction of user acquisition campaign.


Case studies


[💎@35:05]


Based on 145 conversion lift studies, containing 240 campaigns

[💎@37:25] 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.


[💎@37:58] Different objectives for different cohorts. Link clicks might be relevant if:

  • Cohort of users with already a high purchase rate (hard to improve)
  • If audience too small to get good signal/significance with optimizing for purchases


Re-engagement and hyper casual are not a match.

Early engagement works well in the casino space (re-engage after a week or so).

Casual/Strategy: a lot of lapsed users re-engagement.

Retargeting big on e-commerce and travel.