Luka Rade (App Sales Director at Outfit 7) talks about the differences between IAP-driven and Ad-driven games when it comes to user acquisition, the best way to calculate Ad-LTV, increase ROI and what to focus on.
Data has an immense value. Just like the big networks collect data to improve their algorithms, so should you - proprietary data is of the utmost importance when doing paid user acquisition.
One effort to better calculate your Ad LTV is to focus on knowing how many impressions each user has seen from each provider and the ad type (banner, video, interstitials). So get daily reports from your ad providers on the revenue generated and the number of impressions (per ad type, platform, country, etc.) then combine with MMP data to aggregate data on dimensions like country, platform, network and publisher. This might become tricky!
The biggest way a UA team of an Ad-driven game can have an impact on ROI is to have an impact on costs, by increasing IPM (install per 1000 impressions) which can be done through improving creatives. This will in turn increase the reach of UA.
Having the UA team share numbers from creatives with the product team can have an impact on retention because you can incorporate what performs well in acquisition in the actual product. Example: emotional hooks in outfit 7's creatives that can be added to the game.
Despite the progress of machine learning on Google and other platforms to acquire engaged users, focus your campaigns on targeting for shallow events (level 2/3, watching rewarded video ads, etc.) because a big percentage of users reach them and reach them fast + they are good indicators of high quality users.
Deeper events don't necessarily work because the LTV deviation between players are usually fairly small. If you optimize for events that are not reached often the increase in cost of your campaigns will not be compensated by the increase in LTV.
Still because of the low LTV deviation between players, Ad-driven businesses have the advantage of allowing to test new geos cheaper and faster: a small amount of installs allows to accurately estimate the value of users in certain markets. This allows you to focus on improving IPM through creatives and localization.
Because there is not enough scale at the creative level to have a strong signal enough on ROI, beyond IPM Nordeus looks at the Cost per D1 retained users as a proxy metric. [Nordeus]
The most important thing to test currently is which events to optimize for, and this includes custom events (as long as they happen enough in the first few days and are predicting high user quality). [Nordeus]
You can get good results optimizing for "watch X rewarded videos" events. To do this you want to know the % of users reaching these kind of events, how fast they are reaching these events and how these events correlate with being a higher quality user.
Targeting for Ad-driven games is usually broad because anyone can generate revenue and LTV is limited by retention. The key UA campaign focus is therefore mostly on IPM in order to decrease the eCPA (vs. ROAS for IAP-driven games).
Calculating your Ad LTV in its most basic form (a function of ARPDAU and retention) is not enough because the value generated by users from different markets/networks/publishers differs. So you need more granular data.
Data has an immense value. Just like the big networks collect data to improve their algorithms, so should you - proprietary data is of the utmost importance when doing paid user acquisition.
One effort to better calculate your Ad LTV is to focus on knowing how many impressions each user has seen from each provider and the ad type (banner, video, interstitials). So get daily reports from your ad providers on the revenue generated and the number of impressions (per ad type, platform, country, etc.) then combine with MMP data to aggregate data on dimensions like country, platform, network and publisher. This might become tricky!
The biggest way a UA team of an Ad-driven game can have an impact on ROI is to have an impact on costs, by increasing IPM (install per 1000 impressions) which can be done through improving creatives. This will in turn increase the reach of UA.
Having the UA team share numbers from creatives with the product team can have an impact on retention because you can incorporate what performs well in acquisition in the actual product. Example: emotional hooks in outfit 7's creatives that can be added to the game.
Despite the progress of machine learning on Google and other platforms to acquire engaged users, focus your campaigns on targeting for shallow events (level 2/3, watching rewarded video ads, etc.) because a big percentage of users reach them and reach them fast + they are good indicators of high quality users.
Deeper events don't necessarily work because the LTV deviation between players are usually fairly small. If you optimize for events that are not reached often the increase in cost of your campaigns will not be compensated by the increase in LTV.
Still because of the low LTV deviation between players, Ad-driven businesses have the advantage of allowing to test new geos cheaper and faster: a small amount of installs allows to accurately estimate the value of users in certain markets. This allows you to focus on improving IPM through creatives and localization.
Because there is not enough scale at the creative level to have a strong signal enough on ROI, beyond IPM Nordeus looks at the Cost per D1 retained users as a proxy metric. [Nordeus]
The most important thing to test currently is which events to optimize for, and this includes custom events (as long as they happen enough in the first few days and are predicting high user quality). [Nordeus]
You can get good results optimizing for "watch X rewarded videos" events. To do this you want to know the % of users reaching these kind of events, how fast they are reaching these events and how these events correlate with being a higher quality user.
Targeting for Ad-driven games is usually broad because anyone can generate revenue and LTV is limited by retention. The key UA campaign focus is therefore mostly on IPM in order to decrease the eCPA (vs. ROAS for IAP-driven games).
Calculating your Ad LTV in its most basic form (a function of ARPDAU and retention) is not enough because the value generated by users from different markets/networks/publishers differs. So you need more granular data.
Data has an immense value. Just like the big networks collect data to improve their algorithms, so should you - proprietary data is of the utmost importance when doing paid user acquisition.
One effort to better calculate your Ad LTV is to focus on knowing how many impressions each user has seen from each provider and the ad type (banner, video, interstitials). So get daily reports from your ad providers on the revenue generated and the number of impressions (per ad type, platform, country, etc.) then combine with MMP data to aggregate data on dimensions like country, platform, network and publisher. This might become tricky!
The biggest way a UA team of an Ad-driven game can have an impact on ROI is to have an impact on costs, by increasing IPM (install per 1000 impressions) which can be done through improving creatives. This will in turn increase the reach of UA.
Having the UA team share numbers from creatives with the product team can have an impact on retention because you can incorporate what performs well in acquisition in the actual product. Example: emotional hooks in outfit 7's creatives that can be added to the game.
Despite the progress of machine learning on Google and other platforms to acquire engaged users, focus your campaigns on targeting for shallow events (level 2/3, watching rewarded video ads, etc.) because a big percentage of users reach them and reach them fast + they are good indicators of high quality users.
Deeper events don't necessarily work because the LTV deviation between players are usually fairly small. If you optimize for events that are not reached often the increase in cost of your campaigns will not be compensated by the increase in LTV.
Still because of the low LTV deviation between players, Ad-driven businesses have the advantage of allowing to test new geos cheaper and faster: a small amount of installs allows to accurately estimate the value of users in certain markets. This allows you to focus on improving IPM through creatives and localization.
Because there is not enough scale at the creative level to have a strong signal enough on ROI, beyond IPM Nordeus looks at the Cost per D1 retained users as a proxy metric. [Nordeus]
The most important thing to test currently is which events to optimize for, and this includes custom events (as long as they happen enough in the first few days and are predicting high user quality). [Nordeus]
You can get good results optimizing for "watch X rewarded videos" events. To do this you want to know the % of users reaching these kind of events, how fast they are reaching these events and how these events correlate with being a higher quality user.
Targeting for Ad-driven games is usually broad because anyone can generate revenue and LTV is limited by retention. The key UA campaign focus is therefore mostly on IPM in order to decrease the eCPA (vs. ROAS for IAP-driven games).
Calculating your Ad LTV in its most basic form (a function of ARPDAU and retention) is not enough because the value generated by users from different markets/networks/publishers differs. So you need more granular data.
Notes for this resource are currently being transferred and will be available soon.
Before this presentation, there is a presentation by Andrej Kugonič (User Acquisition Lead at Nordeus - Top Eleven) on "Post COVID-19 UA strategy for IAP-driven business". At the end there is a Q&A with both Luka and Andrej, included here.
[💎 @45:30] Data has an immense value. Just like the big networks collect data to improve their algorithms, so should you - proprietary data is of the utmost importance when doing paid user acquisition.
[💎 @47:02] Targeting for Ad-driven games is usually broad because anyone can generate revenue and LTV is limited by retention. The key UA campaign focus is therefore mostly on IPM in order to decrease the eCPA (vs. ROAS for IAP-driven games).
[💎 @48:13] Calculating your Ad LTV in its most basic form (a function of ARPDAU and retention) is not enough because the value generated by users from different markets/networks/publishers differs. So you need more granular data.
In an ideal scenario we would know the exact LTV of each user but it is very unlikely because you work with a lot of partners.
[💎 @52:45] One effort to better calculate your Ad LTV is to focus on knowing how many impressions each user has seen from each provider and the ad type (banner, video, interstitials). So get daily reports from your ad providers on the revenue generated and the number of impressions (per ad type, platform, country, etc.) then combine with MMP data to aggregate data on dimensions like country, platform, network and publisher.
Because monetization is a ARPDAU x Retention, it is hard for the UA team to have a big impact on revenue.
[💎 @55:24] The biggest way a UA team of an Ad-driven game can have an impact on ROI is to have an impact on costs, by increasing IPM (install per 1000 impressions) which can be done through improving creatives. This will in turn increase the reach of UA.
How to collaborate with product and creative team to increase Ad LTV and be able to enter specific markets?
[💎 @56:54] Having the UA team share numbers from creatives with the product team can have an impact on retention because you can incorporate what performs well in acquisition in the actual product. Example: emotional hooks in outfit 7's creatives that can be added to the game.
[💎 @58:43] Despite the progress of machine learning on Google and other platforms to acquire engage users, focus your campaigns on targeting for shallow events (level 2/3, watching rewarded video ads, etc.) because a big percentage of users reach them and reach them fast + they are good indicators of high quality users.
[💎 @59:23] Deeper events don't necessarily work because the LTV deviation between players are usually fairly small. If you optimize for events that are not reached often the increase in cost of your campaigns will not be compensated by the increase in LTV.
[💎 @01:02:04] Still because of the low LTV deviation between players, Ad-driven businesses have the advantage of allowing to test new geos cheaper and faster: a small amount of installs allows to accurately estimate the value of users in certain markets. This allows you to focus on improving IPM through creatives and localization.
LTV calculations are even more important in Ad-driven games because the margin of error in Tier 3 countries (India, Indonesia) can be so small that 1 or 2 cts can be a determining factor.
Focus on automatization because it will keep you flexible and responsive to market trends and limit errors. Leverage APIs and algorithms.
Luka mentioned Brazil and Russia.
On calculating the K factor and ROI of campaigns
How to attribute and measure the effect of traditional media (tv, radio, etc.)?
How do you set ROI target for your UA campaigns? Do you have targets for all campaigns separately or for GEOS, platform, etc.
Creative optimization: have you seen phenomenal IPM from a creative that did not translate into a good ROI?
Ways to test creatives?
Automation/machine learning vs. being control freak?
Best possible event to start optimization with? Is it worth bidding towards Ad Events like "Ads watched: 10 ads watched, 30...") and not just "reach level X".
Changed anything in the game during covid?
CPI decrease with LTV constant: depended on channel?
Before this presentation, there is a presentation by Andrej Kugonič (User Acquisition Lead at Nordeus - Top Eleven) on "Post COVID-19 UA strategy for IAP-driven business". At the end there is a Q&A with both Luka and Andrej, included here.
[💎 @45:30] Data has an immense value. Just like the big networks collect data to improve their algorithms, so should you - proprietary data is of the utmost importance when doing paid user acquisition.
[💎 @47:02] Targeting for Ad-driven games is usually broad because anyone can generate revenue and LTV is limited by retention. The key UA campaign focus is therefore mostly on IPM in order to decrease the eCPA (vs. ROAS for IAP-driven games).
[💎 @48:13] Calculating your Ad LTV in its most basic form (a function of ARPDAU and retention) is not enough because the value generated by users from different markets/networks/publishers differs. So you need more granular data.
In an ideal scenario we would know the exact LTV of each user but it is very unlikely because you work with a lot of partners.
[💎 @52:45] One effort to better calculate your Ad LTV is to focus on knowing how many impressions each user has seen from each provider and the ad type (banner, video, interstitials). So get daily reports from your ad providers on the revenue generated and the number of impressions (per ad type, platform, country, etc.) then combine with MMP data to aggregate data on dimensions like country, platform, network and publisher.
Because monetization is a ARPDAU x Retention, it is hard for the UA team to have a big impact on revenue.
[💎 @55:24] The biggest way a UA team of an Ad-driven game can have an impact on ROI is to have an impact on costs, by increasing IPM (install per 1000 impressions) which can be done through improving creatives. This will in turn increase the reach of UA.
How to collaborate with product and creative team to increase Ad LTV and be able to enter specific markets?
[💎 @56:54] Having the UA team share numbers from creatives with the product team can have an impact on retention because you can incorporate what performs well in acquisition in the actual product. Example: emotional hooks in outfit 7's creatives that can be added to the game.
[💎 @58:43] Despite the progress of machine learning on Google and other platforms to acquire engage users, focus your campaigns on targeting for shallow events (level 2/3, watching rewarded video ads, etc.) because a big percentage of users reach them and reach them fast + they are good indicators of high quality users.
[💎 @59:23] Deeper events don't necessarily work because the LTV deviation between players are usually fairly small. If you optimize for events that are not reached often the increase in cost of your campaigns will not be compensated by the increase in LTV.
[💎 @01:02:04] Still because of the low LTV deviation between players, Ad-driven businesses have the advantage of allowing to test new geos cheaper and faster: a small amount of installs allows to accurately estimate the value of users in certain markets. This allows you to focus on improving IPM through creatives and localization.
LTV calculations are even more important in Ad-driven games because the margin of error in Tier 3 countries (India, Indonesia) can be so small that 1 or 2 cts can be a determining factor.
Focus on automatization because it will keep you flexible and responsive to market trends and limit errors. Leverage APIs and algorithms.
Luka mentioned Brazil and Russia.
On calculating the K factor and ROI of campaigns
How to attribute and measure the effect of traditional media (tv, radio, etc.)?
How do you set ROI target for your UA campaigns? Do you have targets for all campaigns separately or for GEOS, platform, etc.
Creative optimization: have you seen phenomenal IPM from a creative that did not translate into a good ROI?
Ways to test creatives?
Automation/machine learning vs. being control freak?
Best possible event to start optimization with? Is it worth bidding towards Ad Events like "Ads watched: 10 ads watched, 30...") and not just "reach level X".
Changed anything in the game during covid?
CPI decrease with LTV constant: depended on channel?
Before this presentation, there is a presentation by Andrej Kugonič (User Acquisition Lead at Nordeus - Top Eleven) on "Post COVID-19 UA strategy for IAP-driven business". At the end there is a Q&A with both Luka and Andrej, included here.
[💎 @45:30] Data has an immense value. Just like the big networks collect data to improve their algorithms, so should you - proprietary data is of the utmost importance when doing paid user acquisition.
[💎 @47:02] Targeting for Ad-driven games is usually broad because anyone can generate revenue and LTV is limited by retention. The key UA campaign focus is therefore mostly on IPM in order to decrease the eCPA (vs. ROAS for IAP-driven games).
[💎 @48:13] Calculating your Ad LTV in its most basic form (a function of ARPDAU and retention) is not enough because the value generated by users from different markets/networks/publishers differs. So you need more granular data.
In an ideal scenario we would know the exact LTV of each user but it is very unlikely because you work with a lot of partners.
[💎 @52:45] One effort to better calculate your Ad LTV is to focus on knowing how many impressions each user has seen from each provider and the ad type (banner, video, interstitials). So get daily reports from your ad providers on the revenue generated and the number of impressions (per ad type, platform, country, etc.) then combine with MMP data to aggregate data on dimensions like country, platform, network and publisher.
Because monetization is a ARPDAU x Retention, it is hard for the UA team to have a big impact on revenue.
[💎 @55:24] The biggest way a UA team of an Ad-driven game can have an impact on ROI is to have an impact on costs, by increasing IPM (install per 1000 impressions) which can be done through improving creatives. This will in turn increase the reach of UA.
How to collaborate with product and creative team to increase Ad LTV and be able to enter specific markets?
[💎 @56:54] Having the UA team share numbers from creatives with the product team can have an impact on retention because you can incorporate what performs well in acquisition in the actual product. Example: emotional hooks in outfit 7's creatives that can be added to the game.
[💎 @58:43] Despite the progress of machine learning on Google and other platforms to acquire engage users, focus your campaigns on targeting for shallow events (level 2/3, watching rewarded video ads, etc.) because a big percentage of users reach them and reach them fast + they are good indicators of high quality users.
[💎 @59:23] Deeper events don't necessarily work because the LTV deviation between players are usually fairly small. If you optimize for events that are not reached often the increase in cost of your campaigns will not be compensated by the increase in LTV.
[💎 @01:02:04] Still because of the low LTV deviation between players, Ad-driven businesses have the advantage of allowing to test new geos cheaper and faster: a small amount of installs allows to accurately estimate the value of users in certain markets. This allows you to focus on improving IPM through creatives and localization.
LTV calculations are even more important in Ad-driven games because the margin of error in Tier 3 countries (India, Indonesia) can be so small that 1 or 2 cts can be a determining factor.
Focus on automatization because it will keep you flexible and responsive to market trends and limit errors. Leverage APIs and algorithms.
Luka mentioned Brazil and Russia.
On calculating the K factor and ROI of campaigns
How to attribute and measure the effect of traditional media (tv, radio, etc.)?
How do you set ROI target for your UA campaigns? Do you have targets for all campaigns separately or for GEOS, platform, etc.
Creative optimization: have you seen phenomenal IPM from a creative that did not translate into a good ROI?
Ways to test creatives?
Automation/machine learning vs. being control freak?
Best possible event to start optimization with? Is it worth bidding towards Ad Events like "Ads watched: 10 ads watched, 30...") and not just "reach level X".
Changed anything in the game during covid?
CPI decrease with LTV constant: depended on channel?
