Thomas Petit (Growth Consultant) talks about creativity in marketing, how to build a world-class marketing stack, challenges and consideration of subscription apps.
Investing in data is usually an excellent choice, regardless if your build or buy (there are more solutions nowadays).
The big focus of the data stack at 8fit was how to predict revenue the earliest possible and in the most accurate way in order to know what to re-inject in acquisition.
If you focus too much on people converting to free trial you might attract more people who don't convert after. For example younger audiences are a lot more aware about how to cancel a free trial so you might get good CPAs but a cohort that converts poorly.
Constant testing on offers and pricing is the only way to survive for subscription apps. What you might have learned 3 years ago might be different today: audience you are approaching or overall market could be different, there might be subscription fatigue, etc.
Something that worked is putting a longer funnel before the install. Example: sending people from Outbrain/Taboola to a landing page (article form) where 8fit had a lot more space to tell people what makes the app different.
On the App Store there is a bottleneck: everybody sees the same thing no matter which USP you advertise. Via web you can create a more coherent journey so people understand why they are going to see the App Store. It created a virtuous cycle (better install conversion, better place in the auction, ASO, etc.).
Even though "inspirational" videos did not perform well on Facebook, what did work well was to retarget the video viewers with direct response ads and a more direct messaging.
Turns out Google has already developed internally predictive testing and can predict by 70% if a creative is going to outperform the current one or not before they spend money.
When you find a creative that really performs on the top of the funnel (clicks, installs, etc.) it is extremely fast to see it. The algorithm makes most of the delivery go to it, you can scale your campaigns, etc. It is obvious. But it doesn't mean that it's the best for the long term because the cohort needs to mature.
When using AEO and VO it's about which event you're feeding the algorithm/machine. Maybe you don't optimize for sign ups, or free trial, but for "free trial + this event completed within 24 hours".
Investing in data is usually an excellent choice, regardless if your build or buy (there are more solutions nowadays).
The big focus of the data stack at 8fit was how to predict revenue the earliest possible and in the most accurate way in order to know what to re-inject in acquisition.
If you focus too much on people converting to free trial you might attract more people who don't convert after. For example younger audiences are a lot more aware about how to cancel a free trial so you might get good CPAs but a cohort that converts poorly.
Constant testing on offers and pricing is the only way to survive for subscription apps. What you might have learned 3 years ago might be different today: audience you are approaching or overall market could be different, there might be subscription fatigue, etc.
Something that worked is putting a longer funnel before the install. Example: sending people from Outbrain/Taboola to a landing page (article form) where 8fit had a lot more space to tell people what makes the app different.
On the App Store there is a bottleneck: everybody sees the same thing no matter which USP you advertise. Via web you can create a more coherent journey so people understand why they are going to see the App Store. It created a virtuous cycle (better install conversion, better place in the auction, ASO, etc.).
Even though "inspirational" videos did not perform well on Facebook, what did work well was to retarget the video viewers with direct response ads and a more direct messaging.
Turns out Google has already developed internally predictive testing and can predict by 70% if a creative is going to outperform the current one or not before they spend money.
When you find a creative that really performs on the top of the funnel (clicks, installs, etc.) it is extremely fast to see it. The algorithm makes most of the delivery go to it, you can scale your campaigns, etc. It is obvious. But it doesn't mean that it's the best for the long term because the cohort needs to mature.
When using AEO and VO it's about which event you're feeding the algorithm/machine. Maybe you don't optimize for sign ups, or free trial, but for "free trial + this event completed within 24 hours".
Investing in data is usually an excellent choice, regardless if your build or buy (there are more solutions nowadays).
The big focus of the data stack at 8fit was how to predict revenue the earliest possible and in the most accurate way in order to know what to re-inject in acquisition.
If you focus too much on people converting to free trial you might attract more people who don't convert after. For example younger audiences are a lot more aware about how to cancel a free trial so you might get good CPAs but a cohort that converts poorly.
Constant testing on offers and pricing is the only way to survive for subscription apps. What you might have learned 3 years ago might be different today: audience you are approaching or overall market could be different, there might be subscription fatigue, etc.
Something that worked is putting a longer funnel before the install. Example: sending people from Outbrain/Taboola to a landing page (article form) where 8fit had a lot more space to tell people what makes the app different.
On the App Store there is a bottleneck: everybody sees the same thing no matter which USP you advertise. Via web you can create a more coherent journey so people understand why they are going to see the App Store. It created a virtuous cycle (better install conversion, better place in the auction, ASO, etc.).
Even though "inspirational" videos did not perform well on Facebook, what did work well was to retarget the video viewers with direct response ads and a more direct messaging.
Turns out Google has already developed internally predictive testing and can predict by 70% if a creative is going to outperform the current one or not before they spend money.
When you find a creative that really performs on the top of the funnel (clicks, installs, etc.) it is extremely fast to see it. The algorithm makes most of the delivery go to it, you can scale your campaigns, etc. It is obvious. But it doesn't mean that it's the best for the long term because the cohort needs to mature.
When using AEO and VO it's about which event you're feeding the algorithm/machine. Maybe you don't optimize for sign ups, or free trial, but for "free trial + this event completed within 24 hours".
Notes for this resource are currently being transferred and will be available soon.
Back then, people with experience were in gaming and it was hard to attract them. So hired people based on mindset (even if they were in the web) rather than experience and not only UA people.
They understood very only on that they needed a solid data stack and accessibility to data. They built they own data stack, scalable and custom to what they needed.
[💎@06:00] Investing in data is usually an excellent choice, regardless if your build or buy (there are more solutions nowadays).
Subscription acquisition flow is not that simple: start a free trial or not, do they convert, do they refund, modelize the renewals, etc.
[💎@08:00] The big focus of the data stack at 8fit was how to predict revenue the earliest possible and in the most accurate way in order to know what to re)inject in acquisition.
Another important part was uniting all networks and costs to have all conversions in one place: every single step of the funnel from every single source and being able to segment by anything (OS, gender, activity, etc.). There are now new products in the market for that.
Challenge #1
Most subscriptions use "start a free trial" as an event for optimization (Facebook, UAC).
[💎@09:23] If you focus too much on people converting to free trial you might attract more people who don't convert after. For example younger audiences are a lot more aware about how to cancel a free trial so you might get good CPAs but a cohort that converts poorly.
In short: the algorithms from FB/UAC really deliver what you ask for.
Challenge #2
On the user side there is only one subscription to buy but on the developer side you have many (can be 100+): iOS and Android, 7 or 30 day trial, prices different by country, super premium subscription. This increases complexity as well.
For 8fit it was in the company mindset to constantly test pricing: introductory offers (free trials), removing the free trial, etc.
[💎@12:12] Constant testing on offers and pricing is the only way to survive for subscription apps. What you might have learned 3 years ago might be different today: audience you are approaching or overall market could be different, there might be subscription fatigue, etc.
For 8fit, they came from the nutrition angle so that USP was doing good on conversion. That's therefore where a lot of the UA efforts were placed, as well as: "it's useless to go train everyday if you eat like shit".
8fit was against the kind of "quick fix" message and were into long-term healthy choice in life, education, etc.
Focusing on the long-term and specific positioning might not bring some of the quick wins but it helps building over time.
Thomas had a couple of cohorts showing that pushing free stuff was not the right choice in the long term. He banned the word "free" from the communication of the company besides inside the app: no "free download", no "free trial".
Replaced the attractiveness of the free trial with a push on personalization. So that when people hit the paywall they have a reason to subscribe: the product is built specifically for them.
When pushing 2 messages at the same time, it would not work. Example:
→ Had to deliver 1 message only. Video helped.
[💎@21:20] Something that worked is putting a longer funnel before the install. Example: sending people from Outbrain/Taboola to a landing page (article form) where 8fit had a lot more space to tell people what makes the app different.
Because of the drop off on the landing page the install would be more expensive but there was higher conversion, retention and renewal were compensating this effect. Long term gains were everywhere: users subscribed to push, were more receptive to promos, engaged on social, etc.
👉 Check [RESOURCE #16] Better Paid Content Marketing Copy (GEM MINING #16 - Better paid content marketing copy with Sandra Wu, Content Marketing Lead at Blinkist) for even more information on this (GrowthGems.co membership needed)
Did a playable geared at putting more time into the education before people hit the App Store.
[💎@23:45] On the App Store there is a bottleneck: everybody sees the same thing no matter which USP you advertise. Via web you can create a more coherent journey so people understand why they are going to see the App Store. It created a virtuous cycle (better install conversion, better place in the auction, ASO, etc.).
This was done in parallel with app campaigns (not all spend went to campaigns going through a landing page).
Playables are another version of this: people want to see more. Same with video on the store: it's about showing more before people download the app.
8fit created "inspirational" videos (like a big brand would).
[💎@26:15] Even though "inspirational" videos did not perform well on Facebook, what did work well was to retarget the video viewers with direct response ads and a more direct messaging.
They also did this with influencer videos, where they retargeted a specific influencer's audience with DR ads.
Sometimes it works, but sometimes it fails. So you have no certainty. It lessens the value of learning but hightens the value of consistently trying new things.
Very excited about streamlining the creative process: how to produce more creatives but without putting too much resources before you know what works, move variants, etc. Here is the post (I think) by Eric Seufert that Thomas mentions.
A company called Network (used to have a very successful mobile game) developed a technology to deploy a large quantity of creatives and act on it fast (technology fast) to select winners a lot faster.
Doing this yourself would not work if you're spending 1 million / day, not really with small budgets. Smaller players could automate their testing much more.
[💎@31:00] Turns out Google has already developed internally predictive testing and can predict by 70% if a creative is going to outperform the current one or not before they spend money.
Today is about automation, maybe tomorrow is about prediction.
It can take many months to really understand the value of a specific creative.
[💎@32:45] When you find a creative that really performs on the top of the funnel (clicks, installs, etc.) it is extremely fast to see it. The algorithm makes most of the delivery go to it, you can scale your campaigns, etc. It is obvious. But it doesn't mean that it's the best for the long term because the cohort needs to mature.
Humans can balance quick data response with long-term maturing cohorts, brand consideration, what's happening in the space, etc.
For complex products you need to understand the full user journey, and the full journey take months in many case.
[💎@35:30] When using AEO and VO it's about which event you're feeding the algorithm/machine. Maybe you don't optimize for sign ups, or free trial, but for "free trial + this event completed within 24 hours".
Back then, people with experience were in gaming and it was hard to attract them. So hired people based on mindset (even if they were in the web) rather than experience and not only UA people.
They understood very only on that they needed a solid data stack and accessibility to data. They built they own data stack, scalable and custom to what they needed.
[💎@06:00] Investing in data is usually an excellent choice, regardless if your build or buy (there are more solutions nowadays).
Subscription acquisition flow is not that simple: start a free trial or not, do they convert, do they refund, modelize the renewals, etc.
[💎@08:00] The big focus of the data stack at 8fit was how to predict revenue the earliest possible and in the most accurate way in order to know what to re)inject in acquisition.
Another important part was uniting all networks and costs to have all conversions in one place: every single step of the funnel from every single source and being able to segment by anything (OS, gender, activity, etc.). There are now new products in the market for that.
Challenge #1
Most subscriptions use "start a free trial" as an event for optimization (Facebook, UAC).
[💎@09:23] If you focus too much on people converting to free trial you might attract more people who don't convert after. For example younger audiences are a lot more aware about how to cancel a free trial so you might get good CPAs but a cohort that converts poorly.
In short: the algorithms from FB/UAC really deliver what you ask for.
Challenge #2
On the user side there is only one subscription to buy but on the developer side you have many (can be 100+): iOS and Android, 7 or 30 day trial, prices different by country, super premium subscription. This increases complexity as well.
For 8fit it was in the company mindset to constantly test pricing: introductory offers (free trials), removing the free trial, etc.
[💎@12:12] Constant testing on offers and pricing is the only way to survive for subscription apps. What you might have learned 3 years ago might be different today: audience you are approaching or overall market could be different, there might be subscription fatigue, etc.
For 8fit, they came from the nutrition angle so that USP was doing good on conversion. That's therefore where a lot of the UA efforts were placed, as well as: "it's useless to go train everyday if you eat like shit".
8fit was against the kind of "quick fix" message and were into long-term healthy choice in life, education, etc.
Focusing on the long-term and specific positioning might not bring some of the quick wins but it helps building over time.
Thomas had a couple of cohorts showing that pushing free stuff was not the right choice in the long term. He banned the word "free" from the communication of the company besides inside the app: no "free download", no "free trial".
Replaced the attractiveness of the free trial with a push on personalization. So that when people hit the paywall they have a reason to subscribe: the product is built specifically for them.
When pushing 2 messages at the same time, it would not work. Example:
→ Had to deliver 1 message only. Video helped.
[💎@21:20] Something that worked is putting a longer funnel before the install. Example: sending people from Outbrain/Taboola to a landing page (article form) where 8fit had a lot more space to tell people what makes the app different.
Because of the drop off on the landing page the install would be more expensive but there was higher conversion, retention and renewal were compensating this effect. Long term gains were everywhere: users subscribed to push, were more receptive to promos, engaged on social, etc.
👉 Check [RESOURCE #16] Better Paid Content Marketing Copy (GEM MINING #16 - Better paid content marketing copy with Sandra Wu, Content Marketing Lead at Blinkist) for even more information on this (GrowthGems.co membership needed)
Did a playable geared at putting more time into the education before people hit the App Store.
[💎@23:45] On the App Store there is a bottleneck: everybody sees the same thing no matter which USP you advertise. Via web you can create a more coherent journey so people understand why they are going to see the App Store. It created a virtuous cycle (better install conversion, better place in the auction, ASO, etc.).
This was done in parallel with app campaigns (not all spend went to campaigns going through a landing page).
Playables are another version of this: people want to see more. Same with video on the store: it's about showing more before people download the app.
8fit created "inspirational" videos (like a big brand would).
[💎@26:15] Even though "inspirational" videos did not perform well on Facebook, what did work well was to retarget the video viewers with direct response ads and a more direct messaging.
They also did this with influencer videos, where they retargeted a specific influencer's audience with DR ads.
Sometimes it works, but sometimes it fails. So you have no certainty. It lessens the value of learning but hightens the value of consistently trying new things.
Very excited about streamlining the creative process: how to produce more creatives but without putting too much resources before you know what works, move variants, etc. Here is the post (I think) by Eric Seufert that Thomas mentions.
A company called Network (used to have a very successful mobile game) developed a technology to deploy a large quantity of creatives and act on it fast (technology fast) to select winners a lot faster.
Doing this yourself would not work if you're spending 1 million / day, not really with small budgets. Smaller players could automate their testing much more.
[💎@31:00] Turns out Google has already developed internally predictive testing and can predict by 70% if a creative is going to outperform the current one or not before they spend money.
Today is about automation, maybe tomorrow is about prediction.
It can take many months to really understand the value of a specific creative.
[💎@32:45] When you find a creative that really performs on the top of the funnel (clicks, installs, etc.) it is extremely fast to see it. The algorithm makes most of the delivery go to it, you can scale your campaigns, etc. It is obvious. But it doesn't mean that it's the best for the long term because the cohort needs to mature.
Humans can balance quick data response with long-term maturing cohorts, brand consideration, what's happening in the space, etc.
For complex products you need to understand the full user journey, and the full journey take months in many case.
[💎@35:30] When using AEO and VO it's about which event you're feeding the algorithm/machine. Maybe you don't optimize for sign ups, or free trial, but for "free trial + this event completed within 24 hours".
Back then, people with experience were in gaming and it was hard to attract them. So hired people based on mindset (even if they were in the web) rather than experience and not only UA people.
They understood very only on that they needed a solid data stack and accessibility to data. They built they own data stack, scalable and custom to what they needed.
[💎@06:00] Investing in data is usually an excellent choice, regardless if your build or buy (there are more solutions nowadays).
Subscription acquisition flow is not that simple: start a free trial or not, do they convert, do they refund, modelize the renewals, etc.
[💎@08:00] The big focus of the data stack at 8fit was how to predict revenue the earliest possible and in the most accurate way in order to know what to re)inject in acquisition.
Another important part was uniting all networks and costs to have all conversions in one place: every single step of the funnel from every single source and being able to segment by anything (OS, gender, activity, etc.). There are now new products in the market for that.
Challenge #1
Most subscriptions use "start a free trial" as an event for optimization (Facebook, UAC).
[💎@09:23] If you focus too much on people converting to free trial you might attract more people who don't convert after. For example younger audiences are a lot more aware about how to cancel a free trial so you might get good CPAs but a cohort that converts poorly.
In short: the algorithms from FB/UAC really deliver what you ask for.
Challenge #2
On the user side there is only one subscription to buy but on the developer side you have many (can be 100+): iOS and Android, 7 or 30 day trial, prices different by country, super premium subscription. This increases complexity as well.
For 8fit it was in the company mindset to constantly test pricing: introductory offers (free trials), removing the free trial, etc.
[💎@12:12] Constant testing on offers and pricing is the only way to survive for subscription apps. What you might have learned 3 years ago might be different today: audience you are approaching or overall market could be different, there might be subscription fatigue, etc.
For 8fit, they came from the nutrition angle so that USP was doing good on conversion. That's therefore where a lot of the UA efforts were placed, as well as: "it's useless to go train everyday if you eat like shit".
8fit was against the kind of "quick fix" message and were into long-term healthy choice in life, education, etc.
Focusing on the long-term and specific positioning might not bring some of the quick wins but it helps building over time.
Thomas had a couple of cohorts showing that pushing free stuff was not the right choice in the long term. He banned the word "free" from the communication of the company besides inside the app: no "free download", no "free trial".
Replaced the attractiveness of the free trial with a push on personalization. So that when people hit the paywall they have a reason to subscribe: the product is built specifically for them.
When pushing 2 messages at the same time, it would not work. Example:
→ Had to deliver 1 message only. Video helped.
[💎@21:20] Something that worked is putting a longer funnel before the install. Example: sending people from Outbrain/Taboola to a landing page (article form) where 8fit had a lot more space to tell people what makes the app different.
Because of the drop off on the landing page the install would be more expensive but there was higher conversion, retention and renewal were compensating this effect. Long term gains were everywhere: users subscribed to push, were more receptive to promos, engaged on social, etc.
👉 Check [RESOURCE #16] Better Paid Content Marketing Copy (GEM MINING #16 - Better paid content marketing copy with Sandra Wu, Content Marketing Lead at Blinkist) for even more information on this (GrowthGems.co membership needed)
Did a playable geared at putting more time into the education before people hit the App Store.
[💎@23:45] On the App Store there is a bottleneck: everybody sees the same thing no matter which USP you advertise. Via web you can create a more coherent journey so people understand why they are going to see the App Store. It created a virtuous cycle (better install conversion, better place in the auction, ASO, etc.).
This was done in parallel with app campaigns (not all spend went to campaigns going through a landing page).
Playables are another version of this: people want to see more. Same with video on the store: it's about showing more before people download the app.
8fit created "inspirational" videos (like a big brand would).
[💎@26:15] Even though "inspirational" videos did not perform well on Facebook, what did work well was to retarget the video viewers with direct response ads and a more direct messaging.
They also did this with influencer videos, where they retargeted a specific influencer's audience with DR ads.
Sometimes it works, but sometimes it fails. So you have no certainty. It lessens the value of learning but hightens the value of consistently trying new things.
Very excited about streamlining the creative process: how to produce more creatives but without putting too much resources before you know what works, move variants, etc. Here is the post (I think) by Eric Seufert that Thomas mentions.
A company called Network (used to have a very successful mobile game) developed a technology to deploy a large quantity of creatives and act on it fast (technology fast) to select winners a lot faster.
Doing this yourself would not work if you're spending 1 million / day, not really with small budgets. Smaller players could automate their testing much more.
[💎@31:00] Turns out Google has already developed internally predictive testing and can predict by 70% if a creative is going to outperform the current one or not before they spend money.
Today is about automation, maybe tomorrow is about prediction.
It can take many months to really understand the value of a specific creative.
[💎@32:45] When you find a creative that really performs on the top of the funnel (clicks, installs, etc.) it is extremely fast to see it. The algorithm makes most of the delivery go to it, you can scale your campaigns, etc. It is obvious. But it doesn't mean that it's the best for the long term because the cohort needs to mature.
Humans can balance quick data response with long-term maturing cohorts, brand consideration, what's happening in the space, etc.
For complex products you need to understand the full user journey, and the full journey take months in many case.
[💎@35:30] When using AEO and VO it's about which event you're feeding the algorithm/machine. Maybe you don't optimize for sign ups, or free trial, but for "free trial + this event completed within 24 hours".
