# Scaling Your Subscription App with Meta Ads – Marcus Burke, Independent Consultant

Channel: Sub Club by RevenueCat
Video: https://www.youtube.com/watch?v=Pb5TuG77O7M
Duration: 1 hr 1 min
Language: English
Words: 11746
Transcript page: https://viewrankai.com/tools/youtube-transcript/Pb5TuG77O7M

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[0:00] hello I'm your host David Barnard and my guest today is Marcus Burke a subscription growth consultant who specializes in unlocking meta as the primary growth channel for your app on the podcast I talk with Marcus about the past current and future of meta ads tactics to scale subscription apps on meta and why you should probably exclude younger audiences in your targeting hey Marcus thanks so much for joining me on the podcast today hi David super excited to be here I listen to like almost every episode of this since 2020 one of the few podcasts I listen to very regularly so yeah really really excited for this

[0:38] one awesome well it's so good to have you on and long time coming I've been following your work as well you've done a lot of great posts on LinkedIn anybody who doesn't follow Marcus on LinkedIn go to the show notes or search his name Marcus Burke and find him on LinkedIn because he's constantly sharing all sorts of great stuff so I've been meaning to have you on the podcast a while and I'm especially excited about the topic today so we are going to talk about meta ads so Facebook Instagram you probably know all the different plac I don't even know all the different placements that you can have with meta

[1:10] ads but you know a lot of people have used meta ads over the years successfully unsuccessfully it's one of the more talked about channels in subscription apps and so the first thing I wanted to discuss is why like why meta ads why is it such a talked about about Channel why should sub subscription apps consider it should all subscription apps consider it so let's just start with that like why meta ads you should definitely consider it I don't think there are many apps out there that can just say meta isn't for me one of the big things of course is that meta's family of apps is just

[1:47] gigantic I think they recently shared again that they have around about four billion monthly active users across all of their apps so as you already mentioned Facebook Instagram but then also all the sub placements do you know if they D that so would that be is that like actually 4 billion unique humans or is that 4 billion like maybe there's a billion overlap of people who use both Facebook and Instagram do you know I think it's duped but it also includes WhatsApp of course which isn't all like I think there's ads by now on WhatsApp but you don't run them through meta ads manager so that of course kind of bloats

[2:23] it up a bit but still even if it's just three or two billion um you can pretty surely find your target audience on this channel the other important thing is that meta has a very very strong algorithm they're good at finding your audience for you it got a bit more complicated with at and scan and I'm sure we'll talk about that later on but still meta's algorithm is really unchallenged they had their SDK in many many of the apps out there for years they had their pixel on almost every website for the last 10 years so they sit just on a ton of data and they

[2:57] really know how to build these customer profiles and use the signal that you're sending them to find your perfect customer and that's really where the magic happens so that you don't have to care that much about how do I Target and like who is my Target customer but you can really find them in collaboration with meta yeah that's one of the interesting things I mean if you think about and this is why I think it was important to start with their scale and the reach and why they matter cuz when you think about those two things combined three 4 billion people whatever it is fully duped or people who actually

[3:34] see ads it's in the billions and so then you combine most people that would ever subscribe to your app are probably using one of meta's products that they do see ads in and then meta is really good at targeting your specific product to the niches that it would appeal to so like you know maybe like a super duper duper duper Niche app would just not have the scale to work on meta ads and then there's all sorts of other reasons with funnel and other reasons that meta ads might not be performant for an individual app but broadly speaking any human that might subscribe to your app

[4:16] is probably a meta and meta is really good at finding them so it kind of makes sense this is why they make tens of billions of dollars of profit a year right yeah it's a strong combination and what I also like about the channel is that in in the end all of it is native advertisement and you can really go onto these placements you can see what those ads look like you can see what other content is on these placements so you get a pretty good understanding of like what can I do here how can I be creative to really insert my product onto them while with a lot of other channels they

[4:49] are like display networks you don't know what's going on there's fraud which doesn't happen on meta so really it's a safe environment you have a good understanding of what your final ad will look like and I I think that's just a very big Advantage as you're trying to validate your app in the beginning because if you don't know the context in which your ads was shown then you can never be sure if the data that you gathered was in the end even valid yeah and then another thing too is that the ads on meta are actually kind of good I mean people kind of talk about it a

[5:18] little bit and then there's of course like privacy Circles of like all ads are bad or whatever some of the Twitter noise in the communities I'm in will talk about ads being bad and all this kind of stuff but when I get on Instagram I kind of like the ads I actually had to remove Instagram from my phone finally because I was getting enticed by too many products because it's almost like ads as content that I actually enjoyed the ads and would almost go to Instagram not just to see the content from the people I follow it's like a great product discovery engine in a way because they're so good

[5:57] at targeting you the specific things that you would be interested in I think that's another thing it's like the two biggest players in ads in this context actually make a ton of sense because Google is search intent is it when you go search something you're telling a text field on the internet that you have a problem and that you're looking for a solution and so it makes sense why Google can monetize ads at that level and then with Facebook like it kind of makes sense how well they do with their own monetization for helping apps monetized because the ads are actually really good glad to hear that marketers are

[6:36] doing a good job here yeah I think like looking at into many ad accounts every month I think ads could still be better and I still see a lot of things where I feel like okay and this is more of like a very classic Banner ad or something and it's not really well thought through but I mean definitely it seems to work for them that they have been increasing ad load more and more and users are still enjoying the experience so it feels like people get value out of the ads and not just out of the Native content because by now I would say like 50% of what you see on those feeds is

[7:08] ads or then influencers posting native content but then they do product placements so it's really much of a product discovery engine as you mentioned here and then the web is like the opposite kind of to your point earlier like banner ads tabula ads on news sites it's like the ad experience on the web broadly is actually the exact opposite of that including Google's display networks and things like that that they do and then especially the way Google and we're not going to talk much about Google today but the ways that Google forces you to do these broad campaigns that might end up in these really poor display ad networks versus

[7:46] the kind of higher intent with meta it's like you know your ad is going to show up in these quality higher intent places even versus Google that actually brings me to the next question I wanted to ask you is that how do you think about Facebook in the G Channel mix for a subscription app as they're scaling up to me it would usually be one of the first channels to launch because it's a Discovery Channel you know you can get some volume there and it's a bit more representative of an audience you will Target in the future than for example search search is super interesting because you know the quality behind it

[8:22] so if you're trying to validate your funnel your pricing strategy then Apple search ads might be an interest one just so that you know you're buying qualitative traffic and don't have to worry about was my ad good did I reach the Right audience because you know if someone searches for a weather app and you have a weather app then you can be sure it's the right user but it's not as scalable it's super competitive so it's going to be hard for you to compete with the big players that might have a better funnel higher pricing So Meta really stands out because you can have a competitive advantage through good

[8:55] creative and through mastering the channel and that's why I like to start it early on and most apps care about iOS a little bit more than Android which also gives meta a competitive advantage over Google ads because Google tends to be very strong on Android where they own the ecosystem it's much easier for them to track performance while on iOS they've rather been struggling in the recent years all right so with the broader context out of the way I wanted to dive into history which may seem odd but I do think is important in the context of meta ads because there has been a lot that's changed over the last

[9:32] four to five years and of course everybody listening to this podcast will know what I'm alluding to and that's app tracking transparency and so before app tring transparency meta was the go-to channel because with a level of deterministic measurement with the idfa it was just like magic for so many apps and and I think it's maybe even lost on folks who don't follow the industry super close but a lot of the apps that are big today are big specifically because of the Arbitrage of them having scaled so quickly so efficiently on meta ads preat and then ATT happened so tell me I mean maybe I've already given enough of the

[10:15] history but like what's your perspective on the preat and then we'll get into like how ATT changed and how things are evolving definitely it's a bit unfair of like how much easier things were back in the days compared to today So Meta Rel had one of the strongest device graphs out there so they really built super strong customer profiles based on these idas and as I already mentioned they just tracked everything everyone had the meta SDK everyone had a pixel on their website from them so basically they were tracking the whole internet and were able to create these super strong customer profiles with ATT that changed

[10:51] making things a bit harder maybe a small anecdote like most of my preat time I spent in the gaming industry working for in games a big German strategy games developer and yeah I followed the whole development from like browser games into mobile games and we started meta as a channel somewh around like 2012 2013 when I joined the company and we scaled it super rapidly always jumping on the newest products when they launched app event optimization for the first time when they launched value optimization for the first time those were always magic moments in the end like it was really a gold rush we turned those campaigns on we didn't have to

[11:29] care about targeting anymore they were just looking for the right customers for us and we were within the month doubling our budget again and again while overachieving our return on investment targets and it was crazy like we had times where management told us please don't spend so much money we know it looks amazing but we're scared that's how it worked back then and that just really changed with ATT and you have to do your homework a lot more closely these days you have to dive deeper into like your funnel into creative strategy and do a lot more qualitative work while back in the days it was really data

[12:05] driven you could push out just a ton of creatives meta would find the Right audience for them and you didn't even have to care like why did this work it's like okay it works we're just going to do it over and over again and that's kind of the biggest change that happened with the implementation of ATT so as attt rolled out it feels like there was an especially tumultuous year year 18 months two years where things just weren't working what were you seeing during those times or what lessons can you take from that time and then it does seem like and we'll get to this that

[12:41] things have started improving but tell me about for history's sake what was that like 18 months to two years like as somebody spending a ton of money on behalf of clients and Publishers trying to make it work thankfully actually around that time I moved from a UA focus into growth Focus so I had some time where I could dig into the topic before I was spending bigger budgets again which was great but I mean the main issue was there was a lot of uncertainty really around what's going to happen and what this is going to look like I think the whole communication by Apple wasn't great and for the longest time it wasn't

[13:16] clear what this would do to your data models what this would do to meta's algorithm and those things really only were figured out as it was life and then we saw a massive drop in performance everyone skilled back meta's stock took a hit and their advertising income took a hit and only from there on things recovered so I think probably there would have been ways to make this transition a bit smoother but this was just what we had to deal with and I think like advertisers really had to relearn how to make predictions on how valuable this channel actually is because you don't have down funnel

[13:52] tracking anymore most apps only track a trial start within their ads manager and that's all they see they don't know what's happening afterwards do these people convert do they renew how long do they stick around which put meta in a bit of a tough position because it's always been a more expensive channel in the end on the upper funnel cpms are high there's a lot of pressure in the auction very competitive but the value came with that great targeting and the good down funnel performance so conversion rates were often higher for this traffic which is something you now weren't able to see and you also were

[14:26] unsure if meta is still doing a good job and that of course creates a lot of uncertainty and you don't want to spend a million a month without knowing is this going to be profitable in the end and that was the main issue that took one to two years I would say now that advertisers are in a more comfortable position again they've find you their data models so that they can predict value based on that trial event and all the other information that they're Gathering From users in onboarding now at what point did you start seeing a shift because I I feel like maybe it's you specifically that

[14:56] I've been following on LinkedIn for a while it feels feel like it's not just you but that I've just been hearing more and more folks say in the last I don't know 12 months or so and then especially again specifically for subscription apps that it's like working again so how much of that is Facebook having invested so much into these targeting algorithms and non-deterministic measurement and those things how much of it's like Publishers getting smarter like you were talking about like what are all the contributing factors to it start stting to get better again and then when did you start seeing that shift happen to me it also feels

[15:34] like there was a shift definitely in the last year again while also already prior to that like the best advertisers definitely didn't take until a year ago like they were already ramping this up again much quicker and I think it's a combination of these factors really it's not just I mean meta of course keeps improving the algorithm and gets better at modeling performance again based on the signals that they're getting from scan but also from much more videos on the platform now I mean they're scaling reals like crazy most of my accounts is actually one of the biggest placements by now and it functions a lot more like Tik Tok so

[16:09] they have a lot of Engagement signals watch rates which they can feed into the algorithm and based on that I think they got pretty good at modeling performance even from these very upper funnel metrics and on the other hand also advertisers just got better and better and I think by now everyone has covered the groundwork and they know we need like a where did you hear about a service green onboarding so that we can create sample data on channel level and look into differences of organic versus meta versus Tik Tok most advertisers are asking for age and gender of users which is super helpful because meta always

[16:43] reports you their data on agent gender level and if you then in your product also apply that level again then you can basically cross check and look into I don't know I'm seeing in my app that age group 45 to 54 is 5X more valuable than 18 to 24 so is my CPI of 18 to 24 actually 5x lower or is there a mismatch and these other kind of things that I think first really needed to be dealt in and then data models needed to be improved so that advertisers really gain a lot more trust again in what they're spending on these platforms yeah I think

[17:20] that's a really good summary and again I think this is all going to be super helpful to folks who've heard oh things are changing like is it time to like come back to the platform or if we didn't find success in 2021 what can we do today differently that will find success and I think understanding this kind of context of why it was so good preat the kind of tumultuous time and then why it's starting to get better I think is super helpful for people to kind of frame their decisions around when and how to be ramping up meta ads so the last thing on this topic though before we move on

[17:59] is scan three and we'll talk about scan 4 because I haven't fully ramped that up yet but how much do you think scan three has helped and then how has it helped with meta getting better at finding these users again like most advertisers are really not happy with what scan 3 can do still compared to what things look like beforehand you really have a very limited time frame that you can track users you have privacy thresholds meaning you need a certain level of installs per day so that you can actually get a post back with your app events attached to it and also there is postback delay so it usually takes two

[18:40] to three days for your posts to arrive and then you need to match that to what you've been doing two to three days ago so your spend levels from three days ago need to match the app events that you're receiving three days after which isn't great I mean you want your data to feel like it's scientific and you can rely on it and this always feels a bit Scrappy and it's not great based on what you've did before but still scan 3 I think has allowed a lot of advertisers to spend a lot more money again and especially sub apps were a bit in a comfortable

[19:11] position because their payment usually happens in onboarding already so you start a trial event there meaning you have a pretty good value indicator early on in the product experience that you can feedback to that model so that meta at least gets a post back within these two to three days while other advertisers were hit a lot more strongly like talking about gaming before for games usually monetization happened not as fast still like good payers would already purchase something early on but a lot of especially in the strategy games genre the whole business model was based on some people paying a lot and finding these people got a lot harder

[19:52] because signal quality isn't as strong they might not pay that big amount of money on the first day meaning you can't track it and hence meta can't find you these people anymore so any product that was a bit more Niche with purchase patterns that were later in the product basically had a hard time and hence also product development and how you treat your funnel played a big role here so really if you want to succeed on scan you have to work a bit deeper into product and think about what are my conversion events going to be what can I feed to these platforms so they still

[20:23] make smart decisions for me and luckily sub apps just were in a good position for having that trial and onboard I want to dive into fast practices I'm sure folks listening are like all right now tell me what to do but before we transition to that I did want to get any other kind of thoughts around how to think about subscription apps why they work so well on meta and then you know a lot of the noise around at was even around DTC direct to Consumer kind of physical products and things like that so any other top of Mind differences so when somebody reads ah meta is not

[20:58] performing but it's from like a dtoc perspective how do they frame their thinking around that compared to running a subscription app and trying to scale a subscription app on meta I mean I would actually say that d2c has an easier game because most of what their tracking is still happening on the web and so you have Facebook conversion API that you can send your pixels through directly your events through directly from Shopify there's an easy integration and then you're out of this whole at mess if if you're trying to send traffic into your app for like a bigger I don't know fashion brand or something that is

[21:33] trying to optimize for app traffic that might be a bit tough but as you're on web I've seen actually that d2c advertisers had a much easier game and also a lot of the recent uptick I think was actually from d2c and the whole Advantage Plus initiatives that were launched from meta so they really automated more and more of what's happening in the app account for the better or the worse depending on what you're looking at I think for d2c it works nice because they are sending a purchase event which is their business goal in the end and the algorithm knows this person has purchased potentially even for how much while if you send a

[22:10] trial event the algorithm can't be as strong for you because it's not directly correlated with business value a trial only converts in 30 to 50% of cases so they might be optimizing for the other 70% basically I've seen stronger performance in these d2c accounts d2c is maybe better better which that's interesting I hadn't heard it framed quite that way so again really good to get that perspective and then games and delayed monetization is maybe where things are still going to struggle any other thoughts on subscription apps and how they vary against other types of ads on meta yeah I think the conversion event is really the most important one

[22:48] so like as I mentioned your trial isn't a real business value for you in the end and trial conversion can vary massively on an audience level I already mentioned on AG group it's usually big differences older people will just convert better because they have deeper Pockets so that's where it's special on the subscription front because you need to be much more aware of what's Happening after the trial and guide the algorithm in the right direction while a d2c Advertiser will probably get along with using more of the automation because the algorithm is just much closer to their actual business goal I mean they also have recurring purchases and people that

[23:28] sign up for the email list and don't so I don't want to say it's easy game for them or they less skilled but definitely there are special complications with advertising a subscription app and optimizing for trial all right well let's move into best practices and so we've talked about all the challenges how do you as a subscription app best use meta to find those payers that are actually going to become subscribers I mean I think it's pretty commonly known that you want to use a Consolidated account structure so that you really try to maximize the amount of signal you get on each of your adsets so in the end as

[24:05] the algorithm is supposed to help you find your users you want to maximize the signals that it has to basically do that and in the account you have a campaign level at set level at level and if you set things up very granularly and you might be running I don't know 10 campaigns with 50 adsets in them you would be scattering data across all of these so the algorithm will have a very tough time in actually using those signals to find your users while if you just run one campaign with one adset in it all that data will be used to basically fine tune targeting for you so

[24:40] you always want to find the right balance of where do I need granularity to guide the algorithm and maybe you want a country split in your campaigns because you know the us is going to be much more profitable than Brazil so you want to have these splits but then still don't add too many so that there's a lot of signal under each of your campaigns that would then maybe be something that as you scale up so like if you're just starting out and you're spending that first 40K to like start figuring out if you can get the algorithm trained that's when you want to be almost completely

[25:14] Consolidated and then as you're spending more and more and more then maybe you do have more flexibility to branch that pretty much so yeah try to start very Consolidated and then as you're adding span you can add additional campaigns and adsets based on the levels that you want to Target separately and this is also going to really help you pass privacy thresholds which are in scan 3 is still a big pain for especially smaller advertisers because you need to reach 88 installs per day per scan campaign ID and actually meta doesn't even tell you where they apply a scan campaign ID it's definitely on adset level but it might even be more Nara

[25:52] because they receive better data on each of these IDs so they might be creating in the background two IDs for Facebook and for Instagram even though you target these in one campaign it might be two IDs you don't know so basically you want to make sure a lot of installs are coming through on each of your campaigns so that privacy thresholds are passed and then you actually get postbacks in so that meta sees your trials and can optimize for you so start Consolidated don't go in there set up different interest targeting and think that you know better than the algorithm because in the end you don't so are there any

[26:28] scan specific best practices or at this point does meta handle most of that for you like when you install the Facebook SDK is there customization and best practices on scan or is that mostly just kind of like handled by the SDK and by default I mean I would say one best practice is to optimize for your trial event and make sure that's basically the highest value one in your scan schema other than that there is not much you need to handle in terms of how you set up your campaigns you have a choice choice between tracking through scan and through a it's really not that you need

[27:03] to take special care of a scan campaign by setting it up differently what you need to consider is how you evaluate your data because as I mentioned before your events are going to come in later so you actually when you kick off your campaign for two days you're not going to see any results and that can be scary especially if you're spending a lot and it says you've spent 5K and you have zero installs and zero trial tracked but that's just due to the fact that postbacks are coming in delayed and you need to make sure that you're basically evaluating campaigns that way and always match data from two days back with the

[27:37] right spend level additionally I would say also give meta a little bit more time because they don't get these events super quickly so you shouldn't be evaluating results after three four days because then only one day of data came in meaning the algorithm wouldn't have been able to really do a good job for you yet so give it some time at least a week I would say before you start evaluating performance that's kind of the biggest thing to take care of when you're handling a scan campaign because of that is a 3-day free trial a better tactic even if maybe you're not going to convert quite as well but you're going

[28:13] to get better data on a three-day trial than a 7-Day free trial or do both kind of work well as long as you know that and are factoring that into your data measurement in the end right now in scan 3 it's not going to make a big difference I mean yeah for conversion there might be one or the other will be better for your app but you're still only going to track your trial start event and then you're going to have to look into TR conversion in your product data and of course having a 3-day window means you're getting a conversion quicker meaning you can more quickly

[28:44] evaluate your results but that's really the only benefit here and I would say if a 7-Day trial is actually converting five 10% better for you then rather go with that and don't reduce performance just so that you have results quicker because over time your modeling and knowledge over that data will become strong enough that you can also predict results with a 7-Day trial what are your other kind of best practices on the basic level and then we can get to like more advanced stuff with the subscription app you're always going to optimize for a trial start generally like try to get your conversion event as close to a business value that you can

[29:20] for the subscription app it's mainly going to be that don't Target to narrow these algorithms usually need a lot of reach so that they can use data to then find the Right audience for you if you apply a ton of targeting restrictions on top then usually you pay a premium for targeting more granularly while performance not necessarily is better so I would usually with a new app rather start with a broad targeting choose your core markets and I do usually exclude younger users below 25 sometimes even below 30 depending on app because of what I mentioned earlier that the conversion rate from trial to paid is so

[29:59] different for older and younger users and while you're still early and you don't have a good understanding yet of how this is going to convert I rather start a bit more conservatively use only audiences that I know have a good chance of converting and only once I've gathered data and get a better understanding I might broaden that to like 25 plus but really the age group below 25 is really neglected by most of the advertisers because they are usually very cheap to buy because they use social media more often and they don't have high purchasing power so cpms are low meta really loves spending on them

[30:34] because they're so cheap and they're going to create a cheap cost per trial but then the conversion down funnel is going to be very very poor so I haven't seen anyone really correct this issue yet of how can you spend towards these audiences and still convert them with I don't know a good discount strategy or whatever you might be apply in the back to it and yeah 25 plus is kind of the best practice I would say in the industry and one other thing is I already mentioned like meta also pushing for Advantage Plus more and more which is their automation features you by now will see probably 10 different things

[31:10] that are called Advantage Plus in the UI which gets very confusing at times and it's basically what you mentioned with Google before that they are trying to make you opt into everything that maximizes reach on their end so that they can sell away any kind of inventory for some advertisers it works very very well especially if your conversion event again is close to your business goal but with a trial just be careful I wouldn't say it's not a strategy to use but you want to know the algorithm Works before allowing the algorithm to do everything for you so I usually start on manual campaigns set up things myself have

[31:49] separate adsets per country and then exclude younger age groups while only later in the lifetime of an account I might layer on these fully automated campaigns because then I know okay my conversion rate for these users is going to be that much lower so I'm bidding less aggressively on them to have a cost per trial that is cheaper so that the equation works out be skeptical of all that automation meta likes to kind of append recommended labels to everything but it's not really recommended for everyone for sure you kind of mentioned you don't want to go super narrow on your targeting but then you mention you

[32:24] do want to Target via age so you're saying there is some level of penalty in the auction in a way like you're going to just have to pay more in the auction to get these holder users but that's the one place where being more granular and your targeting really tends to pay off yeah with h definitely that's the biggest lever if you're struggling with traal conversion rate then Target older people and you're meally going to bring it up if you need good numbers for your next VC round then that's definitely a good a good trick to make those numbers look better are there any more kind of

[32:57] Advanced Techniques that you start to get into as you scale that people should be aware of there's a few ones I would want to highlight that I tend to see in accounts where people just stay a bit too basic and don't optimize on that deeper level to really fully leverage that channel one of those I would say is creative diversity which is linked with placement performance and really optimizing for each of these placements as you already mentioned there's a ton by now I also don't know all of them by heart anymore but really there is totally different audiences behind any of these with some it's really different

[33:32] apps like Facebook versus Instagram but then also on Instagram the people that use the Instagram feed compared to the people that use Instagram reals are totally different reals is much closer to a Tik Tok audience it's younger lower intent while the feed is a bit more qualitative but also usually more expensive so you really want to find out like what are the placements that I can find my target audience on and from there go really deep into optimizing your creative for these placements so you want to actually use these apps if you find Facebook is still working for me then use Facebook you probably haven't been on there in a while but it

[34:10] changed a lot people still use it and you want to know like what does this placement look like what's the typical content people are sharing here and how can I insert an ad here that makes sense that is close to the same experience so it's not just a banner ad like on a website but you want to really work with this native feeling and I feel that's where often people just look at performance on a too high level they look at okay this creative has had a cost per trial of 10 EUR and the other one has 15 so one has done better than the other but you really want to dig

[34:42] into okay where did I deliver with these and meta shares all that data you can always break down creative performance on a placement level on an age level on a gender level so you can learn a lot really about where do I need to run these ads to get good performance in my app and creative strategy is really where it's at then so that you tailor things to the right placements that are going to serve you well gotcha and then of course once you've run the ad so much of the potential performance for that ad ends up being what then happens in your app how do you think about the funnel

[35:22] optimization from onboarding to pay placement to surveys to other things that are going to happen happen early in that product journey in ways to kind of optimize that toward being more successful on meta yeah that's definitely one of the changes I would say I've seen that UA managers need to be more and more product focused as well and they need to look into full funnel performance not just purely on the ad side because much of that efficiency from the algorithm is gone and you need to find additional levers to improve performance and I try to always use meta as my channel for experimentation to find angles that I can use for

[36:00] advertising certain like copywriting that works well and styles and then take my winners and inform my full funnel with it so if I found something is working very well on meta then I can bring these learnings into abore optimization easily oftentimes it's as easy as reusing your ad as a video or just a screenshot of it in your app store so if meta is one of your main channels then that already creates coherence people click through they see I've landed in the right place and this is the app that I was looking at that often already creates an uplift and you can take that then also down the funnel

[36:37] if you found that okay we are seeing best results from an audience on Facebook with this type of creative that is between 35 and 44 and female of course there's a lot in there that you can hypothesize around to create experiments throughout your funnel and tailor to that audience if you showcase reviews in your boarding then you might want to take a user review from someone that is actually in that demographic or at least make it look like it I mean you can always just say this is from a woman 44 from the US for example Additionally you also want to make sure that other parts of that onboarding really work in

[37:15] sync with what you're trying to achieve on meta so if you are for example driving a lot of traffic through res and you're targeting relatively young users you're pricing needs to fit that strategy and I often see a mismatch there where people are using a lot of these ugc videos these days user generated content Styles so it's like someone filming themselves on their phone talking to the camera and just why they're loving this product basically and this video style is what's native to real placements and it's portrait mode short form usually below 30 seconds and that placement drives younger traffic but if your app actually I mean maybe

[37:59] your app is already tailored to older users which means you shouldn't be doing this anyways but even if it's an app that is a bit younger and your pricing is very high let's say 80 90 bucks a year you're going to have a hard time converting these people because they're just not as high intent and don't have as much purchasing power so your pricing what your pay wall looks like in the onboarding needs to be really synced up with the ads you're running and the placements you're on so that things even get a chance to convert are there any signals that you get in the app from

[38:32] Facebook like I know back before at you could actually get from the Facebook SDK the specific ad or Channel or you got a lot of very granular data where you could even personalize your onboarding and your pay wall and pricing or anything else based on even the specific ad they saw do you get any signals from Facebook now or do you pretty much have to do that personalization based on a short survey or other context um you don't get any data anymore really it's all aggregate mostly you don't even know the amount of traffic coming from meta because what's tracked with scan often has a bigger

[39:13] discrepancy often around even 50% of Trials are under reported so what you get is an aggregate number that is not all the users and you don't know like which specific user came from the channel so that's why it's super important to really have an onboarding that enriches that data again so that you ask questions how old are you what's your gender where did you hear about us and the data you gather here is never going to match like what's happening on the UA front one to one it's not that you can ask users where did you come from and then only attribute the ones that said I came from meta because a

[39:47] user Journey usually looks a lot different from like just seeing one ad and clicking on something so they might have heard about your app from a friend then they saw a meta app but in the survey they're still going to answer I heard about you from a friend because that was the first touch point so it's really just meant to create these data samples that allow you to compare and you're going to see differences in someone that answered I came from Tik Tok compared to someone that answers I came from matter for sure and same for age groups which is then the data that you can use to personalize and to inform

[40:20] your data modeling to actually know how valuable your user acquisition efforts are to you and then how are you doing this measurement like you've talked several times about modeling and I guess maybe we should talk about this at different levels like when you're first starting out how do you think about that measurement and probably not even like fully modeling you might not even have like a data warehouse that you're dumping product data and analytics data and everything else into how do you think about then measuring this yeah usually as soon as people have like a data warehouse and an analytics team then I don't need to do that work

[40:55] anymore so what I do is usually a bit more MVP which is really using a few data sources to build trust in the channel and one of them is incrementality so you want to switch ads on switch ads off and see what's happening to your Baseline which is always of course a good position for smaller developers if you don't have a ton of organic yet if you don't have 10 channels running it's going to be much easier to see okay I'm spending now 2K a day on meta and my Baseline has increased by 50% that's probably the effect from the ads the more advanced you are the more you will need a data

[41:30] science team for this because the fluctuations on your Baseline are going to be much smaller and it's going to be harder to see what's going on to other data points going into this are your ATT optin audience so there's still a smaller set of users that you are able to match to a channel as you were used to back then so that again creates a sample where you can see I don't know 5% of people I was able to match on meta 6% I was able to match on Tik Tok and I can compare how valuable are these so just to inform should my cost per trial on

[42:01] meta be 3x 4X higher on Tik Tok or should it be a lot cheaper because traffic quality is less than that the other one is this where did you hear about a survey so that users actually give you a qualitative answer on this is the channel I came from which can be a third input into this model and from there it's really a bit of setting your cost per trial goals accordingly so that you know meta is always 2x as valuable as Tik Tok we need to adjust our cost per trials based on that level gotcha and then as folks scale up what are you seeing with these more sophisticated

[42:37] apps working and tooling too I mean at what point do you layer on an MMP or if Facebook's your only channel are you just dumping everything into a data warehouse and having your internal analytics team do aspects of what an MMP would be doing for you anyway yeah I mean you don't need an MMP to run meta they have their own SDK that you can Implement and you can insert your scan model there um track your Tri event and then it's doing the job depending on what your Ambitions are how quick you want to scale I would say like if you're a small developer start out with that

[43:12] and don't add on additional tooling because it creates another breaking point something can always go wrong and also it's going to cost you money at some point I mean usually there's a bit of a free plan in the beginning if you have very few TR events but then it quickly gets to be another cost center and usually like Revenue cat is even one of the tools that is used for these early devs to like look into then trial performance how do my trials convert into paid how is that different on a country level and that would even inform modeling so really I think like most of

[43:45] the de developers I'm working with are like 10 to 20 people teams and they don't have very s sophisticated tooling for this yet and it's not really needed at this early stage when you say modeling I mean this is spreadsheets and intuitive quote unquote modeling not necessarily like having to build an algorithm to be creating some sophisticated matching and all that kind of stuff it's more looking at your data half of the models kind of in your head as you're running the ads looking at the data kind of making decisions based on that right yeah it's not super sophisticated as I said it's very very Scrappy it's really just getting an

[44:24] understanding of the audience that you're driving how valuable it is on a country level AG level and a channel level and then setting your cost per trial gos accordingly and of course don't forget about also rechecking if your assumptions are right because it's always just assumptions so if you're assuming I can spend 50k a month on meta and will get my money back within I don't know maybe even instant payback so on day eight after the tri converts then check your actuals like don't rely on just the modeling but make sure that you're actually recouping money in the time frame that you're predicted it would happen but often in the early days

[45:01] it's quite Scrappy and then as teams grow they would collect all of this in a data warehouse usually have someone that is a bit more skilled in all of this so a data engineer analytics person who then looks into how can we create a predictive model and take in all our UA data what we have on scan what we get from the MMP and feed a UA manager then with a estimated Ras pretty quickly so that they can take decisions but with most of the teams I've seen even bigger ones it was still at that Scrappy level because as I said it's not scientific anyways it's all just predictions so I

[45:38] haven't seen anyone of the small to midsize teams getting super Advanced on it yeah one of the things I I think a lot about to kind of seeing behind the scenes at Revenue cat you know as we roll out things like our experiments feature and even our charts and so many other things we have 30,000 developers now and all checking our math on everything we find bugs that we have to fix but like we're finding all the bugs because we're working with so many different developers with so many different needs and find so many different edge cases but I often think about that like any one app if you are a

[46:12] larger app and you've created this sophisticated algorithm and you have a data team you still need to be going back and at very high levels and in less sophisticated ways like double-checking all of those assumptions because one little like mismatched SQL query is just going to blow things up in a way that's going to cost you a lot of money and so like while as you grow you can get more sophisticated you need to be careful about that sophistication because things can go wrong and do go wrong totally totally yeah even back in the days when I was working gaming and everything was still tracked on an idfa level we had

[46:50] very Advanced models on all of this predicting lifetime value for the next two years but those were usually built on like h amounts of data and then I'm optimizing on an ad level and of course a model can go wrong the more granular you go and a big part of the job was really digging into that data and proving the data team that they're making a wrong assumption and my ads might actually be doing better than they think they are so definitely always go back also like check historic data you had an assumption now you have three months worth of data look into how did this perform is it better or worse than

[47:24] what I had predicted to find you this over time yeah and then by definition any model breaks as you make big changes so like you said three months ago you have a Bas line and you think it was working and then you roll out a big new feature well that breaks your model by definition because those assumptions that went into the model that has been working now doesn't work because you have new features that you're promoting in a way that may be greates higher intent and drives higher value per user and things like that so yeah I mean it's funny how Scrappy teams in some ways are

[47:57] better off because you're just doing the math in simpler ways and the more sophisticated it gets the more places it can break it's just a matter of being smart about it and triple checking assumptions and not forgetting the basics when you're doing all this kind of stuff if the UA team also handles the modeling themselves then it's much easier to get these changes in while once another team is actually responsible for it then it's company politics and you need to prove them that maybe something needs to be changed and that just makes things more complicated yeah the big elephant in the room we haven't discussed yet is creative and

[48:29] that's like so make or break for these campaigns so how do you stand out how do you use these short little interruptions or content in Facebook and Instagram and other places to get people's attention to drive that intent to find the right audiences I would always recommend like using these apps and being close to that content like many people love to dig through competitor ads to make ads and I think there is a lot of value in this especially if you look at big Brands and you know they're doing well on the platform but in the end what you're trying to achieve is that your content

[49:07] is similar to the content that people engage with when it's not an ad and for that you need to be using these placements and understand what makes them tick so that would be my biggest advice here especially talking about Facebook no one I know my age kind of in their mid-30s is using Facebook anymore but for meta ads it's still a super valuable placement it's a bit older audience a lot higher purchasing power so you want to know what's going on there and how you can tailor to these audiences I would also recommend like try to look outside your bubble so follow some sides that you wouldn't

[49:42] usually because your target audience will be a lot more diverse than what you're looking at so I tend to like follow for example a broad spectrum of news sites on Instagram and on Facebook because I want to see what are the headlines how click baity are they what other styles they're using in their images because then I can tailor my content to them so while I don't agree with anything they write I still follow Fox News on Instagram and Facebook just to see what's the content some of my target audience might be consuming yeah that makes a lot of sense in this new paradigm where you do especially early

[50:18] on have to run very Consolidated campaigns how do you test creative and how many creatives are you running and how do you scale that up I would say like the times are gone where you created thousands of creatives and you just throw them in there and hope for meta to do the job because creative testing has gotten a lot more expensive and data quality has decreased so you want to be a bit more in charge and make more qualitative bets here so my creative testing volume has gone down quite a bit in terms of best practice structure always test your creatives in a separate campaign not in the one that

[50:51] you're scaling and that is running smoothly because it will interrupt them and things can go wrong there's this Infamous learning phase so whenever you make a change to your running campaign it resets it meaning the campaigner recalibrates and that can lead to fluctuations and performance so if you upload a new creative into your skilled campaign you want to make sure it's a good one you want to have tested beforehand and know this is actually going to improve performance and not decrease it and other than that I always try to test them in a similar environment that I'm also scaling in so I don't know if my scale campaign is

[51:25] optimizing for trial and I have 10 core markets that I usually advertise in then my testing campaign is going to run in the cheapest of those markets but in the ones that I'm also Al in and it's also going to optimize for a trial it's not going to be a totally different campaign setup which I often see advertisers do because they want to save money and they only optimize for an install and they go into markets like Thailand or Mexico where there's cheap ctms but then the audience is going to be a lot different if you're not live in these markets you usually then of course people haven't

[51:59] heard of your brand yet so the effect there is going to be different so I rather try to be a bit closer to my actual audience when testing it's going to be more expensive but then results are just going to be easier to Port over to my skill campaign the last thing I did want to talk about real quick is the future and like meta has already made some advancements scan 4 is coming out does the future look bright for continuing to improve upon the improvements we've already seen and then what are the things that are contributing or potentially going to contribute to increasing performance over time yeah I would say yeah the

[52:36] future does look bright scan for as you said is on the horizon there are a lot of things in there that are going to help you with the channel two main benefits I see is for one privacy thresholds are coming down so it's now called crowd and unimited tiers and based on some First Data that EPS Flyers shared from what they see in their client data it's only going to be 20 installs per day down from 88 which is quite the difference and especially for more Niche advertisers that have high cpis it's going to make a difference for sure and the other big thing is that

[53:09] we're getting these two additional post backs and that's also very interesting to again feed that model so we have the word that you hear about us data the at optin audience and now on top we can also look at the scan second and third post back just to F that model and look into what does my trial to paid conversion look like from first to third post back on meta compared to Tik Tok compared to Google which is going to get you even more Security in what you're doing there the other things happening we mentioned it quickly before was a which is aggregate event measurement it's basically metas form of

[53:45] fingerprinting it's been a bit of a buzz I see some people saying hey just like switch everything over to a you're going to have same data as you used to before scan others are a lot more cautious I'm a bit in the middle there I think it is a great tool and I've seen advertisers that this really helped especially the ones that have high cpis and can pass privacy thresholds so basically with aggregate event measurement they're getting instant postp data and meta is basically just doing the magic on the back end but also in the end is a blackbox everything is modeled they're just looking into kind of IP addresses

[54:20] and trying to match these users and then add their modeling on top hence you cannot really trust it neither can you scan because there is discrepancies but I would say be a bit cautious about it because Apple might take that away as well like if your whole strategy is based on running am campaigns and you don't invest into your scan infrastructure and how you evaluate performance under scan then at any point you might be in the similar situation to when at launched so I like to build my account foundation on scan because I know it's future proof it's what's Apple wants us to do and it's also getting

[54:57] better with each version I mean scan 5 is already on the horizon while we've been waiting a long time for scan 4 to be finally rolled out and then I use a for scaling if I see performance is good with it then why not use that opportunity I wouldn't just not do it because it's unsecure I rather take my chance there but it shouldn't be the foundation and your P Performance Based on it because you can't be sure what's going to happen when do you expect scan and four to be rolled out enough where we're going to fully see the benefits I have no idea I also don't want to take

[55:34] guesses I would have expected it to happen way faster because of these benefits but like the whole industry has been quite slow there is this nice singular tracker from where you can see basically the post backs that are coming on scan 4 versus scan 3 for all the different networks and meta has ramped up to like 40% scan for some when February I think now they actually went back down to 20% again so I have no idea when it's going to happen and this is already their second attempt at rolling it out like the last one was somewh back in September 2023 so they seem to be

[56:08] taking their time they seem to not be really sure yet how to make sense of the new postp data and hence I won't make any guesses but yeah I'm excited for it because of the benefits that I mentioned and there are a few more where data granularity is just going to be better on this model well that's a good place to wrap it's nice to kind of leave on a high note that not only has it gotten better in the last couple years but that it's likely to get even better not to dive to chase another rabbit as we wrap up but it is like personally frustrating

[56:38] to me that scan 4 is probably what scan two should have because scan 2 is what Apple rolled out when at was rolled out and had they spent more time talked to the industry like really understood the problem of measurement better before rolling out at I think we'd be in a way better place today and we could have avoided a lot of the challenges of the last few years I'm always frustrated with apple and how slow they are I was so frustrated with Apple pay was like why don't you just pay all these Merchants to like install the new hardware and take Apple pay and let's

[57:11] get the show on the road well like they're patient and it's fully you rolled out and almost nowhere I go doesn't take Apple pay anymore and so it's like they're patient and eventually it gets there and so I think that's at least a hopeful place to end the podcast is that scan 4.0 is coming and it's going to keep improving and scan 5.0 and scan 6.0 and like it is going to get better and I mean the cool thing like personally I'm a bit of a privacy Zealot myself and so it is exciting too that as someone who cares about privacy and not wanting to have my user data just

[57:48] hoovered up by a million different people and selling data and all the kinds of things that have gone on preat it's exciting to me that all of this is happening in a privacy friendly way that you're not having to violate user privacy to do it that's all pretty exciting to me totally and I really like like how the job has changed due to this I really like that you have to go deeper and go into qualitative data and I don't know like doing user interviews is super helpful because you learn how to do better advertising and it's not just all sitting in front of your data analytics

[58:18] deack and throwing hundreds of ads added every week but you actually have to do your homework a little bit better and to me it's a lot of fun and it's kind of moved things in the right direction and of course for Apple there's also the benefit that Apple search as 5x in Market size or something yeah for those of you who are running Facebook ads since I'm not you've probably been like shouting into the void asking questions that you hope I would have asked Marcus you know more advanced questions more tactical questions the good news is Marcus actually did an AMA in the sub Club Community if you go to chat. club.com

[58:56] and look for the AMA and that's public you have to join to get to some of the private channels but we've been doing public amas there on the subclub community so he's actually answered a lot of kind of more tactical questions in that ama and then I'm put you on the spot but I want to have you back on and do another AMA because you those amas it's like you know when people are Flying Blind and not working across multiple apps like you do it's just so helpful to be able to talk to an expert who's doing it so yeah join the community and we'll do more am with

[59:26] Marcus with Thomas with other kind of experts in these fields in the future and then you can check out what people already asked Marcus on the AMA that happened a couple weeks ago so yeah anything else you wanted to share as you wrap up I know you're actually accepting clients right so if people do need to scale and as much as they've learned from this podcast maybe still need a helping hand are you open to clients right now yeah always happy to kind of work on fun challenges I have a big backlog due to LinkedIn going quite well but basically I'm always looking for like a fun challenge so feel free to

[59:57] reach out give me a follow on LinkedIn I try to post every day also more tactical stuff and feedback has been great so I think it's useful and I'm also planning to launch some digital products soon probably like an email course on meta ads and how to make it work for subscription apps so hopefully by the time this airs I might even already have a sign up for that on my page so definitely give that a visit very cool and we'll have in the show notes links to his LinkedIn and other places to find Marcus and U make sure we link to the am as well check the show notes and get in

[1:00:31] touch with Marcus thank you so much this was a blast like we could have gone two or three hours but I do try and keep the podcast around an hour we've gone a little over but this is so fun thank you so much for joining me thanks a lot David was a lot of fun thanks so much for listening if you have a minute please leave a review in your favorite podcast player you can also stop by chat. sub club.com to join our private [Music] community

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