We met with Laura Nys, Gaming & App Measurement Lead of Northern Europe, from Google who focuses on measurement and she answered some of our top burning questions around driving app campaign performance in Northern Europe post iOS 14+. 

Here’s what she had to say.

Enjoy!

Transcript:

“All right. Good. Let’s get started. 

Hi everyone. My name is Laura from Google, I work for the Northern European Gaming & Apps team. My focus is measurement, and I’m here to talk you guys through a few burning questions on the use of SKAdNetwork and more generally the trends we’ve seen as a consequence of iOS 14. 

How to optimize your conversion values and measurement windows. Should you extend beyond the 24-hour window?
The short answer to this great question is it depends, but I will give you guys the long answer.

It’s true that the faster you can send a value, the faster you’re able to respond to changes in cohort behavior, but according to the garbage in, garbage out principle, sending inaccurate values to a campaign will not improve the quality of your UA efforts. Essentially you’re striking a balance between speed and accuracy. If a user’s behavior in those first 24 hours is very meaningful to predict their value to your business over time, say if your app has high usage frequency and low conversion hurdles, then stick to the 24-hour window. However, if you feel you do need more time to capture some of those valuable signals, don’t hesitate to extend to a longer window. I think ideally that would be three days.

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How important are concepts like LTV and prediction modeling and how do you go about it with PredictSK?
Availing of a simple and powerful way to compute an approximation of LTV beyond direct attribution has never been as important as it is today. It will become even more important in a privacy-centric future. Analyzing LTV is an essential part of competitiveness and long-term profitability. PredictSK is a simple and powerful way to do that. Using an existing AppsFlyer integration and in-app measurement that’s already embedded in the SDK.

PredictSK can provide you with an understanding of how your channels are performing and where to adjust strategies by giving you an LTV bucket between 1 and 9 within three days after the install. It will be a complement to the strategy you’ve chosen for your SKAdNetwork conversion values, and it will result in a more holistic view of your performance over time.

Finally, how do you think the performance marketing world in Northern Europe changed both iOS 14 and potentially in comparison to some of the other markets?
Northern Europe has an especially strong developer footprint, Helsinki being a hotbed for mobile gaming globally, so I believe we will find smart new ways to go about marketing beyond the narrow scope of unique identification and rely more on first party data and predictive analytics.

The bottom line is that heavily performance-focused marketing is under a lot of pressure, and it’s being forced to shift away from individual identification. We need to get creative about how we look at profitability of marketing efforts in this new world, which is turning out to be very challenging, but you know what they say, never waste a good crisis.”

The post How to optimize performance app marketing in a post iOS 14 world appeared first on AppsFlyer.

Welcome to Edition 31 of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Andy Carvell, the co-founder at Phiture, a mobile growth consultancy that helps apps to grow, and focus on acquisition and user retention. 

Andy shares how to improve retention using attribution data, and how to create a tailor-made on-boarding experience, achieve cross-team alignment and ultimately improve user loyalty.

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

So what exactly is retention? It’s the percentage of users who are actually coming back and using your app again and again in a given time frame, or in a succession of time frames. And healthy retention is really the cornerstone of growth. If you don’t have that retention in place, that at least some percentage of the users are sticking around over the long term, you’re gonna find it really hard to grow your app sustainably over time. 

One of the problems that I often see is what I call a silo mentality, so this is what I’m showing here, where we have, for example, a brand team, user acquisition team, and a product team who are not really aligned and not really talking to each other. 

What can happen here is that you get a situation where you get what I call a disconnect, where the user expectation is not clearly aligned and you’re not delivering clearly on that user expectation through these different functions of your company. 

So the brand is maybe setting a certain expectation, certain message, user acquisition is then advertising, trying to get installs, maybe at a cheap price, which is maybe optimizing for that price, and maybe adjusting the message in order to get that price. And then in the product, it’s also delivering a certain experience, which maybe is not aligned with the expectation the brand was setting and the expectation of the adverts and the app store itself. So if you don’t have these things in alignment, this is where you can really start to have problems with your retention. 

Retention starts with acquisition

So retention starts with acquisition.

The user expectations are gonna be set from the very first touchpoint they have, if they see an advert for the app and it’s advertising certain features, the user’s gonna expect those features or benefits when they come into the app. 

And if they’re not there, this is where you have a disconnect, and it’s gonna be very hard to retain those users. So it’s not really helpful for the user acquisition team to say, well, retention is a product’s problem. It’s actually everybody’s responsibility to have good retention. Here’s an example from a PDF app. 

Let’s say you have an app which opens and allows you to view and maybe edit PDF files.

Hopefully, that’s made clear in the advertising, that you’re advertising these benefits. Hopefully, that’s clear from the app store presence, from the screenshots, from the description. And hopefully, that’s clear when, in the first use of the app, the user opens it up, and they can actually find that they can edit and open PDF files. And if the user opens the app, having installed something which they expect to open PDF files, and they find it can only open word documents. Then you’ve got this kind of disconnect where it’s gonna be very hard to retain those users. That’s a pretty extreme example, but my point is that you need to really carefully align the product experience with the expectations that you’re setting in the advertising and in the app store. 

Measuring retention

So how do we measure retention? Well, the first thing to understand is that you need to be measuring every user session in order to actually do this kind of analysis. If you were using a commercial package like an attribution provider or an analytics solution, this is gonna be measured automatically by the SDK.

If you’re doing your own in-house analytics, you need to make sure that you’re recording every session that every user has, so that you have those timestamps to refer back to when doing this analysis. 

Let’s look at some of the common visualizations for retention.

What we have here is a cohort table. A cohort, in this case, is a group of users who’ve had their first session on a given day. That’s what we see on the left there. And in the columns going through on the right, we see what percentage of users were retained on each of the subsequent periods.

In this case, it’s showing daily retention, so we see day zero, actually, 100% of users are always there on day zero, because that’s the day which they were first seen in the app.

Day one, it’s looking at what percentage of that cohort actually came back and were seen in the app, and had a session on day one.

Day two, et cetera. In this example, we’ve just used shading, often a cohort table has shading, but also a percentage number which shows the exact percentage of users who came back from that cohort. More intense shading means higher retention, less intense, lower retention. 

We can read the cohort table two ways:

• One is scanning across from one particular line, which shows how well that particular cohort was retained.
• We can also scan down the columns.
  • So if we look at, for example, day one retention, it’s usually a fairly critical number which companies want to improve, that’s showing the trend in day one retention and how it’s evolving over time. Ideally, it’s improving over time. Which would indicate an increasing level of product-market fit, which could be achieved by different marketing activities, which are bringing better users in, or improvements in the product, or some combination? 

So let’s look at retention curves.

A retention curve is essentially a plot of a blended average retention for your entire user base over a particular period. What we see here is we have two examples.

• The blue line, showing very healthy retention. It levels off at a particular point, showing that, actually, a decent percentage of users are sticking around over the long term.
• The red line is actually more typical. It’s showing that actually it’s trending to zero over time, which means that, for any particular cohort, there’ll be basically none of that cohort left if you look at a long enough time horizon.

This is unsustainable growth, but it’s often masked by throwing lots of new users in so that the active user number never actually dwindles to zero. But if your retention curve looks like this one, the red line, eventually, if you stop acquisition, you’re not gonna have any users left. 

Finally the stacked retention graphs.

This is essentially another way of looking at, and best visualizing, how monthly active users are affected by retention. So here on the y-axis, we’re actually looking at monthly active users. And what we see here is essentially a bunch of stacked retention curves, each of these representing one cohort. You can think about this very much like the curves that we see here above. Each of these cohorts is showing how it’s contributing to monthly active users.

The example on the left, these cohorts are trending to zero, so it’s kind of like the red line example above. And so it gets harder and harder to sustain monthly active user growth, and it starts to level off because these cohorts are dwindling to zero, it doesn’t matter really how many more you throw on top, it’s very hard to sustain the growth. 

On the right, we have a much healthier example. These curves are flattening out, which means that some percentage of users are sticking around over the long term. So as you acquire more and more cohorts, this is slowly building and building and building on your monthly active user number. 

How to influence and improve retention?

So let’s bring all this together.

• We’ve understood why retention is important, and some of the common pitfalls.
• We understand how to measure it, so how do we actually influence it and improve it.

Well, it’s a tricky topic, and it’s a big topic. Everything can improve retention, or it can influence retention, including external factors such as competition, as well as the marketing and user acquisition activities that you do, as well as anything that’s going on in the product. So there’s a lot of inputs to retention, and retention is an output metric. 

Let’s focus on the actual input levers which I think are most easy to actually tackle. That would be new user activation, existing user engagement, and reactivation. So new user activation, this is how well you can actually get people to experience the value of the product in their first session, and get them activated such that they’re likely to actually come back and have more sessions within the app. So this is all about new users. 

Existing user engagement

How well are you able to keep users engaged over the long term. This might be through releasing new content or providing other mechanisms that keep users engaged and they keep finding value, not just in the first session but time after time. 

And finally, reactivation.

Are you able to bring users who’ve lapsed or churned back into the app, maybe with a special offer, or some new information which reinvigorates their desire to use the app and to come back again? And if you’re able to improve any one of these, retention’s likely to increase, albeit slowly, it’s a lagging indicator. 

New user activation is usually where you can see the results quickest, and it’s usually where there’s the most upside, which is why a lot of people focus on that. So I would really advise working cross-functionally, both to get that alignment of your marketing message with your first-time user experience to improve your activation rate, and also to maybe consider doing what I call adaptive onboarding, which is to provide a slightly tailored or personalized onboarding experience based on the acquisition channel, or the acquisition campaign, that the user came in on.

Using your attribution data, you can actually identify which campaign a user came in with, maybe even what creative they clicked on, and then provide a very tailored onboarding experience that really speaks to that message that they saw in the advert that they clicked on. This is the ultimate in achieving that alignment. 

So my final message really is that improving retention is tough, it takes a lot of work, but it’s totally worth it. And to work cross-functionally in order to achieve that. That’s it for today.

The post Do’s and don’ts for optimizing UA and performance ad campaigns appeared first on AppsFlyer.

Welcome to another edition of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Christian Eckhardt, CEO & Co-Founder at Customlytics, a leading full-stack mobile technology and marketing consultancy in Germany.

Ahead of the holiday season, Christian talks about common challenges that marketers face when they want to run paid advertising for an eCommerce app, and offers proven tips how to overcome those with Dynamic Product Ads.

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Welcome to this episode of the AppsFlyer MAMA Boards.

My name is Christian and I’m the co-founder of a company called Customlytics. We help apps being successful by delivering consulting for all things mobile marketing, so that goes from strategy to technology to actual tool integrations.

Today I’m here with you to talk a bit about common challenges that marketers face when they want to run paid advertising for an eCommerce app. And how to overcome those with Dynamic Product Ads.

So we will start off with the situation that occurs if you want to run paid advertising for an eCommerce app, and what you’ll probably end up doing in that case is that you have a media mix that is spread across a lot of different channels, like paid social which is essentially Facebook, Twitter, and other social networks, paid search which is the big search engines like Google, and some additional ad networks that you can of use to promote your ads.

And all of the issues that come from that are related to the fact that as an eCommerce business you will have a lot of different products, and what you do in advertising is essentially showing products to potential customers, and that kind of leads us onto the issues that come from running paid ads for an eCommerce app.

Top challenges for eCommerce app marketers

The issues are related to the ad creation primarily, so if you have a lot of products, what you have to do is you have to have a lot of ads, so you have to manually create a lot of those ads to run on all those different channels, and that’s just a lot of work.

The next problem that’s kind of even related to this is ad wearout, so in paid advertising what happens is that your ads over time will just wear out, meaning that they will decrease in performance so click-through rates and conversion rates will go down over time, and what you have to do in this case is you have to come up with a new ad design and if you have so many ads running at the same time, then this is again a lot of manual work.

The third problem really is that with the manual ads and just showing them to your customers, you will end up having quite a low relevancy because it’s not targeted. You just show whatever product to whatever user, and maybe it’s a good fit for the user but maybe it’s not. And the last point to that is that if you’re working with a media agency, it’s kind of getting even worse because they also need to request new creatives from you all the time, meaning that they just will bug you and kind of annoy you.

The solution: DPAs

And the solution to all of this is Dynamic Product Ads, or DPA’s the acronym for it. And Dynamic Products Ads are a mechanism to show ads to users in an automated fashion, especially for cases where there’s a lot of products.

So how it works is that there is basically a product catalog in place which is a big list of all the products that you have in your business and that are available on sale. It has all the different information that come with the products, and that is gonna feed it to the advertising creatives in an automated way, and all your campaigns are then running automatically based off of this product feed. So next up we will look at the different components that are needed to run Dynamic Product Ads.

And in easy words, everything that’s needed to run Dynamic Product Ads is a product catalog which is a list of all the products that are available in your eCommerce app.

It has to carry some specific information which we’ll go through later, but essentially think of it just as a long list of all the products that are available, together with all the information that belong to that specific product.

The second thing you need to make all of this personalized and targeted and – if you want to call it this – make it smart, is user signals. User signals is information about behavior of the user, essentially stuff like products that user would have already looked at, products that user would have already purchased.

Information like this which you need to run your Dynamic Product Ads in a smart way. And those two components together with the creative templates which are the design framework that you come up with, to show all of the different products, those elements are gonna form your Dynamic Product Ads. And next up we will go through the required elements in a bit of a greater detail. And if you break it down it’s basically five different components that are needed.

The first component is deep links, and deep links are like URLs on the web

It actually looks very similar, looks like a regular URL, and it’s a path that has information about where user should be redirected to inside of your app. So in this case it would be a specific product page, and that deep link would live behind the call to action of your Dynamic Product Ad, and then when a user clicks on it, he would get redirected to the correct product that he was looking at in your ad.

Deep links is something that your app developers have to define in the code of the app, so they have to go there and introduce a syntax, a logic for the app to understand how deep links work, and which kind of deep link triggers which page in the app, so it’s something your app developers have to do, make sure that it’s well documented and it’s aligned across all the different operating systems.

The second component is the product catalog itself

The easiest form of the product catalog is just a spreadsheet that has all the information about all available products in your business.

There are some requirements to how this product catalog has to look like, also depending on the platform that you’re using to run your retargeting campaigns. For example Facebook and Google have specific requirements on what you need to put in terms of columns in your product catalog. Information that has to be there for sure is stuff like the name of the product, the price of the product, if you have then something like a user rating for the product.

The deep link that we discussed before also has to be in that product catalog, so that the provider that you’re using to run the retargeting the campaigns in the end actually knows which page in the app the user should be redirected to. That’s a product catalog.

More sophisticated versions of the product catalog can be done in an automated fashion where you have some kind of feed going on of for example your own backend that has all the products and that’s then pushed automatically to the provider that runs your retargeting or Dynamic Product Ads.

The third component that you need for your Dynamic Product Ads is event and parameter measurement

So what that means is that you have to have information about the behavior of your users inside of your app. Without this you cannot run any targeted or personalized campaigns really.

That lives with your mobile measurement partner, and has to be integrated into the app as well by your developers. Important stuff to capture there is information about products that have been viewed by the user, products that have been purchased by the user, anything that you can think of that you would potentially use later on to fuel your Dynamic Product Ads campaigns should be captured as events and parameters that you measure inside of your app.

Component number four is the targeting strategy

The targeting strategy is something that you come up with from a marketing perspective. It’s basically your targeting plan of, this is the group of users that I want to target, and this is the common behavior that they all share. This is what you come up with in the targeting strategy.

Depending on the partner that you work with for your Dynamic Product Ads, they will probably also have some suggestions and there are some usual suspects when it comes to targeting strategy in eCommerce that is stuff like users have already purchased a specific product, and you want them to purchase something else. Or they have put something into the cart, but didn’t went to check out yet.

This is a target group abandoned basket that you will probably want to look after with your eCommerce advertising. And then the very last component number five is the creative templates, and the creative templates is really the design framework or the universal design that you want to use for all your dynamic product ads. It is something that has to be designed by your designer most likely, and it should be a kind of universal, not just background but also kind of just design language of how you want to display the different products that you have.

It can also be different depending on the category of products, so that got the more sophisticated versions, so for example all the shoes should have this or that kind of creative template, and then other products might have other templates.

Again, important to mention here is that there are external requirements to this, so if you want to run your Dynamic Product Ads on Facebook, there are specific requirements that you have to follow with your creative templates.

They have to fit the advertising formats that they are from. After going through the theoretical elements of the Dynamic Product Ads, we will now focus on the optimization.

The optimization of Dynamic Product Ads happens in a very similar fashion than any other online marketing campaign. It starts with the rollout, which is your first release of the campaign.

Then there’s the learning phase, which is you collecting data about user behavior, ad creative performance, and anything else that you can think of.

Generate learnings from that, and then apply it in the optimization phase which is where you come up with new design formats, maybe new targeting strategies, and roll that out again to kind of close the loop. And to make it a bit more practical, I’ve gathered three tips for you to follow when you want to run Dynamic Product Ads yourself, or you’re maybe just starting off with them.

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Pro tips

Tip number one is to use different creative formats depending on the advertising platform that you use to run your ads on.

What I mean by that is that you should not go out there with a generic design template, one resolution one aspect ratio and give that to all the different advertising partners that run your ads. What you rather should do is look at the requirements of formats they have on offer, so that you can make full use of them. And what you will see from that is that performance will just be much better.

Tip number two. You should keep an eye on unprofitable products in your Dynamic Product Ad campaigns. So you should be looking at the per product performance of your campaigns, and maintain a blacklist of products that are just not performing well. There are different reasons why that can happen. It can be that the product looks appealing on the ad, but then people are just not buying it, or a lot of other reasons.

What you should do is remove those products from the product catalog because they will just decrease your campaign performance overall.

And then tip number three. You should always be looking for a new user signals, or new targeting strategies that result from user signals of your dynamic product ads campaign.

To give you an example, you might end up realizing that users that have bought some winter shoes might be also prone to buying a winter jacket. So that’s the most basic form of recommendations. Not even talking about machine learning here, this is something much more sophisticated. But really just come up with new strategies of how you want to target your users based on user signals.

So the bottom line of this is that there’s an evolution going on in the role of paid Marketing Managers, so the responsibilities of a paid Marketing Managers are changing drastically from repetitive daily tasks which is essentially the Marketing Managers are going to some dashboard and manually adjusting the bids up and down to optimize the campaign performance, towards machines taking over that piece of the equation and the human beings focusing on the remaining parts of that.

And the two remaining parts here really are the creative side, so that is coming up with new creative templates, coming up with new creative formats that might be yielding a good performance in the campaign, and the second piece is the technical foundations or the measurement, the deep links, all of that has to be set up by a human being once so that the machine can actually do its job properly.

So in short Dynamic Product Ads are not a luxury they are a necessity. And you should get going with this rather soon to be part of that revolution.

So this was it for today guys. If you want to watch more MAMA boards, click on the link on the top here, and other than that, thanks a lot for tuning in today and see you soon.

The post Scaling eCommerce app marketing with dynamic product ads appeared first on AppsFlyer.

Welcome to another edition of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Sandra Wu, Paid Content Marketing Lead at Blinkist, a leading book-summarizing subscription service with million of users.

Sandra walks us through in detail what it takes to make Taboola and Outbrain drive scale and performance for your app. Their reach can be massive, but getting this channel to work is a challenge.

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Hi, everyone, welcome to another edition of MAMA Boards by AppsFlyer. My name is Sandra Wu. I’m the Paid Content Marketing Lead at Blinkist, which is an app that summarizes key insights, from nonfiction books. So you can read as many of them as you can. And today, I’m going to talk to you about one of my favorite topics, which is how to master Outbrain and Taboola to grow your app.

What exactly is Outbrain and Taboola? Well, you might not be familiar with their names.

But if you’ve read an article online, you’ve been exposed to one of their ads, they control the section underneath articles, under ‘you may like’ or ‘from around the world’. And if you click on any of these ads, you will leave the current website you’re on and go to an article, a landing page or possibly even the App Store. And the aim is to convert you while you’re in that reading mode. And right now, they have a portfolio of over 10,000 publishers, the likes of BBC, CNN, The Guardian, New York Times, so network is really vast.

Let me put it this way: If Facebook and Instagram gives you everyone who is active on social media, Outbrain and Taboola gives you those who are reading news online. And depending on the age group of your target audience, the latter might actually give you more reach.

One of the main reasons that I advocate going after these channels, is that it’s a really good way to diversify your marketing mix.

At Blinkist, about four or five years ago, we used to be really reliant on Facebook. And that’s no longer the case. This is our current marketing mix. And what you notice here is that it’s very evenly distributed across five or six big channels. And Outbrain and Taboola is pretty much on par with Facebook and Instagram.

Another reason that I would recommend going after these channels, is that there’s such high barriers to entry. And that’s because it’s so hard to get these channels right. I only know about a dozen advertisers who are able to make it work for performance, which may sound like it’s really daunting to tackle these channels.

But that also means is really not that competitive compared to Facebook or AdWords.

So why is this so difficult to get these channels working?

Well, the number one reason is that most people have the wrong expectations.

They’re thinking, I’m going to launch some campaigns on Outbrain and Taboola. And in about a week’s time, things will start working, and I can start thinking about actually scaling these channels. That’s really not the case. What you should be expecting it’s actually weeks or months of hard work.

Typically I would recommend that, in the first test budget around ten thousand, and then pick two or three articles, run a few campaigns for maybe two weeks, the numbers will be horrible, but that’s okay. Because you’re going to take these campaigns offline, and then you will try to figure out what exactly, is the weakest part of your funnel, and then figure out a few tests to actually improve these numbers.

When you’re ready, launch another set of campaigns, budget another 10K, and try to conclude some of these tests and see if you’re moving the needle at all with any of these tests. And then take these campaigns offline again, do some analysis and you just basically repeat this process for another few weeks or months.

For us, actually that took about six months before we actually saw positive results. This chart here describes what the first 14 months at Blinkist was. For us when we launched Outbrain and Taboola.

The green line represents ROI, which is our metric to optimize towards, and the blue bar is Spend. And what you can see in the first six month is that performance was really relatively flat and numbers didn’t look great at all. And in the seventh month, actually, things are looking a little bit better.

But it wasn’t until about like a year, after we launched the first campaign, where we started catching up to Facebook. So it did take a while. So just make sure you have the right expectations. This is not going to work overnight, but that’s okay.

The second reason why it’s so hard to get these channels right, is that most people don’t advertise the right content. So with Blinkist all of our ads lead to our magazine, so we use this content marketing flow. And a lot of apps out there do this as well. However, most of these apps would just think about recycling whatever works on Facebook.

They’re thinking, “Oh, these taglines work really well on Facebook, let me just turn that into an article.”

And that’s really the wrong approach, because Outbrain and Taboola and Facebook, they’re completely different animals.

So with Facebook, there’s really advanced targeting, like lookalike, or interest targeting. So you don’t necessarily have to get your message perfect, you’ll still reach the right people. On the other hand, with Outbrain and Taboola, the targeting is very broad. So you gonna make sure that your headline attracts exactly the right people. So I would say just forget whatever creative you have on hand, that works for other channels, and just start from scratch and think about what headlines and article angles would actually work for you.

And there are some questions you can ask, like, “Why does your app exist?” or “What is the mission that you’re trying to achieve?” or possibly even, “Why should people pick you over the competition?” so these are really good questions to ask to get you thinking about what headlines will actually work with your target audience.

And I think everything will be quite easy from there.

The third reason why it’s so hard, to get these channels working, is that most people just don’t make the right conclusions. Remember when I said the first campaigns were supposed to be disastrous. Well, most people don’t take that as a challenge. They’re more thinking, wow, these numbers are so horrible. I don’t wanna have anything to do with these channels. They just don’t work for my product. And that’s a mistake, because they’re supposed to figure out what exactly went wrong.

Identifying the problem

So you have the funnel here where people will click on Outbrain and Taboola ad, they will land in the magazine, and then off to the App Store.

Which part is the problem?

Is it the CTR, or the signup rate, or possibly something else like the purchase rate. With the CTR it’s easy to influence. So you just got to figure out if the titles and images you use were good enough. If not, you can think about including different buzz words, maybe thinking about different article angles you can try.

And with images, you can think about black and white, maybe including food or animals in your photos, or depending on the season maybe you want to consider outdoor or indoor images.

If it’s a signup rate that, you’re struggling with, then you got to think about doing everything you can to make sure that the article converts the best possible. So this is where we focus, most of our efforts at Blinkist on. And we do things like evaluating everything, we’ve written in the article, and thinking about how we can actually add more value. To convince the reader to be able to convert more.

We also think about different layouts so we can increase, we can improve the user experience, for example. We also have tried a lot of tests, on just working on our call to actions. I will probably not focus too much on trying to convert people at every opportunity, because this is a reading experience. So this is not a banner ad, where you would have multiple buttons to trick people into clicking. And you can also think about maybe different designs, so that people would perceive your product in a more positive way.

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If something else is the problem, like purchase rate, that’s something that you might want to tackle with the rest of your team, and it might not be unique to Outbrain and Taboola.

So do you also struggle with this with Facebook?

If that’s the case, then you want to figure out, maybe you want to try different pricing models, or a different onboarding experience inside the app. But if you want some benchmarks to work with, I’ve had a lot of experience working with different companies.

And I would say that the benchmarks, you can, probably compare it to Facebook. So I would say the signup rate is around 10% lower compared to Facebook, because there’s an extra step in the funnel which is the article, but that’s okay, because it pays off in the purchase rate which will be around 20% higher.

Why?

Mainly because people have read 800 words about your product, they’re more convinced that they should actually try out the whole thing. Versus if people just go straight to the App Store and then they convert there.

Bottom line

So the bottom line is, these channels are not easy to get right. You’re gonna have to do a lot of testing over several weeks, even months, but that’s okay. Because once you do get it right, it’s going to pay off big.

You’ll see that a lot of people, recognize your brand because they see it everywhere. And you might actually be able to use a lot of the content on other channels and help them expand as well. So this really is the gift that keeps on giving. That’s it for today. For more MAMA Boards just click here. Thank you for watching and goodbye. So good it’s done.

The post How to master Taboola and Outbrain to drive app growth appeared first on AppsFlyer.

Welcome to another edition of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Yury Bolotkin, Growth Specialist at Popcore, a leading creator of Hyper Casual games.

Yury dives into how Popcore automates creatives and campaigns, specifically their management and bidding. He also reveals what kinds of tools they use to make the process more efficient. There’s plenty to gain from automating some of your processes so you can focus on things that matter and require humans!

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Hello, welcome to another edition of MAMA Boards by AppsFlyer.

I’m Yury, growth specialist at Popcore. Popcore is a Berlin-based game developer creating beautiful, oddly-satisfying, hyper casual games. And today we’re going to talk about marketing automation.

Before I start, I would like to mention that there are a number of tools that you need to have in order to achieve that. I will get down to the tools a bit later.

And now, we come to the first point, which is, “What can you automate?”

Here at Popcore, we decided we can focus on two things, which are creatives and campaigns. And campaigns are also broken down by management and bidding in the campaigns.

Okay, let’s go to creatives automation.

As you all know, iterating on creatives is a long process. It’s basically an infinite loop. So when our creative team comes up with an idea, and then produces video content, lots of video content, we want to run a test. And to do that, we ask our script to upload everything to our ad network that we select. And we run the test, and overnight, we get the results, which we can communicate back to our creative team, or discuss internally to find out who are the winners or who are the losers in our test. And therefore, when we got the results, we want the best of the best results to be implemented into our live campaigns.

That’s how we’ll close the loop, and that’s how this circles starts all over again.

Why do we do this? Easy, just in one week, our creative team can produce hundreds of video creatives. And we need to test all of them and select the best ones.

And also, to upload all of this, even winning creatives, to our live campaigns, we will spend hours. And with the help of a script, we can do it in just ten minutes.

Now let’s talk about the second part, which is campaign automation. In this part, I’m going to talk about the script, which also can be used in the first part, for example, refresh your best-performing creatives in your live campaigns.

So campaign automation consists of two things: campaign management and campaign bidding. In campaign management, we mean at Popcore the set of rules that we run using our in-house developed tools or some third-party tools that are pretty simple, actually.

For example, if your day one retention hits a certain threshold and your count of installs is more than a certain amount, so you make sure you have enough figures to make a decision, then you do your decision. You change your bids, you change your caps. As a vivid example, Google Ads, a network that requires simultaneously hundred and hundreds of campaigns running, when for example we scale our new title.

To keep all of these campaigns aligned, and doing what we want from them, we run these simple rules on top of our campaigns to keep them intact.

For example, we limit their bids, we increase their bids, we limit their daily caps, or we increase daily caps. Campaign bidding using scripts is done using a small piece of code written in Python with the help of our data science team. So, as an input, we use our export from a network of choice, an export of campaign structure. And a second input is an export from our statistics, from BI or any reporting tool that we use. Combining those two data, we have a third file as an output that can be used to upload to the same network, to change your campaign structure, your bids, your creatives.

As an example, we use as an input old campaign structure using old creatives, and then we have a list of new creatives that we tested and that we know that will work even better than the previous ones.

Combining that with a Python script, we can produce a wholly new campaign structure using new creatives and upload them in just about several minutes to our network of choice. That saves us a lot and a lot of time every week. And also, we can manage hundreds and hundreds of line items that are currently live in all of our campaigns.

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So now, the tools.

Which tools we had to have in order to do this automation at Popcore?

So first, we had our BI system or reporting. Of course, it’s basic.

Then, our media partners that support CSV file export/import or API management tools. The third part is a bare minimum of Python knowledge, which, in our case, was solved by our data science team. And a backend engineer helped us with more advanced stuff. For example, what can be advanced stuff is uploading creatives into sophisticated systems.

For example, Facebook requires some API backend work to move your files from, let’s say, Google drive into their system. A Python script won’t help you there.

What are the key takeaways here? First of all, define your bottlenecks and low-hanging fruits. At Popcore, we realized that we spent a lot of time uploading and testing creatives for our campaigns.

Therefore, we decided to automate it. Then, draft your first “hacky” MVP, which is a quick-and-dirty solution to prove your concept. Once you realize the concept works, and you’re comfortable using it, then you go down to productionalize it so more users can access it and you can scale your product.

I was talking about marketing automation here, but if there is only one thing that you can take away from this presentation is, saving your time. So when you do your routines, they can be boring and also time-consuming.

But automating things can save your time and time of your company to do things that are more exciting and that may evolve into something more exciting. So, automate things, please. All right, that’s it for today.

Thanks for watching, I hope you enjoyed.

Bye.

The post Making user acquisition work in the age of automation appeared first on AppsFlyer.

Welcome to Edition 34 of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Volkmar Reinerth, Marketing Director at Kolibri Games, a mobile game studio aspiring to be the most player-oriented gaming company in the world.

Few verticals are as savvy about ad monetization as Gaming apps, but how do they choose which ad formats and placements to actually serve users? Volkmar steps in with the answers, deep diving into the top 3 formats for Facebook ads and, particularly, how to leverage playable ads for real revenue. Let the games begin!

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Hi, welcome to MAMA Boards by AppsFlyer. My name is Volkmar, I’m the Marketing Director at Kolibri Games, and I’ve been leading our UA team for three years now. Kolibri Games is a mobile game studio based in Berlin. Our aspiration is to be the most player-oriented games company in the world.

Today, I will be talking about Facebook ad formats, and how to leverage the power of playable ads.

Before I start, please remember that there’s no magic way to master Facebook ads; it will always involve lots of testing and iterating. However, I hope I can give some guidance on which ad formats are worth spending time on, and which ad set strategies can help.

On which combination of Facebook ad formats and placements should I focus? 

On your official Facebook ads guide, there are 11 ad formats, and as a fun fact, playables are not even in there. This can either mean that they are not proven to be successful yet, or are still a hidden treasure. Today, I will focus on my main three, which are images, videos, and playables. I think they go hand in hand with the placements; for example, images don’t make sense for rewarded video ads.

So, starting with images, as you all know, they are easy to produce, flexible to use, and can still have a great conversion rate. What I think is great about images is that they can be more detached from the actual game play, and that you can use them for creative testing.

For example, testing a set of images with one character versus another character, and unrelated to the exact winning creative. You can see which concept works better as a whole.

So in summary, I would focus 20% of my time on images, and put a special focus on the Facebook news feed.

Videos are the next step up. They are a bit more costly to produce, but you can actually keep them simple – a super short, three-second GIF can work equally well as a longer video, and you can work with screen recordings, which is not a lot of work at all. I would start with square videos – they work in almost all placements, and they are very flexible. If you have more resources, then you can move on to tailor placement-specific iterations of your creatives. But I would start with square videos first.

When you make videos, make sure you have a very catchy first second, especially for skippable placements like audience network interstitial or the Facebook news feed. Make sure the first second is very interesting and engaging. If you have a long skippable placement, like Facebook audience network rewarded, you have a bit more time and can work with story-driven ads, or show real game-play.

In summary, I would focus 35% of my time on videos. I think videos are great, make sure you have them in your creative mix, and put a special focus to the audience network rewarded.

Now moving on to the most interesting ad format of this talk, which are playable ads.

I think playable ads are great, although they are as high-risk as they are high-reward. Realize that they do come with a lot of technical challenges, like bad loading times, crashes, all of which can lead to bad user experience. Make sure you tackle this with good internal QA processes.

Treat every playable like a project all by itself – you often need external resources, which require a lot of communication and is definitely not easy. But in the end, it’s all worth it because, once you actually have the playable ready, players will appreciate that there’s honest gameplay. As in, if they go to the app store, and download from there, they will be more likely to know what your product is about after experiencing the ad. This leads to a good store conversion rate, and also a good day-one retention, because you advertised for actually what your product is about.

Secondly, once you actually have a playable produced, you can tweak little parameters, which can have a huge impact, like the gameplay speed, the background, or the characters. All of this is quite easy to do, and can have a good impact on your conversion rate.

What are some best practices for creating Facebook playable ads for Gaming apps? 

When you make playable ads, focus on some do’s.

Firstly, you must include a clickable CTA all the time. Facebook checks for it, make sure you have a button in there all the time.

Secondly, focus on gameplay. It might be challenging to put a lot of complex gameplay into an abstract 20-30 seconds ad, but you can focus, for example, on a core loop, or on certain features of your game. Just make sure it’s real gameplay. Players will appreciate these honest ads, and it will result in a higher conversion rate. Even though clickbait can be a valid strategy, I think gameplay ads are definitely the way to go. So, playables are great, make sure you have them in your mix, don’t miss out on those.

In summary, I would focus most of my time, relatively speaking 45%, on playables, putting a special focus on rewarded video ads. That’s because, with those ads, you have the time, you have 20-30 seconds to actually show what your game is about.

What kind of Facebook ad set strategy should I use? 

Moving on to the ad set strategy.

If you look at this table, it looks like there are a lot of placement ad format combinations that you should tackle, but actually, it’s not that much work. You don’t have to look at them individually, you can make use of the Facebook auto-placement feature, which is the first strategy.

Here, I would recommend to use five to eight ads of mixed types; for example, two images, two videos, two playables, putting them all together in one ad set. After that, select the auto-placement option. Facebook will decide to which placement and creative combination the spend goes to, and you don’t have to worry much about it. This can work really well on large-scale campaigns with little targeting, set to post-event optimization. It can also be great for a beginning where you just want to see which placements or creatives work well for you.

It’s hard to fit all the ads for all placements. Like I mentioned before, square videos can work well on most of the placements.

However, if you use playables, for example, it can be more challenging to tailor them to placements like audience network rewarded or interstitials or stories. And, what can happen as well is that spend gets allocated to only one winning creative, or a single winning placement. This in turn means you’re not making full use of your potential here, because only one of the placements is actually used.

In this case, I would recommend to go to the second strategy, which is to take control yourself. Here you would take a specific placement that works well, or that you want to focus on, and split it out into its own ad set, using only placement-specific ads. For example, if you want to focus on Facebook audience network rewarded, you can make full use of rewarded video playables.

This approach is great for testing and iterating on creatives because it’s very focused. You can really see which creatives work well for the specific placement. And since it is a very focused production, you don’t need to worry about too many requirements, yet you still have full control over the budget and other KPIs that you can monitor for your placement.

Both strategies are valid, and have their pros and cons. I think they can also work well together, so it’s not an either-or; you can simultaneously have some ad sets on auto-placement while others are on manual placement. And, they can go hand in hand, just make sure they don’t overlap too much.

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What is the future of Facebook ad monetization? 

What will the future bring? I think Facebook has a very unique market position. Facebook has massive data and can find users across multiple placements, like discussed in the autopilot strategy. I would expect to see much more features like this in the future, like, for example, Campaign Budget Optimization (CBO).

On one hand, yes, it can mean that you’re losing some control over your campaigns. However, Facebook can make real use of its data, and find users, for example, across multiple placements, or allocate budgets efficiently so there’s not much audience overlap. So, this is a good trend that’s happening in general, because it saves work for you, and it can make the whole advertising ecosystem more efficient from a market perspective.

The second part of my predictions concerns creatives themselves. Right now, it’s about the battle of eyeballs, how to stick out and get attention. Even though clickbait can be a valid strategy, if that works for you, I would still go for a more holistic, and long-term approach, and which I think is gameplay ads. Your audience will appreciate seeing honest gameplay ads, and what your product is actually about, which can lead to a very good user experience, and a good user quality in the end as well.

So I would definitely go for that approach – it’s more long-term, it’s more user-centric – but whatever works for you. However you win the battle of eyeballs, it’s ultimately up to you.

So that’s it for today, If you want to see more MAMA Boards, click here. Thank you for watching. Goodbye.

The post How mobile games can leverage playable ads on Facebook appeared first on AppsFlyer.

Welcome to Edition 33 of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera. For today’s mini whiteboard master class, we have Inga Teiserskyte, Product Manager at Onefootball, a football media company covering over 100 international football leagues and competitions.

Many marketers are already familiar with successful ad monetization in the Gaming vertical, but what about for the others? Inga walks us through this non-linear process, and how we can leverage audience segmentation and diverse ad formats, among other tools, to make a profit. Let the money flow! 

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Hello and welcome to the new edition of MAMA Boards by AppsFlyer. My name is Inga and I’m from Onefootball, which is a platform about all things football. Mobile advertising has often been analyzed from the gaming perspective; however, today I want to share how Non-Gaming apps can benefit from serving advertising.

How does a Gaming app user journey differ from a Non-Gaming app user journey? 

So let’s take a look at the user journey. While Gaming apps usually have a linear user journey that usually starts at level 1 and then continues to levels 2, 5, and 200, Non-Gaming apps usually look a little bit more like trees with all kinds of directions. This means that if you have a group C, which is 50K daily active users, and group E, which is 350K users, even though they are in the same app, they might be overlapping only by 5%.

So how do we solve this? The best way is to use user segmentation and multiple ad formats in different placements.

What should I consider when building a Non-Gaming monetization business case?

So what else to consider when building your business case?

First, geographical split for the users is really important when it comes to the CPM levels, since they can vary extremely. For example, in the US and LATAM, it can be as high as 5 to 1.

Second, if you have a huge group of users in a certain region, you can solve it by working with the local demand partners who have the expertise and relationships in the region with the media agencies. Don’t forget that user privacy acts add additional complexity into the story.

Third, additional demand can be added from working with the brands directly since Non-Gaming apps usually have really valuable demographic data. However, you have to remember that this adds additional internal costs such as ad ops and accounting, as well as a need for additional ad server capabilities.

What are the best ad formats for Non-Gaming apps? 

So let’s take a look into the best ad formats for Non-Gaming apps.

To start, full screen interstitials require finding the right moment in your user journey; however, they have a huge global demand and competitive CPM.

Next, banner ads might decrease your user experience. On the other hand, they’re easy to integrate and have huge global demand, especially in emerging markets where other ad formats do not have it.

Third, native ads usually look really great. You can adjust the size according to your app and content. However, they have low demand, especially in the markets where banner ads do not face this issue.

Finally, rewarded videos are often used in Gaming apps. However, Non-Gaming apps have to find either cheap in-app purchases or other rewards to use them, aiming for 30, 50 percent engagement rate. The biggest benefit are their global demand and very high CPM.

How do I calculate potential profit from monetization? 

So how do we calculate all the potential and all the money you can make?

First of all, please split your daily active users into groups and segments. For example, if you have a lot of different countries and none of them are reaching 10 or 20%, just group them into the language groups. Let’s say English-speaking markets and Western European can be together.

Second, get their realistic CPM expectations from your demand sources and, best-case scenario, take a little window so you are on the right path.

Third, don’t forget the engagement rate if you are using rewarded ad format such as rewarded video. If you were expecting engagement to be between 30 and 50%, take 30, 40, and 50 percent just to be sure that it’s actually according to your expectations.

Example

Now let’s take a look at an example for the interstitials. Let’s assume your app has 250,000 daily active users: 100,000 of them are living in US; 50,000 are in France; and the rest are emerging markets in the rest of the world.

We got their CPM expectations and we calculated the potential revenue.

In the US, we are expecting to reach $13; in France, $5.5; and in the rest of the world, $2.5. This means that if we assume that 50% of other users are reaching the point where they see the interstitial and they see 1.2 of them per day, $21 would be our revenue potential from interstitials per day.

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Conclusion

It’s definitely often a very complex task to bring all of these points together to find the right way to serve ads inside your app without sacrificing user experience. However, this high-margin revenue stream is definitely worth your effort.

That’s it for today. For more of MAMA Boards, please click here, and thanks for watching.

Bye.

The post The other side of the coin: Monetization for non-gaming apps appeared first on AppsFlyer.

Welcome to Edition 32 of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Jegor Ivanov, Growth Product Manager at Jodel, one of the largest European social networks with millions of active users.

Jegor gives us a crash course on launching massive product growth through virality, including the minimum tech required and the complete ideation, scoring, and monitoring processes. He suggests that better prep will lead to earlier execution and, ultimately, more successful product traction. Ready for your viral launch? 

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Hi everyone, and welcome to the new edition of MAMA Boards by AppsFlyer. My name is Jegor and I’m a growth product manager at Jodel, one of the largest European social networks with millions of active users. Today, we’re gonna talk about the product experimentation process for viral growth, as told by the product manager and formerly a marketer. 

What is viral growth?

So what is viral growth anyway? For a social product like Jodel, most new users are coming from existing users, especially in existing markets. In short, what this means is that more users on the platform generate more content, which means more content being shared, and then, finally, the new users that are checking out that content are hopefully downloading the app as well. 

We have two possible growth levers around virality in Jodel currently. One is the user-generated content that I mentioned already. The second one is referrals. Basically, the users invite other users and they’re credited with karma, or internal currency, for it. 

Now, the difference between linear channels, like paid user acquisition and viral growth, is that the viral growth self perpetuates. If one user invites another user, or more, your product is bound to grow. The paid user acquisition can be complementary to viral growth because if you pay for a user but this user invites another one, then you’re better off with the ROI. 

What is the minimum tech stack I can have?

Finally, it’s not entirely in the domain of the product people to run experiments, right? If you’re a marketer that is aiming to switch over to product, or perhaps your product team is small, but the marketing team has got some power to support, you may use the following tech stack in order to start iterating and experimenting with viral growth. 

First and foremost, you’ll need an experiment framework. You want to trust the results of the data of the experiment. That means you need to assign randomly into two groups so that you can compare the results of the experiment. 

Second, no less importantly, you need an attribution partner. One thing to measure are the shares inside of the app and the recipients getting the links, but what you care about the most are the installs at the end. Without the attribution partner, you can’t measure this. 

Finally, all of these growth hacks and optimizing a viral loop should not get in the way of the retention and engagement of your existing users. For that, you want to be able to measure user performance in-app and on the web in case of the user-generated content flow. 

How can I find a good idea for a viral growth loop? 

How do you come up with a good idea for a viral growth experiment? Truth be told, unless somebody has been practically looking after virality in your product, the data can probably tell you quite a bit. 

If you fetch the data for all the funnels that are involved in the user-generated content flow, specifically how many people are sharing outside of the app; how many people are receiving those links and clicking on them; and how many people get into the store and download the app, you’ll probably learn quite enough and would be able to find the strongest lever out there. If you benchmark these numbers against the industry standard, you would probably find out that one of the sections of the flow is easier to optimize than the others. 

Remember, you want to take note of the fact that not every stage of the user-generated content viral loop can be optimized. For instance, you can’t select which messenger the user will use, but you can optimize the share snippet. We’ve all seen those large share URLs with a lot of UTM parameters and nobody really wants to click on them. So if this was substituted with a nice little share snippet with an image and a preview of the landing page, that could also be an idea for you to grow through virality. 

What else does the ideation process involve? 

The second part of ideation comes down to people. One group of the people involved is your users. I wouldn’t recommend asking your users for the solutions, but the users can often point out the problems in the flow. Even when it comes to sharing content (in the case that sharing is not working), they might as well tell you. Therefore, talk to users proactively and try to identify the problems, especially in the viral growth loops. 

The second group involved in ideation is the team, specifically the customer care people. They do hear from the users a lot and even when it comes to virality, they may have a good idea or two on how you could generate more invites. Maybe there’s something the users are interested in receiving and this could be the incentivized referral idea for you. 

The third group involved is actually the competition. If you use the rival app for one week with all the notifications on, with all the channels on, you would probably learn a thing or two and maybe there is a good nugget of a campaign idea that you could implement in your flow as a result. 

Are there any models to help illustrate viral ideation? 

Moving more to the theoretical now, there are a few models out there within behavioral economics that might help you structure your thinking around triggering users to perform certain actions. 

The first model I would like to briefly introduce is the Fogg Behavior Model. This line here [on the chart] is the action line. Basically, in order for the action to occur, Fogg suggests that there needs to be motivation, or how psyched users are to actually complete the action. There also needs to be ability. The user needs to know how exactly to share from your app, for instance. And then, there needs to be a timely trigger. If all three of those come together, then the behavior happens. If not, then it doesn’t. 

Now, how does this process help you think about ideation? If the user is low in motivation, you may think of priming the user so that they understand why this action is important, thus moving them to the action line. If the user is low on ability, they may not know how to share out of your app. Perhaps a little tooltip explaining how the share function works in the app could move them over to the action line, again yielding the action for you. 

Another interesting model that helps one ideate around product growth is the hook model. It suggests that once the user is triggered and completes the action, the user needs to be rewarded for the variable amount. 

At Jodel, we ran one experiment around variable rewards specifically, where we changed the amount of karma that we assigned for each successful invite from a static to a random one, and ended up lifting the numbers significantly. Thus, the user traveling through this hook establishes a habit. The model suggests that such a habit is at the core of healthy growth, as opposed to just hacking it together. 

What role do scoring and prioritization play in executing your idea? 

Okay, so how does one move from ideation to execution? The answer is, you really want to know what your first priority is, probably because you’re strapped on resources or aren’t the product manager yet. 

Now, you want to avoid bias, because everybody thinks their idea is the best, and this is what you want to cut out. Invite those four or five relevant stakeholders, perhaps the people that have also fed ideas into your flow. Something that we used for scoring in the past is the R.I.C.E model. The model is super simplistic. You basically just score all of the ideas that are sourced on a scale of one to five based on four components: reach, impact, confidence, and effort. 

The output of the model is the sorted list of all ideas that sets the ground for a healthy, unbiased, or hopefully, less biased discussion around the ideas. As a result, you may even clarify a single idea that is ready to bring over to the product team. In my experience with product development, coming more prepared usually yields a much higher chance of development. I don’t recommend involving more than four or five people that are already invested with this idea. That makes it more powerful in the organization. 

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Summary

So, what have we covered here? What’s the bottom line? First and foremost, viral loops can be a source of free new users for you, so you may want to deep dive into that if you have the resources. 

Second, by looking at your funnels that are involved in the viral loops, you may have just found the next great idea for a future experiment. 

Next, you want to involve multiple stakeholders in the team, because their investment in the process means that it’s more likely to happen. You want to be unbiased and use the scoring technique so that you know that everyone is in agreement on what the next idea is. 

Finally, and I couldn’t highlight it more, the more preparation that goes into product development, the more likely it is to be executed soon. We’ve all probably worked with that one engineer or product manager that told you off for wasting their time by being unprepared with your request. So hopefully with this information here, you’re better prepared to ship your first viral growth experiment, or any experiment on the product. 

And that’s it for today. If you have further questions or comments, feel free to tweet at me.

The post Product growth through virality: A product marketer’s take appeared first on AppsFlyer.

Welcome to Edition 31 of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Andy Carvell, the co-founder at Phiture, a mobile growth consultancy that helps apps to grow, and focus on acquisition and user retention. 

Andy shares how to improve retention using attribution data, and how to create a tailor-made on-boarding experience, achieve cross-team alignment and ultimately improve user loyalty.

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

So what exactly is retention? It’s the percentage of users who are actually coming back and using your app again and again in a given time frame, or in a succession of time frames. And healthy retention is really the cornerstone of growth. If you don’t have that retention in place, that at least some percentage of the users are sticking around over the long term, you’re gonna find it really hard to grow your app sustainably over time. 

One of the problems that I often see is what I call a silo mentality, so this is what I’m showing here, where we have, for example, a brand team, user acquisition team, and a product team who are not really aligned and not really talking to each other. 

What can happen here is that you get a situation where you get what I call a disconnect, where the user expectation is not clearly aligned and you’re not delivering clearly on that user expectation through these different functions of your company. 

So the brand is maybe setting a certain expectation, certain message, user acquisition is then advertising, trying to get installs, maybe at a cheap price, which is maybe optimizing for that price, and maybe adjusting the message in order to get that price. And then in the product, it’s also delivering a certain experience, which maybe is not aligned with the expectation the brand was setting and the expectation of the adverts and the app store itself. So if you don’t have these things in alignment, this is where you can really start to have problems with your retention. 

Retention starts with acquisition

So retention starts with acquisition.

The user expectations are gonna be set from the very first touchpoint they have, if they see an advert for the app and it’s advertising certain features, the user’s gonna expect those features or benefits when they come into the app. 

And if they’re not there, this is where you have a disconnect, and it’s gonna be very hard to retain those users. So it’s not really helpful for the user acquisition team to say, well, retention is a product’s problem. It’s actually everybody’s responsibility to have good retention. Here’s an example from a PDF app. 

Let’s say you have an app which opens and allows you to view and maybe edit PDF files.

Hopefully, that’s made clear in the advertising, that you’re advertising these benefits. Hopefully, that’s clear from the app store presence, from the screenshots, from the description. And hopefully, that’s clear when, in the first use of the app, the user opens it up, and they can actually find that they can edit and open PDF files. And if the user opens the app, having installed something which they expect to open PDF files, and they find it can only open word documents.

Then you’ve got this kind of disconnect where it’s gonna be very hard to retain those users. That’s a pretty extreme example, but my point is that you need to really carefully align the product experience with the expectations that you’re setting in the advertising and in the app store. 

Measuring retention

So how do we measure retention? Well, the first thing to understand is that you need to be measuring every user session in order to actually do this kind of analysis. If you were using a commercial package like an attribution provider or an analytics solution, this is gonna be measured automatically by the SDK.

If you’re doing your own in-house analytics, you need to make sure that you’re recording every session that every user has, so that you have those timestamps to refer back to when doing this analysis. 

Let’s look at some of the common visualizations for retention.

What we have here is a cohort table. A cohort, in this case, is a group of users who’ve had their first session on a given day. That’s what we see on the left there. And in the columns going through on the right, we see what percentage of users were retained on each of the subsequent periods.

In this case, it’s showing daily retention, so we see day zero, actually, 100% of users are always there on day zero, because that’s the day which they were first seen in the app.

Day one, it’s looking at what percentage of that cohort actually came back and were seen in the app, and had a session on day one.

Day two, et cetera. In this example, we’ve just used shading, often a cohort table has shading, but also a percentage number which shows the exact percentage of users who came back from that cohort. More intense shading means higher retention, less intense, lower retention. 

We can read the cohort table two ways:

• One is scanning across from one particular line, which shows how well that particular cohort was retained.
• We can also scan down the columns.

So if we look at, for example, day one retention, it’s usually a fairly critical number which companies want to improve, that’s showing the trend in day one retention and how it’s evolving over time. Ideally, it’s improving over time. Which would indicate an increasing level of product-market fit, which could be achieved by different marketing activities, which are bringing better users in, or improvements in the product, or some combination? 

So let’s look at retention curves.

A retention curve is essentially a plot of a blended average retention for your entire user base over a particular period. What we see here is we have two examples.

• The blue line, showing very healthy retention. It levels off at a particular point, showing that, actually, a decent percentage of users are sticking around over the long term.
• The red line is actually more typical. It’s showing that actually it’s trending to zero over time, which means that, for any particular cohort, there’ll be basically none of that cohort left if you look at a long enough time horizon.

This is unsustainable growth, but it’s often masked by throwing lots of new users in so that the active user number never actually dwindles to zero. But if your retention curve looks like this one, the red line, eventually, if you stop acquisition, you’re not gonna have any users left. 

Finally the stacked retention graphs.

This is essentially another way of looking at, and best visualizing, how monthly active users are affected by retention. So here on the y-axis, we’re actually looking at monthly active users. And what we see here is essentially a bunch of stacked retention curves, each of these representing one cohort. You can think about this very much like the curves that we see here above. Each of these cohorts is showing how it’s contributing to monthly active users.

The example on the left, these cohorts are trending to zero, so it’s kind of like the red line example above. And so it gets harder and harder to sustain monthly active user growth, and it starts to level off because these cohorts are dwindling to zero, it doesn’t matter really how many more you throw on top, it’s very hard to sustain the growth. 

On the right, we have a much healthier example. These curves are flattening out, which means that some percentage of users are sticking around over the long term. So as you acquire more and more cohorts, this is slowly building and building and building on your monthly active user number. 

How to influence and improve retention?

So let’s bring all this together.

• We’ve understood why retention is important, and some of the common pitfalls.
• We understand how to measure it, so how do we actually influence it and improve it.

Well, it’s a tricky topic, and it’s a big topic. Everything can improve retention, or it can influence retention, including external factors such as competition, as well as the marketing and user acquisition activities that you do, as well as anything that’s going on in the product. So there’s a lot of inputs to retention, and retention is an output metric. 

Let’s focus on the actual input levers which I think are most easy to actually tackle. That would be new user activation, existing user engagement, and reactivation. So new user activation, this is how well you can actually get people to experience the value of the product in their first session, and get them activated such that they’re likely to actually come back and have more sessions within the app. So this is all about new users. 

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Existing user engagement

How well are you able to keep users engaged over the long term. This might be through releasing new content or providing other mechanisms that keep users engaged and they keep finding value, not just in the first session but time after time. 

And finally, reactivation.

Are you able to bring users who’ve lapsed or churned back into the app, maybe with a special offer, or some new information which reinvigorates their desire to use the app and to come back again? And if you’re able to improve any one of these, retention’s likely to increase, albeit slowly, it’s a lagging indicator. 

New user activation is usually where you can see the results quickest, and it’s usually where there’s the most upside, which is why a lot of people focus on that. So I would really advise working cross-functionally, both to get that alignment of your marketing message with your first-time user experience to improve your activation rate, and also to maybe consider doing what I call adaptive onboarding, which is to provide a slightly tailored or personalized onboarding experience based on the acquisition channel, or the acquisition campaign, that the user came in on.

Using your attribution data, you can actually identify which campaign a user came in with, maybe even what creative they clicked on, and then provide a very tailored onboarding experience that really speaks to that message that they saw in the advert that they clicked on. This is the ultimate in achieving that alignment. 

So my final message really is that improving retention is tough, it takes a lot of work, but it’s totally worth it. And to work cross-functionally in order to achieve that. That’s it for today.

The post Mobile app retention: A mobile marketer’s primer appeared first on AppsFlyer.

Welcome to the twenty eighth edition of MAMA Boards, an AppsFlyer video project featuring leading mobile marketing experts on camera.

For today’s mini whiteboard master class, we have Jean-Sebastien Laverge, VP of Data & Growth at Tilting Point, a free-to-play games publisher which helps independent game developers scale their games.

Despite a 51% lift in the average revenue per paying user in Gaming apps for those that ran remarketing campaigns, only a mere 15% of them actually do! To bring greater awareness to this mission-critical marketing tactic, Jean-Sebastien discusses the core pillars of running powerful Gaming retargeting campaigns, as well as the 3 key elements that make them successful. 

Real experts, real growth. That’s our motto.

Enjoy!

Transcription

Welcome to another edition of MAMA Boards by AppsFlyer.

I am Jean-Sebastien Laverge, VP of Data and Growth at Tilting Point.

Tilting Point is a free-to-play games publisher which helps independent game developers scale up their games. Today, we’re going to talk about “How Retargeting Can Drive Success for Your Mobile Games” and how adding retargeting to your re-engagement campaigns can help you boost your retention. 

According to AppsFlyer data, only 15% of mobile games run retargeting campaigns compared to 50-60 percent of eCommerce apps.

Why is this common practice in eCommerce not used in mobile gaming? 

Today, we’re going to discuss how to run retargeting and how you can achieve great return on ad spend (ROAS) and boost your retention.

What are the core pillars of Gaming retargeting campaigns? 

We’re going to start with the core pillars of our retargeting campaigns. First and foremost, it starts with dynamic segmentation. You want to build segments of not only your lapsed users, but also your most engaged users; your paying or non-paying users; your mega whales or small minnows. What matters is building dynamic segmentation, meaning that your segments are alive and players enter and exit the segments as they take action inside the game or they re-engage with your game. 

For example, every hour, you would like segments of lapsed players that didn’t play your game for 7 days, 14 days, 30 days, or even two or three months. It depends on the games you’re working on. It all comes down to the segments you build with your data science team and the proportion of your users to play and engage with your games. 

Second, once you have your segments, you want to build an engaging player journey. When a user starts to play your game, depending on the conditions, they will enter campaign one or campaign two. It all depends on what they do inside the game or not do inside the game. In either case, what matters is that you build a coherent experience for your users, so you should propose an exciting and engaging journey/experience to them. 

Lastly, make sure that you reiterate your re-engagement loop, as players are going to go through your re-engagement and retargeting loops multiple times. You want to make sure you vary your messages to make them more efficient. 

What are the main communication channels used for retargeting? 

Next, once you’ve built your segments and your player journey, you’re going to look at your communication channels. We use push notifications, email, and ads, and on each of them, we A/B test the messaging to make sure they run at maximum efficiency. The main challenge that we’ve been facing is linking our dynamic segments in real-time to each communication channel. 

In order to achieve this, we build our segments in-house and push them to each tool we use to make sure they communicate the best message to the user. In turn, this allows us to vary the order of the messaging and always use the best message or the best timing to retarget and re-engage users with an ad.

Which messaging is most effective in bringing user’s back to your game? 

Once you’ve settled your communication channels, you’re going to work on your messaging and what you actually tell your players to drive them to return to the game. This can range from a simple call to action, “Come back and play now,” to a personalized message, “Ajax 67, you’re getting raid, come back and defend yourself,” to an incentive, “Hey, come back. You have a great chest that is waiting for you.”

No matter your choice, you need to ensure that you’re using universal deep linking in order for the user experience to be coherent with your messaging. That is, when you tell them to come back to get a free chest, you need to make sure that when they enter the game, they’re going to receive and open the chest. 

Additionally, you want to use rich and enticing content to drive engagement, like new features, gifts, sales, or events, so that when you re-engage your user, you multiply the iteration and the messaging that you’re sending to them. 

After setup, how can I most effectively measure my app retargeting efforts? 

Last but not least, measurement. First, what do you need to measure the efficiency of your retargeting campaign? You’re going to need your game data, attribution data, and ad network data all in the same place, centralized, to get a holistic view of your campaigns and make sure you’re able to measure your ROI efficiently. 

Second, you want to measure everything throughout your campaign from actual reach – i.e. how many people did we put in our segments? Did you start in your campaign one, and did you actually reach? How many people actually re-engaged with or reinstalled the game? How much revenue was generated to put a return on ad spend number against your campaign?

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Third, always keep control group for measuring your lift. There’s always the idea of just spending advertising money to retarget users, but you don’t know what the natural re-engagement lift would be from your game, and you want to measure your costs and your ROI accurately. Measuring cost is, again, how much money did you spend to run a push notification, an email, or an ad, versus how much additional money you actually generated to the natural re-engagement of your game. 

Always keep control groups for measuring your lift. Your game has a natural re-engagement level, and you always want to keep a control group to for both measuring this level and measuring your re-engagement and retargeting efforts against it. 

Fourth, you want to measure your ROI accurately, so you need to take into account your costs for running push, e-mail, ads against this whole new lift to make sure you have a positive ROI. 

Fifth, tiny details always matter. Whatever you do, you should always test the player experience and the journey you are building to ensure it makes sense. The players should be presented with a coherent message to make sure that your messaging makes sense when they enter the game again and play. 

Overall, we’ve seen the best return on ad spend on our retargeting campaigns and I can only encourage you to test it on your games. 

That’s it for today. If you have comments, please leave them below this video, and if you want to watch more MAMA Boards by AppsFlyer, please subscribe here. 

Thanks for watching! Bye bye.

The post How to run remarketing campaigns on mobile games appeared first on AppsFlyer.