In the last year or so, we’ve seen a trend in the Gaming industry in which an increasing number of monetization and UA teams are merging together, as various game companies realize the virtues of a holistic growth model.

This shift began in the hyper-casual space, where the business model is reliant on ad-based monetization and shorter retention, but after it proved its ability to catalyze growth, it is now slowly making its way to developers in other casual game genres.

In the big picture, a unified UA and monetization team enables faster, more efficient optimization of the growth loop, allowing better prioritization of where resources should be spent. Below, we explain why exactly this is the case, giving specific examples to show how operating with someone or a team of people who do both UA and monetization can benefit a mobile games business.

A virtuous cycle of growth

In any Gaming company, there are those who are working on bringing in new users, and those who are working on providing a valuable experience for those users – which (hopefully) generates revenue. That revenue gets reinvested into bringing more, even higher-quality users who (hopefully) go on to monetize, as well.

It almost seems obvious that looking at it from this perspective, growth is not a funnel that starts with user acquisition and ends with monetization, but rather a loop that’s powered by both. Each side of the business, after all, is dedicated to driving game growth, and every decision made by either one of them impacts the cycle.  

In a unified team, of course, it’s easier to see how that cycle works, as the team would most likely be optimizing their growth strategy towards a single goal, such as profit. However, separate UA and monetization can still benefit from increased communications and a better picture of how the other side works and what they’re working on. 

Executing on the right KPIs

In a strong growth team, decisions are made based on data and the right metrics. But without a look at the other side of the business, the data these conclusions are based on is skewed and only tells half the story – making it unreliable.

By being connected to each other and aware of what’s happening outside their own domain, monetization and marketing units can make more informed decisions to accelerate the loop – enabling them to execute, iterate, rinse and repeat. Let’s break down a few examples. 

How monetization impacts UA KPIs

The launch of a new ad placement or tutorial by the monetization and product team certainly impacts all of the downstream metrics on the UA side. Not only is it important for the UA side to understand why ARPU suddenly increased (the new ad placement is performing great), but it’s also critical that they’re immediately made aware that it is in fact increasing.

Notified of the new high-performing ad placement, the UA side could quickly adjust CPIs to bid higher in that moment, and drive more scale – rather than losing precious time and the opportunity cost of waiting sometimes weeks or even months before reacting. 

In addition to impacting the metrics, any change on the monetization and product side could also skew how certain users behave in the game, including the monetization behavior of key demographics for the UA team – say, users in tier-3 countries who are highly engaged offerwall users. This would require the UA side to rethink their strategy, creatives, channels, for acquiring that demographic of users. 

How UA impacts monetization KPIs

Similarly, any updates to UA activity, such as a new CPE campaign to reward users for finishing level 10, will impact monetization metrics and how the monetization side makes their decisions.

The acquired users who complete the level may skew the product team’s numbers and distort their understanding of the level’s quality – they might see high numbers simply because of the offerwall campaign, which might not reflect the actual quality of the level itself. 

Likewise, turning on a new paid channel or running a specific campaign, which would deliver an influx of a new type of user, depending on the demographics of that channel’s supply pool, would be met with an impact to monetization metrics – for example, running a rewarded video campaign may bring in users who are likely to engage with rewarded video.

The uptick in rewarded video engagement and ARPU could trigger the product and monetization team to make ill-informed decisions relating to their placement and ad unit strategy. The same is true for retargeting – because users brought back into the game from retargeting campaigns have higher purchase rates and retention rates, being in the loop and knowing that there is an explanation from the UA side for these metrics is evidently critical for decision making. 

Combining forces for better segmentation

Once the foundations are in place for increased communications between monetization and marketing, there are several activities which the two sides can collaborate on to accelerate the growth loop – namely, audience segmentation, which can massively impact KPIs across the board. 

The two sides of the business can work together to better segment their product by taking source level UA information like organic, paid, and even which paid channel into consideration. Seeing that organic users engage more with IAPs but less with ads and by contrast, paid users generate higher ad revenue, which is logical seeing as they were converted in the first place by an ad.

As such monetization managers can segment their ad unit strategy to maximize ad revenue from paid users while not hurting IAP revenue from organic users. Meanwhile, they can feed this information back to the UA side to shape their strategy.  

Let’s take the offerwall as an example, which only works in games with deep in-app economies and generally requires a careful placement strategy. In order to get the most engagement out of the offerwall, the product team should know which users converted into the game through a paid offerwall campaign, as they can assume that those users will go on to engage with their offerwall as well.

In this case, it’s best practice to be more aggressive and show the offerwall in the first or second session to this user, increasing the number of traffic drivers and pop-up notifications – which isn’t a common offerwall strategy otherwise. 

Summing up 

The most pertinent benefit of unifying UA and monetization teams is that it creates a structure that revolves around a shared interest and operational focus. This has a powerful impact on your business, streamlining the way teams approach game growth and ensuring one cohesive unit working towards the same goals.

The post Merging Game Monetization and Marketing Teams for Accelerated Growth appeared first on AppsFlyer.

Getting quality consumers to your Shopping app can be hard. Keeping them there over time while driving ongoing revenue with relevant and attractive offers can be even harder. 

Given increasingly high expectations, and numerous other competitors to buy from, users can easily and rapidly lapse into inactivity (or even uninstall). 

On the marketing side, rising user acquisition costs means driving new users to your app is increasingly expensive, while retargeting is much cheaper. As such, it is worthwhile to leverage intent data and advanced audience segmentation capabilities to re-engage with existing users — whether they are already inactive (lapsed) or are predicted to become inactive.

But practically speaking, what is the best way to set up these campaigns? How can marketers fully optimize the value of retargeting for their app? 

This blog post will cover the what, why, and how of retargeting for Shopping apps so that you can make the most of your marketing budgets and meet your goals. Plus, be sure to check out the complete practical cheat sheet on Shopping app marketing at the end of this post that includes: 

• Top vertical-specific goals + the KPIs to measure 
• Which and how many in-app events to configure
• Selecting media sources to run with
• Recommended segments

↓ Download full cheat sheet for Shopping apps ↓

What is retargeting and why use it? 

Retargeting is the marketing method to re-engage existing app users through paid and owned media channels. 

As the data from our new State of App Retargeting report shows, retargeting efforts deliver strong results and are especially well-suited to Shopping apps, which had a 50-72% adoption rate and a 25-60% increase in the share of retargeting conversions (depending on the region) as of Q1 2020. 

More importantly, we found that retargeting led to significant uplift in the share of paying users across regions: US (+90%), France (+70%), Italy (+43%), to name a few examples.

Retargeting in Shopping is a natural fit because direct response campaigns encourage action, which drive purchases of products users expressed interest in. In fact, according to Criteo, re-engaged shoppers show more than four times higher conversion rates in-app than they do on mobile web or desktop, making the app the ultimate touchpoint and a must-have channel for Shopping brands. 

Clearly, mobile web is not an alternative but rather a complimentary mobile channel whose primary role is to cater to users who land from search.

What to measure

Running an effective retargeting campaign starts with measuring the right data to guide your analyses. This is especially true for Shopping apps, who need to drive lapsed users to make their first purchase, while also retargeting non-paying and paying users before they lapse (usually within the week after install).  

To get you started with focused measurement for retargeting, we’ve included the following table showing some of the most common vertical-specific goals and the KPIs to support them:

Timing retargeting campaigns

There are multiple factors that go into deciding when to begin running your retargeting campaigns. These include the type of audience segment, the level of engagement, whether you’re seeking to recover users or simply re-engage, your budget, and others. 

Retargeting campaigns may be launched as early as immediately or 24 hours after install, within the first week, or later. Remember that one size does not fit all and choosing your timing depends ultimately on your own goals.

The same is true of stopping your campaigns, which is again determined by your expected marketing goals and the limits of your campaign budget. For Shopping apps, the most successful marketers tend to end campaigns after 7, 14, and 30 days, though one perspective suggests that all projected engagement will occur within 14 days. 

Remember it is important to avoid overexposure which could severely hurt your brand and lead users to rapidly uninstall your app. 

Audience segmentation for retargeting

So you’ve mapped your target KPIs and set up the scope of your campaign, but it’s still not time to launch your retargeting campaigns. Understanding your audience, and being able to use your data to segment audiences effectively, is critical for delivering targeted and effective ads. 

Below, we’ve included some of the most common audience segments Shopping marketers use to guide their retargeting efforts:

For Shopping-specific UA audience segments, as well other practical guidelines for Shopping app marketing, check out the cheat sheet below. 

The post The What, Why, and How of Retargeting for Shopping Apps appeared first on AppsFlyer.

The gaming market is booming. eMarketer predicts that the US market for in-game advertising across all devices will exceed $3 billion this year alone, with mobile comprising 47% of that spend. All trends are leaning towards significant growth for in-app advertising (IAA). In some gaming sub-categories, like hyper-casual games, IAA can even account for 100% of their revenue.

Greater transparency into in-app revenue is thus becoming essential for gaming optimization success.

Today, we are thrilled to announce a partnership between Facebook Audience Network and AppsFlyer, bringing a first-to-market campaign-level return on advertising spend (ROAS) measurement solution to gaming app developers.

Tying the revenue generated by a cohort of users from Facebook Audience Network to the user acquisition source will allow app developers to more precisely optimize their user acquisition strategy, improving ROAS and LTV accuracy.

This cycle between monetization and user acquisition can be referred to as “completing the growth loop” for every gaming developer; and as we see a surge in gaming engagement, this solution is key for gaming UA and monetization managers alike. It’s also inevitable that both user acquisition and monetization teams will need to optimize their strategies together to achieve real success.

The growth loop: connecting the dots between monetization and user acquisition

While one team handles bringing in quality users, the other focuses on providing a more ideal experience for those users, which usually leads to more revenue for the app developer. That revenue then gets reinvested to again bring high-quality users to the app. This process is best described as a loop, since one directly impacts the success of the other and the success of both directly impacts the profit of the gaming app.

In the past, ad networks only provided eCPM averages at differing levels of granularity. In this scenario, revenue could still be used as an indicator in measuring ROAS and building LTV models to a certain extent, but those weren’t as precise, which in some cases might lead to less informed decisions that affect publishers’ margins.

Without granular insight at a campaign level, ad revenue from monetization networks can only be tied back to the user acquisition campaign through heuristic calculations. These estimates cause gaming advertisers to make decisions with incomplete data, because frankly, it’s the only option advertisers have had.

To take it to the next level, Facebook Audience Network and AppsFlyer have come together to provide Campaign-Level IAA ROAS Measurement.

”Our new campaign-level measurement solution, developed in collaboration with MMP partner, AppsFlyer, fulfills a critical gap in the market by helping advertisers on our platform truly understand if their return on ad spend is both accurate and profitable” says Mat Harris, Facebook Audience Network Director of Product Management.

This new Facebook API allows for more accurately measuring the ad revenue generated by cohorts of acquired users, eliminating the need for any heuristic calculation.

While having a strong monetization strategy to optimize for the success of the ads shown within these apps is important, without understanding the true value of the users (LTV) who install the app to begin with, the full cycle of optimization is incomplete. Sharing this insight with both the user acquisition teams and monetization teams will allow both to optimize towards the same goal: positive ROAS.

In-app advertising in itself can be a very lucrative revenue stream for gaming apps, resulting in greater insight to allow for optimization, which is critical and should become the industry standard.

AppsFlyer is in a unique position to connect the dots between the monetization network, the user acquisition channel and the mobile attribution data. By having the attribution data, using the device IDs coming from installs, AppsFlyer is the only player that can properly tie back the revenue generated to the user acquisition network.

“With measurement being the precursor to optimization, together with AppsFlyer, we’ve taken the initiative to create a product that helps game advertisers and publishers understand the effectiveness of the ad campaigns they run on the Audience Network platform. Our new campaign – level measurement solution, developed in collaboration with MMP partner, AppsFlyer, fulfills a critical gap in the market by helping advertisers on our platform truly understand if their return on ad spend is both accurate and profitable. Audience Network is committed to helping game developers build sustainable businesses and this represents another opportunity to provide them with accurate, granular insight and the right tool to grow and improve their business.” 

Mat Harris, Facebook Audience Network Director of Product Management.

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We are thrilled to roll out this collaboration with Facebook Audience Network to bring additional value to our mutual customers. Facebook Audience Network is providing a significant foundation to perform precise optimization for gaming advertisers.

With AppsFlyer as an instrumental attribution measurement partner, gaming developers can leverage their trusted attribution data to more precisely calculate ROAS and the true LTV of their users, and in turn, optimize both their UA and monetization strategies.

To learn more and participate in our beta program click here.

The post Facebook Audience Network and AppsFlyer have come together to provide campaign-level In-App Advertising ROAS measurement appeared first on AppsFlyer.

Our dedication to the success of our customers and partners remains steadfast as ever. At AppsFlyer, we are guided by an all-in mindset, meaning we will always go the extra mile to ensure we meet the needs of our customers. As Chief Customer Officer, I’m honored to announce that our customers around the globe have named AppsFlyer the clear leader in the mobile attribution.

AppsFlyer was founded with a customer-centric approach. We truly are customer obsessed. While our products led our growth, it was the trust of our customers and partners that allowed us to be the leader we are today – the trust to be responsible stewards of their data and the trust that we will always act with their best interests in mind.

As the only enterprise-grade attribution provider with attribution at its core, AppsFlyer’s growth and expansive market presence proved to G2 that the Mobile Attribution category was not only warranted, it was mission-critical to create in order to accurately reflect the current state of mobile marketing. Our customers spoke and G2 heard them loud and clear. In G2’s own words:

“The mobile attribution software category on G2 was created in October 2019 to reflect the market for software that measures and attributes every app install and in-app engagement to the marketing campaign or source that drove it. Unlike attribution software, which has been a software category on G2 since 2015, mobile attribution software enables businesses to track where users first learn about an app and also connects them with multiple identifiers to measure the pre and post-install app journey.” 

-Emily Malis, Market Research Manager, G2

When I think of what makes a best-in-class enterprise attribution solution, one word comes to mind – scale. AppsFlyer measures over 28 billion in ad spend per year, features over 6,000 technology partner integrations, and runs 89,000 active mobile apps. This scale is one of the most important factors in our ability to deliver the most accurate and reliable attribution data in the industry. It also powers advanced use cases, including a market-leading fraud detection and prevention solution.

When we say AppsFlyer has a global footprint, we mean it. For the past 9 years we have worked to create an extensive network of international partnerships, and developed use case solutions that fit the needs of regional markets at a global scale. We can’t fully understand and meet our customers’ needs if we don’t speak our customers’ language, both literally and figuratively. We are always looking to understand what jobs need to be done in our customers’ organizations so we can meet those needs. Our focus on listening and acting on feedback has not only allowed us to identify where we can improve our platform, is has provided us with an indication of what our customers most value about our offering.

Thank you for your positive reviews and ratings. We hope to have you as a customer for life. Your feedback is the North Star that guides our innovation and our success. Without it, we wouldn’t know what we are doing right, and more importantly, we wouldn’t know we can be doing better to serve you. We welcome you to continue to provide your invaluable feedback on G2. 

Sincerely,

Ziv Peled, Chief Customer Officer

“AppsFlyer’s unique scale and machine learning deliver the most comprehensive anti-fraud solutions in the world. For their technology and data, for their service, for their attention to detail, AppsFlyer is simply irreplaceable.”                               

Irene Vaquero Sánchez de Ibargüen, User Acquisition Manager at Genera Games (EMEA)

“AppsFlyer is our source of truth when it comes to attribution. No marketing tech stack is complete without it.”

Sadie Daryan, Global head of Display and App Marketing at eBay (North America)

“AppsFlyer is the cornerstone of our marketing, allowing us to measure our traffic across every channel and geo in the world, helping us understand our performance and efficiently drive our business growth.”

Zhang fu Tao, Marketing Director at DH Games (APAC)

“AppsFlyer is an essential partner, delivering a deep understanding of our mobile business performance and driving our app campaign optimization. Their focus on customer success and technology innovation have raised the bar, helping us reach a new level of sophistication.”

Arthur Santos, Marketing Director at iFood (LATAM)

The post G2’s Spring 2020 Report Solidifies AppsFlyer’s Position as the Mobile Attribution Leader appeared first on AppsFlyer.

Thanks to global ad platforms and freely available market data, it’s now easier than ever for digital businesses to enter new markets. With fewer barriers to entry than ever before, we constantly see mobile apps growing internationally. 

However, the ability to grow internationally isn’t equal across all industries. Exclusively digital businesses and those in non-regulated industries have a significant advantage over hybrid or regulated ones. 

As industry analysis is fundamental to business growth, we analyzed 9,000 apps and 1.5 billion non-organic installs to better understand how internationally diversified the top 20 industries are, and identify each industry’s fastest-growing markets. 

This analysis provides actionable insights for apps performing industry analysis and those expanding internationally. 

Why analyze international diversification? 

International growth offers apps many benefits across marketing, product, strategy, and revenue generation. Comparing diversification across industries holds insights into unlocking growth and strengthening defensibility and market positioning. 

Apps advertising in multiple countries can attest to the benefits, namely: 

• Driving demand and ensuring user growth.
• Acquiring users in under-served countries and languages, often with large populations and lower CAC.
• Generating revenue from ROI positive activity, even in low-scale markets. 
• Mitigating risk of revenue loss due to region or geo-specific economic factors.
• Receiving product feedback from a broader and more diverse audience. 
• Reducing competitors’ market share. 

Which industries are most internationally diversified? 

We’ve organized the top 20 app categories and sub-categories, broken down by the average number of countries where apps are generating non-organic installs. 

We’ve broken down the results by the size of the app (large = top 20th percentile, medium = 50th-80th percentile, small = bottom 50th percentile). For significance, we’ve limited the apps in our data study to those with a minimum of 1,000 installs per country per month, and removed the outlying percentiles. 

Here are a few insights from the data we analyzed. 

1. International diversification and app size go hand in hand 

The chart below shows the distribution of international diversification by app size. The larger the apps, the more diversified they are. Or, the more diversified they are, the larger they are. Of course this is the case, as apps running globally are available to more people. This is the first and most obvious benefit to advertising globally. 

While 64% of large apps advertise in more than one country, and 29% in 11 or more, the vast majority of small and medium-sized apps are only advertising in one. That is likely a result of resource constraints and strategy prioritization. 

2. Understanding industries that are less diversified

The majority of the top 20 industries are exclusively digital and non-regulated. And the lower-ranked industries have additional hurdles when expanding internationally. 

• Shopping is a hybrid industry; businesses must maintain a physical supply chain, which makes selling globally more difficult. 
• Entertainment has strict laws and contracts which govern international content rights and distribution. 
• Finance is a heavily regulated industry, subject to different regulations for each sub-industry and market. 

3. Do the lower-ranked industries have an opportunity for global expansion?

Yes. Absolutely. Here are two examples. 

Shopping: When looking deeper, we found that 57% of Shopping apps are only advertising in their home countries, and 77% are only advertising in one country. The opportunity for global expansion exists in part as a result of the combination of the following reasons: 

1. 1. Demand is global.
   2. People spend more time using Shopping apps than they spend shopping via mobile web. 
   3. The popularity of direct-to-consumer or direct-to-distributor/fulfillment center dropshipping continues to grow, helping businesses avoid many supply chain concerns. 
   4. Regional economic treaties (USMCA and APEC, for example) encourage trade within economic zones

Finance: Considering the regulatory barriers to entry, it’s odd to say Finance apps have an opportunity to be more international. Currently, 83% of Finance apps are only in one country, and 13% are between 2 and 10. However there are a few reasons to expect international growth for Finance industry apps.

1. 1. The traditional finance industry is being disrupted, and consumers want this disruption to happen quickly (4.5% of all app installs are Finance apps, up 2.5x from 2017). 
   2. Like Shopping, demand for digital Finance apps is global. 
   3. It’s already started. Some Finance apps have prioritized international expansion. For example, Revolut supports customers in 36 countries, Ecobank in 33, N26 in 23 and Monese in 20. 

4. The sub-industry can drastically differ from the overall industry

The reasons why some sub-categories don’t follow their overall category’s behavior isn’t clear, which makes the importance of marketers capitalizing on global opportunities that much more important. Here are two examples:

1. 1. Finance – Investments: the sub-industry is ranked 4th among the top 28 sub-industries. All other Finance sub-industry are ranked 25th-28th, and the Finance industry is the 3rd least diversified overall industry. 
   2. Gaming is the 2nd most diversified industry. However, Gaming – Social Casino is 15th out of 28 sub-industries.

Which markets should you explore?

There are many benefits of international growth, as we’ve listed above. But the million dollar question is to which markets should you expand? To answer, we identified the fastest-growing markets per category (based on the % change in the number of non-organic installs and the number of apps running campaigns in each country among the top 50 markets). 

Many countries are experiencing a mobile app revolution, appearing on the growth list for many of the sub-industries we analyzed. In order, the top 10 growth markets (industry agnostic) are Russia, Brazil, Saudi Arabia, Vietnam, United Kingdom, China, France, Egypt, Japan and South Korea. 

Others are simply demanding more specific services, such as Australia and Canada, showing nearly 13% yearly growth for Casual Gaming, or China reaching 10% yearly growth for Productivity apps.

Conclusion

Apps advertising internationally benefit across marketing, product, strategy, and revenue generation. Due to the rise in global ad platforms and easily accessible data, marketers now play a bigger role in identifying and capitalizing on international growth opportunities. 

We’ve analyzed international diversification by industry and app size, demand for each sub-industry by country and identified the fastest growing markets overall.  

1. Use this analysis to understand how your company compares to the benchmarks, and if you’re advertising in the growth markets we’ve identified. 
2. Use the AppsFlyer Performance Index to pinpoint the best media sources to test in multiple markets and industries. 
3. Most importantly, utilize in-depth 1st party data analytics and profitability models. 

Spread your wings (but only if the data says so)!

The post Going Global: Insights on International Growth for Mobile Apps appeared first on AppsFlyer.

During COVID-19, we have been thinking about how to best support our customers, partners, the entire ecosystem. In the last couple of weeks, we launched the ‘New Normal’ productivity & efficiency package, introduced our incrementality product beta, a first to market partnership with Facebook introducing in-app-ads ROAS measurement, and today, I am extremely excited to announce Zero. 

So, what is Zero? 

In one of our MAMA events last year, a CMO I spoke with introduced me to the ‘Zero Budget Marketing’ framework: Imagine that your company’s marketing budget was zero, what would you do? Obviously, COVID-19 turned this concept into reality, for many companies.

Some of my best, most creative, and impactful ideas came to life while I was in a ‘Zero Budget Marketing’ mindset. Without any budget, you MUST only use your imagination and creativity to win. While in some aspects money is a commodity, innovation, and creativity are about leveraging your brand and company’s unique assets. 

Working with tens of thousands of marketers worldwide, we witnessed as they made incredible achievements by leveraging our engagement products and APIs in creative and innovative ways. It pushed us to focus on our own innovation and support brands in addressing the basic, most important aspects in marketing: creating organic demand and turning a visitor into an engaged user. 

Today, I am excited to announce Zero. Zero is Geared towards developers, product managers, and marketers. It offers free, state of the art software tools and APIs, to empower brands’ growth and leverage their earned and owned media strategies. Zero includes the powerful tools of AppsFlyer’s complete engagement suite, all for free: Our OneLink deep linking technology, smooth web-to-app & social media-to-app user journeys, Smart Banners, referrals & user invites, SMS & QR codes, cross-promotions, and more.

COVID-19 forced all of us to rethink our marketing strategies and go back to basics. Companies need to focus on having a great product & onboarding experience, and leveraging their most important assets: their owned media, website, social media, and loyal user base. 

This is the time for brands to get creative and grow their business at zero cost. We’re proud to provide product managers and app developers with our technology for free, including open APIs they can use to delight and engage with their existing and new customers.

Give it a try (you can get started in minutes, no payment information needed), and let us know what you think!

Oren

The post From zero to infinity – we’ve got you covered appeared first on AppsFlyer.

When user journeys spread across multiple platforms, devices and channels, it can be tricky to discern what typical conversion paths look like. Answering questions like “how many times is the customer interacting with my brand before downloading the app?” or, “how long does it take the user to finally sign up for a subscription?” can be key to optimizing your funnel.

Looking to data for answers

To help marketers tackle these critical questions and enable smarter, data-driven decisions, we analyzed 68.2 million conversion paths across two weeks in May and June 2020. The conversion paths cover user journeys across both web and app, across devices, platforms and channels. The data spans almost every mobile industry, with users from all over the globe. 

We focused on two types of conversion events: app installs and web conversions, where web conversions include actions that visitors take on the brand’s website (such as registrations, purchases and subscriptions). Our analysis explored how many unique interactions (steps) users had with a brand before converting, and how long, on average, it took users to convert. 

Quick to download, not so quick to convert on the web

Our data uncovered that users convert quite quickly, on average, on mobile. Between the first interaction and installing the app, the average user engaged with the brand 1.3 times (this can include social media, ads, emails, or any other marketing channel), and took about 1.7 days before finally deciding to install the app. Since installing an app requires a rather minimal level of commitment, this isn’t surprising.

Web conversions, on the other hand, take more consideration and thought. Users interacted with the brand an average of 3.7 times before finally converting on the brand’s website, and this took an average of 4.2 days.

Food & Drink apps have the longest paths to conversion

Some decisions are easier to make than others. We’ve already mentioned that conversion paths are typically longer for web events than mobile installs, but even within the realm of web conversions there’s quite the range.

For example, the average user of a Food & Drink brand will take up to 8.9 days to convert on the web (make an order, sign up for a service, etc). Making the decision to install a Food & Drink app, however, will take less than 2 days, on average.

Organic traffic might be your most significant marketing channel

One of the biggest puzzles marketers face with user journeys is understanding how different media channels interact to drive a conversion. AppsFlyer’s Conversion Paths enables marketers to solve this equation. Some channels may be stronger for a first touch interaction, whereas others may be good BoFu catalysts.

Based on our data of both mobile installs and web conversion events, organic web search was the most significant channel for driving conversions, followed closely by direct web traffic.

Organic web searches were the driving force behind 40% of all first interactions with the brand, and 36% of all last touches (before a conversion). 

Direct web traffic (such as a user typing “www.appsflyer.com” into their web browser), indicates a pre-existing familiarity with the brand, and accounts for 31.7% of the first touch interactions and almost 37% of the last touch.

Social media, ads and referrals had similar impact both at the beginning and end of the funnel, whereas email campaigns had a marginally stronger impact as a last touchpoint.

One in three web conversions is happening on mobile

While the term “web” may be strongly associated with desktops, data shows that mobile web should not be ignored.
Of all the web conversion events measured, one-third were performed on mobile web (such as Chrome or Safari for mobile).

Desktop is still king for web interactions, proving as an important marketing property even for mobile-first or mobile-centric brands. Brands that have a strong web presence or are optimizing for a web conversion (such as a subscription via the website), should ensure that their website is optimized for mobile, creating a seamless experience for users on all devices.

Almost 40% of revenue is generated on conversion day

Focusing on revenue-generating events, we did a deep-dive into web conversions to get a better idea of user behavior.

Unsurprisingly, more than a third of the revenue (37.5%) is generated on conversion day. That’s the day the user orders the pizza, buys the shoes, or subscribes to the streaming service.

Over 65% of revenue is generated by users in the first 3 days of converting, the number steadily dropping to about 1.1% on Day 9. Days 10-30 account for about one-tenth of the revenue generated after conversion.

Uncovering your own insights

The user journey can look vastly different from brand to brand, even within the same industry or similar business models. Brands working with AppsFlyer and People-Based Attribution have learned more about their users and user behavior across devices, channels and platforms.

Learn more about People-Based Attribution 

The post Uncovering Conversion Path Trends Across Web and Mobile [Data Study] appeared first on AppsFlyer.

We’ve had a number of significant product announcements in the last few weeks, and we’re thrilled to share another exciting one.

AppsFlyer’s Smart Banners, a no-cost feature which empowers marketers to turn high-intent web visitors into loyal app users, just got even smarter and simpler. How so? The entire Smart Banners experience has been upgraded, simplifying your ability to create and manage banner campaigns. Here’s a summary of the functionality that we released today:

• A new UI flow designed to help you organize your “banner estate” and your banner campaigns
• A new Banner Groups framework enabling you to display banner variants within the same and other Banner Groups simultaneously and with ease
• New settings, or rules, that empower you to show banners on web pages according to geolocation, scheduling, and URL path settings

The release of these capabilities marks a watershed in the continued evolution of our Smart Banners. They allow you to display banners in a more precise way to the right users in the right geolocation at the right time. The new UI also enables you to take advantage of Smart Banners’ proven ability to move users smoothly from your website so that they can enjoy the benefits of your app.

We’ll provide a breakdown of the features below as well as use cases that demonstrate the value of the new Smart Banners experience. But before that, here’s a quick reminder of what Smart Banners are and how they can help you.

“The Why” behind Smart Banners

What makes Smart Banners so important to your marketing mix? It’s simple. Most brands have a mobile website, and nearly 90% of users discover brands during their web explorations. On the other hand, the lion’s share of revenue occurs in apps. In a nutshell, users discover on the web and convert in-app.

Smart Banners are a magic connector, turning web visitors into loyal users, first through an install and subsequently through revenue generation. In fact, you can think of Smart Banners as a digital version of Magic Johnson; just as Magic artfully created scoring or “conversion” opportunities by passing the ball to teammates, Smart Banners “convey” users to an install, in-app content, or another digital destination. And we know for a fact that Smart Banners are highly effective; data from our customers shows a click-to-install rate of 30%, the highest among commonly used owned media channels.

Of course, a key reason that nearly one of three users clicking on a banner installs the app is that each banner has a OneLink deep link behind it. Deep linking ensures a smooth path to the appropriate app store and to the app itself, and superior user experience is a proven way to increase conversions.

Smart Banners: Banner Groups use cases

In this section, we describe two use cases to illustrate some of the benefits of the new Smart Banners experience, one showing how to display banners to users in a certain geographical area, and the other showing how to display banners during the holiday season. 

1. Displaying banners to users in specific locations
Let’s say you’re a marketer of a music app called Mixter, and you’d like to display your banners only to users in New York and New Jersey. With the new UI, it’s simple to create banners with these settings. We call the group of banners that abide by the same settings a Banner Group. So, we’d set up a Banner Group and define the geolocation according to our needs.

The three banners in the Mixter_Routine Banner Group (shaded in pink) will appear only in New York and New Jersey 

Now users with an IP address in New York and New Jersey will see one of these banners — each displayed one-third of the time, sequentially — when they are on your website.

 2. Displaying banners during a specific time period 
For the second example, let’s say you’d like to display a couple of banners during the week between Christmas and New Year’s. You’re especially eager not to work during the holiday week and you’d like the holiday-centric banners to be published automatically. What can you do? Easy: Create holiday-flavored banners in a new Banner Group and schedule the banners to appear during the holiday week. Now you’ll be able to focus on your holiday preparations and your loved ones. 

The two banners in the Mixter_Holidays Banner Group (shaded in pink) will appear during the holiday week

The new Smart Banners experience

Now that you’ve read two (of so many possible) use cases, you have an understanding of what the new Smart Banners experience includes. In fact, while creation of the banners themselves is still a cinch (we even provide templates for inspiration), we’ve overhauled nearly everything else, keeping ease-of-use and simplicity at the forefront. Here’s a deeper dive into the new experience:

1. Banner Groups
Perhaps the most distinctive element of this feature release is the way we’re allowing you to organize banners in groups. Each Banner Group includes banners that behave according to the same targeting and behavior rules.
 
What are these rules? They include the OneLink deep linking template that contains the instruction set for a user’s journey; banner appearance frequency rules; the ability to measure the source from which people arrived to the banner (UTM); and we’ve added three new settings which are described in the next section.

Each Banner Group includes banners that behave according to the same rules

Why are Banner Groups important? Because they allow marketers to put one or more banners into play at the same time on a mobile website. This means you can dynamically enable and disable banners. As well, you can simultaneously execute campaigns with different Banner Groups, for example displaying banners from one group in the New York City area and banners from another group in London.

The bottom line is Banner Groups bring order to the many banners you create and enable. They are your stable of easy-to-create assets that you can enable and disable with a mouse click.

2. New Rules: Location, Scheduling, and URL path
With this release, we’ve added three new rules to help you serve the right banners to the right person at the right time. By enabling you to define the geographic location and day and time that a user will see banners in a Banner Group, you can make banners more relevant to your target audiences and ensure relevant, positive customer experience. In addition to the self-explanatory geolocation and scheduling possibilities, you can also set banners to appear on webpages that include words in the URL path, such as “holidays,” “home,” “music,” “children,” “garden”, “menswear,” “casino,” etc.

3. User interface flow
Last but not least, the entire, refreshed Smart Banners experience is predicated upon a new flow that makes it utterly simple for users to create Banner Groups and Smart Banners and then manage the brand’s “banner estate.” Honestly, seeing is believing, so to get a true sense of what the new UI flow is about, I encourage you to try the magic of Smart Banners by signing up free for AppsFlyer’s Zero plan.

Wait, what? Use Smart Banners at no cost?

Yes, you read correctly. You can start using Smart Banners on your mobile website today, when you sign up for our free-forever plan called Zero. Head to our pricing page for more information and start converting high-intent mobile web traffic into app users today. 

The post AppsFlyer’s Smart Banners: Web-to-app conversion was never more simple appeared first on AppsFlyer.

Successful app marketing depends on an array of platforms within a tech stack and how they are connected. Within that architecture, attribution — which credits a marketing activity that delivered a desired goal — is the key. Only with accurate and granular attribution data can you identify which marketing campaigns work and meet your goals, and which do not. 

But the attribution story doesn’t start and end there…sophisticated app marketers understand that there are multiple attribution models that they can capitalize on to pinpoint data-driven decisions and marketing strategies. Many also use refined attribution techniques to understand the relative value of different touchpoints within the customer journey. 

What is an attribution model?

An attribution model is a mechanism used to determine the value of different marketing efforts, whether an in-app advertisement, an ad on social media or an email campaign. Attribution models aren’t just for mobile marketers — they’re simply a variation on marketing attribution models in general, which are a lynchpin of digital marketing no matter what the context.

Marketing attribution models tell stories with data. In marketing speak, they follow the “customer journey.” Leveraging them successfully is all about making comparisons between the various activities — separately or combined. 

Within the fragmented setting of mobile, a marketer can see clearly which promotions perform well on particular networks or channels. Those data points are critical details in the “story” — the full arc of the role of a specific promotion whether a paid media source or another channel like email — that drives user acquisition, revenue, and ultimately ROAS. 

But when users might install an app at any number of points of exposure with your brand and/or app (the first time they see a promotion, the second, or the fifth), it’s tricky to figure out which source (or sources) gets the credit for that install and is paid for it. 

Within marketing attribution models, there are complex questions that challenge even the best minds in the business. 

Your job is to understand different kinds of attribution models and which provide the data that you need in a particular context. You also need to understand the flaws of each.

Let’s start with the basics.

First-touch attribution model

If you think of attribution as a way to assign credit for an engagement to a media source, then a first-touch attribution model assigns credit to the very first time a user engages with an ad (also known as the first touchpoint). The media source responsible for that first engagement gets paid for it, no matter how many more ads it takes for the customer to install or convert.

The simplicity of first-touch attribution is its main advantage, plus the fact that it’s a great way to measure top-of-the funnel (demand gen) efforts. But it’s very limited in terms of optimization, which is crucial in mobile marketing. What’s more, it’s not an accurate or even fair way to approach attribution when many mobile users require repeated exposure to an app before converting. 

Given the sophistication of other attribution models at this stage in the game, first-touch attribution is not very advanced, but it does have its virtues: it’s easy to implement, simple to interpret, and it sheds light on how top-of-the-funnel exposure leads to conversions. It’s not used in app marketing frequently, but it’s still important to understand as a conceptual starting point with attribution modeling. 

Last-touch attribution model

Like a first-touch attribution model, a last-touch attribution model — also known as a last-interaction attribution model — gives the credit (and pays for) a single touch point — in this case the very last one before the user installs or completes the desired action. 

For example, a hyper casual mobile gamer would go through this progression in terms of ads seen for another hyper casual game:

• Day 1: A click on an ad that appeared in media source 1 but no install 
• Day 2: A click on an interstitial ad for the same game in media source 2 but no install
• Day 4: A view of a rewarded video for the same game in media source 3 an an install

Under the last-touch attribution model, media source 3 gets the full credit for the install. 

For years now, mobile advertising has been largely rooted in the last-touch attribution model. The industry has scaled to the degree that it has because of last-touch, but it’s problematic. 

Most installs are a result of a progression of ads, meaning each of them has value, but with last-touch, only the final click gets the credit. Because marketing is fractured across multiple channels, formats, platforms and devices, the last-touch model undervalues the preceding touchpoints while overvaluing the final one. 

It doesn’t take into account the incremental impact of each touch point preceding the last.  

While there is something to be said for simplicity, the last-touch attribution model oversimplifies a complex story. It obscures your true ROI.

Last non-direct attribution model

The last non-direct attribution model (also known as non-direct click attribution) is quite similar to the last-touch attribution model, but it attributes all of the conversion value to the last marketing activity — not the last touchpoint — the customer clicked through before converting. 

It’s used primarily in the context of web attribution, which is the process of identifying web touch points that lead to a conversion, and when paired with a mobile attribution model can provide a complete picture of the customer journey. 

A progression might go like this:

• A user clicks on a Google ad directing them to a website and then performs a web event. Later, the user visits the website again directly, performs an event and makes a purchase.
• The purchase is attributed to Google Ads because the model is last non-direct. 

The value in this model is that it filters out direct traffic, which is much harder to optimize towards. Many advertisers relate direct traffic to users who already were impacted by a previous marketing effort.

Multi-touch attribution model

Now that we’ve established the advantages and disadvantages to first-touch, last-touch, and last non-direct attribution, let’s explore the multi-touch attribution model. 

With multi-touch attribution, every touchpoint along the way from first impression to install, is built into the assessment. When each touchpoint is acknowledged as a reminder to the user, varying weights can be assigned every step along the way to conversion. 

Within a single device app consumer journey, multi-touch attribution is often referred to as “assisted installs” because each touchpoint pushes the user closer to conversion, whether that’s an install, purchase, or other in-app event. You might also hear it referred to as “fractional attribution.” 

Multi-touch attribution can be limited to a single channel (a single mobile device) or span multiple channels (e.g. mobile phone, tablet, desktop, and TV).

When you look at the attribution data from the multi-touch perspective, you gain insight into which media sources influenced users on what device and where in the funnel. From there you can make smart media and budget allocation decisions.

For example, imagine that a sponsored ad campaign on Twitter that drives 2,500 last-click installs. The last-click is important, but so too are the 1,000 high quality assists, even if other media sources (e.g. Facebook, Google Search) led to the last-click install. 

When you can identify those high quality assists that get the customer closer to conversion, then you can give them credit, determine their value in the customer journey, and budget accordingly. 

Within the multi-touch attribution model there are three primary models: 

• The linear model assigns equal value to each touchpoint along the way to install or conversion. It’s a simple way to give credit to touchpoints in the customer journey (which many regard as linear). It makes budgeting for media sources and campaigns relatively straightforward.

• Then there’s the time decay model, which starts with the proposition that not all touch points in the customer journey have the same value. It also gives more credit to the touch point closest to conversion.

  For example, imagine that you run campaigns on Network A, Network B, and Network C. With time decay attribution, you can see that Network A and Network C do the best with assists, early in the funnel. Network B, however, seals the deal — it drives the most conversions. Then you know that Network A and Network C have value in that they propel the customer forward in the journey, but Network B is more valuable because it closes the deal with conversion. 

  Basically this means you can say, “Network A and Network B did the best job with assists, while Network C did the best with conversion,” so then you can determine the best price to pay each network. 

• Like the time decay model, the U-shaped attribution model (so-called because when it’s graphed, the way in which credit is assigned forms a U) helps to identify which media are the most effective at certain points in the funnel. It differs, however, in that it divides credit among multiple touchpoints: the first and the last get 40% attribution credit and every touchpoint in between — the assists — shares the remaining 20%. 

  If you regard the first and last touch points (brand awareness and conversion, respectively) as the most powerful within the customer journey, but you still want to give some credit to every other assist along the way (again, incrementality), then U-shaped attribution might be the way to go. 

Custom Attribution Model

Finally, there’s custom attribution, an app marketing strategy that works well for apps that already have robust, sophisticated measurement in place and a seasoned team that is ready to take it to the next step. 

Naturally defining your own attribution rules within the customer journey starts with an intimate understanding of your users, your customers’ journeys in relation to your monetization strategy, and as many details as you can gather about your competitors within your vertical. 

With a custom attribution model, you can get incredibly granular with your analysis, for example by assigning a proportional value to every touchpoint; the touchpoint (or channel) that was most influential in the path toward conversion gets the most credit, the second most influential gets the second most credit, and so on. No matter where the most powerful touchpoint is in the customer journey — first, last, 3rd, or 23rd — it gets the most credit. 

Note, however, that customizing attribution doesn’t just take expertise, manpower, and aggressive optimization. It’s complicated, and the more complicated an attribution model is, the more likely it is to introduce errors to your analysis. 

To sum up, attribution modeling is the bedrock of marketing, and in mobile particularly, it is critical to gaining a competitive edge. Depending upon the habits of your users and your monetization strategy, it might be clear which model is the best one to go with. But you can also experiment. Compare the results of several to see which one you can best optimize, and keep in mind that the more sophisticated the model, the more resources it will likely take to apply it. 

The post Marketing attribution models: Which one is right for you? appeared first on AppsFlyer.

On June 22nd, during WWDC 2020, Apple announced several privacy-related updates that will take effect with the release of iOS14 later this year. As an Apple Search Ads Partner, we anticipated that Apple would continue its commitment to create a user-friendly and privacy-centric mobile ecosystem. While we expect that the specifics of the announcements will evolve with the beta versions of iOS14, we believe that the announcement in its current form will create meaningful challenges for partners, customers, and the app economy at large. 

AppsFlyer is fully aligned with Apple’s commitment to privacy and a responsible application of user data. We are also technologically prepared to provide customers with an accurate attribution solution that puts the end-users’ privacy first – even when certain identifiers are absent.

AppsFlyer came into existence back in 2011 as a result of a major industry shift – the creation of the App Store. At the time, app developers were faced with measurement and attribution challenges that didn’t allow them to understand their users or improve their users’ experience. While many thought that attribution was not possible, we felt that this problem must be solved, one way or another, by us or by someone else. Quickly, attribution and measurement became a fundamental part of the ecosystem and an enabler for the entire App Store economy to flourish. Over time, attribution became even more challenging, with the increasing complexity of platforms, methods, and major industry changes. For example, the deprecation of UDID and MAC addresses (both were important moves by Apple), and numerous platform changes pushed us to deliver a layer of measurement abstraction, and accurate attribution data that allowed app developers to grow, with privacy and security in mind. In fact, the more complex the industry became, the more value AppsFlyer delivered. 

Attribution is about measuring the value for the end-users by providing insights into the customer lifecycle. After all, how can app developers know if they succeeded in delivering value without feedback in the form of attribution? How can they improve their apps and experiences without measurement? Attribution is at the heart of marketing. Without accurate attribution, marketers ‘spray and pray’, and the experience for end-users deteriorates.

Lack of, or inaccurate attribution also means that app developers can’t monetize their work; iOS developers are generating tens of billions of dollars in revenue from advertising, which are at risk of disappearing without proper measurement. Some of this revenue is then invested back into the ecosystem to improve and develop new products. That’s the ecosystem I fell in love with 10 years ago, and am still fully committed to today. 

We’ve all learned during COVID-19, that changes could be transformed into opportunities and spark innovation. While we do think about the near term challenges these recent changes are introducing, I am also excited about the opportunities they will open for us as a company, and for the entire app economy. Some of them are already in the works in the form of strategic innovation projects, and others in leveraging our existing products to help app developers generate more value for their end-users.

As the global attribution leader, we are obsessed with both our customers, and the App Store economy. We are fully committed to working on these solutions in collaboration with Apple and the rest of the community, to help app developers flourish, and build trust through better privacy. This is why we have been facilitating expert discussion groups, and will continue to do so in the following days and weeks, with everyone in the industry: App developers and marketers, partners, agencies, attribution companies, privacy executives and business leaders from all over the world, who share our passion for the future of privacy and the App Store economy. If you’re interested in joining these conversations, please sign up here.

The AppsFlyer story was shaped by industry changes and challenges that drove creativity and innovation, ultimately making AppsFlyer what it is today. We’ve faced similar changes in the past, and we’ve always grown as a result. I want to assure our customers, and our partners that this case is no exception.

“Let’s go invent tomorrow rather than worrying about what happened yesterday.” – Steve Jobs

The post The future of the App Store economy, in a privacy-centric world appeared first on AppsFlyer.

Fraud is bad but false positives can be worse.

But are they?

Unlike true positive cases, where real fraud is identified and blocked, a false positive case allegedly penalizes legitimate sources. This could potentially harm an advertiser’s relationship with its quality media partners rather than protect them from malicious ones.

Any responsible fraud solution should aim for the lowest false positive rate possible, in order to maintain its integrity and credibility, while protecting their client’s best interest.

Sounds like a simple thing to do, right?

Well… partially.

What is the False Positive test?

To understand the False Positive test and its repercussions we must first understand the concept of Precision and Recall – two highly important data analytics KPIs – particularly in fraud detection.

False positive test diagram

The false positive test sets the allowed precision level for our fraud detection algorithm. The higher precision level is – the more conservative the fraud detection becomes. 

A lower precision rate, on the other hand, may result in an increase of total traffic detected, but at the cost of higher false positive rates.

Each fraud rule or machine learning algorithm eventually includes a false positive threshold that could be tuned higher or lower based on multiple variables and needs.

The key is finding the right balance between detecting as many fraud incidents as possible, while maintaining a high enough precision level.

An opportunistic point of view

Most marketers act with some restraint and self reflection, which builds their credibility. Unfortunately, fraudsters will note this as an open opportunity. 

We know that fraudsters are always on the lookout for loopholes to exploit in order to gain the occasional advantage over the market. This typically refers to technical loopholes but rest assured that ethical boundaries pose a very big opportunity as well.

It’s become common practice for fraudsters to examine the changes and updates made by fraud prevention vendors and optimize their activity to by-pass detection logic and thresholds. 

A relatively simple loophole to exploit is taking advantage of the false positive test limitation, at which you will see fraudsters deliberately mixing legitimate installs into the fraud mix. 

Don’t confuse this with an attempt to improve their traffic in any way, but rather look at this tactic at face value.  The goal here is to whitewash poor quality traffic.  Any legitimate traffic or install coming in is meant to be later used as a counterclaim whenever other, fraudulent, parts of their activity is blocked. 

Hyper active devices

Click flooding can be generated by a single device, sending multiple (sometimes thousands) clicks a day.  The example below displays identical IDFA for all reported actions – indicating that the same device is being used.  The event_time column displays  that all clicks (1,645 total, as shown below the table) were reported on the same day.  Repeating similar IPs can help identify  that this is the same physical device. However, the OS version and User agent are not the same for all clicks. This means that someone forged these values artificially.

Identical device click report

Can “legitimate” installs generated by such sources still be referred to as legitimate? 

Click flooding, as a general tactic, relies on massive click volumes populated with real device details. However, these details are often obtained using illegitimate means, such as malware on user devices, or even purchased through the dark-net. 

This means that even so called “legitimate” installs are often organic installs that the advertiser never should have paid for – these users went through the install process organically without ever encountering an ad. 

What would be the right approach in this case? Should we block the IP from providing additional clicks or perhaps the IDFA from gaining credit for further attributions.

Whatever our solution is, it won’t be error-free, some risks have to be taken.

 Flooding away

A different, more conservative tactic is based on statistical models that indicate chances of conversion per certain amount of clicks. 

As a result, advertisers are bombarded by a host of clicks for the remote chance of them converting into an app install. Fraudsters could populate fake click information and communicate it to advertisers on behalf of users who have no idea that this is being carried out. 

Click flooding

Another tactic is firing click URLs for every ad impression. Users may be exposed to real ads, but no ad clicks are actually made. A user is very likely to view the ad several times, thus increasing the chances for their “clicks” to convert. Once users eventually download the app, the fraudster wins attribution for an organic install.

Creating and delivering these fake clicks costs almost nothing for fraudsters or their operation, and they don’t mind the low conversion rates, as profitability is high. 

Sneaking under the radar

A similar, but more sophisticated method of click flooding is the break-up attempts of click floods into smaller sites – each associated with a lower number  of clicks.

This could  make each individual site seem “innocent” when examined independently.

However, when looking at the bigger picture this is no different than the example stated above. 

The number of clicks associated with each of these sites is actually a great example of the BI carried out  by fraudsters, to test anti-fraud thresholds, and optimize their traffic distribution towards a number that would keep them unsuspected below respective radars.

Micro site click flooding

Most of these sites will present very low conversion rates; however, the ones that will convert will present better potential conversion rates and will be used as an optimization life-line, presenting these few “good” channels as a means to keep their activity on-going.

When media partners split a single site ID name into thousands of meaningless names – it reflects badly for AppsFlyer, but more so to the advertiser – making it harder for them to accurately measure and further improve successful campaigns. Or alternatively, ditch poor performing sources. Considering that most of these sites will only have a few installs during their lifetime, studying their methods has become almost impossible.

This is where AppsFlyer’s post attribution fraud detection comes into play, as these low scale sites are almost impossible to block in real-time. A retrospective algorithm can trace back this activity and assign it to fraud trends that aren’t applicable to real time detection with high precision.

Some networks who gained a reputation of deliberately manipulating this parameter are treated with more extreme measures. Examining their traffic on a less granular level – meaning these small sites will be aggregated by their prefix name, as well as other similar parameters – they will be “judged” together and blocked in real time. 

We’re aware that a few “legitimate” sites may be affected by these actions as they’re mixed into a cluster of fraudulent small sites. While we work hard on mitigating this edge case as much as possible, the logic shown above highlights just how needed these actions are.

An unforgiving approach

Does the above suggest that we’re abandoning the false positive test?

Of course not.

As mentioned earlier, the false positive test is crucial in order to maintain ecosystem integrity, and a reliable fraud protection mechanism. However, we’re not going to accept whitewashing and a cynical use of low scale “quality” traffic as an excuse to keep harmful sources active. This is a great example of a case where we’re willing to lower our precision rate standards, become more permissive and block more fraudulent installs.

Click flood blocking (among other types of fraud) doesn’t happen instantaneously, it requires time, and carefully examine each and every publisher independently. To do so at scale, we’re constantly improving our cluster blocking mechanism – allowing fraud detection when insufficient information is gathered on the install level.

AppsFlyer will not tolerate abusive behavior from specific media sources that can be  harmful both to the ecosystem, and more specifically to our customers. 

Our goal is to catch fraudulent attempts from various sources earlier, faster, and more effectively. 

The post Click flooding detection and the false positive challenge appeared first on AppsFlyer.

Getting quality consumers to your Shopping app can be hard. Keeping them there over time while driving ongoing revenue with relevant and attractive offers can be even harder. 

Given increasingly high expectations, and numerous other competitors to buy from, users can easily and rapidly lapse into inactivity (or even uninstall). 

On the marketing side, rising user acquisition costs means driving new users to your app is increasingly expensive, while retargeting is much cheaper. As such, it is worthwhile to leverage intent data and advanced audience segmentation capabilities to re-engage with existing users — whether they are already inactive (lapsed) or are predicted to become inactive.

But practically speaking, what is the best way to set up these campaigns? How can marketers fully optimize the value of retargeting for their app? 

This blog post will cover the what, why, and how of retargeting for Shopping apps so that you can make the most of your marketing budgets and meet your goals. Plus, be sure to check out the complete practical cheat sheet on Shopping app marketing at the end of this post that includes: 

• Top vertical-specific goals + the KPIs to measure 
• Which and how many in-app events to configure
• Selecting media sources to run with
• Recommended segments

↓ Download full cheat sheet for Shopping apps ↓

What is retargeting and why use it? 

Retargeting is the marketing method to re-engage existing app users through paid and owned media channels. 

As the data from our new State of App Retargeting report shows, retargeting efforts deliver strong results and are especially well-suited to Shopping apps, which had a 50-72% adoption rate and a 25-60% increase in the share of retargeting conversions (depending on the region) as of Q1 2020. 

More importantly, we found that retargeting led to significant uplift in the share of paying users across regions: US (+90%), France (+70%), Italy (+43%), to name a few examples.

Retargeting in Shopping is a natural fit because direct response campaigns encourage action, which drive purchases of products users expressed interest in. In fact, according to Criteo, re-engaged shoppers show more than four times higher conversion rates in-app than they do on mobile web or desktop, making the app the ultimate touchpoint and a must-have channel for Shopping brands. 

Clearly, mobile web is not an alternative but rather a complimentary mobile channel whose primary role is to cater to users who land from search.

What to measure

Running an effective retargeting campaign starts with measuring the right data to guide your analyses. This is especially true for Shopping apps, who need to drive lapsed users to make their first purchase, while also retargeting non-paying and paying users before they lapse (usually within the week after install).  

To get you started with focused measurement for retargeting, we’ve included the following table showing some of the most common vertical-specific goals and the KPIs to support them:

Timing retargeting campaigns

There are multiple factors that go into deciding when to begin running your retargeting campaigns. These include the type of audience segment, the level of engagement, whether you’re seeking to recover users or simply re-engage, your budget, and others. 

Retargeting campaigns may be launched as early as immediately or 24 hours after install, within the first week, or later. Remember that one size does not fit all and choosing your timing depends ultimately on your own goals.

The same is true of stopping your campaigns, which is again determined by your expected marketing goals and the limits of your campaign budget. For Shopping apps, the most successful marketers tend to end campaigns after 7, 14, and 30 days, though one perspective suggests that all projected engagement will occur within 14 days. 

Remember it is important to avoid overexposure which could severely hurt your brand and lead users to rapidly uninstall your app. 

Audience segmentation for retargeting

So you’ve mapped your target KPIs and set up the scope of your campaign, but it’s still not time to launch your retargeting campaigns. Understanding your audience, and being able to use your data to segment audiences effectively, is critical for delivering targeted and effective ads. 

Below, we’ve included some of the most common audience segments Shopping marketers use to guide their retargeting efforts:

For Shopping-specific UA audience segments, as well other practical guidelines for Shopping app marketing, check out the cheat sheet below. 

The post The What, Why, and How of Retargeting for Shopping Apps appeared first on AppsFlyer.

And why closing the feedback loop is crucial to enabling both

Apple’s CEO, Tim Cook, said in a recent statement that “The App Store is a place where innovators and dreamers can bring their ideas to life, and users can find safe and trusted tools to make their lives better…”.

In order to deliver better user privacy, do users necessarily need to compromise on their experience, and the value they’re getting from their favorite apps? Must there really be a trade-off, or can the two coexist? 

Privacy is a fundamental human right, and a core value that both Apple and AppsFlyer share. Over the years, and most recently in its latest worldwide developer conference, Apple proved that there is a way to maintain both user privacy, and a superior user experience. Let’s review some of Apple’s recent privacy enhancements:

• Photos – When allowing an app to access your photos, you can now give permission to share only specific photos, instead of the entire photo library. 
• Contacts – With iOS 14, you won’t have to share your entire contact list with apps, just the ones your app really needs access to in order to provide its promise. 
• WiFi – Devices will not broadcast your MAC address (a non-resettable, persistent ID) to every WiFi hotspot around you. 
• Camera & microphone – You will now have a clear indication when an app is using your camera and microphone. 

None of these privacy enhancements compromise the user’s experience, or the value they receive from their device or a given app. On the contrary, these measures increase users’ trust in the App Store economy. Eventually – these actions are win-win-win for everyone – consumers, app developers, and the App Store economy at-large.

Apple Privacy Decisions – better privacy AND better user experience

The IDFA/ATT change

Following these important improvements, Apple introduced the AppTrackingTransparency (ATT) framework, which will ultimately eliminate the IDFA. 

The IDFA is a great tool that enables the App Store economy to flourish. On the other hand, it introduces privacy concerns, as it can be used in various ways, some of which could potentially harm user privacy. The IDFA is not inherently “bad” or “good” for users. It depends on how they’re used. 

IDFA risks (the “bad”)

IDFAs can be used to track users across apps, build user profiles without proper consent or worse, attach GPS data to users across apps. In addition, they can be used for questionable practices like selling customer data and trading it for targeting or other uses. Apple is trying to get rid of these practices on their platform, which is without doubt a great move.

IDFA benefits (the “good”) 

User experience 

In most cases, the IDFA is used to provide a stellar user experience and increased value to consumers.

If you can’t measure it, you can’t improve it. There is no doubt that measurement, or closing the feedback loop with attribution, is the key to improving user experience. Did the user find value in what was offered to them? Was the user experience good? 

To properly understand and measure the user’s journey, there is no need for any tracking across apps, no profiling, and surely no selling of user data. Developers measure users’ activity within the scope of their apps, and connect that to their owned media such as websites, social media platforms, emails, user referrals, and their own ads which users interacted with to install their app. 

Monetization and the App Store economy

Measurement allows app developers to monetize their work, so they can continue to innovate and create better products for their users. On the other hand, measurement makes sure that these developers present current and future users with relevant and appealing content, rather than spraying and praying.

Moreover, IDFA introduced a major privacy improvement over its predecessor – the UDID, with the ability to reset and opt-out. Without the IDFA or an appropriate replacement, some in the ecosystem might be pushed to find loopholes, or use far more intrusive techniques. 

Great user privacy and great user experience can coexist

Think about cars, for example; no one would think to suggest eliminating the use of cars altogether, even though they can be very dangerous. We believe that there are ways to increase users’ privacy and to improve their experience, creating a win-win-win for everyone.

Eliminating the IDFA without allowing proper attribution: Great privacy. Negative user experience impact.

In the last two years, we’ve been preparing for an IDFA-less ecosystem. We’ve been investing in multiple products and solutions, which along with our lifelong investment in privacy and security make us very well prepared for the upcoming iOS14 updates. We are excited to share our immediate solutions with our customers and partners in the coming days and weeks, as well as to continue our discussions with Apple on long term ideas to maintain the highest levels of both user privacy and user experience.

“In a challenging and unsettled time, the App Store provides enduring opportunities for entrepreneurship, health and well-being, education, and job creation, helping people adapt quickly to a changing world. We’re committed to doing even more to support and nurture the global App Store community — from one-developer shops in nearly every country to businesses that employ thousands of workers — as it continues to foster innovation, create jobs, and propel economic growth for the future.” Tim Cook 

Oren

The post Does user privacy have to come at the expense of an incredible user experience? appeared first on AppsFlyer.

This post is an update of a March 2019 blog with new benchmarks

Over the last few years, much has been said about the post-install journey — marketers need to measure this data, then connect to a user’s pre-install behavior to optimize their future marketing efforts. Indeed, emphasizing user “value” over “volume” is key to driving marketing profitability.

However, in addition to post-install analytics, it is important to closely monitor pre-install data, which offers key insights that inform creative optimization, ASO, targeting, and bid management (check out this list of 16 KPIs gaming app marketers should measure). 

Marketers rely on pre-install metrics to: 

1. Measure the value of ad creatives and their ability to drive users to perform a desired action (e.g. click, install, registration, purchase). To improve these metrics, marketers need to optimize the creatives (design and copy), a/b test ad placements, improve targeting, etc.
2. Know the quality of media sources they work with by comparing their supply variety, scale, global reach, and business logic (machine learning complexity that determines where, when and to whom your ad will be shown). This will allow campaigns to reach optimal pre-install metrics under their given limitations (budgets, frequency caps, targeting, etc).

This post will cover the importance of pre-install metrics, and provide data-driven trends and benchmarks per genre.  

What are install conversion rates and why are they important?

Among the variety of mobile marketing KPIs, pre-install conversion rates are used to measure the volume of users that make it through your conversion funnel to install. 

While an install is usually not considered the real “end game” of marketing efforts, it is still an important milestone, as users must physically download the app to their phone and invest attention and/or resources into it.

Understanding this significance, in the broadest terms, a pre-install conversion rate can be the number of installs divided by either the number of total ad clicks or ad views, depending on what you’re trying to measure.

In the past, when granular data was less accessible, mobile marketers relied more heavily on pre-install conversion rates, trying to reach high engagement and choose the partners that can help them rapidly grow while maintaining high quality.

Today, pre and post-install metrics are both an integral part of every user acquisition strategy.

Let’s explore each pre-install metric:

Click-through rate (CTR)

Click to install (CTI)

Installs per mille (IPM – Install per 1000 impressions)

Gaming genre trends

The comprehensive data set used in this research covers 470 billion ad impressions, 49 billion clicks, and 2.1 billion installs of 4,500 apps in the past 1.5 years. It included top media sources used by Gaming app marketers, whose inventory is mostly comprised of video placements (rewarded and interstitials). The data set does not include Facebook and Google inventory.

The fact that video covers most of the data explains why the average CTR values are significantly higher than in cases where display (standard static banners) is more dominant.

Advanced technology and constant training of machine learning algorithms drive performance, delivering the highest returns of campaign investments. Some genres do it better.

According to our data, Midcore and Hyper Casual games are the most popular genres, with the largest scale and fastest growth. It is therefore no surprise that these genres are leading all three metrics in the graph above. 

The most impressive growth rate was seen in CTI (click to install), rapidly growing for the past 18 months in most gaming genres (excluding Social Casino), and especially within Midcore and Hyper Casual games — each of which increased by nearly 110% YoY.

Hardcore games are very popular among specific types of players, making it harder to drive scale through UA because of a smaller pool of potential players. However, they’re still growing every year (Q2 vs Q2) in CVR (+18%), CTI (+34%), and IPM (+6%). 

It is important to take into account the impact of COVID-19 and the imposed lockdowns in many countries across the globe. 

As we’ve shown in our COVID-19 dashboard, Gaming apps enjoyed a spike in installs, usage and revenue. With heightened demand for games, pre-install metrics improved as well as we can see in most cases in the chart above.

Regional trends

In our latest State of Gaming report, we’ve shown the vertical’s significant growth across the globe. Clearly, mobile games are engaging more users and more users in every country, increasing its reach in different regions with different cultures. 

As we can see in the chart above, there has been a significant increase in regional pre-install metrics. Rising above is Latin America, where the YoY (Q2 vs Q2) increase across CVR (+28%), CTI (+86%), and IPM (+19%).

The fact that pre-install performance is on the rise across every region worldwide reflects progress in creative optimization, and the ability to match creatives to multiple cultures. As a result, ads are more relevant to the end-users wherever they are.  

Benchmarks

The following tables include pre-install metrics, showing the average per app, market, and gaming genre during H1 2020. In addition, we added the percentile 90 value to show the performance of the top 10% of apps (per the KPI measured) — a great way to put a number by a goal in your game’s genre:

The post CTR, CTI, & IPM: Optimizing The Path to Install Conversion [Benchmarks] appeared first on AppsFlyer.

Over the years, marketers and app developers have learned that attribution is mission-critical to running an app business. As the upcoming iOS14 changes will impact everyone in the ecosystem, I wanted to lay out how attribution also affects app consumers.

Since AppsFlyer’s inception back in 2011, we’ve seen how a conflict of interest can have a negative impact on both consumers and the ecosystem at large. This is why we chose to be neutral and unbiased from the get go, and built great software to represent the marketers and app developers within the app economy. Our independent positioning created a lot of trust in this ecosystem and remains to be one of our key principles and commitments to date.

We always have our customer’s customer, the app user, at the center of everything we do. We are constantly pushing ourselves to unbiasedly represent users and their interests.

When you think about attribution, you might immediately think about the value it provides for sophisticated marketers and app developers. But in fact, attribution makes the entire ecosystem more efficient, providing a better user experience, and improving consumers’ privacy. 

So how do consumers benefit from attribution? We can break this down into four key areas:

1. Attribution improves the user experience

2. Attribution is not tracking

3. Limited attribution →  limited monetization →  limited innovation

4. Attribution promotes consumer privacy

1. Attribution improves the user experience

If you can’t measure it, you can’t improve it. Questions like: Did the consumer find value in what was offered to them? Was the user experience good? Are answered with attribution that measures the true value delivered to consumers.

Good, relevant ads

Without attribution, app developers will not be able to know whether their ads are adding value to consumers or annoying them. Ads don’t have to be a nuisance, they can and should be a way for consumers to discover great products they weren’t aware of. Moreover, without proper ROI measurement, app developers will not be able to justify their ad spend, which will create more irrelevant ads – eventually harming the users’ experience.

A frictionless journey 

Attribution enables the context bridge that allows apps to send their consumers to exactly where they want to be within the app, rather than just to the homepage. Through attribution, many developers leverage deferred deep linking to significantly improve their users’ experience, by seamlessly connecting emails, social media posts, referral programs, and their website into the web-to-app journey. 

Think about browsing for a vacation on your mobile web browser. How can you complete the transaction in an app if you lose the booking details in the process of downloading it? That would be the equivalent of Google search taking you only to home pages, rather than the specific page you were looking for on the site. None of us would accept such a broken experience. 

2. Attribution is not tracking

Attribution connects apps to their own ads running in other apps. Attribution measures the app developers’ owned creative and campaign details, not data from the apps in which the ads are served. Moreover, in most cases, the app in which the ad was served is unknown. Attribution is about answering a very simple question: did the consumers find value in my paid, earned or owned media. 

Not all SDKs are equal. AppsFlyer for example, is a CRM-like SaaS platform that allows app developers to manage, analyze, and secure their consumers’ data. The AppsFlyer SDK acts as an extension to developers’ technology stack and as an interface to the AppsFlyer cloud-based software. While we are essentially a third-party software, we serve as a first-party software that is an integral part of the app developer’s tech stack.

3. Limited attribution →  limited monetization →  limited innovation

App developers generate tens of billions of dollars in revenue from advertising. This stream of revenue, which is invested back into the ecosystem of innovators to improve and develop new products to delight consumers, is at risk of disappearing without proper attribution. 

Without attribution, marketers will not be able to justify their ad spend, which will directly impact app developers that are reliant on ad monetization to innovate and continue to create incredible products we all love to use. 

4. Attribution promotes consumer privacy

Is an extensive user-level profile needed to delight customers with relevant ads? The short answer is – no. 

Machine learning enables ad networks to ensure the ads they display provide value to consumers, without having to build intrusive user profiles, simply by running experiments and observing the results. For example: incorporating non-personalized, contextual parameters such as weather, time, location, sentiment, cohorts, etc, and training their machine learning models on what worked and what didn’t. In marketing, and specifically within the app economy, attribution is the feedback loop.  

The feedback loop, or attribution, doesn’t have to be based on user-level data. Feedback in a certain level of aggregation is enough, which is a huge leap forward for consumers’ privacy.  Proper attribution is key to closing the feedback loop and improving consumers’ privacy.

Bottom line

In the coming days and weeks, we’ll be presenting our iOS14 compatible solutions, placing consumer privacy and experience at the center, while supporting ecosystem innovation. 

What guided us as we’ve been working on these solutions is our unbiased principle, which has led us from day one. This enables us to do the right thing for consumers and support developers across the industry. 

We believe that innovators, creators, and dreamers should have the ability to make an impact and change the world. We believe that freedom, openness, privacy, and safety are core parts of the internet. We also believe that good advertising is a very important component of the internet.

We are, and have always been passionate about doing the right thing for consumers, whether it be in maintaining their privacy, or improving their experience. As counter-intuitive as it may sound, since we are not a direct to consumer company, we see that as our ultimate mission.

The post Why attribution is important for consumers appeared first on AppsFlyer.

The last few weeks have been busy for the entire App Store ecosystem, ourselves included. The AppsFlyer offices around the world have been feeling like an App Store support center, calmly managing hundreds of calls and emails from app developers and ecosystem partners of all shapes and sizes. We are humbled to be seen as a trusted partner by many in the ecosystem, and we take this responsibility very seriously. To make sure we handle this challenge in an unbiased manner, I asked the team to take their AppsFlyer hats off and put their ecosystem hats on, placing our customers and our customers’ customers at the center of every decision we make.

iOS 14 is not about IDFA deprecation. iOS 14 is about users’ privacy

While the IDFA is not inherently “good” or “bad”, the concept of IDFA deprecation has been discussed for quite some time now, and there have been many good reasons for Apple to take action on this. With that, iOS 14 is not about IDFA deprecation, it is about improving user privacy, a value which we at AppsFlyer fully support and embrace.

Since the early days, privacy has been a core value at AppsFlyer, and one of the main reasons for our success. Our long term commitment and investment in privacy pushed us to become experts in the privacy domain. As a CRM-like SaaS platform, AppsFlyer allows app developers to manage, analyze, and secure their consumers’ data. Our privacy-by-design software enables app developers and partners to make the right choices to delight their end-users, protect their privacy, and help them comply with regulations and platform policies.

In our effort to maximize user privacy and user experience, we spent the last few years working on several solutions to operating in an IDFA-less world. From probabilistic modeling, which we introduced over two years ago, to ideas for the App Store and iOS in the form of IDFA alternatives to preserve the benefits of attribution for consumers and maximize their privacy.

Today, I’d like to introduce one of the exciting ideas we’ve been working on for our iOS 14 readiness – aggregated attribution. This aggregated attribution solution is based on differential privacy principles and built-in strict privacy measures to align with iOS 14 privacy requirements. 

What is differential privacy?

Simply put, differential privacy takes data anonymization and aggregation a few steps further in terms of privacy. It makes it practically impossible to tell if an individual is included in the computation or dataset by looking at the output.  

Aggregated attribution based on differential privacy – highlights:

• The solution is based on the principles of differential privacy, deterministic (where allowed) and non-deterministic methods, probabilistic modeling, and signals from Apple’s SKAdNetwork. 
• The core of the solution is aggregated level attribution, reporting, and integrations. To clarify: user-level campaign details might be available only if: (i) the user gave AppTrackingTransparency (ATT) consent on both source app and target app, or (ii) both apps belong to the same developer i.e. have the same IDFV.
• Impression based modeling will be available only on an aggregated level, regardless of whether the user provided ATT consent.

• User-level click and impression data will not be available to anyone and will be used solely for aggregated attribution modeling. Click and impression data will be deleted based on a strict data retention policy.

AppsFlyer’s Aggregated Attribution Solution

The mechanics of campaign attribution modeling are basically implementation details, not the core of it. Implementation details can vary, and can be updated over time. It can be a combination of probabilistic modeling, which is aligned with differential privacy input, data ‘noise’ requirements, and other deterministic or non-deterministic methods, such as a future App Store referral API, or an updated version of SKAdNetwork. 

This solution provides a high level of privacy and aggregated accuracy. It maintains the attribution benefits of the IDFA while keeping the bad out, and allows the ecosystem to continue to focus on consumers. Like many changes our industry has faced in the past, this will require some adaptability from app developers and our partners. 

In the last couple of weeks we’ve been engaging the market to present and discuss this solution. Several partners are already in different stages of implementation, and many customers are excited about it. In the coming weeks, we are going to extend the discussion to the entire app market, to discuss this solution as well as other solutions we’re working on. 

Thanks,

Some additional reading/viewing:

• A short video about differential privacy 
• The Algorithmic Foundations of Differential Privacy – Cynthia Dwork & Aaron Roth

The post Aggregated attribution innovation based on differential privacy appeared first on AppsFlyer.

Much has been written about SKAdNetwork in the past weeks, since Apple’s announcements at WWDC20.

Today, we are excited to share AppsFlyer’s solution for SKAdNetwork, and elaborate on how it will be embedded into our full attribution solution.

Maximizing SKAdNetwork data is an important part in our new Aggregated Attribution solution, as these new signals make the full attribution solution more accurate. SKAdNetwork data, cleaned from all user identifiers by definition, fits perfectly into that model by putting user’s privacy at the heart of the solution.

Overcoming the challenges

SKAdNetwork presents new functional challenges for the advertiser. To name a few:

• No real ROI/LTV – Measures mostly installs. See below more details on how our solution overcomes this
• Granularity – Campaign level only, limited to 100 campaigns
• Postback delay – At least 24 hours, sometimes longer
• Prone to low levels of trust from advertisers – Data is owned and reported by networks
• Ad fraud risk – Data can easily be manipulated
• No re-engagement attribution support.

Aside from the functional challenges, it also introduces structural ones. The postbacks are sent only to the attributed ad network; therefore the advertisers, or anyone processing data on their behalf, are blind to it.

This raises some tough questions:

• How will advertisers collect SKAdnetwork data from all of the different networks? The list of networks can be very long, leading to extensive R&D, integration and support cycles for each of them.
• How will results be displayed in one centralized place, for actionable marketing insights?
• How will data be validated? The data could potentially be manipulated by many players along the way. Dirty data means bad marketing decisions.
• How will networks decipher the actual meaning of the conversion value per app?

We designed our solution to tackle these functional and structural challenges.

Our solution

The main highlights of our solution are:

• Data aggregation: Collecting all SKAdNetwork information from each ad network, on behalf of the advertiser
• Data validation: Ensuring all postbacks are signed by Apple and aren’t manipulated in transit 
• Data enrichment: Matching SKAdNetwork information with other data points, such as impressions, clicks, cost, organic traffic and more, for complete ROI analysis
• Data enablement: Facilitating SKAdNetwork data for convenient consumption by the advertiser, through dedicated dashboards and APIs
• Seamless integration: Full encapsulation, requiring close to zero effort from the advertiser, including for future changes in the SKAdNetwork protocol
• Conversion events: Server side, dynamic and flexible in-app event to conversion value configuration. 

In order to enable this, here are our solution pillars:

Access to postbacks: We’ve defined an easy-to-follow process to enable postback forwarding to an end point by AppsFlyer, together with an integration doc depicting the fields ad networks should expect in each postback.

We’ll be sharing this integration doc with all of our partners in the coming days.

Mutual model enrichment: Last week we introduced our Aggregated Attribution solution. AppsFlyer’s attribution approach is holistic: every new signal enriches all the different models. In our vision, SKAdNetwork is no different in this manner; in the future, these signals will be used to enrich our aggregated attribution model, to provide a full attribution picture.

SDK wrapping: AppsFlyer’s SDK within the advertised app will encapsulate all calls to SKAdNetwork OS functions. The advertiser doesn’t have to do anything beyond integrating our SDK.

Dynamic post-install in-app events attribution for SKAdNetwork

A lot has been said about the conversion value field in SKAdNetwork. We believe in full flexibility here. That’s why we decided to enable advertisers to configure what they want to measure with it; revenue, conversions, engagement or retention.

Advertisers can control or change it on-the-go directly from AppsFlyer’s dashboard, and the SDK and entire system will adjust immediately. To be clear, everything will be controlled from server-side, with zero changes to the app code and no re-submissions to the store.

The screenshot below shows how advertisers choose their preferred mode. According to the active configuration, AppsFlyer will decode the conversion value from SKAdNetwork data, present it to the advertiser, and also report back to the networks for campaign optimization purposes (based on advertisers’ postback configuration):

We’re happy to share that we have enriched this model even further , to enable customers to activate more than one mode in parallel (conversion, engagement and revenue). By randomly splitting the users of the app to different groups, and upon scaling the results, we can support measurement of all KPIs to achieve optimal marketing insights.

AppsFlyer’s SKAdNetwork Overview Dashboard

We’re excited to share a first look at our brand new SKAdNetwork overview dashboard. This dashboard presents all SK data collected from the different networks, after being validated and enriched with all other signals collected externally to SKAdNetwork from the networks and AppsFlyer SDK.

The dashboard presents the entire marketing funnel, per each media source and campaign: impressions, clicks, installs, conversions, engagement and revenue. In other words, measuring your main KPIs per campaign: CVR, ROI, CPI, ARPU, ROAS, eCPA.

The dashboard also shows trends over time revealing insights and comparisons, using your organic traffic as a baseline.

Let’s look for example on the 3 pie charts in the center and the marketing insights they generate. We’ll note that Network A generates a big portion of the installs (35%), but these users aren’t converting well (15% of converted users, and only 9% of paying users). On the other hand, for Network C the results are the opposite. The marketing implications are clear.

What’s expected until the release of iOS14?

Advertisers: In the coming days, we will release our SKAdNetwork-compatible SDK. Advertisers will need to update their SDK to this version in order to enjoy SKAdNetwork attribution functionality.

In parallel, we are updating our Help Center with full documentation about the configuration of post-install attribution, limitations, dashboard and APIs.

Ad networks: We are in the process of releasing our integration spec detailing the interface between AppsFlyer and networks:

• How to send AppsFlyer the SKAdNetwork data 
• Campaign mapping
• Postbacks from AppsFlyer to networks for SKAdNetwork data and campaign optimizations

We welcome all of our partners to approach us to receive the spec and start integrating with our SKAdNetwork solution.

Wrapping it up

We realize and sympathize that SKAdNetwork is not optimal. ROI and LTV measurements are somewhat limited. However, when enriching these postbacks with other data points as presented above, one can still generate significant insights and take the right marketing decisions based on them. Afterall, that’s what attribution is all about.

The post Maximizing SKAdNetwork insights with AppsFlyer appeared first on AppsFlyer.

In the mid-1990s, the use of mobile phones started its swift spread across much of the developed world. At that time, very few thought of Africa as a potential market.

Today, the GSMA’s recently-released report The Mobile Economy: Sub-Saharan Africa 2019 predicts that sub-Saharan Africa will remain the world’s fastest-growing region, adding 167 million unique mobile subscribers between 2018 and 2025. This will take the total subscriber base to just over 600 million, representing around half the population.

In fact, 3G adoption has doubled over the last two years as a result of network coverage expansion and the availability of cheaper devices that connect to the internet.

The GSMA’s report also highlights the large number of young consumers becoming adults and owning a mobile phone for the first time. This segment of the population will account for the majority of new mobile subscribers and, as ‘digital natives’, will significantly influence mobile usage patterns in the coming years.

It’s not all roses, however. More than 800 million people in Africa still don’t have access to the internet, with the lowest penetration rate in the world at just under 40 percent.

Also, when we talk about Africa, we tend to think of a single 1 billion-strong geographic entity. But Africa has 54 countries with more than 2,000 languages, while urban Africa is vastly different from rural Africa. As a result, it is very unlikely that a mobile app can meet the needs of all Africans.

Still, one can draw parallels and learn from patterns, trends, and data. These can better inform those looking to thrive in the African continent.

How best to operate in such a complex and fragmented market?

When it comes to the African continent, there’s a lot to learn from WhatsApp’s success.

This ease and flexibility of use makes WhatsApp the most popular messaging app in most African countries, marking the third year in a row atop the charts.

WhatsApp is used by 73% of internet users in Kenya, 53% of internet users in Nigeria, and 49% of internet users in South Africa. In 2018, WhatsApp accounted for almost half of all mobile data used in Zimbabwe.

WhatsApp boasts the following features that make it so wildly successful in this region:

The app adjusts to spotty internet service
It works on inexpensive feature phones
It can transmit anything from photos to videos to documents to spreadsheets
It allows people to use it for free – which is important in regions where money can be an obstacle
It lets people send audio messages – key in countries with high illiteracy rates
It’s simple – you only need to know a contact’s mobile phone number

So…

If you’re looking to break into the African tech/app scene, here’s what you need to know:

1. You’ll have to use data-light content

The majority of mobile internet activity in Africa comes from web pages and not apps, due to cost.

The Internet remains prohibitively expensive: on average, a gigabyte of mobile internet data costs 8% of average income across the continent—more than anywhere else globally. The reason for the lingering cost of access largely lies in the lack of competition between internet providers in markets across the continent. Aside from cost, internet speed also poses a problem as projections show Africa is at least five years away from faster 4G mobile networks having a major impact.

As a result, services such as Twitter and WhatsApp tend to be more heavily trafficked than the data rich ones like YouTube and Vine.

Source: Atlas

When launching an app in Africa, it’s crucial to recognize the African user. To acknowledge their experience is to recognize the high cost of data in Africa that limits their desire to engage with data-heavy content and campaigns. If you want to reach a larger and broader audience, cater to users with a slower connection that focuses on text and links as opposed to video and photos.

2. You’ll need to build apps that add instrumental value

While this is pretty much the case anywhere in the world, it’s especially important in Africa.

To succeed in the continent, you must not only build power and data-efficient apps, but also develop apps that deliver real value that a consumer with limited storage space and limited data will want to use.

This is why some predict that for African consumers, the super app model (similar to WeChat in China) could likely enjoy much success.

Africa doesn’t have a super app yet, although WhatsApp is closest to it. In Africa, it’s common for people to use WhatsApp to chat with friends and send memes, but also to dispute a bill with their utility providers or book an appointment with a barber. It has become a default method of office communication as well, from grapevine to major planning decisions.

Therefore, for an app to be successful in Africa, it should be of instrumental rather than self-fulfilling value.

To better understand what this means, let’s look at QuickCheck, a mobile app that was specifically designed to solve the difficulties Nigerians face in seeking access to credit.

The QuickCheck app

According to QuickCheck’s CEO Fabiano Di Tomaso, “We believe in double-faced businesses: economically sustainable and socially impactful at the same time – this is QuickCheck, aiming at a meaningful financial inclusion of Nigerians, by enabling consumers’ participation in the economy through our digital financial services”.

Many Nigerians are unbanked and have no access to formal financial services for various reasons. The results of this EFInA survey reported that over 60% of the adult population were financially excluded. With their tech solution, QuickCheck not only bridges the financial inclusion gap but also educates people about financial instruments and risks related to loans and amassing too much debt.

Your app should be instrumental to African users and their lives, enough to give them a reason to “sacrifice” valuable phone storage space and data to use it.

Another African startup, the Eco-Warriors™ app, is the first educational mobile game that teaches kids to recycle waste with the help of gamification.

“When we launched Eco-Warriors™, we wanted to make it available to all school kids. The strategy that we applied was to get in a partnership with the Ministry of Education locally after receiving the first-ever UNESCO Patronage for an Educational Mobile game on the African Continent”, explains company’s founder and CEO Brian Dean.

This partnership helped the company increase the rollout speed of the mobile game and reach more children in a shorter time frame. Today, they are present in 69 schools in the Island of Mauritius. Currently, the team is rolling out the game in the 289 remaining schools.

The spread of COVID-19 has slowed their process but by the end of the semester, Eco-Warriors™ Educational mobile game will be available in all the primary schools of Mauritius and more than 80,000 kids will be able to learn waste recycling.

3. Android apps come first

In Africa, Android phones lead in terms of market share of phones and smartphones with 84% adoption rate. They are far more affordable than Apple devices and therefore more accessible to the vast majority of Africans.

When choosing between Google Play (Android), the App Store (iOS) and Windows, it is common to develop and deploy apps in as many platforms as possible. However, in the African context where financial resources are scarce and data is expensive, you should pick one platform and track early adoption before moving to additional ones.

Data suggests you should build Android apps first, get feedback, and then roll out to other platforms. If your app doesn’t succeed on Google Play in Africa, it won’t succeed on the App Store or Windows. Furthermore, the Android platform seems to provide fewer restrictions in terms of available countries, app registration, and sales.

4. It can be expensive

The dynamics of the African market requires you to definitely do a lot of advertising, mostly TV and radio advertising – and these often don’t come cheap. However, CPIs are usually the lowest in these markets. It offers app developers a chance to acquire users at a lower cost than more developed regions.

An app can become successful over longer periods of time organically, but to drive rapid app adoption a large marketing budget is often needed to create awareness on a wider scale.

Having said that, a limited budget should never be a limitation as there are plenty of cost-efficient ways to promote your app in the market. Let’s take as an example Teheca Limited, a solution that delivers postnatal care services in Uganda.

“We have got great success in the number of installs through mostly Google Ads followed by Facebook Ads, with Facebook being the most efficient and budget-friendly”, shares their experience with the app promotion Ruyonga Daniel Bosco, CEO of Teheca Limited.

Teheca app

For example, for Eco-Warriors™, social media was an efficient tool for brand awareness and brand image, but to get downloads and convert parents into customers, they used other channels like email marketing, partnership,etc.

Africa has shown considerable growth and made its first appearance on the AppsFlyer’s Performance Index. As a result of the continued economic development, the number of smartphone owners is rising at a rapid speed, representing fertile ground for marketers vying to attract and retain new customers.

QuickCheck leveraged not only digital ads but has also partnered with brands such as Jumia and Pay Attitude (to name a few) by providing loans to their existing customers. These digital partnerships support the rapid emergence of new cross-industry business models as everything becomes connected and digital. In other words, look out for unconventional means of reaching out to your target market.

Once dominated by print, radio and television, the Nigerian media landscape is now experiencing a disruption by digital platforms and for this reason, Quickcheck focusses most of its marketing efforts on digital channels.

5. The market is maturing

The app economy in Africa represents a tremendous opportunity for the economic and social development in Africa, and for the businesses developing them.

The GSMA report estimated that the mobile industry will contribute $214 billion to the GDP. Currently, the cost of basic simple smartphones is falling rapidly and they will soon be within reach of the majority of people in the African region. Also, mobile phone coverage in Africa is rapidly improving and many areas now have access to LTE, delivering high speed and advanced functionality.

There are many unique economic contexts within Africa that create opportunities for unique African solutions and they represent opportunities from which app development and marketing can take place.

Conclusion

There’s no one-fits-all strategy when it comes to marketing an app in the African market, as it is in any other market. It’s always about trial and error and finding what works best for your product and industry. Just to sum-up:

• Perform market research and approach entrepreneurs and solutions already active in the market.
• Check the industry benchmarks but always set up your own after a testing period. Try not to focus too much on those examples, because each business’ average numbers can lead you in the wrong direction in the end.
• Always speak to your customers. Try to set up a process when you communicate with at least 5 customers per month.

Keep in mind that the market size is enormous thus, even a small piece of it can give you pretty good returns on your investment.

Ilma Ibrisevic and Stephanie Peter-Omale contributed to this article.

The post Go-to-Market strategy for your app in Africa appeared first on AppsFlyer.

Until recently, very few people had ever heard of the term SKAdNetwork (or SKAd). Recent developments, however, have more and more people realizing that it’s going to play a major role in the marketing ecosystem in the post iOS 14 era.

In a recent blog post, we presented AppsFlyer’s solution towards SKAd, and how we overcame its limitations to maximize the values the ecosystem can get out of it. Our solution includes collection, validation, enrichment, enablement, and seamless integration with both advertisers and ad networks.

In the center of the SKAd mechanism, however, there is one special field: conversion value. In this blog post we will explain why this field is so important, how to get the most out of it, and mostly answer one of the most frequently asked questions as of late: who should control/change the conversion value, and when?

The value of the conversion value

The conversion value is a field with the range 0-63 (integers only), fully controlled by the advertised app. The SKAd postback sent from Apple to the attributed ad network simply forwards that value. Simple, right? Then why is it so important?

To answer that question, let’s imagine what SKAd would look like without it. It’s actually not that hard to imagine, as V1 of SKAd worked precisely like that.

Without this field, SKAd insights would look something like this:
My app had 100 installs from Campaign A last week, and 200 installs from Campaign B. Does this mean that Campaign B is better? Not necessarily. Maybe the users from Campaign A are performing much better as far as my KPIs go?

That’s what the conversion value is all about. It enables the app owners to “grade” their end users. For example: how much revenue was generated from the user? How many levels did they complete in my game? How many times did they click on an ad?

With conversion values, marketing insights are far more, well, insightful. With conversion values, we can reach conclusions like this one: “Though Campaign B generated twice as many installs, the users from Campaign A generated far more revenue and are more engaged”.

Great, so conversion value is all you’ve ever needed, right?

Well, not quite.

First of all, all data needs to be “encoded” to a single field, with a range of 0-63. This means that app owners can’t measure everything. They need to choose what’s mostly important for them to measure.

Second, there are timing limitations based on the timer mechanisms of SKAd. Without going into all the bits and bites:

• Measuring events which happen more than 24 hours post-install is complex. It requires the app to be opened every day until the event occurs, or some background thread to reset the timers periodically.
• Every update resets the timers, causing the (already delayed) postback to be delayed even more. Eventually, this delay makes short term optimization almost impossible.

So it’s controlled by the advertised app. Where’s the dilemma?

Right, eventually it will be controlled by the advertiser.

Can advertisers really maximize its value on their own? The answer is no, and app owners realize this as well. This is the reason big players in the ecosystem are already building solutions to help advertisers with this.

Before diving into the details, it’s important to note that if two separate entities try to control the conversion value in parallel, chaos will ensue; in fact, nothing will work. Even worse, no one will realize that nothing works, and misguided marketing decisions and optimization attempts occur.

App owners – make sure only a single entity is controlling this field on your behalf.

If you’re a provider aiming to control the conversion value – make sure the app owners name you the owners before making changes to conversion values.

Getting the most out of conversion values

As mentioned, getting the most out of conversion values actually means getting the most out of SKAd. Checking off the items below means you made the right choice.

1) Single management
Assumption: Conversion value mapping will be changed frequently by the app owner. As the 6 bits of data can answer only specific marketing questions, the UA manager will change them over time to validate that budgets are being spent efficiently.
Requirement: Mapping changes should be done in a single place, in one dashboard. App owners shouldn’t have to manage these in several places every time they change the configuration.

2) Full server-side encapsulation
Assumption: Conversion values can only be changed on the client side. Does it mean that you’ll need to change your app code for every mapping change? Well, no.
Requirement: Change mapping in a SaaS dashboard anytime you want. The actual mapping should be commanded immediately to the app side, without the need to release a new version to the app store.

3) Simple set up
Assumption: Conversion value mapping will rely on the already existing in-app events of the app.
Requirement: Conversion value configuration should be done by an entity that has already mapped these events, to ensure easy configuration and fewer errors.

4) Flexible configuration
Assumption: In order to get the most out of these 6 bits, full flexibility is needed.
Requirement: Unlimited set of options for the advertiser. Whether they want to measure revenue, conversions, engagement or a combination of these three. Example:

• 2 bits for revenue, 3 bits for engagement, 1 bit for more precise install time
• 4 bits to measure conversions of 4 different events, 2 bits for user’s avatar type
  

Can this full flexibility coexist with simplicity? The answer is YES.

5) Parallel/multi-mode support
Assumption: App owners will have more than one marketing question they want to answer at any given point.
Requirement: Optimize and analyze campaigns for two KPIs in parallel, by splitting the end users, whether based on geo or on a random split.
Eventually, such a split can help app owners to measure revenue in high granularity in parallel to engagement and conversions. That’s priceless when having only one field of data.
NOTE: When using a random split, make sure your dashboard knows to scale up the results accordingly.

6) Configurable window length
Assumption: Some app owners need to focus on events that occur more than 24 hours post-install.
Requirement: Easy configuration (from server-side) of the window length for changing the conversion value. As always, a modification shouldn’t require any code change or release of a new app version.

7) S2S and server triggered events
Assumption: Some advertisers generate events based on server/CRM logic. 
Requirement: A flow to notify the client side based on server generated events, to change the conversion value accordingly (as this can only be done from client side).

8) Predict user quality based on their initial engagement
Assumption: Measuring events 24+ hours post-install is limited in SKAd, so there is immense value in understanding the quality of your users based on their initial engagement with the app.
Requirement: ML/AI algorithms predicting the long term LTV/ROI of a user based on their engagement in the first 24 hours.

9) Minimize potential manipulations on-the-go
Assumption: Advertisers don’t have direct access to the postbacks. To make it worse, the conversion value field isn’t signed by Apple, so they cannot be verified and could potentially be manipulated before being sent to the advertisers.
Requirement: The one controlling the conversion value should be someone you completely trust. Make sure they’re unbiased and represent your interests, and yours alone.
In addition, make sure they rotate the mapping frequently to make malicious manipulation more difficult to carry out. Eventually, fraudsters shouldn’t be able to assume the meaning behind the conversion value and shouldn’t be able to learn its patterns.

10) Validate data by correlating it with other attribution models
Assumption: Some players might still try to manipulate the SKAd data. 
Requirement: Correlate SKAd data with additional attribution models. For example, if SKAd shows completely different numbers than the probabilistic model for a specific campaign, a fraud indication should be raised.

11) Data consumption
Assumption: some app owners would love to see their marketing insights through configurable dashboards, and some through API calls.
Requirement: Translating conversion values before sending them to the advertiser isn’t going to be an easy task. Mapping will change over time, and might differ between geos and users. Sometimes scaling up will be needed. Validate the entity doing that for you takes all of that into consideration.

12) Support campaign optimizations
Assumption: Ad networks need to translate the conversion value in order to optimize their performance upon the KPIs of each app.
Requirement: The entity controlling the conversion value must integrate with all ad networks, and translate to them the real meaning of the conversion value per each postback.

13) Support future changes by Apple
Assumption: v2 of SKAd will soon be replaced by v3, v4 and v5. SKAd will play a major role in the post iOS14 era, and improvements by Apple will surely follow.
Requirement: Work with someone that will stay on top of these changes, and can change logic without the need to update app code or release new versions.

Can advertisers do all of that on their own?

Some of it – Yes, other parts – absolutely not.

With substantial resources, advertisers can create a server-based entity which sends commands to the client side, collects the in-app events, calls the OS functions and rotates frequently. They can also track changes performed by Apple and adopt accordingly.

But then there’s the part where they need to collect postbacks from all of the different attributed networks. Can advertisers do it? Are they ready to go out and collect data from dozens of different networks, deal with the scale and the different integrations?

Even if advertisers theoretically do all of that, they now have an almost impossible mission: posting back the meaning of each conversion value to each of the networks for campaign optimization purposes. App owners simply do not have a mechanism for doing that, and it’s crucial! Without it, networks can’t optimize their campaigns for the benefit of the advertiser. App owners need someone who already has these integrations and mechanisms in place.

So, bottom line: who should control the conversion value?

The immediate candidates are: ad networks, analytics tools, and MMPs.

Why ad networks can’t be the answer
We could say that they don’t have integrations with other networks, so campaign optimizations are at risk. We could say that attribution is not their expertise.

Why MMPs are the best choice
Trusted and unbiased – by definition, MMPs don’t have conflicting interests. Their only goal is to deliver reliable, accurate data to the advertiser.

Campaign optimization – MMPs are the only ones who already have these integrations with all networks. Without them, campaign optimizations can’t work.

Enrichment for the different attribution models – SKAd doesn’t exist in a vacuum, there are other attribution models out there. App owners want all models to enrich each other. Furthermore, only an entity that owns other models can ensure that SKAd data is aligned and that no manipulation has occurred. A “clean” environment is a key element for accurate marketing decisions.

One last recommendation

The entity controlling the conversion value should also control the entire SKAd flow.

If you own an app, choose an expert to work with. Choose a market leader. Choose someone who is experienced in optimizing these processes for tens of thousands of apps. They will maximize the benefit for you. Good luck!

The post Who should own the conversion value in SKAdNetwork? appeared first on AppsFlyer.

We at AppsFlyer have been working intensively to enable our customers, partners, and the entire ecosystem to prepare for the launch of iOS 14. We outlined our full solution for maximizing SKAdNetwork insights, which includes our unique server-side configuration innovation; we elaborated on why attribution is crucial for end-users and the App Store ecosystem, and we introduced how attribution can be done while complying with Apple’s new privacy controls. Our aggregated attribution innovation was received with incredible feedback from advertisers and partners alike. With that, the market has been asking for flexibility combined with privacy-centric attribution safeguards to enable innovative solutions. We’re happy to oblige, and are excited to provide more clarity and updates on what we see as the next evolution of attribution.

Privacy and security experts widely agree that aggregated data is better than user-level data when it comes to mitigating privacy and security risks. Why has this shift not happened to date?  

1. The industry has become accustomed to user-level data granularity, even where it wasn’t required.
2. There was no incentive to evolve from this status quo.
3. There was no other viable solution. In most cases to date, the only option for app developers was to integrate with partners at a user-level granularity. 

Apple’s latest iOS 14 privacy guidelines can drive adoption of the mindset that aggregated data should be preferred when user-level data is not required or permitted with proper user consent. In fact, this is the spirit of both the GDPR and CCPA regulations.

AppsFlyer’s mission in supporting a privacy-centric future

AppsFlyer is a first-party software-as-a-service used by app developers and advertisers as a CRM. AppsFlyer allows them to manage, analyze, and secure their first-party end-user data, while complying with privacy regulations and platform policies, such as the ones recently introduced by Apple.

User privacy is deeply rooted in our culture and products, and is one of AppsFlyer’s four core pillars. It’s what we pride ourselves on, and what we’ll continue to champion. With that in mind, we adopted the strictest privacy and security standards. As a result, we continued to invest in evolving our probabilistic modeling capabilities. Unlike fingerprinting, which seeks to maximize captured data points from each user in hopes of creating an ID, AppsFlyer’s Probabilistic Modeling is employed with the sole purpose of non-deterministically attributing users’ activity within the scope of an advertiser’s apps, and connect that to their owned media such as websites, social media platforms, emails, user referrals, and their own ads which users interacted with. As opposed to ID matching and lookback windows, this form of anonymized attribution is based on machine learning and probability; therefore lookback windows cannot be defined. 

One of AppsFlyer’s core mission statements is to enable market innovation. The innovators and creators we want to empower are the app developers, our partners, and the ecosystem as a whole. That’s why AppsFlyer is committed to maintaining an open platform – a much-needed ingredient for innovation – and to enable full transparency, flexibility, and freedom of choice, while providing the safeguards and tools to align with Apple’s iOS 14 privacy guidelines.

Enabling flexibility while aligning with privacy guidelines

With AppsFlyer’s solution, app developers are in the driver’s seat and have full control over their data, deciding exactly how it is collected, managed, and used by partners via data access permissions. AppsFlyer helps advertisers connect with an extensive list of 7000+ partners. Each of these partners has different integration requirements, terms & conditions, and data privacy policies in place. AppsFlyer doesn’t have control nor knowledge of the agreement terms app developers have with their partners. 

Advertisers will have complete flexibility to choose the datasets they would like to share with each of their partners, by selecting between two modes of partner integrations: 

 To align on terminology, we are defining the following datasets:

(i) Device-level identifiers =IDFV, IDFA – given user ATT (AppTrackingTransparency) consent.

• ^{Later this year we will introduce a strict aggregate attribution mode that is based on aggregated data entirely. Please contact us if you are interested.} 

We recommend that our customers constantly review their partner integrations and move towards advanced privacy mode to leverage our aggregated attribution data wherever possible. To clarify, advertisers will continue to have access to user-level attribution by default, unless selected otherwise. 

iOS 14 introduces new challenges and opportunities for our customers, partners, and the entire industry. We are committed to maintaining AppsFlyer as an open platform and to provide our customers and partners with the flexibility they need to achieve their goals and increase users’ privacy. 

There are almost 100,000 innovators using AppsFlyer’s platform and APIs. We are here to help them navigate the challenges ahead and to identify and capture opportunities in order to continue to delight their end-users while increasing their privacy.

Thanks,

Oren

The post The next generation of privacy-centric attribution, explained appeared first on AppsFlyer.