Author: Alon Nafta

As we get closer to iOS 16 being released, predictions and rumors are increasing around Apple finally forcing the mobile advertising industry to exclusively use SKAdNetwork as originally intended. While some are still theorizing about yet-to-be-endorsed alternatives that can still attribute device-level data, at Singular we have been investing a lot of time and effort into understanding what the next phase of privacy-safe mobile analytics and campaign optimization should look like for iOS.

Our vision for Singular’s SKAdNetwork product is to deliver a reporting experience that’s as close as possible to pre iOS 14 user acquisition, including ad spend and ROAS, cohort metrics, and as many relevant breakdowns as possible … all of which are needed by marketers to optimize their iOS performance. 

At the same time, it is our mission to support campaign optimization, and work with our partners to ensure campaigns can run at scale and deliver results. Ironically, one of the main challenges in SKAN is that these two often come at the expense of each other.

In this blog post we’ll describe how mobile analytics on iOS 14 has evolved in the last 2 years, clarify where it is today, and share why we are excited (or rather, not as worried) about where it’s going over the next few years.

The reporting versus campaign optimization tradeoff

One of the most meaningful things to understand about SKAdNetwork is that in contrast to iOS reporting pre-iOS 14, campaign optimization and reporting are tightly coupled. This is because the signals, i.e. conversion values, that an app reports to SKAdNetwork are used for both optimization as well as reporting. 

In other words, if I’m choosing not to optimize against a specific event, I will not have it in my UA report. This is a huge change compared to the past where everything was always available regardless of what events were sent via postbacks for the purpose of campaign optimization.

A few examples for this:

• Revenue vs. events: if the app is only reporting revenue amounts to SKAdNetwork, ad networks will only optimize based on these revenue amounts and other events won’t be available to directly deduce from SKAdNetwork conversion value data. Recently, Singular added an option to include both by using mixed conversion models where the main trade-off is using less events.
• Measurement periods: the standard today is for apps to update conversion values to SKAdNetwork for the first 24 hours. This allows ad networks to receive SKAdNetwork postbacks roughly 2 days after every install. On the one hand, using a longer measurement period can increase accuracy by introducing additional conversion values. However, this will increase the delay before which ad networks receive postbacks for campaign optimization, which can damage performance.

Reporting in the early days of SKAdNetwork

SKAdNetwork 2.0 introduced a variety of new functionalities vs. the older SKAdNetwork 1.0 and finally became functional enough, albeit very limited, for marketers to use for optimizing while also being able to create a report. 

The IDs available on the SKAdNetwork postback allowed marketers to report on app, campaign, and publisher ID, although pragmatically very few marketers reported on publishers given the extremely low volumes at the time. Getting the IP on the postback also meant that one could deduce the country from the IP address, which provided reasonable accuracy.

Outside of breakdowns, the big new thing for SKAdNetwork 2.0 was of course the new conversion value framework, which means that marketers can optimize and report on conversion events. Despite the limitations, this was a huge advancement for SKAdNetwork and encouraged a lot of new solutions.

In SKAdNetwork 2.2, which became available with iOS 14.5, marketers could run and report on view-through traffic, meaning conversions that Apple would attribute to an ad being viewed and not clicked. And later in SKAdNetwork 3.0, which became available on iOS 14.6, marketers could also understand if certain ad networks had an ad clicked while not being attributed to … AKA “loser postbacks.”

Reporting as it is done in Singular today

So what happened since iOS 14.5 and what marketers can see in Singular today? A lot. 

We can generally categorize our efforts into two main categories:

1. Enrichment with ad network data
2. Estimation and modeling

Enrichment with ad network data
We can extract a lot of information about the campaign from the ad network report. 

It starts with metrics such as Ad Spend as well as Cost, Impressions, Clicks, and breakdowns such as Country (since each campaign is targeting certain countries). Joining SKAN conversion data with ad networks’ campaign data also means that breakdowns such as Campaign Name, Ad Group and others are now available if the ad network tells us how they are mapped – which most of them do. 

Note that the default campaign-id field in SKAdNetwork postbacks is not the ad network’s campaign ID, but rather a “SKAN Campaign ID” whose values are limited to between 1-100 per partner. This SKAN Campaign ID often represents an ad network campaign, and sometimes may also represent a specific ad group and even a specific creative.

Enrichment of SKAdNetwork data with ad network data allows us to create a full-funnel report, connecting top of the funnel ad spend with bottom of the funnel conversions, which is the foundation of effective user acquisition reporting.

Estimation and modeling
In addition to limited metrics and breakdowns, SKAdNetwork also introduced the concept of privacy thresholds – a mechanism designed to protect user privacy and ensure that individual user info can never get exposed through the SKAdNetwork framework. The impact on marketers, however, is negative: some conversion value data is null instead of having a value that can be decoded back to its original meaning. 

On average, nullified data due to privacy thresholds takes approximately 20% of all data, which causes a meaningful accuracy decrease. 

To address this, we are now launching SKAN Advanced Analytics and are introducing the concept of modeling, where Singular is creating approximations for the missing data by leveraging the aggregated data it is able to access under Apple’s guidelines. This allows marketers to compensate for the signal loss, and our beta test customers achieved 87% accuracy.

What’s coming up (and why are we insanely excited)

So, after all of this, where do we stand now? 

The report that marketers access on Singular when running SKAdNetwork is fairly comprehensive, and many of our customers are happy with the performance of SKAdNetwork. However … SKAdNetwork-based user acquisition data doesn’t include Apple Search Ads or organic installs. This is since these data sets are not supported by SKAdNetwork and are reported or calculated differently. 

Metrics available in the report are totals (“actuals”) over 24 hours. This is due to the relationship between the conversions UA is trying to optimize versus. conversions it’s trying to report on. 

These are the truths in today’s SKAdNetwork reality that we are looking to redefine.

So what is the future we are envisioning? It is one where …

1. You can optimize campaigns for a desired measurement period, but reporting is not constrained by that time frame. Marketers should be able to calculate cohorts for any given time period.
2. You can view and optimize by the true KPIs that matter to your app. Whether it’s ROAS or CPA, we want to make sure mobile analytics can support the business metric.
3. You are able to get a single, consistent view of all channels and traffic types on the same table, using the same schema.

But why would this be possible? Well, we’re not certain it is, but we have every reason to believe so. 

In the current state of user acquisition on iOS, marketers rely on the limited data reported back by SKAdNetwork. However there are additional, high-resolution data sets which are left aside. This is where algorithms and estimation theory can come in and leverage additional sources of information to create estimates and predictions, solving for the missing gaps.

Let’s remind ourselves what are the data sets available to us as iOS marketers:

• SKAdNetwork data: This data set captures all paid traffic and provides the attribution source of truth as determined by a last-touch model.

• First-party data (i.e. IDFV data): This is a data set that every app developer collects, and a lot of it is already reported to MMPs and some other vendors (e.g. in-app analytics). It is highly granular and gives us an exact snapshot of device and user activity, minus the attribution information.

• Opt-in data (i.e. IDFA data): This is a data set that many apps who surface the ATT prompt continue to have, and is also, like IDFV data, highly granular. A key point to remember here is that SANs are no longer attributing to IDFA, so this data set is partial and provides a skewed image of reality where only non-SAN partners are attributed.

• Cost and campaign data: This is a fourth data set that is typically highly granular and is very useful for prediction algorithms. Methods such as Media Mix Modeling (MMM) heavily rely on this data set as the input to the marketing equation.

When we look at these data sets above, it is pretty clear that there’s a lot of information out there that our algorithms should be using. But why hasn’t this been done before? Because we didn’t need to. IDFA data was perfect (well, ignoring issues around last touch attribution models) and allowed marketers to calculate any metric needed. 

This also, at least intuitively, tells us about the huge potential for smarter technology used in the space of marketing measurement. The better we can use all of these four data sets, the better accuracy and detail we’ll be able to deliver back to marketers in the world of SKAdNetwork, privacy, and limited data sets.

We are excited about this future and can’t wait to let you try the new products that are coming up soon. As always, we highly value feedback and interest from customers looking to try these out as early adopters.

Today, we are announcing the general availability of Singular Private Cloud: a complete mobile measurement and attribution solution that runs on a dedicated cloud environment that is owned and controlled by advertisers themselves. Singular Private Cloud leverages data clean room technologies that allow advertisers to control, collaborate, and use data in a privacy-safe way best-suited to their individual needs.

We are proud to share that we’ve been live with the Singular Private Cloud solution for the past 18 months (!!), which has allowed us to evolve the technology and environment to dynamically work with any future privacy frameworks. (Anecdotally, when we first started, SKAdNetwork did not even exist in its current form, and this was a major change that was added to the solution during beta testing.)

The Singular Private Cloud solution allows brands and advertisers to maintain complete control over their growth stack, marketing measurement, and sensitive user data in an environment that they own and manage themselves. These new developments in data clean room technology will allow us to continue to innovate, and promise exciting opportunities as the technologies mature (which we’ll dive into later!).

Why Singular Private Cloud?

In today’s advertising landscape, it is no longer uncommon for marketers to learn about a new privacy-driven change and its effect on how they measure.

The recent iOS changes were very notably a striking demonstration, and it seems that every couple of months there is another change that marketers and marketing tools have to account for. The forces behind these changes are often regulatory, but media platforms such as Facebook or Google also often introduce changes to better accommodate for the public’s increased interest and scrutiny in how personal data is being collected, stored and used.

Mobile Measurement Partners, also known as MMPs, are often at the forefront of these changes. This is because:

• We operate globally, so any country legislation has to be accounted for
• We exchange sensitive information with all the media platforms, so any platform-specific requirements have to be met
• By definition, we collect first-party and third-party data, and brands may have specific requirements as to how we should collect, store and/or process a specific data set

Private Cloud can be a challenging concept to understand in a practical way. To add some color to this, let’s think about a few real-life examples:

• We have a customer whose privacy team determines that user data can only be saved for 30 days
• For data originating in certain countries, new legislation declares that IP addresses are private data which cannot be saved in plaintext
• A media partner or ad network shares data with Singular, but only allows certain brands to access it, and only in very specific ways, with custom limitations per brand
• A Fortune-100 company wants to have its first-party data stored in a dedicated database

All of these are real examples that we see at Singular regularly, and need to accommodate. As consumers become more conscious about privacy, new requirements constantly emerge. To solve this, the industry needs to start thinking about MMPs as the facilitators of data clean rooms.

What are Data Clean Rooms?

A data clean room is an environment (for example, a data lake on a public cloud), which allows multiple parties to share data in a mutually agreed upon manner. Sometimes this will be described as privacy-safe, but how the data is used is dependent on specifical rules. In this case, it simply means that there are certain privacy-driven rules applied to how and what data is entering and leaving this environment, and what data is accessible to each party.

In the marketing landscape, this translates to brands (advertisers) whose data is the first-party data collected on their assets such as a mobile app. Other parties include publishers and media platforms: notably the walled gardens such as Google, Facebook, and Twitter.

Interestingly for mobile advertising, MMPs were always somewhat of a data clean room since by their very definition, MMPs can access unique data sets that advertisers cannot … for example, click data for an ad. As such, MMPs always had various sets of rules they had to follow, mandated by the media and hardware platforms themselves.

In the last few years, more and more rules have been added due to a growing number of new privacy regulations. These changes have also led to increasing demand for data clean rooms and new technologies that would allow them to adapt to both new external constraints as well as brand-specific requirements.

Finally, while not strictly mandatory in its definition, it is assumed that for data clean rooms, the environment is completely isolated between different brand and publisher combinations. And this is a big technological leap that MMPs have not taken so far.

MMP evolution in the privacy landscape

MMPs facilitate mobile measurement and attribution and often, such as in the case of Singular, also collect additional data sets for the purposes of rich analytics.

Singular is responsible for collecting a multitude of data sets. There are aggregated data sets such as ad spend, creative, and bid data, which is then combined with device-level impressions, clicks and events. Advertisers often send additional first-party data, for example subscription information, so that ultimately Singular can calculate and visualize the KPIs that matter most towards optimization.

These data sets, especially the device-level ones, are at the intersection of multiple sets of constraints and requirements that MMPs have to accommodate for.

A few examples:

• Apple and Google have platform-specific requirements for mobile apps to get published on the App and Play Stores. Example: only using designated advertising IDs and not hardware IDs such as IMEI.
• Apple and Google have also introduced additional privacy-driven initiatives such as those for kids apps and, of course, iOS 14.5.
• Facebook, Google and all other Self-Attributing Networks (SANs) each have their own unique set of constraints that MMPs must meet, for example, on how and for how long device-level data is saved, and what type of data can be shared with the advertiser.
• Regulations such as COPPA, GDPR and CCPA are both geo-specific and provide room for brands to interpret differently.
• Privacy frameworks such as the UK’s AADC, Privacy Shield and others also translate to additional requirements on the data MMPs are collecting, processing and sharing.

If you generalize all of this, it’s pretty clear that MMPs are acting as data clean rooms. Thus, building the technology to quickly adapt to new requirements is critical in the current landscape and has to be an inherent component of the platform and design process.

Announcing Singular’s latest solution

Privacy technologies are constantly evolving, and new technologies for data processing in a privacy-safe manner are fascinating.

For example, federated learning could allow for some user data to never have to leave the device, without compromising measurement capabilities. Differential privacy methodologies can provide needed marketing optimization insights without accessing sensitive information tied to specific individuals.

All of these will play a rule in tomorrow’s data clean room.

But today, we can already provide to customers the ability to run on a dedicated measurement environment that provides the same exact set of capabilities you’re getting from our public platform. This means that mobile and web attribution, fraud prevention, cost aggregation, and ROAS and cohort reporting are all available in a dedicated environment, allowing your data to live and breathe in complete isolation from other brands’ data. Brands can apply customer-specific privacy rules to accommodate their specific privacy and legal teams’ unique requirements.

Over the past 18 months we’ve built the technology that allows us to operate these environments in a reliable and scalable manner, and we’ve been running with a few major brands as beta partners to bring this to general availability. I am excited to share that we can now offer Singular Private Cloud to additional customers.

What’s next?

Privacy tech is constantly changing and new technologies will allow us to take this even further. But most importantly, more and more brands adopting these platforms will meet us with additional needs and will accelerate our learnings as we continue to build for the purpose of providing better data and better measurement to the market.

Want to take ownership of your data and leverage Singular’s data clean room technology? Have more questions? There are a few more details in our press release.

Better yet, schedule time with one of our product experts.

The future of mobile measurement on iOS is a single view that brings back KPIs, cohorts, and ROAS analysis. Marketers want a single source of truth for iOS like they once had, and I think we’ll be able to provide it.

Of course, there’s mileage to travel before we get there.

While we can’t predict exactly what SKAdNetwork changes are coming, I think we can all agree that the split between SKAdNetwork and MMP data that inherently exists today is not good enough. But, there’s good reason to have hope that it will improve, and that mobile marketers will have the data, insights, and tools they need to optimize growth.

Where we are today: mobile measurement on iOS

Over the past year, many of the fundamental truths we’ve known about mobile app attribution have changed. User acquisition teams learned a brand new set of terms, definitions, and best practices. The mechanics of how you optimize campaigns — and even how you run campaigns — is now different.

Wishfully, some parts of the industry, and perhaps not-so-small parts of it, have seen the iOS changes and the new Apple privacy policies as no more than a technical hurdle. But as we’ve seen throughout the year, SKAdNetwork is here to stay.

Recognizing this leads to an obvious conclusion – we are in need of new technology.

Apple has laid out the basics. There is a way to get attribution on iOS, but it’s neither as good nor as easy as it was with IDFA. The good news, however, is that there is plenty of technology potential out there, and that the measurement market is more competitive than ever. Solutions are coming.

MMPs’ evolving role in iOS marketing measurement

Not surprisingly, we’ve seen a vast change in MMP philosophy towards iOS measurement and SKAN over the past year.

Let’s be honest: even today, the vast majority of MMPs are not really ready when it comes to supporting advertisers with running and reporting on SKAN campaigns. However, in sharp contrast to the past, it’s clearly sensible to argue that the MMP is the optimal SKAdNetwork facilitator: running your conversion value updates against iOS devices and collecting all the SKAdNetwork data from your media partners. Plus, good SKAdNetwork systems will support as many measurement models as possible so your mobile app can best utilize the limited bitwise representation of conversion values that Apple has given us. And better SKAdNetwork systems will decode those conversion values back to the original events so that reporting makes sense to advertisers and they can optimize on actual KPIs.

I’m not shy to say that Singular has led the way in defining what is needed from a good SKAdNetwork system.

Early on, we defined (and then released) the common models we believed should be suitable for most mobile apps. We released multi-day measurement even when no ad network was supporting it, as soon as it became clear that doing so would help push the market to adopt this. We worked with partners to create powerful integrations that would help everyone exchange information on the relationship between the SKAdNetwork campaign and ad network campaigns, as well as define how in-app events are encoded to SKAdNetwork bits.

But today we are not going to talk about what a good SKAdNetwork system looks like. Today we are going to define what the best SKAdNetwork system will look like — even if it’s not quite available just yet.

An important first step: Fix the data

The first thing to acknowledge about SKAdNetwork data specifically and SKAdNetwork in general is that it’s hard. It is genuinely hard, and this is mostly because Apple has made it as such.

But we need to remember that this is a new framework, and we have to think in new terms. It is no longer about attributing a single user: small data sets that expose individual users are no longer supported, and SKAN’s random timers make it even harder to get information about individual users.

What this all means is that even if you’ve gone through the effort of implementing SKAdNetwork in your mobile apps and are running SKAdNetwork campaigns with partners, there is still a fairly decent chance that the data you see, likely in your MMP dashboard, can range between bad and non-usable.

That is horrible.

Let’s just be honest about it. It is really, really bad when marketers adopt the new standard, put in a ton of hard work, and see no reward for their efforts.

A lot of it is due to privacy thresholds as well as common errors that people are making when it comes to running SKAN campaigns. For our customers, we see Singular as a key piece in solving for that, and this is where our new technology comes in. In last week’s release, we announced SKAN Advanced Analytics and its first milestone – Modeled Metrics.

Modeled Metrics fixes the data gaps caused by privacy thresholds using data science and statistics.

It is not magic, but it is some very cool tech that allows our customers to worry less about the common SKAdNetwork pitfalls that would otherwise compromise SKAN data and enables them to just do their jobs. In other words, SKAN Modeled Metrics from Singular supplies clean, actionable insights that lets marketers make allocation and optimization decisions with a high degree of confidence that their data is complete.

The future of iOS mobile attribution: A single view

To understand where we are going with SKAN Advanced Analytics, let’s think about the potential for MMPs as a powerful technology provider for advertisers:

• Our SDK is in the app
• We operate SKAdNetwork updates
• We collect unattributed in-app data, and cover 100% of users
• We still collect attributed opt-in IDFA data for some apps/users, which typically covers 20-40% of users
• We collect SKAdNetwork data from all media partners via postbacks and APIs
• In Singular’s case, we also collect granular ad spend data that tells us where advertiser budgets are going in a very accurate way

Now, let’s think about the problem.

Marketers want reporting to be as it was, which means cohorted, full-funnel metrics, against every meaningful breakdown that can teach us something about the campaign.

Some breakdowns, such as creative, are not readily available by the framework. We expect these to either get added by Apple or supported indirectly by increasing the limits on skan_campaign_id. Improving the conversion model will improve the underlying accuracy. Combining the knowledge we have from all these siloed data sets should teach us a lot more than what we can learn based on the SKAdNetwork dataset alone.

As I mentioned initially, we can’t predict what changes Apple will make in SKAN.

We can argue, however, that the future of iOS measurement is a single view of marketing reality: a single source of truth that returns KPIs, cohorts, and ROAS analysis. And, a source of truth that offers predictive values for KPIs like revenue.

What that means: how we see iOS measurement evolving

As we’ve previously established, SKAdNetwork data is determined by the following aspects:

• The conversion model determines the underlying accuracy.
  • For example, an optimized revenue model for an app with in-app purchases happening in the first 24 hours will generate better data than a basic six-event model that doesn’t optimize on value.
  • In another example, a model that uses a 72-hour measurement period will generate better data for an app that has the vast majority of conversions happening in the first three days than a model that only looks at the first 24 hours.
• Statistical algorithms can aid in reconstructing partial data due to privacy thresholds and timer skews
• Predictive analytics can leverage multiple data sets collected by MMPs to create an easy to understand and easy to use report.

If we look at these components we can also imagine additional improvements:

• Machine learning can help cluster users better, thus further optimizing the conversion model to distinguish between high value and lower value users. We expect this to be common for Singular and other MMPs as the operators of the model.
• Machine learning and other technologies can help calculate the predictions of previously mentioned KPIs, and any improvement in the underlying data will further improve such predictions.

To summarize, we believe that over the next year iOS measurement will go through a dramatic change. SKAdNetwork will continue to grow in adoption as the de facto standard, and marketers that rely on non-compliant workarounds may find themselves lagging behind while others are taking advantage of the gigantic pool of growth opportunity that is the iOS ecosystem.

We do recognize that supportingSKAdNetwork for the past year has been quite taxing, but these new technologies provide our advertisers with a real edge. The simple fact is that advertisers that use MMPs that are betting against SKAdNetwork will be left behind.

It is only a question of when.

As technologists, we are excited by the enormous opportunity that lies ahead of us. We are going to be building a lot in this upcoming year, and we can hardly wait to reveal what will be coming next.

What’s next, and how to get better insights yourself

We are only getting started with SKAN Advanced Analytics, and the recent release, SKAN Modeled Metrics, already gives our customers improved visibility to SKAN performance. Stay tuned for our upcoming posts that provide more detail.

In the meantime:

If you want to take ownership of your iOS marketing measurement or have more questions, schedule some time with one of our product experts.

Welcome to the third blog in our cross-device attribution series, where we discuss how advertisers can leverage web campaigns as a meaningful acquisition medium for mobile apps and other platforms. Check out our previous posts on this topic, Part one and Part two, to get up to speed.

This recent post will address some of the critical data and reporting challenges that marketers face when implementing cross-device attribution. We’ll then present different methods to generating meaningful analytics when the underlying datasets are collected across multiple platforms.

Providing the correct views and analysis to marketers can often create the difference between having the ability to distinguish between your stronger and weaker channels and activities vs. running reports that lead to misinformed decision-making.

Cohort Analysis for Cross-device Attribution: The Basics

Let’s start with the basics and remind ourselves how we define cohorts in marketing analytics:

A cohort is a group of users with a common property. By looking at users with a common property, marketers can often isolate findings and effectively identify trends.

There’s no strict outline of how cohorts should be defined; however, certain industries have their own standard conventions. In mobile marketing, advertisers are often looking at app install campaigns, so Mobile Measurement Partners (MMPs) provide install-based cohorts, users grouped by install date. So if you go to your Singular dashboard and run a report that includes cohorted metrics, these metrics are calculated for a given install date, such as ad revenue, the total revenue during the first seven days after the install. A sharp reader may also call out that seven days can be calculated “on the calendar” or in 24-hour increments, and both would be valid! The MMP can decide on one of these or provide the option to the advertiser to choose the cohort definition that best works for them.

Lastly, we should also call out that cohorts are defined as groups of installs in mobile marketing, not users. This is because a new install might belong to the same user, but to the standard MMP, this would be a new install added to the cohort. Similarly, in web marketing, cohorts are often defined based on the user’s acquisition date, which is when (i.e., the date when) the user first landed on your webpage, which is not too different from the install date on mobile.

Cohort Analysis for Cross-device Attribution: User vs Device

When we look at cross-device, a single user can interact with ads and consequently convert on multiple devices and platforms, so the cohort definition needs to be redefined. With a single platform, the norm is to look at when the device converted. While with cross-device, we’d want to look at when the user is converting. In most products, this would be when the user sets up an account or signs in for the first time.

Let’s demonstrate this by example. My company built the latest social network, which offers 13-second chats with users you’ve never met before around the world! We have built this new network on mobile and web, so we get installs from the mobile stores and website visits from desktops and mobile phones. Users click ads, but as soon as they try to use the product on the respective platform, they need to sign up and log in. Another thing worth mentioning is that a user may click on multiple ads across multiple platforms before deciding to sign up and start using the product. And this is what we are interested in as the point of reference.

When thinking about this problem in terms of your standard marketing tools, an attribution provider that is limited to a single platform will present a partial snapshot of reality — and consequently, when working with several tools, there will be overlap, leading to inaccuracies in each tool’s cohorts. Determining a certain campaign’s LTV becomes difficult since you might be including existing users in that campaign, thus inflating the LTV from user acquisition (UA) activity.

Another thing worth mentioning is that even on a single platform, looking at users instead of installs or devices reveals a lot more insight. In addition to a more accurate LTV, the change in the definition of conversion carries a lot of value to ROI analysis and comparison. For this reason, in certain verticals such as Ecommerce, it is more common for marketers to define cohorts based on the first time a user makes a purchase, for example, which is much more meaningful than the time of install. However, the most important thing is to ensure that the data is accurate given a point of reference.

Cross-device Attribution and Analytics in Singular

By providing the option to select how cohorts are calculated in real-time, marketers using Singular can choose between device-based cohorts and user-based cohorts. This allows you to understand the actual LTV, or any other KPI, for a group of users instead of installs or website visits. It also enables you to understand how those users interact with your product across platforms. For example, you may acquire users at scale on the web, but they use the product on multiple platforms with different retention patterns. A particular web campaign may drive revenue on both mobile and web, depending on the campaign and the channel where the users are acquired.

Similarly, a particular mobile web may drive X new website visits where only a fraction is new users. Understanding these relationships is key to scaling your UA effectively.

This also suggests that on the Singular side, we have to distinguish between the marketing parameters that are attributed to the device (e.g., a new install on iOS) vs. different marketing parameters that are attributed to the user (e.g., Evie who has just signed up for a new account after clicking on an ad). The channel, campaign, creative, and more could be completely different. By getting this data also in raw form, marketers gain complete visibility into the user journey.

What’s next in the series?

Now that we’ve covered how reporting for cross-device attribution works, we’ll continue to discuss meaningful topics for marketers who run across multiple platforms or who want to diversify their UA to better prepare for iOS 14.5. In the next post, we’ll dive deeper into pixels, postbacks, and conversions to understand how your ad partners are also affected by how your cross-device attribution setup and what marketers should do to improve campaign performance. As always, we encourage you to learn more about Singular’s web, web-to-app, and cross-device capabilities and schedule a demo with one of our product experts.