Google Privacy Sandbox is an initiative aimed at enhancing privacy for users while preserving the ability for businesses and marketers to deliver relevant ads and measure their performance. It seeks to phase out third-party cookies, which have traditionally tracked users across websites, and replace them with new privacy-preserving technologies like the Attribution Reporting API (ARA) and several others. These tools allow for ad targeting, retargeting, and measurement without exposing individual user data, making the web and mobile a more secure and private environment. For marketers, this shift requires adaptation to new methods of audience targeting and performance tracking within the constraints of increased user privacy.

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Attribution is the process of assigning credit to touchpoints along the customer journey to determine how they influence a user’s conversion or action within a mobile app. Marketers use attribution data to understand user behavior, optimize campaign strategies, and make informed, data-driven decisions about ad spend and resource allocation.

In the context of mobile marketing, attribution is essential for figuring out which ads, social media posts, quick response (QR) codes, or other digital marketing channels lead to app installs and in-app engagement.

Types of attribution

There are several types of attribution — and which you employ will depend on your specific marketing goals, the platforms you use, and the touchpoints you want to analyze. Each type of attribution provides different insights and advantages, helping to fine-tune acquisition and engagement strategies and maximize return on investment (ROI). Here’s a breakdown of the main types of attribution:

Web attribution

Web attribution involves tracking and analyzing data about how users engage with a website. Web attribution analytics tools track web traffic and metrics related to user behavior, like conversion rates, click-through rates (CTRs), bounce rates, and other data on how users engage with website elements and campaigns aimed at driving web traffic.

Marketers use web attribution insights to understand user behavior and preferences as well as to determine the effectiveness of their content and campaigns. For example, by analyzing bounce rates on specific landing pages, marketers can identify dropoff points on the path to conversion and take action to redesign those pages or adjust the call to action (CTA) to better align with user expectations.

App attribution

App attribution, or mobile attribution, is the process of tracking how users interact with mobile apps to determine which marketing channels drive app installs and engagement.

Most marketers rely on mobile analytics tools and mobile measurement partners (MMPs) like Branch for app attribution. These tools use software development kits (SDKs) to track app-related metrics such as installs, user engagement, and other in-app interactions. They are crucial for marketers to understand how user acquisition and retention campaigns perform and attribute these outcomes back to specific marketing channels or touchpoints.

Cross-channel attribution

When marketers use multiple marketing channels to reach customers, cross-channel attribution provides a more complete view of the user’s journey, allowing them to understand how various channels work together to drive conversions.

Cross-channel attribution uses different attribution models, including first-touch, last-touch, and multi-touch attribution. For mobile marketers specifically, there’s also install-touch attribution. For more on the difference between install-touch and last-touch, check out our blog: “What’s the Difference? Last-touch vs. Install-touch Attribution.”

Cross-channel attribution tools work by allocating credit to multiple marketing channels depending on how each influences user behavior. This method considers user behavior across different devices — such as tablets, mobile phones, and desktop environments — to accurately attribute conversions. It considers different platforms, including social media, web, email marketing, and apps, to determine which is most effective at achieving conversions. This information helps marketers optimize their marketing strategies and budgets to maximize revenue.

Full-funnel attribution

Full-funnel attribution is a more comprehensive approach that takes a broader look at the entire customer journey.

Unlike other methods that focus on specific touchpoints or channels, full-funnel attribution tracks user engagement across the entire marketing funnel, from awareness to conversion. It aims to attribute the influence of each interaction that a customer encounters along their path to conversion, providing a more holistic view of marketing performance. Full-funnel attribution insights are useful for determining the effectiveness of marketing efforts at every stage of the customer journey and, in turn, facilitating more strategic, data-driven decision-making.

Types of marketing attribution models

Attribution models vary in how they determine which marketing touchpoint gets credit for a conversion.

Last-touch attribution modeling, for example, credits conversions to the very last touchpoint a user made before converting. If a user clicked through an ad to download and install an app, the ad would receive credit for the conversion. In contrast, first-touch attribution gives full credit for a conversion to the first touchpoint the user interacted with. If a user clicked on an ad to install an app as the very first interaction with a marketing campaign and then eventually did download the app, the ad would receive credit for the conversion.

Multi-touch attribution models, alternatively, spread credit across multiple touchpoints along the customer journey. This model lets marketers assign credit based on predetermined rules or weighting strategies. Some methods include linear distribution attribution  (where every touchpoint receives the same weight); time-decay attribution (where more recent touchpoints receive more credit); U-shaped attribution (which applies the most credit to the first and last touchpoint while applying the least credit to touchpoints encountered in the middle); and W-shaped attribution (where the first and last touchpoints receive more credit). By distributing credit, marketers can understand the relative impact of each touchpoint. It’s worth mentioning that in modern marketing, privacy policies are forcing companies away from deterministic last-touch measurement, and a variety of alternatives — SKAdNetwork (SKAN), Google Privacy Sandbox, modeled SAN outputs — are emerging. Some companies are finding new approaches, like combining traditionally separate measurement methods or “triangulating” the results from different measurement methods, including attribution data and media mix modeling (MMM).  Learn more in our “How To Combine Attribution Data With Media Mix Modeling” blog.

Mobile measurement partners (MMPs)

MMPs are third-party platforms that specialize in mobile app attribution. They provide the tools and services needed to track app downloads and installs, engagement, and user behavior.They give unbiased insights into campaign performance on paid channels. Check out this blog to learn more about MMPs.

Attribution challenges

The variety of attribution models, user touchpoints, channels, and platforms make attribution challenging. Common obstacles include data accuracy, cross-device tracking, and compliance with privacy regulations that influence how brands collect and use consumer data, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Learn more about how marketers are addressing these challenges in our “Future of Measurement” blog.

Conclusion

Understanding attribution is critical for marketers to gauge the effectiveness of marketing efforts and allocate their resources efficiently.

With technology and consumer behavior evolving at a rapid clip, it’s important to stay up to date on the latest trends and developments in attribution. Marketers who stay ahead of the curve can gain a competitive advantage and maximize their ROI and return on advertising spend (ROAS).

FAQs

How does last-touch attribution work? What are its advantages and limitations compared to other attribution models?

Last-touch attribution credits conversions to the last touchpoint a user made before converting. This model helps marketers understand the final marketing effort that drives conversion. However, it does not consider the influence of other efforts along the customer journey that helped contribute to the conversion.

How does multi-touch attribution offer insights into the customer journey?

Multi-touch attribution considers a wide range of touchpoints along the customer journey and gives each one credit. This model lets marketers understand how every touchpoint contributed to the user’s eventual conversion. It requires a stable concept of identity over time, which is problematic in today’s age of privacy.

What are the key differences between first-touch and last-touch attribution models, and when is each model most appropriate to use?

Last-touch attribution credits conversions to the last touch a user made before a conversion, and first-touch attribution gives full credit for a conversion to the initial touchpoint experienced by the user. It’s a good idea to align the attribution model to your desired objectives. For example, first-touch attribution can be useful when you are trying to convert new users. Last-touch attribution is a good way to measure success with users who are already aware of your brand. In mobile marketing, some marketers use both at once to gain broader insights into user behavior across the spectrum.

How does linear attribution help with understanding user behavior?

Linear attribution gives equal credit to every touchpoint a user encountered before converting. This model helps marketers understand the combination of marketing efforts that had the most impact when used together.

How does time-decay attribution prioritize touchpoints closer to the conversion event?

Time-decay attribution accomplishes this by allocating greater attribution credit to touchpoints that are closer to conversion. Using this model helps marketers understand how effective different touchpoints are along the entire customer journey.

Do certain industries or businesses benefit most from using specific attribution models?

Yes. Depending on their marketing strategy, unique customer journey, sales cycles, and other variables, certain industries or businesses may benefit more from certain attribution models. First-touch attribution can be helpful for marketers that are launching new products or building brand awareness. In contrast, last-touch attribution can be especially beneficial for industries that are focused on conversion-driven campaigns, such as subscription services, e-commerce sites, and businesses in the travel and hospitality industry. It’s important that marketers consider their strategy and objectives when choosing an attribution model.

How do marketers determine which attribution model is the right attribution model for their campaigns and business objectives?

It starts with gaining insights into the complete customer journey and having clearly defined objectives, which may influence which model is best aligned with a business’s strategy. Marketers should also consider which industry they operate in and explore which attribution models their competitors are using. For example, if a company is working to increase its online sales, last-touch attribution may help it identify which marketing touchpoint resulted in the purchase. If marketers are working to increase awareness of a product or service, first-touch attribution will help them recognize which efforts generated initial user interest. Whichever attribution model a marketer chooses, it’s important to test it thoroughly to ensure it is yielding data insights that are meaningful and actionable.

What challenges do marketers face when implementing and interpreting attribution data from different models?

First, different attribution models each have their own limitations based on the way they are designed, including biased attribution. For example, last-touch attribution credits conversions to the final user touchpoint, which ignores the influence previous interactions may have had. In addition to each model having its own built-in bias, sometimes the data collected is incomplete or inaccurate, which can lead to skewed attribution. Other models, such as multi-touch attribution, result in a complex body of data that can be challenging to interpret. Whatever model a mobile marketer chooses, it’s important to understand its limitations.

How has web attribution evolved with the rise of cross-device and cross-platform interactions?

As cross-device and cross-platform user interactions have increased, technology has adapted. Today’s attribution models use advanced data collection techniques, machine learning, and other emerging technologies to create a clearer picture of user behavior across devices and platforms. This enables marketers to gain clear insights from data so they can optimize their marketing efforts in an increasingly dynamic environment.

What are some emerging trends in web attribution methods and technologies?

Attribution modeling technology is evolving to meet the needs of marketers who want to capture increasingly complex user interactions, such as cross-device and cross-platform use. New and upcoming models use real-time machine learning and predictive modeling to capture a clear picture of the user journey for marketers. Privacy is becoming more important in the industry, too, so attribution modeling is also evolving to enhance the way it protects sensitive user data. Learn more about the future of measurement.

How might advancements in AI and machine learning impact the future of web attribution modeling and analysis?

AI and machine learning are helping to shape the attribution models of the future. For example, multidevice tracking technology can capture user data across mobile phones, tablets, and computers. Newer unified data collection tracking methods are also able to capture data across different platforms, such as mobile apps, web, and offline use, to gain a more complete picture of user behavior. Attribution models are becoming more advanced as well, and some can attribute credit to a range of touchpoints along the user journey, which helps marketers understand which efforts are moving the needle on engagement and sales. On the data management side, AI and machine learning are helping to protect the privacy of users while at the same time helping marketers more accurately pinpoint which elements of their marketing campaigns were most successful.

What insights can businesses gain from web attribution?

Web attribution helps marketers understand the effectiveness of their marketing efforts so they can optimize their marketing strategies and budgets.

What role do MMPs play in optimizing user acquisition strategies for mobile apps?

MMPs provide the expertise, tools, and services marketers need to implement mobile app attribution models so they can gain insights into user behavior and data on how successful their marketing strategies are.

The post Attribution appeared first on Branch.

A privacy manifest file is a document that describes the various ways an app handles user data. This information includes which data the app’s third-party SDKs collect, how the app uses the data, who the data is shared with, and how it will be protected.

Why privacy manifest matters to mobile marketers

Privacy manifest files are invaluable to mobile marketers for many reasons, including the following:

• They help marketers build trust with consumers. App users are care about how their data is being used and how well it’s being protected. An app’s privacy manifest shows them that the app developer and marketer care about safeguarding their personal information.
• They help foster stronger user engagement. When users trust apps, such as those using iOS, they are more likely to interact with them, which can help boost user engagement and retention.
• They help marketers comply with laws and regulations. Mobile marketers are required to adhere with privacy rules, and having strong privacy manifest files in place helps them remain in compliance.
• They help marketers avoid fines and penalties. When marketers don’t comply with privacy regulations and laws, they are met with penalties and fine. Having a privacy manifest in places helps them steer clear of these punitive actions and remain in good standing.

Understanding third-party SDKs and required reason APIs

Third-party SDKs and required reason APIs are important elements of privacy manifests for iOS apps that operate on Apple devices. Here’s how they work to make sure user data is handled appropriately:

• Third-party SDKs: Third-party SDKs are tools or libraries of tools that app developers integrate into their apps to collect user data for analytics, social media, advertising, and other functions. These tools are provided to app developers by third parties to help them leverage data for marketing and advertising purposes.
• Required reason APIs: When an app wants to access features on a user’s device, such as location, the camera, the microphone, or personal data, the app must explain to the user why it wants access. A required reason API enables the app to send the request for information to users in the app. This feature helps empower users to have a say in how their data is collected and used.

Considerations about privacy manifests for iOS and Apple

Apple products and iOS have a unique privacy manifest with a special emphasis on user education that empowers them to make their own decisions about their data.

Privacy manifest files for iOS are well regarded for their comprehensive transparency, strict data collection and sharing guidelines, industry-leading privacy technology, and other related features that are often seen as the industry standard for mobile. In fact, Apple requires that apps running on iOS disclose if any data is collected by a third-party SDK and, if it is, which data is collected, shared, and used.

Staying on top of current trends

It’s important for marketers to stay informed about current trends that may impact the way privacy manifest files are developed and deployed. This includes an increase in regulations and stricter data privacy laws. Marketers must ensure their data manifests are detailed enough to account for enhanced requirements as they continue to evolve.

Privacy manifests are also becoming more user-centric and are written in language that is clear and easy for non-technical users to understand. There’s a growing trend to offer users more control over how their data is used, as iOS does, and some privacy manifests contain simple instructions to help users opt out of data collection or edit or delete their information.

Apps often share user information with third parties, such as advertisers, using third-party SDKs. Today, privacy manifests typically explain which user data is shared and who it is shared with.

Looking ahead

Privacy manifests will continue to evolve as technologies and data privacy laws change.

• Artificial intelligence will influence the way privacy manifests are created and will be able to tailor them to each user based on how they engage with the app and what their preferences are.
• Interactive tools may be used to allow users to control how their data is handled in real time.
• International privacy standards may emerge, which will make it easier for mobile marketers to ensure they are in compliance with regulations, wherever their users may be.
• Blockchain technology may be used to make data and privacy manifests even more secure.

Privacy manifests are essential to marketers, ultimately helping them build trust with consumers and enhance user engagement and retention.

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Privacy-enhancing technologies (PETs) are tools and methodologies designed to protect personal data and uphold user privacy rights in digital environments. PETs help organizations navigate the complex privacy landscape while ensuring the safety and integrity of user data. 

The FTC’s perspective on PETs emphasizes the gradual shift toward minimizing or even eliminating user data access: “On one end of the spectrum, a company has access to all of an individual’s private information and relies on internal policies and procedures to ensure this information is not misused or breached. On the other end of the spectrum, there are technologies which allow a company to offer products and services without ever having access to a user’s data. PETs are approaches that allow companies to move towards the latter end of the spectrum — some reach the end goal of a company truly not having access to the data of any individual, and others reside in the middle, where they limit access but still have some reliance on a company’s policies and practices.”

Examples of PETs

PETs encompass a wide range of tools and techniques, including those designed to enhance data security, privacy, and compliant data processing practices. Examples include: 

Data clean rooms: Perhaps the best-known PET use case, data clean rooms facilitate the aggregate, anonymization, and analysis of first-party data from various sources in a secure environment. Their primary application is data sharing for advertising and analytics purposes.

End-to-end encryption (E2EE): E2EE is a secure communication method that protects data from unauthorized access or interception by third parties. The cryptographic technique safeguards sensitive information during transmission and storage from device to device, ensuring that only the intended parties can access encrypted data.

Pseudonymization: Pseudonymization replaces identifiable information with artificial identifiers, or pseudonyms, to reduce the risk of reidentification. Anonymizing personal data enables organizations to process and analyze data while minimizing privacy risks.

Differential privacy: Differential privacy enables organizations to analyze datasets while preserving the privacy of individual data points. It adds noise or “randomness” to query results to prevent the disclosure of sensitive information while still allowing for analysis and insights.

Obfuscation: Obfuscation introduces noise into datasets to protect sensitive information from unauthorized access or misuse. By obscuring the meaning or structure of data, obfuscation enhances privacy protection during data analysis and processing. Unlike differential privacy, which focuses on protecting individual data points, obfuscation operates at the broader dataset level. 

Trusted execution environment (TEE): A trusted execution environment (TEE) is a secure area within a device’s main processor that ensures confidential computing. It isolates code execution and data processing from the rest of the system, protecting it from unauthorized access and threats. Even if the operating system is compromised, the TEE keeps the data safe. 

Blinding: Blinding is a cryptography technique that masks specific data points or attributes within a dataset, preventing organizations from identifying individuals or sensitive information. 

Why do PETs matter now?

PETs aren’t new; they have existed for decades, but their relevance has surged in recent years. With the exponential growth of digital data — and growing concern over data breaches and sensitive data protection — organizations are struggling to leverage data insights while simultaneously protecting individual privacy. PETs offer a privacy-safe approach to tackle these challenges, in turn helping organizations and providers to: 

• Foster trust: Investments in PETs signal to customers and business stakeholders, including partners, investors, and regulators, that an organization is committed to protecting user data. This helps build brand loyalty and strengthen customer relationships. 

• Comply with data privacy regulations: Data protection laws, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA), have pressured organizations to rethink their data collection and usage practices. PETs have become useful tools for navigating the regulatory landscape, providing mechanisms to comply with evolving regulations and avoid penalties. 

• Reduce risk: Data breaches have significant consequences for organizations, not only in terms of financial losses but also in damaging reputations and fracturing customer trust. PETs help mitigate the risk associated with user data collection, storage, and sharing by minimizing the likelihood of unauthorized access or misuse. 
• Execute critical business functions: Finding the balance between user privacy and meeting business objectives can be difficult. PETs, in theory, are not just safeguards against data breaches; they enable brands to carry out operations, explore new avenues, and innovate without compromising user privacy. 

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A data clean room serves as a secure environment where companies aggregate, anonymize, and analyze first-party data from multiple sources to derive insights while ensuring user privacy. These privacy-compliant spaces enable advertisers and publishers to utilize data insights for ad campaign targeting, performance measurement, and attribution analysis, all while safeguarding user-level data.

How do data clean rooms work?

Data clean rooms allow companies to upload their first-party data, which is then matched and analyzed with data from other sources within the clean room. This process enables targeted advertising campaigns and performance measurement without the use of individual identifiers or personally identifying information (PII). The most common use case is between advertising providers and publishers to analyze overlapping user data.

Here’s how the process works: 

1. Data upload: Advertisers and publishers transfer their first-party data to a secure data clean room. 
2. Data processing: Next, the platform cleans and processes the data using privacy-protection techniques, such as pseudonymization and encryption. Once processed, the platform analyzes the data to match users or create cohorts with similar attributes. 
3. Activation: Advertisers and publishers analyze reports generated by the data clean room to fine-tune their campaigns. These reports provide insights into audience behavior, such as click-through rates across different demographic segments. In theory, these reports help advertisers and publishers make real-time adjustments to improve campaign performance. 

What are the benefits?

Data clean rooms offer a fundamental advantage: They prioritize data privacy. As privacy laws evolve and the deprecation of third-party cookies approaches, data clean rooms have become an even hotter topic of conversation. They provide a privacy-safe environment for brands to leverage combined data, enabling audience and customer behavior analysis, targeted advertising, and campaign performance measurement. By aggregating and anonymizing data within a clean room environment, brands can protect sensitive user information and ensure compliance with privacy regulations like the General Data Protection Regulation (GDPR).

Plus, data uploaded to a clean room stays within the platform, ensuring that data owners maintain complete control. This reduces the risk of misuse and fosters stakeholders’ trust in data security and privacy.

What are the drawbacks?

Despite their benefits, data clean rooms also pose challenges and limitations. One drawback is the potential decrease in the accuracy of aggregated data compared to user-level data. While privacy measures are essential for protecting user information, data aggregation can affect the precision of targeting and measurement efforts. Similarly, if organizations are reluctant to share their first-party data in clean room ecosystems, it severely limits the insights available to advertisers and publishers. 

Another challenge is that brands must unify data before uploading it to the clean room platform. In other words, they have to ensure that data from different sources is standardized and structured consistently. This is a major headache, often requiring significant effort and resources. To make matters worse, the lack of universal standards for data clean room setups creates interoperability and compatibility issues across different platforms and vendors. As a result, brands may struggle to integrate data from multiple clean room sources and consolidate insights.

Data clean rooms in the wild

While not new technology, brands across various industries are increasingly adopting data clean rooms to navigate the digital advertising landscape. Retail companies like Hershey’s are investing in these platforms to gain deeper insights into advertising effectiveness and consumer behavior. By establishing its own data clean room, Hershey’s can analyze data from other retail partners, enabling it to optimize loyalty programs, assess the impact of advertising campaigns, and refine its marketing strategies. Other major players like Unilever utilize data clean rooms to address cross-platform measurement challenges, integrating and analyzing data from multiple sources within controlled environments. 

And data clean rooms are not limited to retail and consumer goods industries. Even companies in the media and entertainment industries, like Disney, have embraced this technology, enabling advertisers to access valuable audience insights from anonymized data sets. 

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Sideloading is the process of installing an application onto a mobile device without using the device’s official app store or marketplace. Users transfer files between two devices, such as a computer and a mobile device, or install software packages –– usually an application file or Android Package Kit (APK) for Android devices –– from a website or other unofficial source and manually install it onto their device. Sideloading allows users to access more mobile apps than are officially available on the app store, but it poses risks, as sideloaded apps are not screened for malware and can lead to security breaches. 

How do users sideload apps?

The method of sideloading varies across platforms, and while it’s relatively straightforward on some devices, it requires additional steps or even jailbreaking on others. On Windows computers, for example, users can sideload files from one device to another via a cable or memory card. Sideloading is typically associated with Android devices as the operating system provides more flexibility in installing software from sources other than the Google Play Store. To enable app sideloading, Android users simply check a box in security settings to allow installs from “unknown sources” and download the application file to their device. On iOS devices, including iPhones and iPads, it’s more complex and usually requires third-party app stores or tools.

What are the risks of sideloading?

Sideloading allows users to download apps not available on official app stores, like custom-developed Android or iOS apps, beta versions, or apps removed or banned from app stores. However, sideloading apps can be dangerous, putting users at risk of:

• Malware infection: As sanctioned app stores do not screen them, sideloaded apps may contain malware, leading to data breaches and other security risks. 

• Security vulnerabilities: Sideloaded apps can introduce security vulnerabilities to a device, which is why many device manufacturers or operating systems restrict the practice.

• Lack of updates: Sideloaded apps may not receive regular updates, leaving them vulnerable to cyberattacks. 

• Privacy risks: Some sideloaded apps request unnecessary permissions or access to user data without appropriate controls and oversight. 

• Fraud: As they’re often unregulated, sideloaded apps can include fraudulent versions of legitimate apps, exposing users to legal risks. 

How do Apple’s January 2024 changes to iOS, Safari, and the App Store in the European Union impact sideloading?

In response to the Digital Markets Act (DMA), Apple’s recent update in the EU allows for app sideloading with some restrictions. Effective with the beta rollout of iOS 17.4, the update permits sideloading but mandates a “Notarization” process, which involves a combination of automated checks and human review. Apple also announced a “Core Technology Fee” of €0.50 for each first annual install per year over a one million threshold, regardless of whether the apps were sideloaded or downloaded from the Apple App Store. This fee is intended to discourage the use of alternative app stores. Although these changes comply with the DMA’s requirements, Apple has expressed concerns about the potential privacy and security risks for users. Apple is also urging developers to continue distributing apps through the App Store rather than alternative methods, stating that these solutions help mitigate some of the security risks created by the DMA.

The post Sideloading appeared first on Branch.

The concept of a conversion value is crucial in the world of mobile app marketing, particularly within the frameworks of iOS attribution measurement and Apple’s SKAdNetwork (SKAN). A conversion value refers to the numerical value (0 to 63) assigned to specific in-app events or actions. These values provide app developers and marketers with valuable insights into user behavior, helping them measure the effectiveness of their marketing efforts while complying with the latest user privacy regulations. 

Why are conversion values necessary?

Conversion values became a critical tool in mobile marketers’ toolboxes following Apple’s introduction of App Tracking Transparency (ATT). With ATT, Apple cracked down on user privacy, implementing stricter regulations on user data tracking and collection. It then introduced SKAN, a privacy-centric measurement framework and alternative to traditional attribution methods. SKAN requires that app developers and marketers use conversion values instead of traditional identifiers like device IDs (e.g., IDFA) to attribute and measure user actions within apps. This change marked a significant industry shift from relying on granular user-level data to aggregated, privacy-compliant data. 

How do conversion values work?

Each mobile app defines “conversion” differently depending on its industry, business model, and userbase. A conversion can indicate any kind of user action, including an app install, in-app purchase, subscription, or engagement with a piece of content. Most apps track multiple types of conversions to understand how users behave over their entire lifecycle. To translate these actions into quantifiable data, apps assign a graduated scale of conversion values. These values range from 0 to 63, with 0 automatically assigned to the “install” event by Apple, 1 representing the least significant conversion event, and 63 representing the most significant or valuable user action. By using this 64-value system, developers and marketers gain more granular insights into app user behavior and campaign performance.

When a user performs a specified action within an app, the corresponding conversion value is assigned to that event. 

With SKAN 3.0, measurement was relatively straightforward: when a user clicked on an ad and installed your app, a 24-hour postback timer started. Each time a user completed an in-app action of a higher conversion value than the last, the timer reset to 24 hours. When the timer reached 0, a SKAN postback containing the install and the highest conversion value completed was sent to the ad network, the advertiser, and the mobile measurement partner (MMP). 

However, SKAN 4.0 introduced additional complexity and capabilities: instead of the previous 24-hour window, SKAN 4.0 offers multiple measurement windows, including 0 to 2 days, 3 to 7 days, and 8 to 35 days post-install. Each of these windows corresponds to a postback, which provides a more representative view of user behavior over time. 

What are coarse conversion values?

SKAN 4.0 introduced coarse conversion values to provide a more user-friendly approach to tracking than the 64-value system. It enables developers and marketers to assign user actions to broader buckets, such as “low,” “medium,” and “high.” In general, these values simplify the attribution and measurement process while still providing marketers valuable insights into campaign performance. 

Bottom line

Conversion values allow app developers and marketers to accurately measure the success of their marketing and advertising campaigns. By tracking the SKAN postback triggered by each unique action, app developers and marketers can determine the value generated by different marketing efforts. This information helps them identify which campaigns are driving the most conversions and more effectively allocate their marketing budget. 

Yet for most marketers, conversion values have a steep learning curve. Which events you track, how you map conversion values, and how you configure postback windows will all depend on your unique business goals. To help brands navigate the complexity of SKAN 4.0 conversion values, Branch introduced SKAN Magic Set Up. Instead of manually implementing a custom configuration, you can now use a Branch-recommended conversion setup to save valuable time and resources. To learn more, request a demo with our team. 

The post Conversion Value appeared first on Branch.

An impression refers to the number of times an advertisement or piece of content is viewed by a user. Ad impressions are used to measure the overall visibility and reach of an ad campaign, and drive important metrics like cost per mille (CPM) and click-through rate (CTR). Even a view-through impression, where an ad is seen by a user but not clicked, is valuable in mobile marketing.

Say you drive by the same billboard advertisement every day for work. 

Some days you’re stuck in bumper-to-bumper traffic and you read that billboard copy ten times before you finally drive past it. Other days you speed by, barely registering its presence at all. Each time you drive by this billboard is an impression (also called a view-through) — it doesn’t matter if you speed by and hardly notice it or read and think about its message.

The same is true of digital advertising and mobile marketing. Imagine you’re on social media and you scroll past a Facebook ad: whether you read the ad or rush past it, you count as an impression for that advertiser.

Impressions: Pros and cons

Measuring impressions tells an advertiser how many times their ad is seen — a useful metric all on its own. In digital marketing, impressions are a relatively simple, easy-to-calculate measure of a specific advertisement’s or advertising channel’s reach. The higher the number of total impressions, the more times the ad was served to an audience.

But impressions are not a perfect metric. For example, one person could scroll past the same ad 10 times and they would count as 10 impressions rather than one. Impressions also don’t tell advertisers anything about engagement and whether or not views actually took action after viewing the ad. To better gauge the true visibility of an ad, some brands distinguish between:

• Served impressions: refers to an ad that has been displayed or “served” on a mobile app, webpage, or other platform. When an ad is retrieved from the ad server and displayed to a user, it counts as a served impression — regardless of whether the user truly saw it.
• Viewable impressions: an impression that has met specific criteria to be considered “viewed” by a user, such as being at least 50% in view on a user’s screen for more than one second. This avoids counting a quick scroll-by as a true ad view and is a more meaningful indicator of ad visibility.

Impression counts can also be easily skewed by bot traffic and non-genuine views. Particularly on social media platforms, it can be difficult to discern accurate impression numbers.

However, most advertisers measure impressions because it helps them to properly purchase ad inventory. Tracking impressions is also the first step in calculating even more useful metrics like CPM, ROAS, and CTR.

Additional metrics calculated with impressions

Other metrics useful for advertisers which are calculated using the number of impressions are:

CPM

Advertisers purchase a certain number of impressions — say 1,000 — for a set amount of money. This purchasing method is called CPM which stands for cost per mille or cost per thousand (M is thousand in Roman numerals). So if an advertising campaign has a CPM of $20, the advertiser pays $20 for every 1,000 impressions.

ROAS

An advertiser may use an impressions metric to gauge their ROAS (return on advertising spend) and compare different platforms’ effectiveness. ROAS measures how much revenue is earned for every advertising dollar spent. If an advertiser runs a campaign on both Facebook and Instagram, it can use impressions and ROAS to compare apples to apples campaign performance.

CTR

Finally, measuring impressions is necessary to determine click-through rate (CTR), a crucial metric for most advertisers. CTR is the number of people who click on the ad to go where directed — whether that’s an app, a webpage, or elsewhere. To calculate CTR, an advertiser needs to know their number of impressions. CTR is calculated by dividing the total number of clicks on an ad by the total number of impressions.

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Daily active users (DAU) is a metric that measures the number of unique users interacting with a mobile app within a 24-hour period. DAU reflects user engagement and retention and is used to determine the lifetime value (LTV) of an app.

“Active” is a subjective term that individual companies can define for themselves. One company might define “active” as opening the app, another as logging in, and another as making an in-app purchase. As a general industry term, active usually means a user has downloaded or opened the app. 

A daily active user is identified with an IDFA (identifier for advertising) or other IDs, and information a company collects via email, cookies, or from a user opting in on their mobile phone.

DAU benefits and limitations

Daily active users are typically considered a vanity metric — one that shows a snapshot of success. 

Imagine a celebrity shares a link to your app on their social media. You get 100,000 new users downloading your app to check it out. Your DAU goes way up. This is exciting! But those 100,000 downloads don’t tell you much except for your success on that one day. However, inflated DAU’s that aren’t repeatable aren’t a good gauge of what engagement looks like on your app every day. It’s more of a reflection of press than overall or sustained success. 

DAU isn’t a key metric in every industry. It only makes sense for businesses where users actually use the app on a daily basis, like news, social media, or gaming. And, because every company can define “active” however it wants, no one is comparing apples to apples industry-wide, making metrics like DAU unhelpful on their own. 

This is not to say you shouldn’t measure DAU. It’s just more helpful if used in tandem with other metrics, or as the building blocks for other metrics.

DAU helps to gauge an app’s “stickiness,” or the regularity users engage with it. Measuring monthly active users (MAU) or even weekly active users also indicates stickiness, but over a longer period of time. The best metric to gauge consistent engagement and retention, though, is the DAU/MAU ratio. 

The DAU/MAU ratio is calculated by dividing the number of daily active users by the number of monthly active users over a given time period. It measures the proportion of monthly active users who engage with the app on a daily basis. In other words, it measures how well your app retains returning users, giving you a more accurate view of interaction with your app. 

Generally, a DAU/MAU ratio above 50% is considered good. It means that approximately 50% of monthly active users engage with the app daily. By tracking the DAU/MAU ratio overtime, brands can better understand and benchmark their app’s stickiness and growth potential. 

Most companies want to measure a user’s lifetime value (LTV). LTV is an indicator of the total revenue a business can expect to generate from an individual user. To do this, you need to calculate user retention rates, which rely on DAU.

Bottom line: DAU is crucial to measure, but it’s not indicative on its own.

Strategies to boost DAU

There are several ways to achieve an uptick in their daily active users. In today’s complex digital ecosystem, brands need a multi-pronged user acquisition strategy that focuses on acquiring app users from every touchpoint they interact with. Here are a few examples of proven tactics: 

• Remind your user base to use your app via emails, texts, or push notifications. With so many apps available to consumers, it’s easy for them to get distracted or forget yours exists. Continuous app promotion is critical for getting users to tap “download.” 
• Optimize your app for visibility in the app store. App store optimization (ASO) is a key component of any app growth strategy, as it determines whether users can easily find your app in the Apple App Store or Google Play Store. 
• Remove barriers to entry. If it’s too onerous to sign up, sign in, click through or scroll around, people won’t use your app. Use deep links to make the transition from other marketing channels like email, SMS, or search engine results pages (SERPS) seamless and convenient for users. 

Key takeaways

• DAU is just one metric to gauge interaction with your app. Evaluating it over the long term can help you determine the success of campaigns and customer experience.
• DAU is best used as an input for more indicative KPIs like lifetime value, churn rate, and retention rates.

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An in-app purchase (IAP) is a product, feature, functionality, content, or subscription that a user can buy within a mobile app. Users make in-app purchases via the app store, a debit or credit card, or through a third-party provider like PayPal or Stripe. Available through Apple App Store for iOS and Google Play Store for Android devices, in-app purchases generate revenue and boost user engagement, conversions, lifetime value, and retention rates. 

In-app purchases vs. other mobile app monetization

Most apps are created with the intention to monetize their use. In fact, consumer mobile app spending is on the rise, reaching $33.8 billion during the second quarter of 2023.

There are a few ways developers can encourage mobile app spending. A prime example is charging for app downloads. However, this creates a barrier to entry before a customer ever interacts with your app. This is why almost 97% of Android apps in the Google Play store are free to download. Additionally, you can display ads within the app. Unfortunately, in-app advertising is expensive and is often ignored by users. 

Due to the drawbacks and challenges of charging for app downloads and in-app advertising,  many app developers choose to offer in-app purchases, which are more enjoyable and less intrusive than ads and tend to boost user engagement. 

Types of in-app purchases

There are four types of in-app purchases:

1. Auto-renewal subscriptions: Products or services, like streaming services, that app users purchase on a recurring basis. Example: Spotify
2. Non-renewable subscriptions: Products or services purchased for a period of time. Example: Magazine subscriptions
3. Consumables: One-time-use products (like extra lives in a game) that can be used up and then repurchased. Example: In-game currency
4. Non-consumables: Products like ebooks or advanced features that can be purchased once and reused indefinitely. Example: Advanced editing tools in a photo app

Pros and cons of in-app purchases

As with any monetization method for apps, in-app purchases have their pros and cons. 

Pros

• Revenue generation: Because most apps are free to download, in-app purchases give developers a way to generate revenue without charging for the initial app download.
• Increased engagement: If someone invests money in the app, they’re more likely to continue using and exploring it.
• Understand user behavior: Tracking in-app purchases makes for valuable data about user behavior. This data provides insights to help you make your app more relevant and appealing.
• Cross-sell opportunities: If you offer multiple products or services within your app, you can use in-app purchasing as a way to cross-promote.

Cons

• Poor user experience: Overzealous developers may include too many in-app purchase opportunities that lead to a poor user experience. When users feel bombarded by purchase prompts, they disengage.
• Development and monetization challenges: In-app purchases make developing, implementing, and managing an app more complex and costly. Not all apps are suitable for in-app purchases.
• Competition: With millions of apps available on users’ mobile devices, your pricing and features must be competitive to attract customers.
• Regulations: Depending on the country and region, there may be specific restrictions on in-app purchase offerings.
• Fraud: Fraudsters often take advantage of digital platforms like apps, using fake credit information to make purchases. Cost-per-action campaigns are particularly vulnerable to fraud.

Best practices

When done right, in-app purchases can drive significant revenue for mobile app businesses. Here are some things to keep in mind in order to experience the best chance at success:

1. Transparent pricing: Clearly display the price of in-app purchases and indicate if any charges are billed on a recurring basis. 
2. Valuable offerings: Your in-app purchase offerings should provide real value to users and be differentiated from your free app offerings. Make sure users know exactly what they’ll get if they make a purchase. 
3. Seamless user experience: Reduce the likelihood of dropoff with smooth purchase experiences and convenient payment methods.
4. Special offers and discounts: Incentivize users to convert with limited-time discounts on in-app purchases. Use in-app messaging and push notifications to make users aware of special deals. 
5. Free trials: If applicable, let users try before they buy. Make sure you clearly communicate the trial terms — and if they’ll be automatically billed when the trial ends!

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