As audiences spend more time streaming, connected TV (CTV) has emerged as one of the fastest-growing media channels, prompting a surge in digital TV advertising spend. To keep up, marketers must not only design seamless, cross-device campaigns but also find ways to measure engagement accurately across CTVs, mobile devices, desktops, laptops, and tablets.

Introducing Branch Household Measurement

Household Measurement, Branch’s solution tailored for today’s multi-device households, gives marketers a clear, comprehensive view of cross-device engagement. Household Measurement bridges the gap between ad interactions and conversions across devices like CTVs, mobile phones, desktops, laptops, and tablets. By deduplicating and mapping user touchpoints within a household, Branch enables marketers to measure ad performance accurately, optimize campaigns, and substantiate ad spend with holistic data.

Household Measurement overcomes the limitations of traditional attribution, which often fails to capture the flow of cross-device engagements. With this solution, marketers gain newfound clarity into omni-channel campaigns, enabling data-driven decisions, real-time performance optimization, and more precise budget advocacy based on true campaign impact.

Value through real-world use cases

Branch Household Measurement goes beyond basic attribution, offering a robust suite of use cases that demonstrates its powerful impact on campaign effectiveness. Here are a few ways top brands are utilizing Branch’s Household Measurement.

Analyze CTV ad impact on multi-device conversions

As more consumers turn to streaming, companies are investing heavily in CTV ads to capture this growing audience. To allocate marketing budgets confidently, brands need clear insights to justify the return on investment (ROI) of these campaigns.

For example, a quick-service restaurant (QSR) running a CTV campaign promoting food order discounts can’t facilitate orders directly on the TV. However, with Branch’s Household Measurement, the QSR can trace mobile conversions back to the CTV ad, revealing its downstream impact. By understanding how CTV ads drive sales on other devices, brands can measure return on advertising spend (ROAS) across channels and gain a holistic view of each channel’s contribution to their strategy. This clarity allows marketers to validate their CTV investments and make data-driven decisions to optimize their omni-channel approach.

Measure engagement across multiple household CTVs

Many households now have multiple CTVs, meaning engagement can happen across more than one CTV in the same home. Measuring and attributing activity across these devices are crucial to fully understanding user behavior.

For example, a streaming service promoting a new series might run CTV ads that play a teaser and prompt viewers to click through to the series’ landing page, where they can watch the trailer, view the cast, or add it to their favorites. Later, they may start the series on a different CTV in the home. With Branch’s Household Measurement, the streaming service can track viewers who saw the ad, clicked through, and then watched the series on another device. These insights help the streaming service see which ads drove deeper engagement, allowing them to refine strategies for boosting viewership and make data-driven decisions for future campaigns.

Understand mobile ad influence on CTV engagement

To maximize marketing effectiveness, companies must understand how their mobile ad spend impacts viewer behavior on CTV platforms. Recognizing how mobile ads drive traffic to CTV apps is essential for optimizing advertising strategies and enhancing viewer interactions.

For example, consider a professional sports league running a mobile ad campaign during its championship season, promoting exclusive behind-the-scenes content alongside live games on its CTV app. While users may initially engage with the league’s ad on a platform like TikTok, marketers need to know how that engagement translates to viewership of games and exclusive content on their CTV app, where most viewers stream the games. By measuring these cross-device interactions, the organization gains valuable insights into which campaigns convert the most mobile users into active CTV viewers.

Build seamless engagement with cross-device linking

Brands can connect CTV ad interactions to mobile and desktop conversions, guiding users directly from CTV ads to in-app offers. With Branch-powered QR codes, viewers can instantly access mobile apps from CTV ads, seamlessly driving conversions and engagement across devices.

Media and entertainment companies can leverage cross-device linking to enable quick, streamlined login experiences via Branch-powered QR codes on CTVs. Grocery delivery companies can place Branch QR codes within CTV ads to effortlessly direct viewers into their mobile apps to place orders. This innovative cross-device linking not only boosts user engagement but also empowers brands to deliver cohesive, omni-channel experiences across screens.

Improve retargeting strategies

With consumers frequently switching between mobile devices and CTVs, tracking reengagement efforts can help brands refine their strategies and foster deeper connections with their audiences.

With Household Measurement, a fitness app can run mobile ads promoting exclusive CTV workout sessions featuring popular trainers and glean insights into in-app workouts that would have otherwise been untrackable. By sharing these conversion events across devices, brands can better data that allows them to later tailor their retargeting and customer reengagement efforts, ensuring they maximize the value of each ad dollar spent.

Privacy-centric household measurement

In today’s advertising landscape, user privacy can’t be ignored. Performance marketers are facing increasing pressure to justify ad spend and demonstrate ROI, all while navigating a shifting privacy landscape. Apple’s App Tracking Transparency (ATT) and the removal of the identifier for advertisers (IDFA) have transformed iOS campaign measurement, greatly limiting access to previously relied-upon data. With many users opting out of tracking, marketers are struggling to drive conversions, allocate budgets, and refine growth strategies. And this challenge is no different when measuring performance across household devices.

Branch alleviates this challenge and enables marketers to effectively measure campaign performance across households while still respecting user privacy with Predictive Aggregated Measurement (PAM). PAM provides marketers with more comprehensive iOS performance data while prioritizing user privacy. Utilizing advanced attribution modeling and differential privacy measures, PAM safeguards user data while providing timely and granular insights into campaign effectiveness. PAM allows marketers to more accurately measure iOS campaigns across household devices, allowing them to make data-informed decisions to optimize ROAS and justify their ad spend.

Driving success in a dynamic advertising landscape

Consumer behavior is rapidly shifting toward streaming, and privacy regulations are reshaping the marketing landscape. Brands must adapt to thrive. Leveraging innovative solutions like Branch’s Household Measurement enables companies to gain deeper insights into cross-device interactions, helping to justify every marketing dollar spent.

If you’re interested in building stronger connections with your audiences, meeting users where they are, and driving long-term success in a competitive environment, reach out to our team to learn more about Branch’s Household Measurement solution.

The post Break Through Cross-Device Attribution Challenges With Branch Household Measurement appeared first on Branch.

Performance marketers today face mounting pressure to justify ad spend and prove return on investments (ROI), all while navigating an evolving privacy landscape. Apple’s App Tracking Transparency (ATT) framework and deprecation of the Identifier for Advertisers (IDFA) have reshaped how you measure campaign success on iOS, significantly limiting the data you once relied on. With many users opting out of tracking, you may be in the dark about how to drive conversions, allocate budgets, and refine growth strategies. 

While tools like Apple’s SKAdNetwork (SKAN), Google’s gBraid, and Meta’s Aggregated Event Measurement (AEM) offer solutions to these challenges, they introduce complex, highly technical systems across multiple platforms, each with its own set of nuances and challenges to navigate. SKAN, for example, lacks the data granularity needed for actionable insights and demands significant technical expertise to implement. This complexity can disrupt marketing workflows, shifting your focus from campaign optimization to troubleshooting, and leaving teams frustrated and less capable of delivering impactful results.

While SKAN presents its own set of challenges, we’ve made it easier with solutions like our SKAN Magic Setup, which streamlines implementation and simplifies management. But we know SKAN alone isn’t enough. That’s why we’ve taken it a step further. Our commitment to simplifying privacy-centric measurement continues, and we’re excited to bring you another best-in-class solution to keep you ahead in this evolving landscape.

Introducing Branch Predictive Aggregated Measurement (PAM)

In response to these challenges, Branch has developed Predictive Aggregated Measurement (PAM) — an innovative approach to mobile campaign attribution designed to provide marketers with expansive iOS performance data while preserving privacy in an ever-changing privacy industry. 

PAM uses advanced attribution modeling mechanisms and differential privacy controls to protect user data while delivering accurate, granular insights into campaign performance. By dynamically adapting to the available data, PAM uses predictive modeling to select the best attribution method for each scenario, enabling marketers to focus on performance analysis and strategy optimization. PAM empowers marketers to work smarter, not harder.

“Branch’s Predictive Aggregated Measurement (PAM) takes the complexity out of iOS ads measurement, allowing marketers to focus on what matters: analyzing return on ad spend (ROAS) and driving growth.” 

– Justin Furstoss, Senior Product Manager 

Accurate attribution that boosts performance

Marketers investing in campaigns today across iOS face a critical data gap for opted-out users, which blinds them from measuring a significant portion of their audience. With limited visibility into campaign data, marketers are left guessing on key decisions such as how much to invest, which user segments are most engaged, and how to optimize for growth. While SKAN aims to address this, it lacks the granularity and real-time insights needed to make data-driven decisions, leaving marketers uncertain about the effectiveness of their iOS campaigns.

PAM solves this by delivering granular, real-time insights that fill the gaps left by SKAN. By aggregating and analyzing data in a privacy-preserving manner, PAM eliminates guesswork, allowing marketers to regain visibility into iOS campaigns and capture previously unaccounted conversions. PAM changes this by offering a privacy-preserving, predictive solution that fills in the gaps left by SKAN. With granular, real-time insights, PAM empowers marketers to fully understand the performance of their opted-out users and make informed decisions about campaign optimization. By providing a clear view of user engagement and conversion patterns, PAM helps marketers confidently refine their strategies and maximize growth potential, without the technical complexity or incomplete data that comes with SKAN.

Strategies built to last

As the privacy landscape changes, marketers need solutions that ensure lasting success. PAM isn’t just a short-term fix; it’s a durable, future-proof tool designed to help marketers seamlessly adapt to ongoing changes in the ecosystem while driving sustained growth.   

PAM maximizes attribution coverage by using privacy-centric mechanisms that support compliance while delivering the insights needed to optimize returns. It integrates across multiple attribution sources, including SKAN, AdAttributionKit, and opted-in users. As the digital landscape continues to shift, particularly with upcoming changes on Android, PAM will evolve to meet new challenges, ensuring reliable performance measurement.

Ensure future success

Today’s marketers face immense pressure to drive growth and demonstrate returns while navigating privacy-centric attribution mechanisms across ad networks and mobile operating systems. Safeguarding user privacy often comes at the expense of timely and accurate data, making it challenging to optimize campaigns confidently and effectively.

Branch is committed to providing marketers with exceptional performance measurement tools that remove the guesswork and support growth  — all while helping to support regulatory compliance. We continue to invest in advanced attribution techniques that provide marketers with durable, adaptable measurement strategies, ensuring business continuity and optimized performance as the industry evolves.  

Reach out for a demo or contact your Branch Account Team to learn more about how PAM can enhance your attribution and improve your marketing efforts today.

The post Overcoming iOS Attribution Challenges: Introducing Predictive Aggregated Measurement appeared first on Branch.

AdAttributionKit: The new SKAN 5?

Apple announced its next iteration of privacy-centric attribution, ditching the SkAdNetwork (SKAN) moniker for the much catchier AdAttributionKit.   

AdAttributionKit is “built on top of SkAdNetwork fundamentals,” meaning the overall concepts are nearly identical, which leads us to believe that AdAttributionKit will eventually replace SKAN. For now, SKAN and AdAttributionKit will be interoperable, allowing for streamlined conversions to the new framework. 

AdAttributionKit introduces new features that the industry eagerly anticipated (or really, crossed their fingers for) with SKAN 5. 

Finally, support for reengagement campaigns

SKAN 5 promised reengagement support, which is now included in AdAttributionKit. This will finally allow marketers to measure how many users return to their app. Best of all, you can continue to depend on Branch deep links for your reengagement campaigns because Apple will use Universal Links to power its AdAttributionKit reengagement flows. Note, however, that AdAttributionKit reengagement campaigns only support click-through attribution. 

Reengagement campaigns can also run in conjunction with acquisition campaigns; the AdAttributionKit framework will provide the corresponding postback depending on whether the app was installed. With reengagement support, we expect greater insights through additional postbacks, as we are no longer limited to the initial three postbacks from the app’s launch. 

Apple opens its gates to support third-party marketplaces

With AdAttributionKit, postbacks will now support third-party marketplaces through a new ‘marketplace-identifer’ field, enabling the identification of download or redownload sources. 

Major improvements to Developer Mode

Previously, Apple’s stringent privacy controls made testing SKAN difficult, requiring approximately 20 installs per campaign to initiate feedback, further complicated by randomized timers. This year, Apple introduced major enhancements to testing with AdAttributionKit Developer Mode, which removes the time randomization, shortens conversion windows, and quickens postback transmission. Developers can toggle this mode within their devices’ iOS Settings in the Developer menu. 

Anticipated value from AdAttributionKit

We’re eager to explore two key areas further:

1. Additional granularity in engagement and placements. AdAttributionKit introduces two new ad display methods, providing finer granularity in performance and reporting beyond the known SKAN placements of SKOverlay ads and SKStoreProductViewController ads. 

• • Custom-click ads: A newer iteration of SKAN for Web Ads offers a generic means to register a click and support click-through ads for reengagement, third-party marketplaces, and destinations outside the Apple App Store. 
  • View-through ads: Provides a generic means to register an impression, covering any custom ad presentation for view-through attribution. 

2. More granular conversion value (CV) insights in lower funnel postbacks. While SKAN limits postback 2 and 3 to coarse conversion values, Apple indicated during the “Meet AdAttributionKit” session that CV granularity correlates directly with crowd anonymity. We can speculate that AdAttributionKit may now support finer CV granularity in postbacks 2 and 3 if corresponding crowd anonymity tiers are met.

Apple Intelligence

Thought AI stood for artificial intelligence? Think again. Apple has redefined “AI” as Apple Intelligence, marking its bold stake in the next era of innovation.

Apple’s positions its AI as “AI for the rest of us.” While introducing familiar AI features seen in tools like Midjourney, Adobe Illustrator, and Grammarly, Apple is now integrating similar capabilities, such as generative AI writing and image creation, as on-device add-ons to existing Apple apps. Notably, Apple has partnered with OpenAI to seamlessly provide ChatGPT across its OS suite, a move that diverges from its traditional brand.

What garnered the most interest here at Branch, however, is the ongoing development of AppIntents. The “What’s new in App Intents” session highlighted that “AppIntents is core to building experiences for Apple Intelligence.” This statement, coupled with the announcement that AppIntents can now be exposed by Universal Links in iOS 18, sparked significant excitement at Branch, where we have made significant investments in Universal Links.

Although AppIntents has not achieved widespread adoption since its release with iOS 16, we anticipate that Apple Intelligence will drive a renewed demand for user personalization and engagement. AppIntents offer a powerful framework for app developers to create personalized and captivating user experiences across devices. While Universal Links in AppIntents is still a new concept, at Branch, we see immense potential for unlocking valuable new opportunities in the future.

Conclusion

As WWDC comes to a close, Apple’s keynote brought notable improvements with AdAttributionKit, though it lacked groundbreaking announcements. Despite not fully resolving SKAN challenges, reengagement support is a step in the right direction. With promising developments in developer tools and AI integration under Apple Intelligence, the future holds potential for better app experiences. 

To delve deeper into Apple’s announcements and explore their implications for the industry’s future, replay our webinar: AdAttributionKit: What Marketers Need to Know From WWDC 2024

The post From Predictions to Reality: AdAttributionKit Unveiled at WWDC 2024 appeared first on Branch.

Where we stand today

Given Android’s complex OEM ecosystem, the GAID deprecation is expected to unfold more slowly than iOS’s App Tracking Transparency (ATT) changes, with no significant impact on Android marketing expected before the end of 2024. But, we also expect Google is likely to announce GAID changes before covering all Android devices, mirroring ATT’s phased introduction with iOS 14.5 and above. 

Globally, Android holds a 42% larger market share than iOS, making the implications from Google’s Privacy Sandbox for mobile marketing substantial. Even imagining a GAID restriction limited to just Android versions 13 and 14 would impact about 23% of the entire Android marketing industry, underscoring the urgency for mobile marketers to align with ad tech companies on the Privacy Sandbox integrations.

Keeping up: iOS vs. Android privacy frameworks

Although the mission of prioritizing user privacy remains the same, ad tech giants Google and Apple have different nuances to their privacy-centric attribution frameworks. 

Addressing today’s business needs under the Privacy Sandbox

In light of the evolving privacy frameworks within both the iOS and Android ecosystems, it’s evident that user-level tracking will become increasingly restricted, including ID limitations and reduced cross-app and cross-site tracking. Additionally, third-party SDKs’ permissions and data access will be further constrained.

In the face of the evolving changes surrounding privacy-centric attribution, businesses have expressed interest — or really concern — in sustaining their marketing capabilities in key areas. Google’s Privacy Sandbox introduces several innovative solutions to address these business needs while prioritizing user privacy.

Build and manage target audiences

	Protected Audience API	Topics API	Protected App Signals API
User acquisition
Reengagement
Retention and activation

Marketers today have relied heavily on user-level identifiers to effectively build and target key audiences for reengagement, retention, and activation campaigns. While this has been a great way to accurately reach target audiences, the focus on user-level privacy changes the way marketers will have to think about audience building and management, which Google aims to address with: 

• Protected Audience API – for retargeting. To effectively engage with users, marketers often need to build campaigns based on how users have previously engaged with their app. This is commonly thought of in re engagement, retention, and activation campaigns. An example might be a pet store app that wants to advertise to users who have left an item in their cart in the past 24 hours to remind them to complete the purchase. This API enables the grouping of users based on criteria specified by the app, facilitating targeted advertising while maintaining user privacy.
• Topics API – for personalization. In mobile advertising, advertisers aim to present ads that align closely with a user’s interests. For instance, to create a more engaging and personalized experience, someone who frequently engages with running apps would likely find ads about running topics more appealing than unrelated ads. The Topics API plays a crucial role here by generating broad interest signals, known as topics, directly from a user’s app interactions. These topics are determined on-device, ensuring privacy, and are then provided to advertisers. This allows marketers to build more relevant advertising based on a user’s demonstrated interests without the need to track individual user activity across different apps.

While the examples above illustrate the primary use cases we expect, advertisers and DSPs can ultimately select the APIs that best suit their specific needs.

Understand how your marketing is performing

Historically, most marketers have understood their attribution and measured campaign performance by leveraging Advertising IDs, such as GAID. However, in a world where marketers cannot rely on user-level identifiers like GAID, they will need an alternative way to continue measuring attribution and curating accurate reports on their marketing performance. Google’s Privacy Sandbox aims to address this need with:

• Attribution Reporting API – for measurement. The Attribution Reporting API enables privacy-focused measurement of digital ad performance across apps and the web by removing the reliance on cross-party user identifiers. This will ensure cross-app-and-web attribution while prioritizing user privacy. Marketers at a gaming company, for example, may want to measure which country had the best performance with their newly released game, which they could understand at an aggregate and event level with this reporting API.
• SDK Runtime – to isolate SDK execution. To add more value, the SDK Runtime provides a secure environment for executing third-party SDKs, such as a unified “SDK Store.” This isolates third-party SDKs from direct access to user data and app resources and enhances privacy and security by controlling the SDK’s interaction with the host app and user information.

While several other APIs in the Privacy Sandbox framework can be explored, we believe the ones discussed here represent the core components essential for understanding the shift toward more privacy-conscious advertising. These APIs lay the foundation for a new era in digital marketing, where user privacy, effective targeting, and performance measurement coexist.

How Branch is navigating a post-identifier deprecation landscape

Google’s Privacy Sandbox evolution signals a continued shift toward a more privacy-focused digital marketing ecosystem, urging ad techs and apps to embrace new marketing technologies and strategies.

At Branch, our ongoing focus is on offering privacy-compliant attribution and measurement. We are actively collaborating with Google’s Privacy Sandbox team, participating in live tests, and contributing feedback to the designed initiatives. Our goal is to enhance the privacy-focused mobile advertising ecosystem together while continuing to support thriving digital businesses.

As the industry shifts toward an ID-restricted landscape with increased noise added to reporting, Branch is also developing a new holistic-based reporting system. This system will strategically manage noise and offer customization options — along with our expert recommendations on measurement goals, tracking granularities, and aggregation periods — to ensure that Branch continues to deliver actionable insights. Beyond probabilistic approaches, Branch is also advancing toward using multi-data and sophisticated modeling to provide deeper insights into ad spend allocations and more.

While we navigate changes like the deprecation of third-party cookies, Branch also ensures that our deep linking and Journeys offerings remain unaffected. We are supporting your marketing efforts as before. 

We will continue to share practical insights and updates on our navigation of the Privacy Sandbox. We are moving into live testing and welcome interested apps to join us in this initiative. If you’re ready to take that step, we invite you to contact us! For more on Privacy Sandbox, check out our recent TechTalks webinar, “The Unpopular Truth About the State of Privacy.” 

The post Google’s Privacy Sandbox and the User Lifecycle: Thriving in a Post-Identifier Deprecation Landscape appeared first on Branch.

What is the Cross-Events Export API?

The Cross-Events Export API is Branch’s enterprise data exporting solution, designed to streamline workflows, remove data limitations, and maintain data integrity across sources. By seamlessly exporting multiple datasets through a single query, it simplifies the process of handling high data volumes and eliminates the manual labor and complexities traditionally associated with data management. The Cross-Events Export API empowers data science, insights, and marketing teams to efficiently handle data from diverse sources, leading to better data-driven decision-making and improved marketing performance.

Retrieve data without limitations

Export your desired data without limitations on the number of rows, dimensions, or maximum number of days in a single query. This enables more comprehensive analysis and strategic decision-making without data volume constraints. Our goal is to enable you to leverage all unified data within Branch, without hindrance or hesitation.

For instance, consider a retail company aiming to analyze customer behavior throughout its busy holiday shopping season. To get an accurate picture, it would need to retrieve an entire season’s worth of transaction data. However, it encounters row limits when attempting to do so, resulting in incomplete datasets. With the Cross-Events Export API, the team can export an unlimited amount of data with one query to capture all critical inputs. Plus, if the data spans more than 200,000 rows, the results are automatically split into multiple files to avoid inflated file sizes.

Consider another example: A financial services company wants to segment its customer base by analyzing interactions across multiple dimensions, including transaction types, geographic locations, and customer demographics. With the Cross-Events Export API, the company can export unlimited dimensions in a single query, providing granular insights to inform targeted marketing strategies or product offerings.

Unify data from multiple sources

Marketing teams often face challenges in consolidating data from multiple sources, leading to a disjointed understanding of both marketing performance and customer engagement.

Within the Branch Dashboard, you can access a comprehensive view of attribution and campaign performance, including campaign data from all attribution methods, such as SKAdNetwork (SKAN), IDFA, IDFV, Apple Search Ads, and Android. The Cross-Events Export API empowers you to seamlessly export datasets from multiple sources. This enables you to leverage your Branch Unified Analytics in external tools with confidence, ensuring cohesive analysis of reliable data.

Imagine an online streaming service that wants to consolidate viewer engagement metrics across different platforms (e.g., website, mobile app, third-party services). To assess overall performance, the company wants a singular, unified view of its analytics, without the time-consuming work of exporting and re-merging data. The Cross-Events Export API allows the team to combine multiple data sources into a single dataset, providing a holistic view of engagement and informing more strategic marketing decisions.

Leverage Branch data across any platform

The Cross-Events Export API streamlines data exportation by tackling challenges around data volume restrictions, data fragmentation, and limitations on query dimensions. This allows you to prioritize efficient and comprehensive data analysis, empowering you to make better, data-driven decisions regarding your growth strategies.

Learn more about Branch’s Cross-Events Export API in our help documentation. To get started, request a demo with our team.

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