Key over-the-top (OTT) and connected TV (CTV) terminology

The world of television has undergone a dramatic transformation, moving away from traditional broadcast methods to embrace internet-connected devices that deliver streaming content. This paradigm is known as Advanced TV—the evolution of television viewing and advertising beyond traditional linear TV. Advanced TV comprises a range of technologies and platforms, including connected TV (CTV), over-the-top (OTT) streaming services, addressable TV, and programmatic TV. It provides a more flexible and measurable approach to advertising, bridging the gap between digital and traditional TV landscapes. This enables advertisers to deliver more targeted and personalized ads by leveraging data-driven insights, enhancing viewer engagement and improving ad effectiveness.

Advanced TV has given rise to a plethora of new CVA (concepts, vocabulary & acronyms—see what we did there?) that are often bewildering. As you peruse this guide, you will encounter a range of advanced TV advertising terms that define this dynamic space. Our goal is to demystify so much alphabet soup, offering straightforward definitions and illustrative examples to enhance your advanced TV understanding. We aim to convey the knowledge needed to navigate and understand the intricacies of OTT and CTV, empowering you to engage confidently with this exciting field.

OTT (Over-the-Top)

Over-the-Top, or OTT, describes the method of delivering television and film content directly to viewers, streamed “over the top” of the internet and thus bypassing traditional broadcast, cable, or satellite platforms. OTT content can be accessed from apps or websites via a variety of devices, including connected TVs, desktops or laptops, mobile phones, and tablets. OTT represents the content delivery aspect of the advanced TV ecosystem, offering viewers the flexibility to watch what they want, when they want, on their preferred device.

CTV (Connected TV)

CTV, or connected TV, describes televisions that are either directly internet-enabled, such as smart TVs, or those that use external devices to stream content over the internet. These devices include streaming media players (Roku, Amazon Fire TV, Apple TV, Chromecast with Google TV); smart TV operating systems (Samsung, LG, VIZIO, Roku); and gaming consoles (PlayStation, Xbox). The CTV ecosystem also encompasses various streaming services: subscription-based services (Netflix, Disney+, Max, Amazon Prime Video); hybrid services combining subscription and ad-supported models (Hulu, Peacock, Paramount+); and free ad-supported streaming television (FAST) services (Pluto TV, Tubi, Freevee, Xumo Play, Plex, the Roku Channel). CTV represents the hardware aspect of the advanced TV ecosystem, providing the means to access a wide array of OTT streaming services and content.

ACR (Automatic Content Recognition)

ACR is a technology built into smart TVs that identifies in real-time the content playing onscreen, whether from streaming services, gaming consoles, or even traditional cable boxes. This enables more precise audience measurement and ad targeting in the CTV environment. ACR helps advertisers understand what content viewers are watching as well as which ads they are exposed to, making it a cornerstone technology for advanced TV advertising measurement, viewability, and frequency management.

Ad Pods

Ad pods are groups of ads that play back-to-back during a commercial break in a video stream. Advertisers can optimize these pods for efficiency by rotating ads or showing them in specific sequences.

Addressable Advertising

Addressable advertising in the advanced TV ecosystem enables different ads to be shown to different households watching the same program, based on sophisticated targeting capabilities. This technology has evolved far beyond basic demographic targeting (i.e., age and gender) to incorporate multiple layers of audience intelligence. Advertisers can now reach viewers based on affluence, education, and life stage indicators, as well as behavioral patterns such as viewing habits and content preferences. Geographic precision ranges from broad DMA (designated market area) coverage down to specific zip codes or neighborhoods, effecting locally relevant messaging. Addressable advertising also considers interest-based factors, analyzing data about hobbies, purchase intentions, and lifestyle choices to ensure ad relevancy. Brands can create custom audience segments using first-party data or specific brand criteria, with the ability to adjust targeting mid-campaign based on performance metrics. This dynamic approach ensures optimal audience reach and campaign effectiveness in ways traditional TV advertising could never achieve.

Addressable TV

Addressable TV uses programmatic technology to segment audiences automatically to target ads at the household level. These addressable TV advertisements are served to viewers across set-top providers (cable platforms) and VOD (video on demand) inventory in real-time. Addressable TV enables advertisers to target customers and geographies more precisely.

Advanced TV Advertising

Advanced TV advertising refers to the delivery of ads on television sets that are connected to the internet, through streaming devices (Roku, Amazon Fire TV, Apple TV, Chromecast with Google TV), smart TV platforms (Samsung, LG, VIZIO), and gaming consoles. This innovative form of advertising enables brands to serve targeted, often interactive ads to viewers who consume content via CTV platforms and OTT streaming services.

ATSC 3.0

ATSC 3.0 is the latest version of Advanced Television Systems Committee standards for broadcast television, also known as NEXTGEN TV. It combines over-the-air broadcasting with broadband internet, revolutionizing both viewing experiences and advertising capabilities. For viewers, ATSC 3.0 delivers 4K ultra high-definition video quality, theater-like sound, mobile reception, and enhanced emergency alerts. For advertisers, it enables breakthrough capabilities including dynamic ad insertion at the household level, geographic targeting down to neighborhoods, interactive advertising overlays, and real-time ad performance measurement. The standard also supports dialogue replacement that enables broadcasters to customize audio elements of ads for different audiences. This convergence of broadcast and digital creates new opportunities for advertisers to deliver personalized experiences while maintaining the broad reach of traditional television.

AVOD (Advertising-Based Video on Demand)

AVOD is a streaming service model that offers free, ad-supported streaming video (Tubi, Freevee, Pluto TV, Xumo Play, Plex, the Roku Channel). Some hybrid streaming services (Peacock, Paramount+, Hulu) also offer ad-supported tiers alongside their subscription options. Viewers can watch content for free, but advertisements occasionally appear during their view time.

Channel Stores

Channel Stores are app distribution platforms where viewers can launch various streaming channels through a single interface. Major examples include Amazon Channels, the Roku Channel Store, Apple TV Channels, and Samsung TV Plus Video. These aggregation platforms offer viewers a convenient means to access and subscribe to different content providers within the advanced TV ecosystem, often with unified billing and content discovery.

Digital TV

Digital TV transmits television signals across digital media such as desktop, mobile, or tablets rather than traditional linear television delivery.

DAI (Dynamic Ad Insertion)

DAI technology enables advertisers to insert ads into a video stream in real-time based on viewer data, device information, and content context. Unlike traditional linear TV advertising, where the same ads broadcast to all viewers, DAI creates individual decision points within the content stream where customized ads can be inserted. This process happens in two main ways: client-side DAI (CSAI), where the insertion occurs on the viewer’s device, or server-side DAI (SSAI), where ads are stitched into the content stream before delivery. DAI technology considers factors such as viewer demographics, viewing history, geographic location, and even time of day to determine the most relevant ad to serve. This capability is particularly powerful in OTT/CTV environments, combining the high-quality viewing experience of traditional TV with the targeting precision of digital advertising.

FAST (Free Ad Supported TV)

FAST refers to a growing category of streaming services that offer content for free, supported by advertisements. This model is similar to AVOD (advertising-based video on demand), but differs from it in that FAST services are often pre-programmed channels, similar to linear TV. Examples include Pluto TV, Tubi, Freevee, Xumo Play, Plex, and channels from connected TV OEMs such as the Roku Channel, Samsung TV Plus, LG Channels, and VIZIO WatchFree+.

Frequency Capping

Frequency capping in CTV refers to the practice of limiting how often a specific household sees the same advertisement across different streaming platforms and services. This is particularly important in the CTV space where multiple family members might be watching through different apps on the same device or the same content might be accessed through different CTV devices in the home. Effective frequency capping helps optimize campaign spending and prevent ad fatigue.

GRP (Gross Rating Point)

GRP is a standard measure in advertising used to quantify the exposure of an ad by measuring impressions as a percentage of the target population. GRP refers to the total exposure as a percentage of the target audience.

HbbTV (Hybrid Broadcast Broadband TV)

HbbTV is a global initiative aimed at harmonizing broadcast TV and broadband delivery of entertainment services to consumers, allowing connected TVs, set-top boxes, and multiscreen devices to access additional online services and content alongside traditional broadcasts.

Household Graph

A household graph is a data structure that maps the relationship between CTV devices, viewers, and households, enabling advertisers to understand and reach specific audiences at the household level. This technology helps solve one of the unique challenges of CTV advertising: multiple viewers using the same device or multiple devices in the same household. Household graphs enable more precise targeting and measurement while respecting viewer privacy, making them essential for advanced TV advertising campaigns.

HVOD (Hybrid Video on Demand)

HVOD combines elements of SVOD and AVOD, offering a hybrid of subscription-based and ad-supported content. Subscribers have the option to pay a higher subscription rate for an ad-free experience or a lower rate for an experience that includes ads. In recent years, SVOD streaming services such as Netflix and Disney+, which previously offered only ad-free subscriptions, have introduced subscription tiers with ad-supported content at a lower price point.

iTV (Interactive TV)

iTV refers to television services that enable viewers to interact with the content they are watching, such as voting, shopping, or accessing additional information, making TV a more interactive medium.

Linear TV

Linear TV is the traditional television form factor in which networks broadcast content that is displayed via a satellite or cable box and watched live at a scheduled time on a specific channel.

MVPD (Multichannel Video Program Distributor)

A MVPD (Comcast, Spectrum, DISH Network) distributes or serves multiple television channels to viewers via cable, satellite, or other distribution technologies. They typically offer bundled packages of channels, typically on a subscription basis, and may also offer video-on-demand.

Programmatic Advertising

Programmatic advertising is the automated process of buying and selling digital advertising using software algorithms rather than traditional human negotiations and manual insertion orders. In the CTV/OTT space, programmatic advertising has seen explosive growth, with market statistics showing that programmatic CTV ad spend reached $19 billion in 2023 and is projected to exceed $25 billion in 2025. This growth is driven by improved targeting capabilities atop the shift in viewer behavior toward streaming platforms. Programmatic technology enables advertisers to purchase individual impressions in real-time while optimizing for specific audience segments, content types, and viewing contexts. Success rates show that programmatic CTV campaigns typically achieve up to 80% completion rates, significantly higher than traditional digital video advertising. This efficiency, combined with the precision of audience targeting, has made programmatic the preferred method for most advanced TV advertising campaigns.

Programmatic TV

Programmatic TV is the automated process of buying, selling, and delivering television ads using data-driven software and real-time bidding technologies. Unlike traditional TV ad buying, which relies on broad demographic data and manual insertion orders, programmatic TV leverages viewing data from set-top boxes (STBs) and smart TVs to identify and reach specific audience segments. The process works through several key components: demand-side platforms (DSPs) that enable buyers to manage campaigns across multiple inventory sources, supply-side platforms (SSPs) that help publishers optimize ad inventory, and data management platforms (DMPs) that organize and activate audience data. In CTV environments, programmatic TV factors in variables such as content genre, viewing time, device type, and household characteristics to make near real-time decisions about which ads to serve. This technology enables advertisers to move beyond traditional age and gender targeting to reach audiences based on behaviors, interests, and purchase intent, while maintaining the brand-safe environment of television.

PVOD (Premium Video on Demand)

PVOD enables consumers to purchase early access to content prior to, simultaneously with, or shortly after its theatrical release. Users can pay a one-time fee in addition to their monthly subscription to access new content before it’s available on other platforms. PVOD is often used for highly anticipated/high-budget films or unreleased movies. PVOD provides an alternative to movie theaters, enabling studios to monetize their films outside of traditional theatrical releases.

SSAI (Server-Side Ad Insertion)

SSAI, also known as dynamic ad stitching, is a technology that seamlessly integrates ads into streaming content at the server level before it reaches the viewer’s device. Unlike client-side ad insertion, SSAI provides a smoother viewing experience that mimics traditional TV. This technology is crucial for OTT and CTV platforms, as it helps ensure reliable ad delivery and reduces buffering between content and advertisements.

SVOD (Subscription Video on Demand)

SVOD describes streaming services (Netflix, Disney+, Max, Amazon Prime Video, Apple TV+) that require viewers to pay a subscription fee, usually at a monthly rate. Viewers can then watch shows from a library of content whenever they choose. While traditionally SVOD services were ad-free, some services now offer both ad-free premium tiers and lower-priced ad-supported options (Hulu, Peacock, Paramount+, Netflix).

t-Commerce (Television Commerce)

t-Commerce involves purchasing products directly through TV platforms. Advanced TV technologies have facilitated commercial interactions as an evolving way that viewers interact with TV content.

Target Rating Point (TRP)

TRP is a standard metric in advertising used to quantify ad exposure, focusing on a specific demographic or target audience. Similar to GRP.

Traditional TV

Traditional TV is the OG means of ad buying in which ad spots are purchased based on viewer data such as gender, age, and other demographics, then served on specific TV ad programs. This type of ad buying reaches a wide range of viewers across linear TV ad spots.

TV Buying

TV buying is the process of purchasing ad space and time on a platform or service to target specific audiences for increasing a customer base. TV buying has evolved over the years: Traditionally, ads were purchased and displayed on cable/satellite television, reaching broad audiences. Now, ad space can be bought and sold automatically, served to specific viewers across many programs and devices.

TV Content

TV content refers to how television can be made available to viewers. Instead of paying for cable or satellite services and watching content available only on the channels provided, viewers can decide what content they want to view and pay for that content in various ways. Advertisers can also determine what type of TV content they serve ads on depending on the audiences they want to target.

There are many types of TV content, and each platform can have a combination of features. For example, Hulu is both an SVOD and AVOD streaming service and provides viewers with the option to buy add-ons that fall under the TVOD category. On the other hand, Netflix operates under the HVOD umbrella because it offers premium ad-free subscriptions and lower-cost limited ads tiers. The following LumaScape shows the multitude of TV content and how it fits into the entire ecosystem. Advertisers choosing where to serve ads need to examine the different platforms, what they offer, and how they display ads to ensure that they are reaching their desired audiences.

TVE (TV Everywhere)

TVE refers to content that can be streamed on any device. Viewers can log in to their chosen platform to view content and pay for their service within the platform.

TVOD (Transactional Video on Demand)

TVOD is a model where viewers purchase download or viewing rights to individual pieces of content—such as a particular film, TV show, or TV episode—on a transactional basis for a one-time fee, allowing them to pay for only the content they want to watch.

vMVPD (Virtual Multichannel Video Program Distributor)

A vMVPD is similar to an MVPD, but distributes and serves channels across digital OTT platforms (YouTube TV, Hulu + Live TV, Sling TV, fuboTV). vMVPDs deliver multiple TV channels to their subscribers with flexible options for cable-like channel bundles that are cheaper than traditional cable. These streaming services typically offer options to watch TV live or on demand.

VOD (Video on Demand)

VOD allows viewers to watch television content when they choose instead of watching programs at a scheduled time.

What is AdAttributionKit?

AdAttributionKit, or AAK, is Apple’s new privacy-enhancing technology (PET) that provides a campaign attribution framework for advertisers while protecting the privacy of individual iOS users. It was introduced with iOS and iPadOS 17.4 and is built upon the fundamentals of SKAdNetwork (SKAN).

To take a deeper dive into how SKAN works for versions 2 through 4, download our Ultimate Marketer’s Guide to SKAN.

Why did Apple release AdAttributionKit?

Prior to the release of AdAttributionKit, Apple had announced the 5th generation of SKAN at WWDC23. However, SKAN 5 never fully materialized. A key catalyst for the shift to AdAttributionKit was the European Union’s Digital Markets Act (DMA). This requires large digital platforms like Apple to allow third-party app stores and marketplaces. The Store Kit Ad Network (SKAdNetwork or SKAN) was built to support only Apple’s App Store. On the other hand, AdAttributionKit is designed to support alternative, third-party app stores.

Will AdAttributionKit replace SKAdNetwork?

Apple has published guidance on the interoperability of AAK and SKAN, and there are currently no announcements about SKAN being deprecated any time soon. That said, it’s clear that the future of Apple’s privacy-durable attribution for advertisers rests with AdAttributionKit. How long SKAN will be around is unknown, but it’s likely to be maintained for some time given that the industry is still running largely on SKAN 3 and just starting to prioritize SKAN 4 adoption.

The following chart shows SKAN version adoption over time. It starts near the rollout of the AppTrackingTransparency (ATT) framework with iOS 14.5 in April 2021, which propelled initial SKAN adoption. V3 dominance is beginning to decline with the adoption of v4.

When will AdAttributionKit be adopted?

While AdAttributionKit became available with iOS 17.4, industry adoption is currently minimal. Per the SKAN version adoption chart in a previous question, the industry is still largely running on SKAN 3, even though SKAN 4 was announced at WWDC22. If prior adoption trends are any indication for the pace we can expect with AAK, it’s likely to be a waiting game.

Mobile measurement partners (MMPs) like Kochava are quickly adding support for AdAttributionKit, which will allow advertisers and their developers to update their apps for support. However, publishers and ad networks must also adopt the new framework for it to properly function end-to-end on an actual campaign. This will be one of the largest dependencies. Ask your growth partners about their plans to support AdAttributionKit.

Click here to see the full list of Kochava integrated partners that support SKAN.

Will I need to update my Kochava iOS SDK to support AdAttributionKit?

Yes. AdAttributionKit relies on newer APIs which necessitate updates to the Kochava software development kit (SDK). The 9th generation of Kochava iOS SDKs, to be released later this fall, will feature full support for AAK. Many server-side controls and dashboard configurations will be backward compatible with what you have already configured in your Kochava account for SKAN, so the transition will be quite seamless.

Do I really need to use SKAN or AdAttributionKit?

If this is a question you’re pondering, we recommend reading To SKAN or Not to SKAN: That Is the Question. Aside from the catchy title, this will help you understand whether SKAN (and eventually AAK) is something you need to adopt in the near term based on your iOS growth strategy and media mix.

Over the long term, SKAN and AAK will become increasingly vital for campaign measurement on iOS, and those who do not embrace and adopt the frameworks are likely to be at a growing disadvantage. The following chart displays the attribution method/source for iOS installs beginning in January 2021. It showcases the rise of SKAN and the correlative decline of IDFA-based and probabilistic attribution (now largely dominated by owned media). As you can see, SKAN is the largest source of iOS install attribution.

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Webinar insights from industry experts at DPAA and Advertiser Perceptions

Digital out-of-home (DOOH) has emerged as a potent medium in the fast-moving ecosystem of digital advertising, melding the impact of traditional billboards with the precision of digital targeting and programmatic capabilities. As marketers seek innovative ways to capture audience attention in an increasingly fragmented media environment, DOOH offers a compelling solution that bridges the physical and digital realms.

In a recent webinar hosted by Kochava, key thought leaders in the field convened to explore the evolving landscape of DOOH advertising. Barry Frey, President & CEO of Digital Place-Based Advertising Association (DPAA); Noah Klas, VP of Membership at DPAA; Sarah Bolton, EVP of Business Intelligence at Advertiser Perceptions; and Grant Simmons, VP of Kochava Foundry, shared their insights on the current state of DOOH, its integration with other media channels, and the opportunities and challenges that lie ahead. Their collective expertise provided attendees with a comprehensive view of how DOOH is reshaping the advertising landscape—and what marketers need to know.

Setting the Stage: The Evolving DOOH Landscape

Frey kicked off the discussion by highlighting the transformative technologies reshaping DOOH, painting a picture of an industry in the midst of a technological revolution. His opening remarks set the tone for a deep dive into the current state and future potential of DOOH advertising.

• DOOH is emerging as a powerhouse in the advertising world, offering unique strengths in brand safety, addressability, and sustainability.
• The global reach and effectiveness of DOOH are surpassing traditional media outlets, marking a significant shift in the advertising landscape.
• QR codes and cutting-edge technologies such as augmented reality (AR), virtual reality (VR), and 3D displays are reinventing the DOOH experience for consumers.

The session then unfolded to reveal several key insights:

1. DOOH Is Gaining Momentum in Advertising Strategies

2. The Blurring Lines Between Media Channels

Simmons highlighted crucial trends that he and his Kochava Foundry team are noting in the DOOH advertising landscape:

• Video content is increasingly permeating all media channels, with DOOH becoming an essential component of this holistic approach.
• Traditional boundaries between social media, digital advertising, and out-of-home are becoming increasingly indistinct.
• This convergence necessitates the development of integrated video teams capable of creating cohesive campaigns across multiple platforms and channels.
• Within this new landscape, programmatic DOOH is emerging as a key advantage, offering flexibility and targeted reach.
• The medium is measurable from both a brand and performance point of view.

This integrated dance among digital media presents both challenges and opportunities for advertisers, requiring a more cohesive approach to campaign planning and execution across diverse media channels.

3. Leveraging DOOH for Customer Acquisition

The webinar revealed promising insights into the effectiveness of DOOH for customer and user acquisition:

• Unlike some digital ad formats, DOOH ads often demonstrate improved conversion rates with higher exposure frequency—and a longer window of customer touchpoint influence.
• This makes DOOH a potentially powerful tool for customer acquisition when integrated into a comprehensive video strategy.
• Advertisers can leverage this trend by strategically increasing ad frequency in high-traffic DOOH locations to maximize conversion potential. The propensity to convert increases with DOOH, unlike other mediums where saturation can cause ad fatigue.

These findings suggest that DOOH is positioned to play a crucial role throughout the marketing funnel, both in building brand awareness and driving conversions over time.

Want access to the full webinar presentation? Contact us.

4. Navigating Challenges in the DOOH Landscape

The speakers went on to identify several key challenges facing the DOOH industry:

• Measurement is perceived as a significant hurdle, with advertisers struggling to accurately track performance and compare results with other media sources. However, DOOH is in fact eminently measurable. (Explore Kochava DOOH measurement capabilities here.)
• Ad fraud is an ongoing concern. While third-party verification offers some protection, the industry must remain vigilant and develop robust safeguards.
• Creative development for DOOH requires a unique approach, necessitating close collaboration among brands, agencies, and media owners.

The experts emphasized that these hurdles can be overcome, unlocking significant potential in the DOOH space, particularly as marketers become more savvy at leveraging available measurement technologies and creative strategies.

5. Future Trends Shaping DOOH

Looking ahead, the panel identified several key trends poised to shape the future of DOOH:

• A growing emphasis on advertiser education to fully unlock the channel’s potential
• Increased integration of DOOH into cross-channel planning and buying
• Technological advancements that open new avenues for creative and engaging ad experiences, including interactive displays and AI-driven content adaptation

These all point to a future wherein DOOH becomes an increasingly sophisticated, data-driven, and integral part of the advertising ecosystem, offering unprecedented opportunities for brands to connect with audiences in meaningful ways.

Catch the Full Webinar on Demand

The complete on-demand webinar, New DOOH Opportunities for Performance Marketers: Results From DPAA Omnichannel Decision Makers Study, is available now! As the lines between digital and physical advertising continue to blur, DOOH is positioned to play an increasingly important role in comprehensive marketing strategies. For marketers looking to stay ahead of the curve, understanding and leveraging DOOH is crucial.

To address the growing need for effective DOOH measurement, Kochava is shaping the future of this dynamic field through innovative solutions including outcomes-based reporting, MediaLift® for calculating incremental lift, and always-on near real-time reporting. These tools enable marketers to measure DOOH campaigns against meaningful outcomes, from app installs to store visitations and more. For a deeper dive into DOOH, readers are encouraged to explore this companion piece about the evolution of DOOH as well as the full DPAA 2024 Omnichannel Decision Makers Study.

As the DOOH landscape keeps evolving, staying informed about the latest trends, measurement methodologies, and industry partnerships is essential for marketers aiming to maximize the impact of their out-of-home advertising efforts. Whether you’re a seasoned DOOH veteran or just beginning to explore this exciting medium, the insights shared in this webinar provide valuable guidance for leveraging DOOH in your marketing mix.

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Perspectives, measurement, and future trend insights with DPAA

Digital out-of-home (DOOH) advertising continues to rapidly transform the advertising landscape, melding the physical and digital realms and creating dynamic, engaging experiences. Imagine walking through a bustling urban square, where dazzling LED billboards capture your attention while adapting content in real-time to meet the interests of the surrounding throngs. Picture digital screens in your local grocery store that display colorful ad specials—and also interact with you, offering personalized promotions based on your shopping preferences. This is an exciting frontier of advertising, where the boundaries between digital and physical blur, creating immersive moments that captivate consumers on the go.

Behind the flashy screens and eye-catching visuals, DOOH now leverages advanced technologies such as programmatic buying, real-time measurement & analytics, and geotargeting to deliver the right message to the right audience at the right time. Its evolution is driven by the increasing digital enablement of out-of-home media, transforming static billboards into dynamic canvases with kaleidoscopic content creating an array of impactful and measurable touchpoints.

DPAA + Kochava

Digital Place-Based Advertising Association (DPAA) recently partnered with Kochava on its research study through Advertiser Perceptions to release the DPAA 2024 Omnichannel Decision Makers Study. The results reveal that DOOH is increasingly recognized as an essential component of the advertising ecosystem, with 79% of study respondents planning to recommend DOOH as part of their media plans over the next 12 months. Of marketers already using DOOH, 96% plan to maintain or increase spend. All told, US advertisers are projected to spend $3.1 billion on DOOH in 2024, a 28% increase from the previous year.

The significance of DOOH in modern advertising is immense: It offers unrivaled opportunities for brands to reach huge numbers of consumers in high-traffic urban spaces, retail environments, transit hubs, and beyond. Let’s unpack how this rapidly growing and rapidly innovating medium is revolutionizing advertising, drawing on key insights from DPAA and Kochava.

First, What Is DOOH?

Digital out-of-home advertising refers to digital media displayed in public spaces, such as digital billboards, screens in transit stations or stores, and interactive kiosks. Unlike traditional out-of-home (OOH) advertising, generally consisting of static images, DOOH utilizes digital technology to display dynamic content that can be updated in real-time, enabling advertisers to deliver relevant and timely messages to their target audiences.

DOOH operates through a network of digital screens managed by tech platforms that control the display, distribution, and timing of ads. These platforms enable programmatic buying, wherein advertisers bid for ad space in real-time based on specific audience criteria, while targeting capabilities ensure that ads effectively reach their optimal audience.

OOH to DOOH: Static to Dynamic

Traditional OOH advertising has long been a marketing staple—think highway billboards or ad content on the side of trains intended to capture the attention of passersby. However eye-catching, the static nature of these ads limits their ability to engage audiences kinetically. DOOH by definition leans into the digital component, allowing for compelling content that can be displayed and updated in real-time.

DOOH Display Innovations

Advancements in DOOH technology in recent years have made the advertising landscape more dynamic, engaging, and interactive. Fundamental to DOOH display is the ability to rotate ads in the same physical space, with digital screens cycling through multiple advertisements. This maximizes infrastructure utilization and increases revenue potential without the need for additional physical installations. Digital screens are more eco-friendly compared to printed billboards, reducing the need for physical materials and waste.

Effects such as innovative lighting installations near DOOH billboards enhance the visual impact by synchronizing with billboard content to create a more striking experience. Full-motion digital billboards feature shifting, personalized creative content, particularly effective for dynamic industries like film. Limitless potential applications are inspiring brands and agencies to develop campaigns specially for full-motion DOOH.

Another attention-grabbing novelty comes from across-space synchronization, in which objects animate over multiple consecutive screens, creating an immersive and cohesive storytelling experience.

Programmatic DOOH & Digital Enablement

The integration of DOOH into cross-channel strategies goes hand in hand with the growing adoption of programmatic buying, enabling advertisers to optimize campaigns in real-time and measure outcomes with greater accuracy. Programmatic DOOH has definitively moved the needle, enabling media buyers to set specific audience and environment criteria such as demographic, time of day, or weather conditions. When these conditions align, the ad automatically displays. This makes the buying process more efficient and ensures that ads are shown to the best audience at the best time.

While awareness of DOOH’s digital enablement is growing among media buyers, it still lags behind reality. Just over half of respondents to the DPAA + Kochava study recognized DOOH as being digitally enabled for everything from data-driven targeting to planning and transacting. In particular, only 51% rated measurement and insights as 4 or 5 out of 5 in their assessment of DOOH’s being fully digitally enabled.

Importance of Measurement and ROI

As DOOH advertising continues to expand its footprint in the advertising landscape, the necessity for robust measurement and attribution solutions becomes increasingly key. Advertisers cannot be satisfied with merely knowing that their ads are being displayed; they require reliable and sophisticated methods to quantify the impact of DOOH investments and how these campaigns contribute to overall marketing success.

Respondents to the question What are your concerns, if any, when considering DOOH in your media plans? reported measurement-related concerns as their top two. Although the percentage of respondents citing these concerns has decreased markedly since 2021, it is still substantial.

Contrary to the outdated perception that DOOH lacks sophisticated measurement capabilities, the reality is that advanced tools and methodologies are capable of providing reliable and detailed insights, and marketers are gradually coming to recognize this. Advertisers can utilize effective measurement solutions to assess the impact of their DOOH investments, enabling them to justify ad spend, optimize campaign performance, and integrate DOOH seamlessly into broader marketing strategies.

For consumers, these advancements mean more enjoyable and relevant advertising experiences. In addition to commercial content, digital screens provide timely, practical information such as weather or transit alerts. Such utilities make DOOH a valuable info source as well as an entertaining advertising medium.

Integration With Broader Marketing Strategies

Integration of DOOH measurement and attribution solutions with broader marketing strategies is paramount. In the DPAA + Kochava study, complementing a digital plan was cited as the top reason media buyers include DOOH in their media plans.

Linking DOOH data with other channels, such as connected (CTV) and mobile, enables advertisers to take in a comprehensive view of their marketing efforts, allowing for more accurate measurement of cross-channel performance and revealing synergies among various marketing strategies. For example, an advertiser might use DOOH to drive awareness, then retarget those exposed to the ad with personalized messages on their mobile or CTV devices. By understanding how DOOH fits into the broader marketing ecosystem, advertisers can maximize the impact of their campaigns and achieve better overall results.

Future Trends for DOOH

As the DOOH advertising landscape continues to evolve, several key factors are poised to shape its future, showcasing the transformative potential of DOOH as it integrates cutting-edge technology and sustainability practices to create more engaging and responsible advertising solutions.

Future DOOH displays will become increasingly intelligent, leveraging location-based marketing to present highly relevant advertising based on where the viewer is. These ads may synchronize with mobile devices, ensuring a seamless and contextually relevant experience. Marketers will devise campaigns that transition smoothly from DOOH to mobile to web, creating a cohesive experience across multiple platforms.

Artificial intelligence (AI) will play a crucial role in dynamically personalizing ad content based on real-time factors. Moreover, AI will offer precise modeled metrics on ad views, including detailed consumer characteristics, enabling advertisers to optimize campaigns effectively and calculate ROI more accurately. Machine learning algorithms promise to facilitate real-time programmatic auctions of DOOH ad spaces, making the buying process even more efficient and tailored to specific audience segments.

Virtual and augmented reality technologies will introduce new layers of interaction and engagement in DOOH advertising. Brands may leverage virtual reality (VR) to offer fully immersive, 360-degree ad experiences within public spaces, captivating audiences and providing a deeper level of engagement. Augmented reality (AR) filters on DOOH displays could enable consumers to visualize products in a real-world setting via their smartphones, enhancing the consumer’s connection to brands and/or products. Both technologies will lead to the creation of on-the-spot, interactive product demonstrations or hands-on experiences.

The development and adoption of low-energy, including solar-powered, displays will significantly reduce electricity costs and appeal to environmentally conscious consumers and brands. An increasing number of vendors will manufacture displays using recyclable or biodegradable materials, furthering the industry’s commitment to sustainability.

DPAA + Kochava White Paper

The DPAA 2024 Omnichannel Decision Makers Study, featuring research from Advertiser Perceptions, offers many valuable insights into the current state and future trends of DOOH advertising. These attest to the growing importance of the medium, as well as the opportunities for marketers eager to explore its full potential and stay on the cutting edge of the rapidly evolving advertising ecosystem.

The study notes a marked uptick in advertiser awareness and understanding of DOOH, with 42% of respondents reporting a moderate increase in their knowledge over the past year. Such heightened awareness is a positive sign for the DOOH industry, suggesting that advertisers are more open to exploring and leveraging DOOH within their broader media strategies.

Moreover, DOOH is considered an extension of TV and video planning by 41% of decision makers. Video Everywhere is widely used as a synonym for DOOH as the genre evolves, as evidenced by the title of DPAA’s annual summit.

Want to Read the Full Report?

The full report and white paper are available to all DPAA members here. If you’re an existing Kochava customer and have not already received your copy of the white paper via email, please contact your client success manager or email Support@Kochava.com.

If you’re an advertiser, an agency, or a DOOH company and need help with measurement, get in touch with the Kochava team here.

CTV’s evolution from passive entertainment to dynamic shopping platform

Among digital marketers, there’s always an air of excitement surrounding the subject of Connected TV (CTV)—every sofa could be a potential storefront. The TV is swiftly transforming living rooms from a passive in-home entertainment environment into an interactive, shoppable TV experience. Unlike the traditional QVC model, which is limited to a specific channel dedicated to shopping, CTV is evolving to create seamless purchase moments across various content platforms. The hours we used to spend simply watching TV are morphing into influential encounters with shoppable ads and interactive content.

Let’s take a look at three watershed moments of this shift.

#1 Contextual Marketing Meets CTV

Contextual marketing, which tailors advertising or content based on the user’s current environment or activity, is increasingly driven by advanced technologies adept at understanding digital content. Integration of these technologies into CTV affords new strategies for over-the-top (OTT) advertising. Dynamic ad insertion (DAI) and contextual intelligence designed for CTV look to shift beyond mere ad placements, or just getting ads in front of eyeballs. The end goal is to integrate ads seamlessly into the content consumers are consuming—in real-time.

At its core, DAI integrates advertisements directly into the content stream, ensuring a cohesive and engaging viewing experience that doesn’t detract from the content or emotional moment. By leveraging advanced targeting capabilities, advertisers can deliver ads that resonate with the intended audience, leading to higher levels of engagement and interaction while reducing ad fatigue.

For example, imagine streaming a travel documentary on a CTV platform. With contextually enabled DAI, advertisers can dynamically insert ads for travel-related products or services—such as airline tickets or vacation packages—into the program, creating a fluid, relevant advertising experience for the moment the viewer is in.

#2 QR Code Explosion and Rise of Shoppable TV

Ad spots (albeit video or native display) featuring a quick response (QR) code are now commonplace. This wasn’t always the case. This shift is another milestone along the journey from TV as a passive to more interactive medium. Widespread acceptance of advertising that integrates itself into the viewing experience and offers direct response touchpoints is crucial when treading into the territory of shoppable TV. This streamlines the shopping experience, making the path from desire to possession a truly linear affair.

Consumer interest in this technology signals not only a shift in shopping behavior, but also untapped potential for TV advertisers and marketers. By syncing advertising content with the users’ needs and expectations, companies foster a deeper connection between their brand and consumers. QR codes can guide viewers to shop or engage with the advertised products on their mobile device instantly—without disrupting the main viewing experience.

Yet, this new wave of tech advancement doesn’t come without its share of challenges. QR codes aren’t always easily scanned within the short time they’re displayed on screen, and advertisers grapple with measuring the effectiveness of shoppable ads and QR codes. Successfully navigating this requires the integration of various measurement technologies and a shift toward thinking like a tech company and accurately marrying advertising content with user preferences.

#3 Evolution of TV Operating Systems

Smart TV platforms are increasingly investing in the evolution of their operating systems (OS), making them conduits for finely tuned, user-focused experiences. Enhanced performance efficiencies and improved ad delivery tactics aim to serve marketing messages that don’t intrude but rather enhance or complement the viewer’s experience.

Such optimization is the future of T-commerce, and select companies are already investing in this direction. For example, enter Telly, a dual-screen television. Telly TVs include a 55-inch theater display with a Harman Kardon soundbar connected to a bottom smart screen display—perfect for serving ads that resonate or complement the content on the top screen. Further, the Telly remote is integrated with smart pay technology, enabling consumers to link a payment method and make purchases from their remote without needing a second device like a phone or laptop. While a pizza commercial is running on the main screen, for example, an ad can run on the soundbar allowing the viewer to order their favorite pizza with a click of the remote. This fluid experience eliminates friction for the user, increasing the propensity to convert in the moment.

This heralds a paradigm shift in how consumers engage with content. It enables the simultaneous consumption of diverse media and relevant ads, thereby redefining the boundaries of TV advertising while minimizing a disruptive ad experience for the viewer. Optimization puts consumers back at the center of exchange in TV advertising.

Additionally, smart TV platforms are creating entirely new home-screen experiences and content hubs around major sporting events (Superbowl, Olympics), awards shows (Emmys, VMAs), and other tentpole events. Tailored content enables consumers to quickly find and watch all content related to the event, at the same time enabling advertisers to reach consumers with focused ad experiences that resonate within the context of the event. This signifies a shift toward actually engaging the engaged. But it’s not just about fostering interactivity for interactivity’s sake. The long-term goal is to generate conversions and drive outcomes, and the key tool for facilitating this conversion journey is an incredible operating system capable of delivering evocative moments. The call to action is clear for TV manufacturers: The demand for smarter devices is on the rise. TVs should not just be passive screens but rather active engagement hubs where advertising and commerce converge.

Yes, Performance Measurement on CTV Is Possible

As CTV advertising continues to evolve, the importance of accurate performance measurement becomes increasingly clear. Analytics and attribution are essential for offering marketers a view into consumer behavior and the effectiveness of their marketing. Kochava offers comprehensive measurement options for CTV, providing advertisers with advanced analytics and insights to track and optimize campaign performance.

To learn more, check out our marketer’s guide to CTV here.

Insights from Embracing Contextual Advertising for Privacy-Safe Growth webinar

As digital advertising rapidly evolves, contextual advertising offers a navigational tool for brands to chart a course through the digital world, exploring privacy-first frontiers and mapping new territories for engaging audiences. In an illuminating webinar hosted by Kochava, a panel of industry experts delved into the realm of contextual advertising and its role in driving privacy-first growth. The panel, comprising Mark Menery, VP, Global Head of Sales at Dataseat (Verve Group); Adrienne Rice, Account Director at M&C Saatchi Performance; and Lane Schechter, Director of Product at Verity, GumGum; shared valuable insights into this timely advertising method and its prowess in negotiating a privacy-forward digital topography. Drawing on their expertise and extensive experience, the guest presenters highlighted the current state of contextual advertising, its relevance to brands today, and best practices for leveraging this approach amid the ever-changing digital advertising landscape.

The Evolving Landscape of Contextual Advertising

The panelists recounted the evolution of contextual advertising, tracing its origins from its prominent past in traditional media to today’s more sophisticated systems. Traditionally, contextual advertising involved aligning advertising messages with the content of a publication or program, such as placing ads for home improvement products in a house and garden magazine. With the emerging digital landscape, behavioral targeting came to the fore, as this type of ad targeting proved highly effective and measurable via individual user data such as past online/mobile behavior and device IDs. Menery delineated the key differences between contextual and behavioral targeting: Behavioral targeting focuses on an individual’s past digital activities and interests in order to serve personalized ads, while contextual advertising relies on understanding the environment and content a user is engaging with at the moment. Shifting into a contextual approach aligns with the growing emphasis on privacy and data protection, making it a compelling option for advertisers.

Embracing the Shift: The Growing Importance of Contextual Advertising

As the digital ecosystem evolved, contextual advertising became more sophisticated, transitioning to picking up contextual signals from publisher apps, keywords, user engagement, time and location, semantics, content categories, and so forth. This has enabled a viable resurgence of contextual advertising as an effective methodology in a landscape increasingly sensitive to the protection of personal information. Because contextual advertising does not require tracking individual user data or behavior, it is inherently a privacy-forward approach.

To this point, the webinar experts emphasized its importance in the face of increasing privacy regulations in today’s digital world. The deprecation of device identifiers has significantly impacted the advertising industry by reducing the availability of user-level data for behavioral targeting. The panel noted that their companies’ respective clients are indeed employing contextual strategies, underscoring the shift to privacy-first methods necessitated by the introduction of various government- and platform-led privacy regulations and resultant phasing out of user-level device IDs as well as third-party cookies on the web. Contextual advertising has become a viable, privacy-compliant alternative for brands and advertisers—who still need to reach their audiences in a targeted and effective manner.

Marketers can leverage these rich contextual signals to move beyond traditional content alignment and deliver programmatic advertising that is truly relevant and resonant with the consumer’s current mindset, mood, and intent. This methodology relies on absolutely no personal user or behavior data whatsoever. Rather, its dimensional comprehension of the environment itself enables the right message to be matched to the right context—ultimately driving higher engagement and performance. Brands and publishers can now leverage fortified contextual signals to deliver relevant and impactful advertising experiences in a fully privacy-forward solution.

Bridging the Gap: Contextual Advertising and Performance Challenges

While the group championed the effectiveness of contextual advertising, they also addressed potential roadblocks advertisers can face in target accuracy, scaling, measurement, and achieving desired performance metrics.

Because contextual targeting relies on understanding the content and environment rather than an individual’s past behaviors and interests, it often requires a more patient “crawl, walk, run” orientation compared to the faster ramp-up of behavioral targeting. To address the challenge of homing in on a desired demographic who might engage with apps or sites that attract a widely diverse audience, the panel recommends a test-and-learn cycle: Start with a wide variety of apps or keywords, refining targeting based on outcomes to tease out high-performing environments.

To optimize contextual campaigns and hit KPIs, the webinar guests recommended working with trusted partners able to provide transparent specificity and guidance. This includes access to detailed, accurate reporting on the specific contextual signals and environments where ads are being served so as to assess their performance and ensure brand safety. By isolating contextual strategies and analyzing which environments drive the best performance, advertisers can refine their approach and allocate budget more effectively.

Schechter noted that the issue of scale, particularly in the CTV space, is a challenge within contextual advertising, as in the video space there is limited access to the signals that enable nuanced contextual analysis of content. The panel is hopeful for increased signal availability commensurate with growing demand.

The webinar experts reiterated the importance of selecting high-quality contextual advertising partners and leveraging their expertise. Clear signals for measuring targeting and optimization testing are critical, as is the resolve to accurately isolate and assess strategies. Because contextual advertising may drive more incremental progress compared to other tactics, the group counseled a patient, iterative approach to test, learn, and refine strategies around elements like targeting, ideal environments, and budget allocation. Optimizing in close collaboration with trusted partners is key.

The panelists also noted the role of methodologies such as marketing mix modeling (MMM) as alternative ways to measure campaign effectiveness. Such solutions enable advertisers to connect the dots between contextual ad exposures and downstream conversions, without relying on individual user data. By utilizing privacy-durable signals and aggregated insights, brands can gain visibility into the impact of their contextual strategies while respecting consumer privacy, bridging the gap between contextual targeting and the achievement of business goals.

The discussion also touched on the role of consent and privacy considerations around measurement within the contextual advertising landscape. The panel agreed that consent is less of a concern within the contextual realm, as focus is inherently on the current context and intent of the consumer rather than individual user data on past digital activities or personal identifiers. However, they emphasized the importance of leveraging privacy-first measurement solutions, such as Apple’s SKAN and Google’s Privacy Sandbox, to ensure accurate and compliant attribution. It is also important to note that post-ad consent becomes necessary when tracking to attribute conversions by users after they interact with the ads.

Expert insights from Kochava webinar with Spotify Advertising and M&C Saatchi Performance

Since its emergence in the early 2000s, podcasting has experienced exponential growth, prompting businesses to adopt marketing strategies to leverage the rapidly evolving medium. In a webinar showcasing podcast advertising, acclaimed industry players—Charles Manning, CEO of Kochava; Adrienne Rice, Director of Media Investment at M&C Saatchi Performance; and Matt Drengler, Director of Marketing Research and Intelligence for Spotify Advertising—shared their insights into the opportunities and best practices within this channel. The insightful session examined the breakthroughs of podcast advertising, its efficacy for advertisers, and its future.

Why Podcast Advertising?

The panel established the impressive scale of the podcasting landscape, emphasizing the medium’s growth and the opportunities this affords advertisers. Millions of podcasts cater to a global listener base projected to exceed half a billion people in 2024. At the same time, podcast advertising revenue is heading upward of $4 billion. This is no longer a nascent, niche medium, but a burgeoning channel with yet-untapped potential.

Rice shared key insights into the demographics and behavior patterns of podcast listeners that marketers might do well to consider, pointing out that 66% of US internet users listen to podcasts (in most cases at least once a week), with the majority of listeners aged 45 or younger and earning higher-than-average household income. As for the dominant podcast genres—spoiler alert, but perhaps not a surprise—comedy and true crime are well established as top listener favorites.

Evolution of Podcast Advertising

The panel recounted the transformative journey of podcast advertising, from its early implementation to the innovative solutions shaping its future. Traditionally, podcast advertising was predominantly purchased directly from shows and embedded within the podcasts themselves, often in the form of host-read ads. This posed significant limitations in scalability, as each ad insertion was manually placed within and inseparable from the episode. Producers and advertisers also had to consider the ongoing relevance and permanence of ad content; after the show was aired, the ad might be forever “baked in.”

The podcast industry embraced technological innovations to solve these challenges, and over the past several years, the landscape has significantly evolved. Drengler took participants through industry advancements that directly addressed the limitations of embedded ads and revolutionized the podcast advertising space, most notably dynamic ad insertion (DAI). This technology, now accounting for 90% of ad volume, enables advertisers to place relevant ads into a designated spot within a desired podcast episode, seamlessly stitched in at the time of download and refreshable as needed. This marked a significant advance toward resolving issues such as scalability, measurability, and systematic targeting.

Automated programmatic ad placement is rapidly taking hold, and there is still much room for growth in this approach. And Spotify’s streaming audio insertion (SAI) represents a cutting-edge breakthrough, leveraging the shift toward streaming podcast content rather than downloading it. This technology has further enhanced ad integration, real-time targeting, dynamic content delivery, and ad measurement capabilities, in particular the ability to measure on real-time impressions, leading to a more engaging ad experience for listeners while offering greater effectiveness and efficiency in optimization for advertisers.

New Possibilities

Manning emphasized that these significant shifts now enable us to comprehend the podcast consumer journey holistically, essentially having blown open the doors for the medium to become fully viable for performance advertising. The webinar panel agreed that new technologies are rapidly driving equivalence with digital ad formats, fully democratizing podcasting as a reliable advertising channel.

Taking full advantage of these advancements, however, necessitates new paradigms in measurement. Spotify’s SAI offers advertisers a more precise measure of reach, impressions, and audience targeting. While this allows for sophisticated metrics, the greater podcast adtech world is still catching up. Case in point—in a digital environment where clicks and downloads are often misleading, distinguishing between podcast downloads and streams is key to tracing listeners’ post-impression actions.

To facilitate such measurement capabilities, Spotify partnered with Kochava to process and analyze a more dimensional profile of podcast stream data in real time. Advertisers on this platform are no longer subject to the limitations posed by engagement ambiguity as revealed solely by tracking downloads or one-touch attribution. The Spotify-Kochava collaboration has enabled third-party verified measurements that open the way for further performance-based initiatives. One actionable metric has revealed that up to 95% of attributed events take place within 14 days of podcast download or exposure.

Effective Campaigns and Best Practices

These insights derived from enhanced measurability reinforce the importance of understanding the customer journey and the role of podcasts in this journey, from introduction to final conversions. Podcast advertising is more than just another channel, but a uniquely immersive experience that provides a focused and uninterrupted space for advertisers. The conversation revealed a bombshell outcome takeaway: One in five listeners who visit an advertiser’s site after exposure to a podcast ad ends up making a purchase. Ponder that!

The panel delineated key practices for devising and executing successful podcast campaigns:

Leverage listeners’ heightened attention: Advertisers need to comprehend the medium’s perceived authenticity and credibility for effective education and audience engagement over a wide range of topics, resulting in a loyal, receptive listener base. The felt connection between host and listener fosters trust in the medium and by extension the advertisers who directly speak to this audience engagement. High-quality, vivid creative is a must to engage podcast listeners who are primed to embrace relevant, compelling ads and brands/products that complement their listening experience.

Deploy a robust measurement strategy: Advertisers need to leverage the wealth of data now available through podcast analytics. Understanding listener behavior, such as when and how they tune in, listen to or skip ads, and engage with content, is fundamental for optimizing campaign performance. Contextual-based targeting, including seamless, real-time topic and conversation-specific ad placements, is a powerful means to tailor creative to podcast contexts and/or home in on audiences by demographic or behaviors and interests. Data derived from such practices can be used to inform and optimize subsequent initiatives relative to desired key performance indicators.

Prioritize privacy issues: With privacy becoming an increasingly important concern, advertisers need to be cognizant of how they collect and use listener data. Ensuring compliance with privacy laws and being transparent with listeners about data usage can help maintain trust and reinforce positive brand image.

Catch the Full Webinar on Demand

The complete on-demand webinar, Capitalizing on Podcast Advertising in 2024, is available now! The discussion is full of fascinating insights on podcast advertising, effective measurement approaches, and future trends, with a fun addition of some of the speakers’ own favorite podcasts. The overall takeaway from this informed panel of industry experts: It is abundantly clear that the podcast medium will continue its upward trajectory, and savvy marketers will be eager to leverage this golden opportunity to apply these webinar insights directly into their digital marketing strategies for a marked competitive edge.

Answers to your questions from the Kochava Foundry webinar

Grant Simmons, VP of Kochava Foundry, and Ethan Lewis, Chief Technology Officer at Kochava, recently hosted the webinar Navigating Google Privacy Sandbox—Part 1, where they unpacked the industry’s upcoming sea change with Google’s rollout of Privacy Sandbox for Android and spotlighted key trends in the shift in mobile toward user privacy. In this follow-up, they have compiled audience questions to address and elaborate upon in further detail.

Check out the full webinar on demand here.

#1 Has Google published the timeline for deprecating their Advertising ID (ADID) from Android?

Google has not yet published a definitive timeline for the deprecation of ADID, also sometimes called Google Advertising ID (GAID), from Android. The ADID/GAID is anticipated to follow a similar path as Apple’s IDFA insofar as its utility for tracking and measurement is expected to diminish. Given Google’s significant stake in the adtech ecosystem, their ADID phaseout may be more gradual compared to Apple’s rapid deprecation of IDFA. There are indications the deprecation process could begin with the phasing out of third-party cookies, expected to start this fall. Google’s active development of APIs for Privacy Sandbox signals a move toward testing with publishers later this year, with a broader rollout and ADID deprecation potentially starting next year. Marketers should prepare for a future where public unique identifiers such as ADID are no longer available and seek alternative privacy-centric measurement solutions.

#2 Is Google going to deprecate Google Play Install Referrer?

While Google has not made a formal announcement regarding this, there are indications they may deprecate the use of UTM parameters, which are critical for mobile tracking as they can be picked up via the Play Store and used to power Google Analytics. The potential deprecation of these links could begin next year, signifying a pivotal shift in mobile tracking and analytics.

#3 How does this compare to iOS App Store data restrictions?

Google’s Privacy Sandbox and Apple’s SKAdNetwork (SKAN) share the goal of enhancing user privacy while providing campaign performance metrics. Both are designed to be anonymous while offering event-level reporting. Their approaches differ, however, with Privacy Sandbox developed through broader community collaboration, while SKAN is an Apple-led initiative. Privacy Sandbox aims to provide tools for targeted advertising without individual user tracking, whereas SKAN offers a more limited framework for iOS app advertising attribution. Advertisers face challenges with both due to reduced granularity of data.

#4 How does this impact MMM, if at all?

Marketing mix modeling (MMM) is likely to thrive, as it relies on modeling of aggregated data as opposed to the granular data necessary for last-touch attribution. MMM platforms, such as AIM by Kochava, can ingest SKAN and Privacy Sandbox data to power their models and help marketers understand influence and incrementality across channel partners. Separately, mobile measurement partners (MMPs) will play a crucial role in understanding data connections, providing tailored measurement solutions, and syndicating measurement data as needed.

#5 How will Privacy Sandbox impact app remarketing, both gaming and non-gaming?

The exact mechanisms for user suppression or retargeting within Privacy Sandbox are not yet clear. However, it is expected that aggregate data will be managed via API, with flags indicating prior customers vs. new ones. Brands will need to differentiate between new and existing customers and communicate this information to the networks they engage with for remarketing. They should also continue to invest in owned media as a pillar of their remarketing strategy.

#6 How will Privacy Sandbox work for user acquisition? How is Kochava thinking about its role working with SDK-less partners, the delegation functionality in PAAPI, and PAS?

Google Privacy Sandbox is set to introduce new frameworks for user acquisition that prioritize user privacy. For instance, the Attribution Reporting API within Privacy Sandbox will enable advertisers to measure campaign performance without relying on traditional identifiers. As an MMP, Kochava is preparing to adapt to these changes by exploring SDK-less integrations and server-to-server clean room integrations. Kochava—an approved testing partner with Google—is actively involved in testing these new mechanisms. The second part of our Google Privacy Sandbox webinar series will delve deeper into how these integrations will function as well as the role of Kochava in this evolving landscape. It will also address to what extent Kochava will interact with the Protected Audiences API (PAAPI) and Protected App Signals (PAS).

#7 How will cookie deprecation impact DSPs and SSPs since they heavily rely on pixels? Do we know what Privacy Sandbox for app tracking will look like? What do we know of the differentiators as compared to SKAN?

Cookie deprecation will significantly impact DSPs and SSPs that have traditionally relied on pixels and third-party cookies for targeting and tracking. With Privacy Sandbox, Google aims to replace these methods with privacy-first alternatives, such as the Topics API for interest-based advertising and Attribution Reporting API for campaign measurement. These changes will challenge DSPs and SSPs to adapt their strategies, possibly leading to increased use of data clean rooms and data lakes. Google’s Privacy Sandbox for app tracking is expected to share similarities with Apple’s SKAdNetwork (SKAN), such as privacy-enhancing technology and anonymous reporting, albeit with its own unique approach to rollout, collaboration, and distribution effects.

#8 Is managing Google Privacy Sandbox on the roadmap for Kochava?

Kochava is an authorized testing partner with Google and actively engaged in managing the transition to Privacy Sandbox. The company is testing the new APIs and frameworks to assess their implications for mobile attribution and develop solutions that align with the privacy-first direction of the industry. As part of their commitment to adapting to these changes, Kochava will be integrating Privacy Sandbox features into services to help clients navigate the new landscape, with a strong initial focus on the Attribution Reporting API.

#9 Is Google Privacy Sandbox going to cost anything for the agencies that use it?

While there may not be direct costs associated with using Privacy Sandbox, the shift to privacy-first attribution methods will require agencies to adapt their strategies and potentially invest in new technologies or partnerships. The changes brought by Privacy Sandbox will be integrated into the adtech ecosystem, and agencies will need to evolve their practices accordingly. This evolution may involve indirect costs related to training, technology adoption, and changes in campaign management.

#10 What is the biggest challenge with Google Privacy Sandbox, and is there an upside of Google Privacy Sandbox from a marketing standpoint?

The biggest challenge with Privacy Sandbox is the shift away from deterministic attribution methods, requiring marketers to adopt more aggregated and model-based approaches to measurement. For the marketing industry, this will demand a new mindset and potentially new skill sets. On the other hand, the upside is an increased focus on consumer privacy, which may enhance trust and potentially improve the public perception of the advertising industry. Marketers will need to become more creative and strategic in how they target and measure campaigns, focusing on privacy-preserving methods that align with consumer expectations.

#11 Is there a POV on retention analytics and how this is going to be impacted/go away?

Retention analytics in the context of Privacy Sandbox remains an area of uncertainty. However, it is expected that technology solutions will be developed to assist with this aspect of analytics. Google has demonstrated a collaborative approach in the development of Privacy Sandbox, which suggests that feedback from stakeholders will influence shaping the future of retention analytics. It is important for marketers to stay informed and adapt to new tools and methodologies that emerge as Privacy Sandbox evolves.

#12 How does identity work in Privacy Sandbox for Android? Is it still based on advertising identifiers?

In Privacy Sandbox for mobile, identity will not rely on publicly available unique advertising identifiers. Instead, Google will utilize aggregated and anonymized data based on user information associated with Google accounts. This approach aims to preserve user privacy while still providing useful data for advertisers. The data will be structured to prevent the identification of individual users, aligning with the privacy-first initiatives of Privacy Sandbox.

#13 As a user, will I be able to opt out of certain interest topics within the Topics API?

While it is unclear whether users will have the ability to opt in or out of specific topics within Privacy Sandbox, it is expected that a new consent mechanism will be introduced on Android, similar to Apple’s App Tracking Transparency (ATT) framework on iOS. This mechanism will likely govern user consent for data collection and use in a privacy-conscious manner.

#14 What about gaming in the Topics API? Will it be broken down by subcategories?

The granularity of the Topics API, particularly for gaming, is not yet fully known. Initially, it is expected that categories may be broad and not provide the level of detail desired by performance marketers in the gaming sector. As Privacy Sandbox matures, however, it is possible that more specific subcategories would be introduced. In the meantime, marketers should focus on leveraging Event and Summary API data, which may offer more actionable insights in the early stages of Privacy Sandbox implementation.

#15 DSPs have spent a lot of time building out high-performance targeting products, but with Privacy Sandbox, they have to work within the browser or on device. How handicapped will their technical capabilities be if they can’t host massive amounts of campaign/targeting data in the browser memory? Or can they?

Demand-side platforms (DSPs) will face significant challenges as they adapt to the constraints of Privacy Sandbox, particularly with its limitations on using browser or on-device storage for campaign and targeting data. The extent to which DSPs can utilize such storage is uncertain, and it is likely that such capabilities will be restricted to ensure user privacy. DSPs may need to explore alternative strategies to comply with the new privacy regulations, relying less on extensive data storage within the browser.

#16 Will event-level reporting postbacks in Google Privacy Sandbox for Android have any kind of delay as with Apple’s SKAdNetwork?

Event-level reporting postbacks within Privacy Sandbox will indeed include delays similar to those in SKAdNetwork. These delays are part of the privacy-preserving features designed to prevent identification of individual users. The specific mechanisms and timing of these delays may differ from those in SKAN, and we expect to be able to clarify further details in the second part of our Google Privacy Sandbox webinar series. Marketers should anticipate adjustments to their reporting and analysis processes to accommodate these delays.

Got more questions on Google Privacy Sandbox?

If you seek clarity on how Google Privacy Sandbox for Android will impact your mobile marketing strategies or have specific concerns about this landmark transition, Kochava Foundry is ready to assist. Our team of experts can provide guidance on navigating these changes and help you adapt your mobile app campaign strategies for success in a privacy-first landscape. Set up an expert consultation with us to explore how we can support your needs and keep you ahead in the evolving digital advertising ecosystem.

Enhancements to MMM make it a powerful tool for advertisers as privacy regulations evolve

What Does MMM Stand For?

Known as marketing mix modeling or media mix modeling, MMM is a statistical data-analysis methodology that gives marketers a better understanding of the optimal mix of marketing strategies across media channels to positively impact sales and other key performance indicators (KPIs). MMM seeks to take into account all advertising channels—print, social, and online advertising (e.g., search, display, video) as well as offline channels.

How Does MMM Work?

Marketing mix modeling relies on aggregate data from marketing and non-marketing sources gathered over an extended period of time. Typically, a minimum of three or more months of historical data (ideally 12+ months) are necessary to reach data significance and account for seasonality shifts. This large volume of data is used to create an accurate demand model to give marketers insights into the most effective channel strategies and forecast the best omni-channel allocation of future ad spend for greatest impact and return on investment (ROI). From these insights, marketers can adjust ad spend allocation across channels and partners for future optimization.

Seems a little abstract? Let’s look at an example.

A CMO at a major fintech company wants to zoom out from granular campaign- and creative-level performance reporting to capture the bigger picture. The CMO’s goal is to understand the incrementality of ad spend across channels and overarching performance peaks and valleys throughout the year. They work with an MMM platform and after plugging in historical data are able to arrive at new recommendations for how much to spend across channel partners at different times of the year. The marketing director and UA manager now can reallocate spend across their various channel partners to drive better incrementality and reduce unwanted oversaturation on any given channel.

And that’s essentially MMM—collecting and processing a lot of data, then presenting it at a high, aggregated level so marketers can glean broad insights into advertising effectiveness, transcending individual outliers and skewed averages.

As digital marketing evolved in the early 2000s, MMM largely took a back seat to direct-response attribution modeling, which relies on user-level interactions on websites and mobile apps. Unlike MMM aggregated data, attribution data is inherently granular—useful for marketers in focusing their efforts on specific users and customers via direct response marketing. This approach facilitates insights derived from customer-level engagements, enabling marketers to drive creative optimization, A/B tests on messaging and creatives, and other personalized marketing tactics tailored toward unique persona profiles.

In recent years, however, MMM has seen a renaissance owing to the data processing and analysis potency enabled by AI and machine learning. Companies and their marketing teams have adopted the advanced analytics and predictive insights afforded by MMM to fuel growth. At the same time, recent developments in user privacy and data use have eroded the availability of granular, user-level attribution data. As a result, marketers are relying more on aggregated data and rediscovering the potential of MMM to inform their marketing strategy. MMM enables them to optimize budgets across channels while respecting privacy policies.

How MMM Is Evolving to Help Advertisers

With the revival of marketing mix modeling, how marketers interact with it has evolved to support the dynamic needs of today’s user acquisition teams. In the fast-paced digital advertising landscape, quarterly or semiannual MMM reports are quickly outdated and lack actionability. Traditional MMM is time-consuming and laborious to manage, making it accessible only to large organizations that have the resources to maintain it in-house or the budget to outsource it.

While historically only such corporations have been able to afford fully leveraging MMM, automated data flows, cloud computing, and machine learning have made MMM more accessible, accurate, nimble, and easily updated. Cutting-edge software as a service (SaaS) next-generation MMM solutions, now accessible to companies of all sizes, have been developed to fit the needs of today’s advertisers. AIM (Always-On Incremental Measurement) by Kochava, a real-time MMM tool, maximizes the effectiveness of the marketer’s budget by providing advanced control over incrementality, channel saturation, and seasonality. AIM utilizes a sophisticated learning system that ingests new data daily and continuously updates and enriches its models. This always-on approach ensures that the insights it produces never go stale and are always ready to use—providing marketers who must make confident decisions with turnkey recommendations for optimized budget allocations.

As user privacy continues to weave itself throughout the adtech ecosystem, next-generation MMM tools will become increasingly indispensable for advertisers in determining the effectiveness of their omni-channel media strategy.

The Conclusion on Marketing Mix Modeling

Next-generation MMM is at the forefront of a marketing revolution, offering actionable recommendations for data-driven decision making in an increasingly privacy-conscious adtech landscape.

Have questions or want more information on AIM and MMM? Check out our Marketing Mix Modeling 101 ebook and explore even more helpful content in the AIM Resource Center.

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How to become an early testing partner with Kochava

Google Privacy Sandbox for Web has recently come increasingly under the microscope as the adtech industry witnesses early signs of third-party cookie deprecation’s impact on ad monetization across the open web. With Google’s 1% third-party cookie deprecation beta for Chrome users starting in early January, initial observations have noted Chrome users without cookies monetizing approximately 30% worse than users with cookies.

Propelling the adtech industry toward a more privacy-first approach is a massive undertaking, especially for the most dominant mobile and browser ecosystem in the world. Google is taking a collaborative approach with the industry to tackle this monumental shift, and Kochava is thrilled to be partnering with industry leaders such as IAB Tech Lab to ensure that Privacy Sandbox meets our customer’s needs. As a longstanding mobile measurement partner (MMP), Kochava is particularly focused on the coming of Privacy Sandbox for Android—and its implications for the mobile ecosystem.

A Refresh on Privacy Sandbox for Android

In August 2019, Google launched Privacy Sandbox as an initiative to develop new standards for websites to access Chrome user information without compromising user privacy. In February 2022, Google announced that Privacy Sandbox would be coming to its mobile operating system, Android. Privacy Sandbox for Android is often likened to Apple’s SKAdNetwork (SKAN), a privacy-enhancing technology for understanding iOS campaign performance in a privacy-first world, although the scope and impact of Sandbox will extend beyond SKAN’s purview.

In their own words, here are Google’s stated goals with Sandbox for Android:

So what are the tools in the Sandbox? As illustrated in the following graphic, Privacy Sandbox on Android consists of four primary technologies. Let’s unpack each in further detail.

Attribution Reporting API

The Attribution Reporting API serves as a privacy-first solution for marketers to measure the effectiveness of their advertising campaigns. It facilitates the aggregation of conversion reporting data (triggers) from different sources (i.e., attribution data from an ad click or impression) while maintaining individual user privacy. Using this API, marketers can assess the impact of campaigns without compromising individual user identities—ensuring privacy compliance while still providing a base level of performance insight for the purposes of campaign optimization.

Similar to SKAN for iOS, the Attribution Reporting API within Sandbox features privacy-preserving thresholds and outputs only anonymous, aggregated performance data. No user or device-level data is available. Unlike SKAN, which originally supported only app-to-app conversion paths (until the release of web-to-app support for Safari in SKAN 4.0), Sandbox will support app-to-app, app-to-web, web-to-app, and web-to-web user paths from the outset.

This API supports observance of measurement data through two types of attribution reports:

• Event Level Reports connect specific attribution sources from an ad click or ad impression with trigger data from conversions. The fidelity of signal output is more limited, as the connection is one-to-one.
• Aggregatable Reports provide a richer fidelity of trigger conversion data, but in only an aggregate format not necessarily tied to particular attribution source data.

Kochava is currently focused on testing Event Level Reports, which more closely resemble the style of reporting through SKAN on iOS.

Why it’s important
The current state of mobile attribution on Android relies on Google Advertising ID (GAID), UTM referrer, and/or other device characteristics, including user agent and IP address, that may be transmitted off device to perform one-to-one attribution between an ad impression or click and the resulting conversion. The Attribution Reporting API will eliminate reliance on this user and device-level data and bring advertising measurement on device. Sensitive signals will no longer need to be sent off device—making them unavailable for unauthorized collection, use, and covert tracking. With the eventual deprecation of GAID, UTM referrer, and access to other device signals, the Attribution Reporting API will be the lifeline through which marketers can understand the performance of their campaigns to inform their optimization decisions.

See Google Developer Documentation HERE.

Protected Audience API (formerly FLEDGE)

Originally named FLEDGE, now affectionately called PAAPI (Protected Audience Application Programming Interface), this set of APIs aims to support on-device auctions for remarketing and custom audience segmentation based on interest groups. The goal is to serve personalized ads to users in line with previous app engagement, but without any third-party data sharing.

Why it’s important
User data no longer needs to be sent off device for the purposes of building user profiles attached to GAIDs or other device/user-data derived profiles for personalized ad targeting across ad networks, DSPs, and other ad platforms. Adtech vendors will be able to tap into anonymous, yet highly accurate signals to inform ad buys based on user behaviors, interests, and historical app usage.

See Google Developer Documentation HERE.

Topics API

The Topics API in Google’s Privacy Sandbox for Android is designed to give marketers a privacy-centric method to target relevant audiences based on their interests. Advertisers can understand the topics engaged by users and serve them personalized and targeted ads without revealing individual user identities—respecting user privacy and maintaining data confidentiality. A topics taxonomy will provide hundreds to potentially thousands of human-curated interest labels that help categorize a user by interests.

Why it’s important
One might liken this to IAB Tech Lab’s Audience Taxonomy, which provides standard nomenclature for the classification of audience segments. The Topics API will provide the new standard for classifying Android users for targeting purposes by leveraging on-device learning. This replaces ad tech platforms collecting user and device data to build their own profiles on users attached to GAIDs or other third-party generated identifiers.

See Google Developer Documentation HERE.

SDK Runtime

SDK Runtime establishes a more secure framework for apps integrating third-party software development kits (SDKs). Because app developers are not always aware of a third-party SDK’s full functionality and data collection practices, SDK Runtime places third-party SDKs into a modified execution environment featuring well-defined permissions and data access rights privileges.

Why it’s important
Over the years, adtech news publications have featured many stories about rogue, third-party SDKs behind advertising fraud schemes, covert data collection, and other nefarious practices. While these SDKs were intended to leverage valuable app functions and features, rogue actors have been known to hide covert functionality deep within their codebase, enabling them to exploit data-access permissions for nefarious purposes, unbeknownst to the developer who integrated them for legitimate use cases. SDK Runtime technology will put third-party SDKs in a dedicated runtime environment that makes such exploitation impossible—giving app developers and the end consumer peace of mind.

The complete library of Kochava Android SDKs will be available through SDK Runtime.

See Google Developer Documentation HERE.

MMPs and the Attribution Reporting API

Let’s zoom in on the Attribution Reporting API—a key focus for the team here at Kochava.

Mobile measurement partners (MMPs) are able to integrate with the API to provide conversion analytics and performance insights for advertisers under the new privacy framework of Sandbox. It’s important to note that while ad network vendors can use the API to receive self-attributed event and summary reports for conversions they drive/influence, only an MMP is positioned to provide cross-network, last-touch attribution by integrating with the array of aggregation services set up by various ad network vendors. Google lays out multiple scenarios for cross-network attribution with an MMP in this developer documentation. Similar to how MMPs work as a unified decoder ring of sorts for the various SKAN-enabled media partners with which a brand is running campaigns, MMPs will again be sitting at the intersection, translating cross-network Sandbox data into a holistic reporting layer marketers can make sense of.

The Attribution Reporting API also provides for lookback window configurability adjustable by the advertiser and/or via their MMP partner. This is more flexibility than we see on SKAN, where such windows are fixed. Sandbox also provides 30 days of post-install event measurement for better user quality and retention insights out of the gate, compared to what SKAN offered at launch.

As neutral third-party measurement services, Kochava and other MMPs play an important role in the advertising ecosystem. The Attribution Reporting API provides both event-level and aggregated attribution reporting to MMPs, which along with other aggregated omni-channel data helps MMPs empower marketers to understand overall campaign effectiveness and optimize spend across multiple media channels. The Privacy Sandbox model creates opportunities for MMPs to innovate with privacy-focused solutions that decomplicate the lives of marketers amid the increasingly complex privacy considerations of digital advertising.

Kochava Sandbox Testing

Kochava engineering and Android SDK development teams have commenced testing of the primary Attribution Reporting API flow:

1. Registering ad clicks or views (impressions) that lead users to a particular app or website to complete a conversion (known as attribution sources)
2. Next, registering triggers (conversions) that signify a user taking a valuable action such as installing an app, making a purchase, or starting a free trial
3. The Attribution Reporting API receiving both attribution sources and triggers, making relevant matches for conversion attribution and sending one or more triggers off device through event-level and aggregatable reports

Are you interested in Sandbox testing with Kochava?

While testing is already underway with a small selection of clients and partners, we’re looking to expand our testing group. Please note that currently our testing is focused on Event Level Reports.

Stay Updated

Privacy Sandbox for Android is a multi-year effort, and Google has not given an exact timeline for general release. Subscribe to our newsletter to stay connected and up to date on future Privacy Sandbox milestones and related updates to Kochava products and services. You can also enroll for notifications directly from Google.