It’s a common scene in households everywhere: the TV is on, but everyone has smartphones in hand. In fact, 88% of Americans use a second screen while watching television, splitting their attention between multiple devices. This behavior creates a significant challenge for advertisers trying to measure campaign effectiveness and understand the true return on their investment.
This fragmentation, often called "audience scatter," makes it difficult to connect the dots of a consumer's journey. When a single user interacts with a campaign on their TV, laptop, and phone, traditional measurement models break down, leading to hidden costs and wasted ad spend. Keep reading to learn more about the frameworks and strategies needed to accurately calculate ROI in a multi-device world.
Understanding Audience Scatter in the Modern Media Landscape
To optimize advertising campaigns effectively, it’s important to first grasp the concept of audience scatter. This modern viewing behavior has fundamentally changed how consumers interact with media, requiring advertisers to adapt their strategies for measuring engagement and performance across a fragmented landscape.
Defining Audience Scatter and Its Impact on Campaign Performance
Audience scatter refers to the distribution of a target audience's attention across multiple devices and platforms, often simultaneously. This stands in stark contrast to traditional linear viewing, where audiences consumed content on a single screen, like a television, at a scheduled time. Today's consumer journey is far more complex, with 90% of people using multiple screens sequentially to accomplish a goal.
This shift means a single campaign message may be seen on a connected TV during a family movie, on a smartphone while commuting, and on a desktop computer during work hours. The most common simultaneous combinations include a smartphone and television (81%) or a smartphone and a laptop (66%). For advertisers, this fragmentation complicates efforts to track user paths, manage ad frequency, and attribute conversions accurately.
The Psychology Behind Multi-Device Viewing Behaviors
The trend of multi-device usage is driven by specific consumer needs and intentions. Different devices serve distinct purposes. A large TV screen is ideal for immersive entertainment, while a smartphone offers immediate access to information and social connections. This is why 76% of second-screen users engage with social media while watching TV.
This behavior can be either sequential, where a user starts a task on one device and finishes on another, or simultaneous, known as multi-screening. During a show, a viewer might use their phone to look up an actor's filmography or find a product featured in an ad. Research shows that 35% of second-screen users shop for products they see advertised, and 41% text friends about the content they're watching, turning a passive viewing experience into an active, multi-layered interaction.
Quantifying the Financial Impact of Fragmented Audiences
When audiences are scattered, measuring campaign performance becomes more difficult, and the financial repercussions can be substantial. Without a clear view of the cross-device journey, advertisers risk overexposing some users while completely missing others. This inefficiency contributes to significant budget waste, with some estimates suggesting that 23% to 56% of all ad spend is wasted.
The complexity of media buying also increases. The average digital campaign runs across an astounding 44,000 websites, making it nearly impossible to manage reach and frequency without sophisticated tools. This oversaturation can lead to diminished returns and annoyed consumers.
Audience fragmentation can also directly influence advertising costs. One study found that a standard deviation increase in television audience fragmentation resulted in an 11% decrease in ad prices during the day but a 7% increase during prime time. This volatility demonstrates how scatter introduces new variables that can impact a campaign's bottom line.
The Attribution Challenge: Connecting Touchpoints Across Devices
Understanding the financial impact of audience scatter is the first step. The next is solving the measurement puzzle it creates. Bridging the gap between a consumer's interactions on different devices is one of the most significant attribution challenges that modern advertisers face.
Traditional Attribution Models vs. Multi-Device Reality
For years, many marketers have relied on legacy attribution models like last-click, which gives 100% of the credit for a conversion to the final touchpoint. In a multi-device world, this approach is fundamentally flawed. A consumer might see an ad on their connected TV, research it on their laptop, and finally make a purchase on their smartphone, but a last-click model would ignore the first two interactions.
Despite its limitations, 78.4% of marketers still use last-click attribution. However, confidence in this model is waning. Only 21.5% believe it accurately reflects long-term business impact, and 74.5% are either moving away from it or want to. This sentiment is unsurprising, as 63.5% of marketers believe last-click attribution isn't aligned with how people actually shop and make decisions in a multi-device world.
Cross-Platform Data Integration Strategies
To overcome the limitations of traditional models, advertisers need to connect user behavior across devices. This is achieved through data integration strategies, primarily using deterministic and probabilistic matching. Deterministic matching uses known user data, like an email address or user ID, to link devices, yielding a highly accurate match rate of 70-80%. Probabilistic matching uses algorithms to analyze anonymous data points like IP address and browser type to infer connections, which is less precise but offers broader reach.
The phase-out of third-party cookies has accelerated the need for new solutions, with 73% of professionals already adopting cookie replacement technologies. This shift emphasizes the growing importance of first-party data collected directly from consumers. Creating unified user profiles through customer IDs and device graphs allows advertisers to see a more complete picture of the customer journey while respecting user privacy.
As privacy regulations evolve, obtaining user consent for data collection is also a key factor. The average consent rate for tracking decreased from 46% to 35% in about a year, highlighting the need for transparent and trustworthy data practices. Building a robust data strategy is no longer just a technical task; it's a matter of building customer trust.
Building Comprehensive Attribution Frameworks
Moving beyond last-click requires implementing a multi-touch attribution model that assigns value to each touchpoint in the customer journey. Over half of marketers (52%) were already using multi-touch attribution in 2024, with 57% stating it is a meaningful part of their measurement toolkit. These frameworks provide a more nuanced understanding of which channels and devices contribute to a conversion.
Common multi-touch models include time decay and position-based frameworks. A time-decay model is useful for longer sales cycles, giving more credit to recent touchpoints. For example, a social media ad seen a day before purchase would get more credit than a CTV ad seen two weeks prior. In contrast, a position-based model values the first and last touches most, crediting a CTV ad for creating initial awareness and a final search ad for closing the deal.
Advertisers can also create custom models tailored to their specific business goals. Implementing these frameworks requires a solid tracking infrastructure, including pixels and SDKs, to collect clean and reliable data from all platforms.
ROI Calculation Frameworks for Fragmented Campaigns
With a comprehensive attribution framework in place, advertisers can move toward a more accurate calculation of return on investment. Adapting ROI models for multi-device campaigns is essential for understanding true performance and making informed decisions about future ad spend.
Unified Metrics That Matter in Multi-Device Campaigns
In a fragmented media landscape, traditional KPIs are not enough. Advertisers should focus on unified metrics that provide insights across all devices. One of the most important is incremental reach, which measures the unique audience a campaign reaches on one platform that it wouldn't have reached on another. For example, between 30-50% of the total reach on connected TV (CTV) is incremental, representing an audience that may not be accessible through linear TV alone.
Other key metrics include true frequency distribution, which tracks how many times a unique user sees an ad across all their devices, and cross-device conversion paths, which map the journey a customer takes from first impression to final purchase. By calculating deduplicated audience metrics (which ensure you're counting a single user who sees your ad on three different devices as one person, not three), advertisers can avoid counting the same user multiple times and arrive at a more accurate cost-per-acquisition (CPA).
Advanced ROI Models for Cross-Platform Measurement
Sophisticated ROI models are needed to account for scattered audiences. These frameworks go beyond simple revenue-to-spend ratios by incorporating weighted attribution values from a multi-touch model. For instance, a model could assign a higher value to a CTV ad view that leads to a website visit than to a banner ad impression that doesn't generate a click.
By analyzing the contribution of each platform, advertisers can calculate a more precise ROI for each channel in their media mix. Success stories highlight the power of this approach. One publisher, The Daily Hodl, saw a 322% lift in net ad revenue after focusing on incremental reach opportunities. Similarly, Kosher.com achieved a 70% increase in average revenue per daily active user by expanding its audience to mobile devices.
These advanced models often integrate lifetime value (LTV) calculations to understand the long-term impact of multi-device journeys. A customer acquired through a cross-platform campaign may have a higher LTV than one acquired through a single channel, a detail that simple ROI calculations would miss.
Benchmarking and Performance Standards
To contextualize performance, it’s important to establish benchmarks for multi-device campaigns. These standards help advertisers set realistic expectations and identify areas for improvement. Since audience fragmentation affects different industries and campaign types in unique ways, benchmarks should be tailored to specific goals, whether it’s brand awareness, lead generation, or direct sales.
Creating meaningful comparison metrics allows marketers to evaluate the effectiveness of their cross-platform strategies over time. To establish your own benchmarks, start by analyzing historical performance from past single-channel campaigns to create a baseline. You can then research industry averages for metrics like Cost Per Acquisition (CPA) and define platform-specific goals, such as awareness for CTV or conversions for mobile search. By tracking performance against these established standards, you can determine if your ROI is improving and whether your budget allocation is optimized.
Strategic Budget Allocation in a Multi-Device World
Accurate ROI measurement directly informs smarter budget allocation. By understanding how different devices and platforms contribute to overall campaign goals, advertisers can distribute their resources more effectively to maximize returns.
Platform-Specific Investment Strategies
A data-driven approach to budget allocation is key. Insights from multi-touch attribution can reveal which platforms are most effective at different stages of the customer journey. For example, CTV might be ideal for building initial awareness, while mobile ads could be better for driving final conversions. This knowledge allows advertisers to invest in each platform according to its strengths.
The multi-screen advertising market is growing rapidly, expected to expand from over $6 billion in 2024 to more than $43 billion by 2034. In the U.S. alone, the market was valued at over $2.2 billion in 2024, showing the scale of investment in this area. Advertisers must also consider the concept of diminishing returns, where adding more budget to a single platform eventually yields smaller and smaller gains.
A truly strategic approach also accounts for platform synergies, where the combined effect of ads on multiple devices is greater than the sum of their individual effects. A well-orchestrated campaign that reaches a user on their TV and smartphone can create a powerful lift effect that drives higher engagement and conversion rates.
Dynamic Budget Reallocation Based on Real-Time Performance
The media landscape is constantly changing, so budget plans should not be static. Dynamic budget reallocation allows advertisers to adjust spending in real time based on campaign performance data. If one platform is overperforming, its budget can be increased, while funds can be shifted away from underperforming channels.
This agility is particularly important given shifts in media consumption. Projections for May 2025 show that streaming represented nearly 45% of all TV viewership, surpassing broadcast and cable combined for the first time. Streaming usage has increased by 71% since 2021, a trend that demands a responsive approach to budget allocation. Automated bidding and machine learning algorithms can help optimize this process, balancing short-term performance goals with long-term audience development across all devices.
Technology Solutions and Tools for Multi-Device Measurement
Navigating the complexities of audience scatter and cross-device attribution is nearly impossible without the right technology. A robust tech stack is the foundation of any modern measurement strategy, enabling advertisers to collect, integrate, and analyze data from a fragmented ecosystem.
Essential Measurement Platforms and Analytics Tools
Several categories of tools are valuable for multi-device measurement. Analytics platforms like Google Analytics provide a baseline for tracking website and app interactions. Customer Data Platforms (CDPs) are used to centralize first-party customer data from multiple sources, creating unified user profiles. These platforms serve as the single source of truth for understanding customer behavior.
Specialized multi-touch attribution solutions go a step further by connecting touchpoints across the entire customer journey and assigning credit appropriately. These tools often use advanced modeling to provide deep insights into channel performance and ROI. When selecting a technology stack, advertisers should consider their specific campaign goals, budget, and the level of technical expertise available within their team.
The right combination of tools allows advertisers to build a comprehensive view of how their campaigns are performing across TV, CTV, mobile, and desktop. This unified perspective is necessary for making data-driven decisions in a multi-device world.
Implementation Best Practices and Common Pitfalls
Deploying multi-device measurement solutions effectively requires careful planning. A common pitfall is poor data quality, which can undermine the accuracy of any attribution model. Advertisers must ensure their tracking is implemented correctly across all platforms to collect clean and consistent data. Integration between different tools can also be a challenge, requiring technical resources to ensure seamless data flow.
Strategic execution can yield impressive results. For instance, the brand Ethnix by Raymond achieved 96% online ad awareness, 90% message with brand association, and 97% purchase intent by implementing strategic frequency capping across CTV and mobile. Another campaign saw its brand search volume surge by 80% through cross-screen synchronization. These successes underscore the importance of both the right technology and a well-executed implementation strategy that prioritizes data governance and privacy compliance.
Future-Proofing Your Multi-Device Attribution Strategy
The challenge of audience fragmentation is not static. Consumer behaviors, technologies, and privacy regulations are constantly evolving. To succeed in the long term, advertisers must build attribution strategies that are not only effective today but also adaptable enough to meet the challenges of tomorrow. This means investing in flexible data architecture, relying on vendor-agnostic solutions, and fostering a culture of data-driven decision-making.
Emerging Trends in Cross-Device Measurement
Several trends are shaping the future of cross-device measurement. The ongoing deprecation of third-party cookies is forcing a greater reliance on first-party data and privacy-centric identity solutions. At the same time, compulsive screen use continues across generations. Surprisingly, Gen X leads in daily smartphone usage of two hours or more (60.4%), slightly ahead of Millennials and Gen Z, showing that multi-device behavior is universal.
Artificial intelligence and machine learning are also playing a larger role, powering more sophisticated attribution models and predictive analytics. These technologies can help advertisers understand complex user journeys and optimize campaigns more efficiently. As new platforms and devices emerge, measurement methodologies will need to continue evolving to keep pace.
Master Multi-Device Campaigns with Mynt Agency
Audience scatter presents a clear challenge, but it also creates an opportunity for savvy advertisers. By moving beyond outdated measurement models and embracing sophisticated attribution, you can turn a fragmented landscape into a competitive advantage. Accurately calculating ROI in a multi-device world is the key to optimizing campaigns, eliminating waste, and maximizing your advertising budget.
With over a decade of experience and exclusive research tools, we create and optimize large-scale campaigns with precision. We understand the nuances of cross-device attribution and can help you build a measurement framework that delivers clear insights and drives real results. Contact us today to learn how our strategic approach can maximize your advertising ROI across all platforms.
