The SaaS Guide to TV/CTV ROI
Measurement and Attribution Modeling
Navigating the Attribution Labyrinth
The strategic importance of Connected TV (CTV) in the modern marketing mix is a market reality. For Software as a Service (SaaS) companies, the allure of CTV's massive audience presents both an opportunity and a profound challenge. As investment pours into this channel, marketers find themselves in a complex ecosystem of fragmented platforms and elusive cross-device journeys that obscure the true return on investment. This guide establishes our core thesis: only a hybrid measurement model can provide the clarity needed to scale with confidence.
The CTV Gold Rush: A Market in Ascent
The migration of audience attention to streaming is one of the most significant media shifts of the decade. This has created a digital-first television ecosystem where SaaS companies can compete for eyeballs on the largest screen. The scale is immense: U.S. CTV ad spend is projected to climb dramatically.
This financial commitment is a direct response to consumer behavior, with streaming now commanding a significant portion of viewing time. The rise of ad-supported video-on-demand (AVOD) tiers has dramatically expanded available inventory, while Programmatic buying aligns the process with digital-native frameworks.
Advids Analyzes: The Investment-Confidence Gap
The data reveals a significant and widening gap. While CTV ad spend grows by double digits, confidence in measuring that spend is critically low. This suggests SaaS leaders are investing based on the potential of CTV but are doing so with an inability to prove ROI, creating immense internal pressure. This disconnect is unsustainable.
70%
of marketers fall short of their attribution goals.
Defining the Measurement Crisis
Despite billions flowing into CTV, measurement precision remains alarmingly underdeveloped. This widespread uncertainty stems from a complex web of interconnected challenges that we term the "Attribution Labyrinth," defined by three primary structural problems.
Platform Fragmentation & Data Silos
The CTV ecosystem is not a single marketplace. It's a fragmented collection of device manufacturers, streaming services, and ad-buying platforms. Each operates as a "walled garden" with proprietary data and inconsistent standards.
"Bringing all of those sources together and making sure that they're de-duplicated...is really important to be able to pin a business outcome to the media that was run."
- Melanie Brown, VP of Advanced TV, Tubi
The Cross-Device Tracking Failure
The central technical challenge is connecting an ad exposure on a TV to a conversion on a different device—a laptop, tablet, or phone. In a world without third-party cookies, this cross-device stitching relies on methods fraught with inaccuracies, making it hard to link cause and effect reliably.
The SaaS Amplification Effect
High-LTV / Long-Cycle Conundrum
The average B2B SaaS sales cycle can last four to six months, involving six to ten decision-makers. A standard 30-day attribution window is rendered almost useless, as a CTV ad viewed in January may only contribute to a deal closed in June.
B2B Buying Committee
A CTV ad is viewed in a consumer context but influences a corporate decision. Connecting these disparate events is notoriously unreliable in a B2B environment with corporate networks and VPNs.
The Path Forward: A Hybrid Model
The complexity of the Attribution Labyrinth demands a more sophisticated philosophy. This report's central thesis is that achieving accurate ROI measurement requires a hybrid model that integrates three complementary methodologies: top-down Media Mix Modeling (MMM), bottom-up Multi-Touch Attribution (MTA), and rigorous Incrementality Testing. By triangulating insights, SaaS leaders can build a comprehensive and defensible measurement practice.
The Incrementality Imperative
To truly understand CTV's value, marketers must shift focus from measuring correlation to proving causation—the rigorous, scientific process of isolating the true, causal lift that advertising generates.
The Illusion of Precision: Why Last-Touch Fails
The persistence of last-touch attribution is a testament to its simplicity, not its accuracy. For a high-funnel, non-clickable medium like CTV, this model is dangerously misleading. It ignores long-term brand-building effects and fails within a long-cycle SaaS customer journey, incorrectly assigning 100% credit to a final click while ignoring the CTV campaign that created the initial intent.
41%
of marketers still rely on a last-touch model.
The Mandate for Proving Causation
The core mandate of modern measurement is to move beyond observing correlation. An incrementality test is a controlled experiment designed to answer one crucial question: "How many of these conversions would have happened anyway?". This is paramount for justifying budgets, as it proves that CTV spend is generating net new demand, not just claiming credit for inevitable conversions.
Contrarian Take
While incrementality testing is the gold standard, an excessive focus on it can lead to "analysis paralysis." The key is balance: use rigorous tests for large, strategic investments, but rely on strong directional data from calibrated attribution models for smaller, tactical optimizations.
Methodology: Geo-Lift Testing
For broad-reach channels, geo-lift testing is the primary methodology. It involves selecting matched "test" and "control" regions, executing the campaign only in the test markets, and then comparing KPI performance. The difference reveals the incremental lift, which is used to calculate the true, causal incremental Return on Ad Spend (iROAS).
Methodology: Holdout Groups
For addressable CTV campaigns, holdout tests are the gold standard. A target audience is randomly split into a Test Group (served ads) and a Control Group (withheld from ads). Conversion behavior is tracked across both groups using your first-party data. Any statistically significant difference can be causally attributed to the advertising.
The Advids Way: Incrementality vs. Correlation Index (ICI)
To navigate the complex claims of vendors, we provide clarity through the ICI. This proprietary scoring system assesses the rigor of any ROI claim by classifying it based on the underlying methodology.
Low ICI Score
Assigned to simplistic models like last-touch. This data carries a high risk of mistaking correlation for causation and should not be used for strategic decisions.
High ICI Score
Awarded only to results from controlled experiments like geo-lift or holdout tests. This is causal evidence that can be trusted for strategic budget decisions.
Medium ICI Score
Applies to data from sophisticated models like calibrated MMM or MTA. This data is directionally useful for optimization but does not definitively prove causation.
The Hybrid Measurement Model
The modern media landscape, fragmented by privacy regulations and cross-device complexity, demands a framework that integrates the strategic view of MMM with the tactical insights of MTA.
The Limits of MTA in the Post-Cookie Era
Privacy & Signal Loss
The deprecation of cookies and stricter privacy controls have severely hampered MTA's ability to deterministically "stitch" together a user's journey across devices.
Offline & Upper-Funnel Blind Spots
MTA is optimized for clickable, digital interactions and is ill-equipped to incorporate the impact of non-clickable media like Linear TV and broad-reach CTV campaigns.
The Resurgence of MMM
As user-level tracking has become compromised, Media Mix Modeling (MMM) has seen a resurgence. This top-down statistical technique is inherently privacy-resilient because it operates on aggregated data, making it unaffected by signal loss. Its greatest strength is its holistic coverage of both online and offline channels, though it can lack tactical granularity if used in isolation.
The Advids Approach to a Unified Measurement Ecosystem
The conflict between MMM and MTA is a false dichotomy. Advanced marketers understand they are complementary components. Our hybrid framework reconciles the macro and the micro for a complete strategic view.
1. Strategic Budget Allocation
Use a robust MMM to establish the overall incremental contribution of each channel, modeling CTV as its own distinct input to accurately capture its unique impact.
2. Tactical In-Channel Optimization
Use MTA to optimize within and between digital channels, answering questions like which search keywords are most effective at converting users exposed to a CTV campaign.
3. Calibration with Ground Truth
Use the results from rigorous incrementality experiments as the "ground truth" to calibrate and validate the outputs of both the MMM and MTA models, ensuring accuracy.
“Advertisers can feel confident about increasing their spend on CTV when they have a MMM solution that clearly measures the incremental value it delivers.”
- Anna Miller, Director of Ad Measurement, Roku
The SaaS Multi-Touch CTV Attribution (MT-CTVA) Framework
A proprietary, hybrid, weighted attribution system integrating diverse data sources to provide a comprehensive view of how CTV advertising contributes to revenue.
A Model Built for the SaaS Journey
The MT-CTVA framework moves beyond simplistic models to map the long, non-linear path of a B2B buyer. It collects data from a wide array of touchpoints and applies a custom weighting algorithm that reflects the unique milestones of the SaaS sales funnel, assigning credit to accurately reflect influence on the journey.
The Four Pillars: Data Inputs
1. Deterministic Signals (The Anchor)
The foundational layer of first-party data captured directly from your systems, where user identity is known and confirmed (e.g., demo requests, trial sign-ups).
2. Probabilistic Household Signals
Connects anonymous CTV exposures to actions using techniques like IP matching. Accuracy can be below 70% and is complicated by corporate VPNs.
3. Glass-Level Exposure Data
Incorporates Automated Content Recognition (ACR) data from smart TVs to identify specific ads being displayed, offering a much stronger viewership signal.
4. Privacy-Enhanced Signals
Utilizes data clean rooms to securely match an advertiser's first-party data against a platform's viewership data using encrypted identifiers, with no PII exposed.
Weighting the Customer Journey
The MT-CTVA framework employs a flexible, custom position-based weighting system with a slow time-decay element tailored for long sales cycles. Credit is distributed across key stages, with customizable weights based on business goals.
How to Implement the MT-CTVA Framework
Unify Your Data
Consolidate data from your CRM, web analytics, and ad platforms into a Customer Data Platform (CDP) to create a single source of truth.
Define Your Key Journey Stages
Work with your sales team to map the critical milestones in your specific customer journey, from initial awareness to a closed deal.
Select a Flexible Attribution Tool
Choose a measurement partner that allows for custom models and supports long, adjustable attribution windows.
Test and Iterate
Start with a baseline weighting (e.g., 30% first touch, 40% opportunity creation, 30% last touch) and iterate based on performance data.
The SaaS Conundrum
Measuring ROI for businesses with long sales cycles and a focus on customer lifetime value (LTV) demands a specialized approach to attribution.
Adapting Windows for the Marathon
A critical error in SaaS measurement is using default 7-day or 30-day lookback windows, which are inadequate for a sales cycle that can be a multi-month journey. Your measurement window must reflect your customer's reality.
Advids Warning
More budget is wasted by SaaS companies using default 30-day attribution windows than almost any other mistake. Your window must match your average sales cycle—typically 90 to 180 days for most B2B SaaS businesses.
Two Sides of the SaaS GTM Coin
Sales-Led (Enterprise) SaaS
The primary strategy is Account-Based Marketing (ABM). Measurement focuses on the account, not the lead, tracking impact on metrics like pipeline velocity and deal size.
Product-Led Growth (PLG) SaaS
Built around a self-service model, where CTV campaigns drive free trials. Focus is on trial-to-paid conversion rates and the quality of Product-Qualified Leads (PQLs).
Integrating LTV for True ROI
For a subscription business, calculating ROI based on initial revenue is flawed. Marketers must incorporate Customer Lifetime Value (LTV). The LTV-to-CAC ratio is the ultimate measure of marketing efficiency.
4:1+
Target LTV-to-CAC Ratio for B2B SaaS
Beyond the Basics: Advanced KPIs for SaaS CTV
Pipeline Velocity Lift
Measures the speed at which deals move through the pipeline, quantifying if CTV helps close deals faster.
Cost per PQL
For PLG companies, measures acquisition efficiency based on users who experience the product's core value.
Attention Metrics
Measures the quality of an ad exposure, like the percentage of the ad on-screen while the TV was on.
Incremental ROAS (iROAS)
The gold-standard, non-negotiable metric that isolates the causal revenue generated directly by an ad campaign.
The 2026 CTV Tech Stack
Implementing sophisticated measurement requires a robust and integrated marketing technology stack to prove ROI and scale investments.
Essential Components of a Modern Stack
CDPs & Data Clean Rooms: The Privacy-First Core
A CDP institutionalizes first-party data collection, the only durable asset for identity resolution. Data Clean Rooms are the critical, secure bridge between your data and the walled gardens of CTV platforms, allowing for collaboration without sharing raw PII.
The Advids 5-Point Partner Evaluation Framework
The Eternal Question: Build vs. Buy
Building a proprietary attribution solution offers customization but is enormously complex and expensive. Buying a commercial platform offers a much faster path to value at a lower upfront cost.
The Advids Recommendation
For the vast majority of SaaS companies, a "buy" or a hybrid approach is the most practical and cost-effective strategy. This allows your teams to focus on generating insights, not maintaining infrastructure.
Balancing Brand Lift and Performance
This is not a trade-off. It's a synergy. Overlooking CTV's brand-building power is a strategic error that harms performance marketing efficiency.
The Danger of Over-Optimizing
An excessive focus on immediate conversions leads to diminishing returns, capturing only the smallest pool of high-intent users.
80%
of marketers use narrow targeting on CTV even for brand goals.
Measuring Brand Lift Effectively
Brand Lift Studies
The gold standard. Survey-based test-and-control to measure causal impact on brand awareness and purchase intent.
Search Lift Analysis
Measures impact on organic search behavior by analyzing branded search query volume in test vs. control markets.
Website Traffic Analysis
Track correlations between campaign dates and statistically significant lifts in direct and organic traffic.
The Synergy: How Brand Fuels Performance
Brand and performance are not mutually exclusive. Investing in upper-funnel brand building via CTV directly improves the efficiency of all lower-funnel channels, leading to higher CTRs, higher conversion rates, and a lower overall Customer Acquisition Cost (CAC).
Advids Interpretation
Sophisticated marketers will move beyond acknowledging this synergy and begin to actively model it, measuring brand investment as a direct multiplier on performance channel efficiency.
The TV/CTV ROI Optimization Blueprint
A strategic framework to bridge the gap between measurement and execution, providing a cyclical approach to continuously optimize media, creative, and strategy.
From Measurement to Action
The blueprint operates as a continuous improvement cycle, transforming measurement into a dynamic engine for growth through four key stages: Measure, Analyze, Optimize, and Iterate.
Pillar 1: Optimizing Media & Targeting
- Channel Allocation: Shift budget away from underperforming CTV platforms.
- Audience Refinement: Identify high-LTV segments to build effective lookalike audiences.
- Frequency Management: Find the optimal number of ad exposures to maximize impact without oversaturation.
Pillar 2: Optimizing Creative
- Message & Offer Testing: Systematically A/B test creative variables like value propositions and CTAs.
- Format & Length Analysis: Analyze the performance of different ad formats, including interactive elements like QR codes.
Case Studies in Action
Paycor: Driving Incremental Pipeline
Problem: Needed to build top-of-funnel demand but lacked a framework for measuring upper-funnel ROI.
Solution: Combined rigorous incrementality testing with CRM metric tracking to connect CTV spend directly to MQLs and bookings.
10x
Increase in MQLs
70%
More Site Sessions
Bolt: Outperforming Digital Staples
Problem: Wanted to build brand with memorable creative while proving performance against channels like paid search.
Solution: Launched a creative-first campaign and used a measurement platform integrated with Google Analytics for real-time, cross-channel comparison.
The Future of SaaS TV/CTV Measurement
Looking toward 2026, measurement will be defined by three powerful forces: AI, privacy-first technologies, and the convergence of linear and digital TV.
Impact of AI
AI will power predictive attribution models that forecast which touchpoints will drive future conversions. By 2026, 90% of advertisers are expected to use AI in ad creation.
Privacy-First Reality
The ecosystem will be rebuilt on first-party data, contextual targeting, and privacy-enhancing technologies (PETs), with data clean rooms becoming standard.
Linear & CTV Convergence
The lines between traditional and connected TV will disappear from a measurement perspective, with new standards for unified, cross-platform measurement.
Advids Future Casting
The most successful teams will use AI as a strategic co-pilot. Your role as a leader is to provide strategic direction and interpret the complex outputs of AI models to make the final, critical decisions.
Advanced Methodologies for Deeper Insight
Propensity Score Matching
A statistical technique that creates a more accurate comparison between test and control groups by matching individuals with similar probabilities of being exposed to an ad, enabling a cleaner estimate of the ad's true causal effect.
Synthetic Control Methods
An evolution of geo-lift testing that creates a "synthetic" control for a test market by constructing a weighted average of multiple control markets. This creates a highly accurate counterfactual to isolate a campaign's incremental lift with greater confidence.
Strategic Synthesis & The Final Imperative
The goal is not just accurate reports; it is to enable confident decision-making. With a credible measurement practice, CTV becomes a powerful, scalable engine for brand equity and qualified demand. The final imperative is to champion a unified framework that proves marketing's contribution in the language of the C-suite: pipeline, revenue, and profitable growth.
The Advids 10-Step Implementation Checklist
Abandon Last-Touch Immediately for upper-funnel channels.
Define Your Sales Cycle and set it as your minimum lookback window.
Establish a Core Multi-Touch Attribution model that reflects your journey.
Invest in a CDP as the central hub for all first-party data.
Plan Your First Incrementality Test (Geo-lift or Holdout).
Explore Data Clean Room capabilities with your key platform partners.
Integrate LTV into all ROI calculations with your finance team.
Evolve KPIs from vanity metrics to business outcomes like iROAS.
Establish a Hybrid Measurement Cadence (MMM for strategy, MTA for tactics).
Create a culture of continuous improvement and experimentation.