Connected TV (CTV) has revolutionized advertising by merging the expansive reach of traditional TV with precise digital targeting, creating new opportunities for mobile app marketers. However, evaluating CTV ad effectiveness remains a complex endeavor due to the cross-device user journeys, technical hurdles in attribution, and political dynamics within the measurement ecosystem. While many mobile app providers, including those using platforms like AdMob, MoEngage, Adjust, Kochava, and AppsFlyer, increased CTV ad budgets significantly since the pandemic, accurately measuring campaign impact involves overcoming challenges unique to CTV. Revisiting attribution models, employing sophisticated probabilistic methods, and leveraging household device graphs have emerged as essential pathways to navigate these complexities effectively.
Technical challenges in assessing CTV ad effectiveness for mobile app marketers
One fundamental difficulty lies in the cross-device nature of CTV ad campaigns. Unlike mobile or desktop advertising where deterministic measurement connects ad impressions directly to device IDs, CTV ads involve interactions dispersed across a TV and a mobile device. This split prevents employing device ID matching, forcing marketers to rely on probabilistic modeling, commonly known as fingerprinting, to associate CTV ad views with app installs.
- Deterministic measurement uses device or advertising IDs to link ads to app installs.
- CTV campaigns require probabilistic models based on IP addresses and device signatures.
- The requirement that both devices share the same household Wi-Fi limits accuracy.
Studies by measurement companies like Adjust indicate that on average, 85% of mobile installs occur on household Wi-Fi networks, but this still leaves significant gaps. Since users often engage with CTV ads outside home or with mobile devices disconnected from home networks, the data suffers from incomplete coverage. This disadvantage creates a headwind compared to social media and search ads, where deterministic attribution dominates.
| Measurement method | Device context | Accuracy | Limitations for CTV |
|---|---|---|---|
| Deterministic | Single device | High | Impossible across TV and mobile devices |
| Probabilistic (Fingerprinting) | Cross-device | Medium | Depends on shared IP and network conditions |
Impact of the attribution waterfalls on CTV ad evaluation
Beyond technical restrictions, mobile app marketers must contend with political and structural issues embedded in attribution methodologies. Popular mobile measurement providers like AppsFlyer, Adjust, Singular, and Kochava implement attribution waterfalls prioritizing deterministic data over probabilistic matches and favoring clicks over impressions. This workflow inherently disadvantages CTV ad networks because:
- CTV ads are only measurable probabilistically due to cross-device constraints.
- CTV ad impressions cannot be clicked, limiting their attribution weight.
- The predominant last-touch attribution model credits conversions to the final seen or clicked ad, overshadowing CTV’s role in the user journey.
The persistence of last-touch attribution benefits dominant networks like social media and search, which maintain measurement data control. This situation complicates fair recognition of CTV contributions in app install campaigns and requires strategic advocacy by CTV ad networks for updated measurement approaches.
Advanced solutions driving accurate measurement of CTV ads in mobile app marketing
Addressing these impediments involves integrating innovative approaches to improve CTV ad measurement capabilities. Leading mobile app marketers and CTV ad networks have begun leveraging comprehensive household device graphs and location intelligence. These tools help establish coherent device relationships across environments, mitigating fingerprinting limitations.
- Household device graphs consolidate sets of devices belonging to the same home for cross-device attribution.
- Location-based insights supplement IP address data for enhanced determinism.
- Measurement providers are encouraged to adopt these solutions despite data privacy concerns, especially under EU compliance frameworks.
The growth of fingerprint-based measurement, accelerated by Apple’s strict IDFA consent policies with opt-in rates near 20% globally, reinforces the importance of probabilistic approaches. Even networks traditionally relying on deterministic data, such as Tune and Branch, acknowledge the necessity of hybrid frameworks that incorporate fingerprinting discreetly.
| Solution | Description | CTV measurement benefit | Challenges |
|---|---|---|---|
| Household device graphs | Maps multiple devices to a household | Improved cross-device match confidence | Data privacy and regulatory hurdles |
| Location intelligence | Uses geolocation data alongside IP | Enhances accuracy of probabilistic matches | Requires user consent and transparency |
| Multi-touch attribution models | Credits multiple touchpoints beyond last-click | More accurate credit allocation for CTV ads | Complex implementation and industry adoption resistance |
Emerging attribution models reshaping CTV ad impact assessment
Several innovative attribution solutions are gaining traction. Sweden-based Funnel offers models that support multi-touch and assistive attribution, enabling marketers to see contributions of CTV ads even if app installs are credited to social media or search platforms. Adjust’s assist dashboard similarly reveals ad engagement across networks, highlighting CTV’s role throughout the funnel.
- Multi-touch attribution accounts for multiple influences along the customer journey.
- Assistive dashboards provide insights into network synergy effects.
- These models encourage collaboration between CTV and mobile measurement vendors.
Future outlook on cross-device app engagement fueled by connected TV
The trajectory of app user behavior itself is evolving towards multi-device ecosystems. Gaming companies illustrate this trend by pushing users to install mobile apps on PCs, supported by tools like Google’s Windows emulator. In parallel, interactive CTV ads are set to merge mobile phones and TV devices into cohesive experiences.
- CTV becoming a central household digital hub.
- Unified interactive ad experiences across mobile and TV.
- Growth opportunities for app marketers using platforms like Segment, Criteo, Tune, and Branch.
This cross-device future implies that CTV ad networks need to position themselves at the core of the ecosystem, bridging big screen creativity with small screen engagement to unlock full user journey insights and optimized campaign returns.
Key takeaways for mobile app marketers leveraging CTV ad campaigns
- Understand technological limitations of cross-device attribution and prepare to use probabilistic methods.
- Advocate for advanced attribution frameworks that fairly credit CTV ad contributions beyond last-touch models.
- Leverage household device graphs and location data to improve measurement accuracy.
- Embrace emerging multi-touch attribution tools such as those from Funnel, Adjust, and Singular.
- Prepare for an integrated cross-device user experience involving CTV as a primary digital interface alongside mobile.
Why is measuring CTV ad effectiveness difficult for mobile app marketers?
Measuring CTV ad effectiveness is difficult because user engagement spans multiple devices, preventing direct device ID matching used in mobile marketing. Probabilistic models like fingerprinting must be used, which have inherent inaccuracies.
How does probabilistic modeling affect CTV ad measurement?
Probabilistic modeling, used in CTV ad measurement, relies on shared household IP addresses and device characteristics to attribute ads, but it is less precise than deterministic methods and can miss conversions when users are outside the home network.
What role do household device graphs play in evaluating CTV ad effectiveness?
Household device graphs enhance CTV ad effectiveness evaluation by mapping devices within the same home, allowing more accurate cross-device attribution beyond simple IP matching, improving measurement confidence for mobile app marketers.
Why is the last-touch attribution model problematic for CTV ad campaigns?
Last-touch attribution credits the final ad seen or clicked before conversion, often ignoring earlier CTV ads that influence user decisions, thereby underestimating CTV ad effectiveness in mobile app campaigns.
Which measurement providers are commonly used by mobile app marketers to evaluate CTV ad effectiveness?
Mobile app marketers commonly use measurement providers such as Adjust, AppsFlyer, Kochava, Singular, Tune, and Branch to evaluate CTV ad effectiveness, each with differing capabilities in handling cross-device attribution challenges.
How has Apple’s IDFA policy impacted CTV ad measurement?
Apple’s IDFA policy requiring user consent significantly reduced opt-in rates, forcing mobile marketers to rely more on probabilistic measurement methods like fingerprinting, which has affected the evaluation accuracy of CTV ad campaigns.
What solutions exist to overcome fingerprinting limitations in CTV ad measurement?
Overcoming fingerprinting limitations involves using household device graphs, location intelligence, and pushing for multi-touch attribution models, thereby improving accuracy and fairness in assessing CTV ad effectiveness for mobile apps.
Are multi-touch attribution models effective for CTV ad campaigns?
Multi-touch attribution models are effective as they allocate credit across multiple ad touchpoints, providing mobile app marketers a comprehensive understanding of CTV ad contributions beyond last-click bias.
How will cross-device app usage affect future CTV advertising strategies?
Cross-device app usage will integrate CTV and mobile devices more closely, transforming CTV into a household digital hub and pushing marketers to design cohesive, interactive campaigns that span devices.
What role do platforms like AdMob and MoEngage play in measuring CTV ad effectiveness?
Platforms like AdMob and MoEngage provide mobile app marketers with tools for data integration and user engagement tracking, aiding in evaluating CTV ad effectiveness within broader media mixes.