Your brand launches an omnichannel marketing campaign across streaming platforms, but when the quarter closes, conversions haven’t moved. The CFO asks, “Where did the ad spend actually work?” and the marketing team struggles to connect the dots.
Cross-screen advertising is a fragmented journey. AI steps in, not as another solution, but as the connective tissue that finally stitches these journeys together and closes the gaps in cross-screen ad spend. Traditional attribution models can’t handle cross-screen journeys. These models cannot process nonlinear journeys, which means underreporting the contribution of upper-funnel and mid-funnel channels.
The following article describes how AI is the solution for cross-screen ad spend.
What Causes Media Waste in Cross-screen Ad Spend?
The reasons for media waste in cross-screen ad spend are as follows.
1. Data in Silos Across Platforms
When the performance data from CTV, mobile, desktop, and social lives in separate systems, marketers cannot create a single unified view of the audience journey.
Example: A SaaS company is running awareness ads on CTV and retargeting on LinkedIn. Because the signals aren’t unified, the same account gets retargeted over and over again.
2. Duplicate Reach and Frequency Inflation
Without cross-screen frequency management, audiences see the same ad numerous times across devices, driving up CPMs and lowering engagement.
Example: A cybersecurity vendor delivers the same webinar promotion to a buying group member across their work laptop, mobile device, and YouTube CTV app.
3. Poor Identity Resolution in a Cookieless World
Traditional identifiers, such as cookies, fail to connect users who shift between corporate networks, personal devices, and privacy environments.
Example: A cloud infrastructure brand is targeting IT directors, but since the IDs do not persist across screens, half the impressions go to irrelevant titles within the same account.
4. Poor Cross-Screen Creative Sequencing
The creative does not align with a user’s journey leading to mismatched messaging across screens.
Example: A marketing automation company is running a product demo ad on mobile before the prospect has even seen a top-funnel brand spot on CTV.
5. Incorrect Screen-level Budget Allocation
The spend is dictated in many cases by channel owners and not by audience behavior. This leads to overspending on high-CPM screens that don’t drive lift.
Example: A fintech SaaS brand over-invests in desktop display, thinking that’s the “work screen,” yet intent spikes actually happen on mobile while commuting or on break.
6. Failure to Connect Buying Group Across Screens
Research into B2B journeys with multiple roles involves the use of different devices, and failure to map these signals leads to wasted impressions.
Example: Only one stakeholder in a buying group is reached across screens, while exposure is not delivered to the decision-makers on CTV or mobile.
How Does Fragmented Identity Tracking Hurt Conversion?
One of the biggest barriers with cross-screen advertising is fragmented identity tracking.
1. Inconsistent Recognition Across Devices
When identity signals break between screens, marketers treat the same user as multiple individuals.
Example: A SaaS buyer sees a CTV awareness ad, but since identity cannot be stitched to their mobile behavior, they are excluded from retargeting.
2. Improper Use of Attribution
Gaps in identity make it impossible to understand which screen actually influenced the decision, thus weakening optimization.
Example: A cloud infrastructure company sees desktop conversions but doesn’t attribute lift from earlier CTV impressions.
3. Frequency Waste and Oversaturation
This fragmented identity means that platforms are over-serving ads, assuming each screen represents a new user.
Example: A cybersecurity company inadvertently serves over 20 impressions of the same whitepaper ad to one stakeholder across laptop, mobile, and YouTube CTV.
4. Not Monitoring Buying Group Behavior Across Screens
In B2B, different stakeholders research on different devices. Missing out on these connections dilutes the conversion.
Example: The IT manager consumes mobile content, the CTO views CTV thought leadership videos, and the procurement lead goes onto the desktop site. For the brand, all these signals, sans identity tracking, become different users.
5. Broken Retargeting and Nurture Paths
High intent behaviors are unrecognized and nurture journeys fail if identity is reset between screens.
Example: A prospect reads a case study on desktop and is not retargeted on mobile because of an ID mismatch, and the momentum is lost.
How AI Solves the Gap in Cross-Screen Ad Spend
AI is the layer that at last closes the gaps in cross-screen advertising.
1. AI Unifies Identity Across Screens
AI uses probabilistic modeling, contextual signals, and behavioral correlations to stitch together user journeys.
Example: A cloud security brand uses AI to identify that the same IT director who watched a CTV ad was later opening a pricing page on desktop, allowing the system to retarget.
2. AI Predicts Intent, Allocates Spend
Instead of pre-fixed budgets per channel, AI allocates spend in real time to the highest-converting screen paths.
Example: Fintech SaaS firm sees prospects usually start their research on mobile but convert on desktop. AI increases mid-funnel mobile spend and conversion-focused desktop spend.
3. AI Eliminates Waste from Duplicate Reach and Frequency
Stitched together with identity, AI caps frequency across screens and makes sure impressions reach decision-makers and not oversaturate the same user.
Example: A CRM vendor earlier hit the same stakeholder 15 times across devices. AI-based frequency management spreads the impressions across the entire buying group.
4. AI Surfaces Buying Group Insights Across Devices
AI identifies which stakeholders are engaging, on which screens, and at which stage for optimization.
Example: On a software deal, AI recognizes the CTO is consuming mobile content, the IT manager is visiting the product page on desktop, and finance is watching CTV explainer videos. The system orchestrates coordinated messaging across all three.
5. AI Automates Nurture Paths Across Screens
High-intent behaviors on one device immediately trigger relevant nurturing on another.
Example: Once a procurement lead downloads a pricing sheet on desktop, AI triggers mobile retargeting with an ROI use case.
Conclusion
The future of cross-screen advertising depends on how marketers are able to unify fragmented journeys. AI enables marketers to shift from a fragmented approach to a unified journey. Ready to close the gaps in your cross-screen ad spend with AI? Let’s build a connected omnichannel together.
