Index Exchange integrates xpln.ai's predictive attention metrics directly into its supply-side platform, enabling programmatic buyers to optimize campaigns based on eye-tracking data before placing bids.
Index Exchange today integrated artificial intelligence-powered attention measurement directly into its supply-side platform through a partnership with xpln.ai. The collaboration moves attention metrics from post-campaign reporting into real-time bidding decisions, according to an announcement made available February 12, 2026.
The integration enables programmatic buyers to activate attention segments directly within Index Marketplaces. Advertisers can prioritize high-attention supply or exclude low-attention environments before campaigns begin, transforming attention from a retrospective diagnostic into a pre-bid optimization signal.
"Attention is no longer just measured after campaigns; it's now influencing which impressions are bought in the first place," according to the announcement materials shared with PPC Land. The system embeds predictive attention signals calibrated against large-scale eye-tracking data directly into the SSP infrastructure, allowing automated inventory filtering based on demonstrated attention patterns.
The xpln.ai platform captures between 20 and 25 exposure signals for each individual impression. These signals encompass share of screen, time fully in view, contextual clutter, content alignment, and additional environmental factors that influence whether advertisements capture genuine human attention.
Machine learning models trained on extensive eye-tracking datasets analyze which combinations of these exposure conditions correlate with actual viewer engagement. The system converts multiple simultaneous variables into a single predictive Attention KPI that can be integrated into programmatic buying workflows.
Traditional eye-tracking methodologies operated as research tools rather than activation mechanisms. The technology could generate insights about attention patterns but lacked the processing capacity to handle real-time decisions across millions of impressions. AI enables the translation of eye-tracking patterns into scalable, automated buying rules executable within SSP infrastructure.
"Pre-AI, eye-tracking was a research tool - useful for insight, not activation," according to the partnership announcement. The technology could not process dozens of variables across millions of impressions in real time or translate attention patterns into automated buying rules that programmatic systems require.
The integration positions supply-side platforms as active quality arbiters rather than passive transaction facilitators. SSPs increasingly shape which publisher environments receive priority treatment and how inventory quality gets defined and monetized upstream in the advertising value chain.
This shift represents a fundamental change from traditional programmatic models where SSPs primarily managed yield optimization and floor pricing. Index Exchange has systematically expanded its platform capabilities throughout 2025, adding contextual intelligence, dynamic pricing mechanisms, and advanced measurement features.
The attention integration builds upon Index Exchange's September 2025 partnership with Gracenote, which embedded brand safety segments and granular content controls directly into programmatic workflows. That collaboration made Index Exchange the first SSP to integrate Gracenote's contextual intelligence for streaming television advertising.
Industry observers note that SSPs are progressively assuming responsibility for inventory quality assurance. This development addresses long-standing advertiser concerns about programmatic advertising effectiveness and brand safety across automated buying channels.
The Index Exchange integration follows significant industry movement toward attention-based optimization throughout 2024 and 2025. Multiple measurement providers expanded attention capabilities during this period, reflecting growing advertiser demand for engagement insights beyond traditional viewability metrics.
IAB Europe's Virtual Programmatic Day panel in July 2025 revealed that attention metrics increasingly influence programmatic advertising decisions. Lucy Wallace, Head of Programmatic at Publicis Next, noted that attention has become "easier than ever to test" during the recent six-month period.
However, measurement standardization remains elusive as different providers employ varying methodologies. The Media Rating Council and Interactive Advertising Bureau released comprehensive attention measurement guidelines in November 2025 after extensive industry feedback, attempting to establish minimum requirements for quality, transparency, and comparability across vendors.
Adelaide has emerged as a prominent attention measurement provider, establishing partnerships with multiple platforms including Nielsen, Comscore, and Uber Advertising. The company's AU metric analyzes placement characteristics to predict whether attention will translate into meaningful business outcomes for advertisers.
Integral Ad Science expanded its Quality Attention measurement to mobile in-app environments in July 2024, combining media quality metrics with eye-tracking data from Lumen Research. The technology demonstrated campaigns achieving up to 130% lift in conversion rates when comparing high-attention impressions versus low-attention impressions.
The technical challenge in operationalizing attention measurement stems from processing complexity. Traditional programmatic systems evaluate bid requests using structured data fields including device type, geographic location, audience segments, and inventory characteristics. These standardized signals enable rapid decision-making across billions of daily transactions.
Attention measurement introduces substantially more complex calculations. The xpln.ai system must analyze dozens of environmental variables simultaneously, compare patterns against eye-tracking training data, generate predictive scores, and deliver results within the millisecond timeframes that real-time bidding requires.
Machine learning algorithms provide the computational infrastructure necessary for this processing at scale. The models learn which exposure signal combinations correlate with actual attention through analysis of large-scale eye-tracking datasets. Once trained, the algorithms can evaluate new impressions rapidly enough to support pre-bid decision-making.
This automation represents a significant departure from historical attention measurement approaches. Previous methodologies required manual analysis of campaign data after completion, generating insights too late to influence media buying decisions. The shift to pre-bid activation transforms attention from a reporting metric into an optimization lever.
Connected television advertising has emerged as a primary beneficiary of attention-based optimization. CTV's media budget share doubled from 14% in 2023 to 28% in 2025, with 72% of marketers planning to increase programmatic investment according to industry projections.
Streaming platforms face pressure to demonstrate outcome measurement capabilities matching search and social channels. The Interactive Advertising Bureau released a comprehensive guide in October 2025 urging industrywide implementation of standardized Conversion APIs to transform CTV into an outcome-driven advertising channel.
Attention metrics provide additional performance indicators beyond traditional CTV measurements including completion rates and reach. Research from Kargo demonstrated that premium creative formats achieved 78% higher attention than industry standards, with 15-second advertisements delivering 50% higher eye-on-screen effectiveness.
Teads launched CTV Performance in October 2025, introducing deterministic measurement capabilities that track site visits, leads, and sales directly tied to connected television exposure. The solution enables advertisers to move beyond standard CTV metrics toward outcome-driven accountability.
The proliferation of attention measurement has sparked debate about appropriate use cases and limitations. Industry analysis suggests attention should complement rather than replace existing metrics including reach, frequency, viewability, and conversion tracking.
IAB Australia's video measurement framework, released December 11, 2025, identified four distinct attention measurement methodologies: data signals analyzing time-in-view and interaction patterns, visual tracking using eye-gaze monitoring, physiological observation measuring neurological responses, and hybrid methods combining multiple approaches.
The framework emphasized that attention should not be considered or used as a measure of outcomes for evaluating campaign performance. Rather, attention represents an important data point in understanding exposure and engagement beyond basic delivery metrics.
Measurement standardization challenges persist as different providers employ varying methodologies. Some measurement companies utilize eye-tracking data while others rely on proxy signals including viewability duration, interaction rates, and placement characteristics. This methodological diversity complicates cross-vendor comparisons and benchmark establishment.
Research demonstrates that attention-based optimization can improve campaign efficiency. EssenceMediacom France achieved 7.3% more cost-effective CPM rates through attention-based custom bidding in Google's Display & Video 360 platform during July 2024. The implementation utilized custom bidding scripts that scored impression combinations based on URL and device parameters.
The attention integration reflects broader artificial intelligence adoption across programmatic advertising infrastructure. Multiple platforms introduced agentic AI capabilities throughout 2025, automating campaign management functions previously requiring human intervention.
PubMatic launched AgenticOS in January 2026, providing unified operating environment for AI agents handling yield optimization, audience discovery, planning capabilities, and deal troubleshooting. The platform synthesizes multi-year investments in agentic artificial intelligence and interoperability into coordinated infrastructure.
StackAdapt introduced Ivy in July 2025, an AI assistant integrated directly into its programmatic advertising platform. The tool provides real-time insights, personalized assistance, and natural language queries to help agencies and brands make faster campaign decisions.
However, industry veterans question whether AI agents can overcome fundamental business problems that prevented programmatic direct success historically. Ari Paparo, founder and CEO of Marketecture Media, suggested that agentic AI could eliminate core demand-side platform functions through automated campaign management and optimization.
The technical infrastructure supporting these AI capabilities requires substantial computational resources. Running large language models at enterprise scale costs hundreds of millions of dollars annually, creating significant monetization pressure on platforms deploying these technologies.
The Index Exchange and xpln.ai collaboration specifically targets the gap between measurement and activation. Traditional attention measurement occurred after campaign completion, generating insights about what worked but providing no mechanism to apply that knowledge to future buying decisions.
Pre-bid integration enables automated optimization based on predicted attention rather than historical performance. The system can evaluate inventory quality before purchases occur, directing budget toward placements demonstrating higher likelihood of capturing genuine viewer engagement.
This capability matters particularly for programmatic campaigns where buyers evaluate millions of potential impressions daily. Manual review of attention characteristics proves impossible at that scale. Automated filtering based on predictive attention scores enables quality-focused buying without sacrificing programmatic efficiency.
The technology also addresses advertiser concerns about inventory quality in open programmatic exchanges. By applying attention-based filters at the transaction layer, buyers can maintain broad programmatic reach while avoiding placements unlikely to generate meaningful engagement.
The attention integration strengthens Index Exchange's competitive position as an independent SSP. The company has invested strategically in artificial intelligence capabilities throughout 2025, including an investment in First Party Capital announced at Cannes 2025 on July 14.
Index Exchange CEO Andrew Casale has characterized the programmatic market as 15 years into its development cycle, with significant opportunities remaining for improvement across the ecosystem. The attention integration represents one approach to addressing quality and transparency challenges that have limited advertiser confidence in automated buying channels.
The company has systematically expanded platform capabilities beyond basic yield optimization. Recent innovationsinclude dynamic pricing models that adjust per-impression pricing to optimize publisher yield, duration-based metrics for streaming television measurement, and containerized real-time bidding for improved ad quality.
Mozilla Ads selected Index Exchange as its first official U.S. programmatic partner in October 2025, providing access to over 210 million Firefox users through Private Marketplace deals. The partnership demonstrates Index Exchange's positioning as a trusted infrastructure provider for premium publishers prioritizing user experience and privacy.
Who: Index Exchange Inc., one of the world's largest independent supply-side platforms, partnered with xpln.ai, an artificial intelligence-powered attention measurement company, to integrate predictive attention signals into programmatic buying infrastructure.
What: The partnership embeds attention metrics calibrated against large-scale eye-tracking data directly into Index Exchange's SSP, enabling programmatic buyers to automatically prioritize or exclude inventory based on predicted attention performance before placing bids. The xpln.ai platform captures 20-25 exposure signals per impression and converts them into a single predictive Attention KPI using machine learning models trained on eye-tracking datasets.
When: The integration was announced on February 12, 2026, moving attention measurement from post-campaign reporting into real-time bidding decisions for the first time within Index Exchange's infrastructure.
Where: The technology operates across Index Exchange's global SSP infrastructure, affecting programmatic transactions across display, video, mobile, and connected television advertising environments where attention-based optimization can influence inventory selection and campaign performance.
Why: The integration addresses fundamental limitations in traditional programmatic advertising where quality assessment occurs after impression delivery rather than informing pre-bid decisions. By embedding AI-derived attention predictions inside the SSP, the partnership transforms attention from a retrospective diagnostic into a scalable, pre-bid optimization signal that enables buyers to prioritize inventory demonstrating higher likelihood of capturing genuine human engagement, addressing long-standing advertiser concerns about programmatic advertising effectiveness and inventory quality.
