Attention Metrics Move Into Real-Time Bidding as xpln.ai Integrates with Index Exchange

For years, attention metrics lived in the post-campaign report — useful for learning, but disconnected from the moment that matters most: the bid decision. That changed in February 2026 when xpln.ai and Index Exchange announced the integration of attention-based segments directly into Index Marketplaces, the SSP's programmatic platform. For the first time, buyers can prioritize or exclude inventory based on predicted attention performance before a bid is placed.

How It Works

The xpln.ai platform captures 20 to 25 exposure signals per impression — including share of screen, time fully in view, contextual clutter, content alignment, and environmental factors — and converts them into a single predictive Attention KPI using machine learning models trained on large-scale eye-tracking datasets.

What makes this technically significant is the deployment model. The attention models run in containerized environments directly on Index Exchange's infrastructure, eliminating the latency of external API calls during bid evaluation. Attention segments are packaged into Deal IDs, making them compatible with existing DSP workflows — including The Trade Desk, which has already integrated the segments.

As xpln.ai CEO Fabien Magalon put it: "Attention sits at the intersection of media quality and creative."

From Diagnostic to Targeting Signal

The shift from post-campaign diagnostic to pre-bid filter is the most consequential development in the attention metrics space since the MRC and IAB released their attention measurement guidelines in November 2025. Those guidelines established standardized definitions and validation requirements. The xpln.ai integration takes the next logical step: making attention actionable at the point of transaction.

Early results from attention-based buying are encouraging. EssenceMediacom France reported 7.3% more cost-effective CPMs through attention-based bidding on the platform. More broadly, Integral Ad Science research shows high-attention impressions drive up to 130% higher conversions compared to low-attention placements, while Kargo found 78% higher attention for premium CTV formats.

Adelaide's 2026 Outcomes Guide Adds More Evidence

The timing aligns with Adelaide's release of its 2026 Outcomes Guide, the most comprehensive evidence set for attention-driven advertising to date. Drawing from 60 case studies across 16 industries, all supported by third-party outcome measurement, the guide found that campaigns optimized using Adelaide's AU metric achieved an average 33% lift in upper-funnel KPIs and 53% stronger lower-funnel impact.

The case studies, covered by ExchangeWire, include specific results that are difficult to dismiss: a bank holding company nearly tripled conversions at lower cost, an e-commerce brand generated 3.8x more checkouts and higher revenue per impression, and a food and beverage advertiser saw up to 60% lifts in unaided awareness, favorability, and purchase intent from CTV inventory meeting AU quality floors.

Adelaide CEO Marc Guldimann summarized the shift: "Attention has moved beyond proof-of-concept. What we're seeing now is execution at scale."

The Expanding Ecosystem

xpln.ai's expansion into the US market in January 2026, with Gina Cavallo as CRO, reflects growing North American demand for attention-based buying. The company has operated in Europe and APAC for years, working with major advertisers, and Index Exchange's SSP gives it global programmatic reach.

Meanwhile, Adelaide continues pursuing MRC accreditation for its AU metric — if granted, it would be the first MRC-accredited attention metric, a milestone that could accelerate adoption among buyers who require accredited currencies.

What This Means for Media Buyers

The infrastructure for attention-based buying is now in place. Buyers can activate attention segments through Deal IDs in their existing DSP workflows, meaning no new platform adoption is required. The business case — supported by Adelaide's 60 case studies and the IAS and Kargo performance data — is stronger than it has ever been.

The question for measurement teams is no longer whether attention metrics work, but whether their programmatic stack supports them. If your SSP or DSP offers attention-based targeting, the data suggests you should be testing it. If it doesn't, that gap is becoming a competitive disadvantage.