Index Exchange today published case study results demonstrating how sell-side decisioning technology deployed within its Marketplaces infrastructure drove a 75% reduction in cost per site visit for a major apparel retailer. The performance gains emerged from deploying predictive intelligence at the supply layer rather than relying exclusively on demand-side optimization, a pattern that reflects broader industry momentum toward upstream decisioning capabilities.

The retailer partnered with a major agency holding company and inPowered AI to address mounting pressure on e-commerce traffic acquisition costs through always-on programmatic online video campaigns. According to the case study published on February 3, 2026, the collaboration applied artificial intelligence models directly inside Index Exchange's infrastructure to evaluate impression opportunities before bids reached demand-side platforms.

Traditional programmatic advertising optimization occurs primarily on the buy side, where demand-side platforms analyze bid requests and make purchasing decisions within millisecond timeframes. This approach limits access to valuable supply-side signals including contextual factors, geographic indicators, and temporal patterns that exist upstream in the transaction path. The retailer's historical reliance on demand-side optimization meant missing opportunities to identify which impressions would most likely generate actual site visits.

inPowered AI constructed a custom predictive model trained on the retailer's historical DSP performance data. The model scored each impression based on likelihood to generate site visits using patterns observed across past campaigns. Deploying this intelligence as a containerized solution within Index's infrastructure enabled real-time evaluation of supply-side signals including time of day, page URL, browser version, operating system, carrier, and zip code.

"Deploying our models inside Index's infrastructure is what makes this approach possible," stated Peyman Nilforoush, CEO and Co-Founder of inPowered AI, according to the case study. "It lets us decision across the full supply landscape, which leads to more consistent and meaningful performance gains."

The model's architecture identified high-potential impressions and applied unique deal IDs enabling spend to route automatically toward those opportunities. This upstream decisioning occurred before bid requests reached the DSP, unlocking performance improvements impossible when optimization operates exclusively downstream. The 75% cost per site visit reduction represented major efficiency gains compared to traditional approaches.

The technical implementation leveraged Index Exchange's Marketplaces infrastructure, which enables partners to deploy solutions including data, algorithms, and measurement capabilities directly within the supply-side platform. This architecture addresses fundamental latency constraints that limit algorithmic sophistication in traditional real-time bidding environments where decisions must occur within 100-millisecond timeframes.

"This is a clear example of what sell-side decisioning can unlock," according to Pirouz Nilforoush, President and Co-Founder of inPowered AI. "Brands that only optimize on the buy side are missing the broader opportunity. By evaluating the full open internet across Index, we're seeing stronger results across nearly every KPI we optimize toward."

The approach represents a departure from conventional programmatic structures where intelligence concentrates on the demand side. Moving computation upstream into supply-side infrastructure enables more sophisticated algorithms to process richer signal sets without introducing additional latency that would compromise auction dynamics. The architectural shift mirrors developments across programmatic advertising where platforms increasingly embed decisioning capabilities at the supply layer.

Containerized real-time bidding approaches have gained traction as advertisers seek access to granular site-level data typically unavailable through standard bid requests. Zillow tested containerized RTB with Chalice Custom Algorithms and Index Exchange in August 2025, enabling quality-focused inventory decisions using artificial intelligence tools with enhanced data access. Similarly, OpenX launched OpenXBuild in January 2026, reporting 70% cost-per-conversion reductions through APIs enabling advertisers to deploy custom logic directly into supply-side infrastructure.

The retailer's results demonstrate tangible business impact from supply-side intelligence deployment. E-commerce advertisers face intensifying competition for site visits as acquisition costs rise across programmatic channels. The 75% efficiency improvement in cost per site visit substantially improves campaign economics while maintaining or increasing visit volume.

Index Exchange positioned Marketplaces as infrastructure enabling partners to build and activate solutions across premium supply without additional platform fees. The company emphasized transparency and control for media owners who maintain full authority over inventory settings and data while accessing demand opportunities through curated deal packages. This positioning contrasts with traditional supply-side platforms that primarily aggregate inventory without embedding partner intelligence upstream.

The case study publication follows Index Exchange's broader strategic initiatives throughout 2025 focused on streaming television optimization, measurement advancement, and programmatic efficiency improvements. The company introduced show-level transparency capabilities with Gracenote in January 2026, duration-based reporting in September 2025, and Transparent Dynamic Take Rates in October 2025.

The sell-side decisioning approach addresses market dynamics where advertisers increasingly recognize limitations of buy-side-only optimization. Media.net launched ELEVATE with Claritas in October 2025, described as the first sell-side attribution and measurement solution for programmatic advertising. PubMatic introduced AgenticOS in January 2026, positioning infrastructure for autonomous advertising execution with processing capacity handling tens of millions of auctions per second.

Retail advertising represents a particularly relevant application for supply-side intelligence given the direct connection between digital advertising exposure and measurable business outcomes including site visits and purchases. The retailer case study demonstrates how upstream decisioning can improve efficiency metrics that directly impact marketing return on investment. The documented performance gains may accelerate broader adoption across retail and e-commerce advertisers seeking similar improvements.

Industry standardization efforts support the technical feasibility of sell-side decisioning deployments. IAB Tech Lab released its Deals API specification version 1.0 for public comment in December 2025, providing methods for deal terms transmission across systems while identifying parties participating in curation and selling. This framework enables more sophisticated programmatic packages incorporating multiple solutions without requiring custom integrations for each participant.

The competitive landscape for sell-side platforms continues evolving as multiple vendors implement curation and decisioning capabilities. Magnite partnered with Cognitiv in January 2026 to integrate deep learning models into ClearLine, expanding real-time curation capabilities across premium video inventory. These developments reflect industry consensus around the value of embedding intelligence at the supply layer rather than limiting optimization to demand-side systems.

The retailer case study provided limited details about campaign scale, budget size, or absolute cost per site visit figures, focusing instead on percentage improvements and strategic approach. This presentation pattern is common in advertising technology case studies where specific financial details remain confidential while directional performance data supports broader market positioning.

For marketing professionals evaluating programmatic strategies, the results suggest potential value in exploring supply-side decisioning approaches rather than relying exclusively on demand-side platform optimization. The 75% efficiency improvement represents substantial cost savings that could justify testing containerized intelligence deployments or curated marketplace packages incorporating upstream decisioning capabilities.

The technical implementation requirements include cooperation between multiple parties including supply-side platforms, algorithm providers, and demand partners. Advertisers considering similar approaches would need access to platforms supporting containerized deployments or marketplace infrastructures enabling partner solution integration at the supply layer. Not all supply-side platforms currently offer equivalent capabilities for embedding third-party intelligence upstream.

The case study timing coincides with broader artificial intelligence adoption across programmatic advertising operations. Google reported 14% Search revenue growth in Q4 2025 alongside expanding AI capabilities, while industry analysis suggests agentic AI could fundamentally disrupt traditional demand-side platform business models through automated campaign management.

Index Exchange maintained that Marketplaces enable media owners to earn more from every impression by unlocking incremental demand opportunities without added fees or extra work. For media buyers, the company positioned the infrastructure as delivering better outcomes with less waste by maximizing working media and optimizing performance through waste minimization and fee elimination. Solution providers including data vendors and algorithm developers gain distribution across Index infrastructure to scale quickly and deliver measurable value.

The retailer results demonstrate that supply-side signals contain predictive value for campaign outcomes when properly analyzed and applied. Time of day, geographic location, browser configuration, and contextual placement all contributed to the model's ability to identify high-potential impression opportunities before auctions occurred. This signal richness exists upstream in the supply path but traditionally remains unavailable to demand-side optimization systems operating within standard bid request constraints.

The always-on campaign structure required consistent performance across extended periods rather than short-term promotional bursts. This operational reality made efficiency improvements particularly valuable since cost reductions compound over time across continuous spending. The 75% cost per site visit reduction would substantially improve return on ad spend calculations for ongoing e-commerce acquisition campaigns.

The case study publication serves Index Exchange's strategic positioning around Marketplaces as differentiated infrastructure enabling partner innovation. The company emphasized transparency, measurement capabilities, and partnership ecosystem as distinguishing factors versus competitors. Whether these claims translate to sustained competitive advantage depends partly on how rapidly competing platforms implement similar capabilities for upstream partner integration.

The documented performance gains may influence advertiser willingness to test newer programmatic approaches including containerized bidding, curated marketplaces, and supply-side decisioning deployments. However, adoption patterns typically lag case study publications as advertisers conduct internal testing, evaluate integration complexity, and assess applicability to specific business requirements before committing meaningful budgets to alternative approaches.

The cost per site visit metric represents a critical performance indicator for retail advertisers because it directly connects advertising investment to measurable consumer behavior with clear business value. Unlike intermediate metrics such as clicks or impressions, site visits indicate actual user intent to engage with the retailer's e-commerce platform where purchase transactions can occur. The 75% efficiency improvement translates to either maintaining visit volume at 25% of previous cost or quadrupling visit volume at equivalent spending.

For an apparel retailer operating always-on campaigns, these efficiency gains compound over time. A hypothetical campaign spending $100,000 monthly on programmatic video at the original cost per site visit could either reduce spending to $25,000 while maintaining visit volume or invest the full budget to generate four times the site traffic. Either scenario substantially improves campaign return on investment and overall marketing efficiency.

The case study distinguished between the retailer's approach and traditional programmatic advertising structures. Most advertisers rely exclusively on demand-side platforms to optimize campaigns using bid-time signals available in standard OpenRTB bid requests. These signals include basic contextual information, device characteristics, and whatever first-party or third-party data the advertiser provides. However, this approach misses richer supply-side signals that exist upstream but are not transmitted in bid requests due to technical constraints and data sharing limitations.

Time of day represents one supply-side signal the inPowered AI model leveraged for prediction. Consumer behavior patterns vary substantially across different hours, with evening periods potentially showing different site visit propensity than morning or afternoon slots. Geographic location at the zip code level provides another signal enabling the model to identify areas with higher likelihood of retailer interest based on demographic characteristics, competitive dynamics, or historical performance patterns.

Browser version and operating system details offer signals about user technology preferences and device capabilities that correlate with conversion likelihood. Carrier information provides additional context about connectivity quality and user demographics. Page URL contextual signals enable the model to evaluate content adjacency and publisher quality factors that influence campaign performance. These supply-side signals exist in the supply-side platform's transaction logs but traditionally remain unavailable to demand-side optimization algorithms.

The containerized deployment architecture proved essential for accessing these signals without introducing latency that would compromise auction dynamics. Traditional programmatic advertising operates within strict time constraints where the entire transaction from bid request generation through auction completion must occur within 200-300 milliseconds to maintain user experience quality. This timeframe limits algorithmic sophistication on the demand side where complex machine learning models cannot execute quickly enough within latency budgets.

By deploying the predictive model within Index Exchange's infrastructure, inPowered AI eliminated network latency associated with transmitting bid requests to external demand-side platforms. The model evaluated each impression opportunity using supply-side signals immediately available within the SSP's systems, applied predictions, and assigned deal IDs without introducing additional auction delays. This architectural approach enabled more sophisticated analysis than possible within traditional demand-side constraints.

The deal ID mechanism provided the technical bridge between supply-side intelligence and demand-side activation. When the inPowered AI model identified high-potential impressions, it applied unique deal IDs that the retailer's demand-side platform could target through private marketplace deals. This enabled the DSP to automatically increase bidding on impressions the upstream model predicted would generate site visits while reducing spend on lower-probability opportunities. The approach combined supply-side prediction with demand-side execution through standard programmatic deal structures.

Index Exchange emphasized that Marketplaces operate without additional platform fees charged to media owners or buyers beyond the standard take rate. This positioning differentiates the offering from data collaboration platforms or measurement solutions that typically assess additional costs for accessing enhanced capabilities. The fee-free structure aims to encourage adoption by reducing economic barriers for partners deploying solutions within the Marketplaces infrastructure.

The retailer campaign focused specifically on online video inventory rather than display advertising or other formats. Video advertising carries different performance characteristics than static display placements, with longer creative durations enabling more comprehensive brand messages but also requiring higher production costs and bandwidth considerations. The 75% cost per site visit improvement suggests the inPowered AI model successfully identified video placement opportunities where creative content would resonate with audiences most likely to visit the retailer's site.

E-commerce advertising faces distinctive challenges compared to other digital marketing objectives. Site visits represent an intermediate conversion metric rather than final purchase transactions, but they indicate meaningful user intent and platform engagement. The relationship between site visits and ultimate purchase transactions depends on website user experience, product availability, pricing competitiveness, and various other factors outside advertising control. However, improving site visit efficiency enables retailers to increase top-of-funnel volume that can then convert through existing e-commerce infrastructure.

The always-on campaign structure reflects standard operating procedures for established e-commerce retailers who maintain continuous programmatic advertising presence rather than limiting activity to promotional periods or seasonal peaks. Always-on campaigns accumulate data over extended timeframes enabling more sophisticated optimization than possible with shorter campaign durations. This data accumulation proved essential for training the inPowered AI model on historical performance patterns specific to the retailer's business.

The case study publication timing in early February 2026 positions Index Exchange to capture attention during planning cycles for major advertising campaigns and annual budget allocations. Industry conferences and planning sessions typically occur in Q1 where marketing teams evaluate technology partnerships and campaign strategies for the remainder of the year. Publishing performance data during this window maximizes potential impact on advertiser decision-making processes.

The broader competitive context includes multiple supply-side platforms and advertising technology vendors implementing similar capabilities around curation, decisioning, and intelligence deployment at the supply layer. This simultaneous development across competitors suggests industry consensus around the strategic importance of upstream optimization rather than limiting value creation to demand-side systems. Whether any single vendor achieves sustained competitive advantage depends on execution quality, partnership ecosystems, and ability to demonstrate consistent performance improvements across diverse advertiser requirements.

The documented case study results provide one data point rather than comprehensive evidence about sell-side decisioning effectiveness across different advertisers, campaign types, or market conditions. Performance outcomes for other retailers might vary based on factors including target audience characteristics, competitive dynamics, creative quality, and baseline campaign efficiency before optimization implementation. Advertisers evaluating similar approaches should conduct controlled testing rather than assuming identical results.

The technical requirements for deploying containerized intelligence or leveraging marketplace infrastructures exceed capabilities available to smaller advertisers or those lacking sophisticated programmatic operations. Enterprise retailers with substantial advertising budgets and dedicated programmatic teams represent the primary target market for these advanced optimization approaches. Mid-market and small business advertisers typically rely on managed service solutions or self-service platforms with less customization capability.

Timeline

Summary

Who: A major apparel retailer partnered with a major agency holding company, inPowered AI, and Index Exchange to improve programmatic online video campaign efficiency for e-commerce site visits.

What: The collaboration deployed artificial intelligence-powered sell-side decisioning through Index Marketplaces infrastructure, achieving a 75% reduction in cost per site visit compared to traditional demand-side-only optimization approaches. inPowered AI built custom predictive models trained on historical DSP performance data and deployed them as containerized solutions within Index Exchange's infrastructure to evaluate supply-side signals in real time.

When: Index Exchange published the case study results on February 3, 2026, documenting outcomes from always-on programmatic online video campaigns that applied sell-side decisioning technology to improve cost per site visit efficiency for the retailer.

Where: The implementation occurred within Index Exchange's Marketplaces infrastructure, which hosts partner solutions including algorithms and data capabilities directly within the supply-side platform, enabling upstream decisioning before bid requests reach demand-side platforms across the open internet.

Why: Retailers face mounting pressure to improve e-commerce traffic acquisition efficiency as costs rise and competition intensifies. Traditional programmatic optimization occurring exclusively on the demand side misses valuable supply-side signals including contextual, geographic, and temporal factors. By moving intelligence upstream into the supply-side platform infrastructure, the retailer accessed richer signal sets and applied more sophisticated algorithms without latency penalties, unlocking performance improvements impossible through buy-side-only optimization while making every media dollar work harder in competitive programmatic environments.

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