ORIGINAL RESEARCH
Findings from 79 client accounts, $389M+ in tracked media spend, and 4M+ view-through conversions — powered by AiTRK and the Atrilyx platform.
Based on aggregate, anonymized data from 79 active client accounts, January 2023 – December 2025.
This report is based on aggregate, anonymized data from 79 active client accounts managed through the Atrilyx attribution platform between January 2023 and December 2025. The analysis period covers three full calendar years of attribution data, providing sufficient longitudinal depth to identify meaningful trends in cross-channel marketing performance, conversion path behavior, and budget allocation outcomes.
The dataset encompasses:
All data is aggregated and anonymized. No individual client data is identifiable in this report. Percentages and benchmarks represent median values across the client base unless otherwise noted. Where trends are cited, statistical significance was validated at the 95% confidence level. Channel-level benchmarks are indexed rather than absolute to protect client confidentiality while preserving the relative relationships between metrics.
The AiTRK tracking architecture uses first-party cookies with CNAME support, ensuring data accuracy across browsers with Intelligent Tracking Prevention (ITP) and similar privacy features. View-through conversions are captured through server-side impression log integrations with each ad network, then matched to on-site conversion events using deterministic identity resolution. This methodology avoids the data loss inherent in browser-only tracking approaches and provides a more complete picture of the customer journey than tools limited to click-based attribution.
The 2026 State of Attribution report reveals a widening gap between what marketers think they know about their media performance and what full-path attribution data actually shows. Across 79 client accounts and $389M+ in tracked spend, five findings stand out as the most consequential for budget allocation, channel strategy, and measurement infrastructure decisions in the year ahead.
1 22% of all conversions involve cross-channel assists invisible to last-click attribution. These are touchpoints from channels other than the converting channel — display impressions, social engagements, video views — that initiated or influenced the purchase but receive zero credit under last-click models.
2 View-through conversions account for 31% of total attributed conversions in programmatic campaigns. Nearly one in three programmatic conversions comes from a user who saw — but did not click — an ad before converting. Ignoring VTCs understates programmatic ROAS by 2.1x.
3 The average converting customer touches 3.4 channels before purchase. The era of single-channel conversion is over. For purchases exceeding $500, the average rises to 4.7 channels. Only 23% of all conversions are truly single-touchpoint events.
4 Mobile initiates 58% of conversion paths, but only 34% of conversions complete on mobile. The 24-percentage-point gap represents users who discover products on mobile and purchase on desktop. Without cross-device identity resolution, these paths break entirely.
5 Advertisers using multi-touch attribution reallocate an average of 23% of their budget after seeing full-path data. The most common shift: increasing programmatic display spend (+15%) while decreasing branded search (-11%), as full-path data reveals that search was harvesting demand created by upper-funnel channels.
Taken together, these findings point to a single conclusion: the marketing industry’s measurement infrastructure has not kept pace with the complexity of the customer journey. Marketers who continue to rely on last-click attribution, single-channel reporting, or browser-only tracking are making budget decisions based on incomplete data — and the magnitude of what they’re missing is larger than most assume.
The sections that follow present each finding in full detail, with supporting data, vertical breakdowns, and actionable implications for marketing leaders and measurement teams.
Across the full dataset, 22% of all conversions had at least one assisting touchpoint from a channel different than the channel that received the final click. These are not edge cases or statistical noise. They represent more than one in five conversions where a marketing touchpoint — an ad impression, a social media engagement, a video view — played a measurable role in driving the conversion but receives zero credit under last-click attribution.
The implications are significant. When a marketer evaluates channel performance using only last-click data, they are systematically overcounting the contribution of lower-funnel channels (branded search, direct, retargeting) and systematically undercounting the contribution of upper-funnel channels (display, video, social). This is not a theoretical concern. It directly distorts budget allocation decisions, often leading marketers to cut the very channels that are generating the awareness and consideration that lower-funnel channels depend on.
Not all cross-channel assist patterns are equally prevalent. Analysis of the 22% cross-channel conversion population reveals three dominant sequences:
The dominant pattern — programmatic display initiating a branded search conversion — accounts for 38% of all cross-channel assists. This is the single most consequential measurement gap in digital advertising today. Display advertising creates awareness and intent. The user, having seen the ad, later searches for the brand by name. The branded search ad captures the click and receives 100% of the conversion credit under last-click attribution. The display impression that created the intent in the first place receives nothing.
This pattern is particularly dangerous because it creates a self-reinforcing cycle of misallocation. The marketer sees strong branded search performance and weak display performance. They shift budget from display to search. Branded search volume declines (because display was driving the searches). The marketer then sees declining search performance and has no explanation for why — because the causal link between display and search was never visible in their reporting.
The 22% aggregate figure masks meaningful variation across verticals. B2B SaaS shows the highest cross-channel assist rate at 29%, reflecting longer consideration cycles and more research-intensive purchase decisions. E-commerce is closest to the median at 21%, while home services shows the lowest rate at 16%, consistent with shorter, more intent-driven purchase paths. Healthcare falls at 25%, driven by the research-heavy nature of patient decision-making.
These vertical differences matter for measurement strategy. Industries with higher cross-channel assist rates have the most to lose from last-click attribution — and the most to gain from implementing multi-touch attribution models that distribute credit across the full conversion path.
The practical consequence of the 22% blind spot is straightforward: cutting upper-funnel spend based on last-click data risks losing the awareness that drives lower-funnel performance. Every dollar removed from display or video may reduce branded search volume, direct visits, and overall conversion volume in ways that last-click attribution will never surface. Marketers who want to understand the true ROI of their upper-funnel investments need full-path attribution data — not the partial view that last-click provides.
In programmatic campaigns specifically, view-through conversions (VTCs) account for 31% of total attributed conversions. Nearly one in three conversions from programmatic display and video campaigns comes from a user who was served an ad impression, did not click it, and later converted on the advertiser’s site through a separate session.
This finding challenges a persistent assumption in digital marketing: that if a user doesn’t click an ad, the ad didn’t work. The data tells a different story. Display and video advertising operate primarily as awareness and consideration channels. Users see ads, internalize the brand or offer, and act on that awareness later — often through a different channel entirely. Requiring a click as proof of ad effectiveness ignores the fundamental mechanism through which these channels generate value.
The median VTC attribution window across all AiTRK clients is 14 days. This means a conversion is attributed as a view-through if the user was served an impression within 14 days prior to converting, without clicking the ad. The 14-day window represents the median; individual client windows range from 1 day (used primarily for direct-response e-commerce) to 30 days (used for B2B and high-consideration purchases). The choice of window length significantly impacts the VTC count, and clients are encouraged to test multiple windows to find the duration that best reflects their actual customer consideration cycle.
View-through conversion rates vary meaningfully across verticals, reflecting differences in purchase complexity, consideration cycle length, and the role of upper-funnel advertising in each industry:
| Vertical | VTC Rate (% of Programmatic Conversions) | Median Attribution Window |
|---|---|---|
| E-commerce | 28% | 7 days |
| Healthcare | 35% | 14 days |
| B2B SaaS | 42% | 21 days |
| Financial Services | 31% | 14 days |
| Home Services | 22% | 7 days |
Median values across 79 client accounts, 2023–2025. VTC rate = view-through conversions / total programmatic conversions.
B2B SaaS has the highest VTC rate at 42%. This is consistent with what we know about B2B buying behavior: longer consideration cycles, multiple stakeholders, and more time between initial awareness and purchase decision. A B2B buyer who sees a display ad for a software platform in January may not request a demo until March. Under click-only attribution, that display impression — which planted the seed — receives no credit whatsoever. Under VTC-inclusive attribution, it receives appropriate partial credit as an assisting touchpoint.
E-commerce shows the lowest VTC rate among major verticals at 28%, which makes intuitive sense: shorter purchase cycles and lower price points mean less time between impression and conversion. But even at 28%, nearly three in ten programmatic conversions are view-through — a figure that many e-commerce marketers would find surprisingly high.
The most immediately actionable finding in the VTC data is the ROAS distortion. Without VTC tracking, programmatic ROAS appears 2.1x lower than actual. This is calculated by comparing ROAS with VTCs included against ROAS with only click-through conversions counted. For a channel where 31% of conversions are view-through, excluding those conversions makes the channel appear dramatically less effective than it actually is.
Consider a concrete example: a programmatic campaign generating 1,000 total conversions — 690 click-through and 310 view-through. A marketer using click-only attribution sees 690 conversions and calculates ROAS accordingly. A marketer with full VTC visibility sees 1,000 conversions. The spend is identical. The actual business impact is identical. But the two marketers reach very different conclusions about whether to continue, expand, or cut the campaign.
This ROAS distortion is one of the primary drivers of the budget reallocation patterns described in Finding 5. Marketers who gain VTC visibility for the first time consistently discover that programmatic display has been undervalued — often significantly — in their previous reporting. The difference between AiTRK and GA4 is particularly stark here, as GA4 does not support view-through conversion tracking at all.
The average converting customer touches 3.4 channels before purchase. This figure represents the median number of distinct marketing channels a user interacts with — across both click and view-through touchpoints — before completing a conversion event. It encompasses the full spectrum of tracked channels: programmatic display, paid social, paid search, video/CTV, native advertising, email, organic search, and direct.
The 3.4-channel average is itself an average that masks considerable variation by purchase value, vertical, and customer segment. For purchases exceeding $500, the average rises to 4.7 channels. For purchases under $50, it drops to 2.1 channels. The relationship between purchase value and path complexity is monotonic and consistent across verticals: the more a customer spends, the more channels they engage with before committing.
Analysis of the most common multi-channel conversion sequences reveals five dominant patterns:
Several patterns emerge from this data. First, search appears in every top-five conversion path, underscoring its critical role as an intent-capture channel. But search is almost never the first touchpoint — it is preceded by awareness channels (display, social, video, email) in every major sequence. This reinforces the finding from Finding 1: search harvests demand that other channels create.
Second, direct visits are the most common final touchpoint, appearing as the converting channel in four of the five top sequences. Under last-click attribution, these conversions would be attributed to “direct” — a classification that provides zero insight into the marketing activity that actually drove the visit. With full-path attribution, the upstream channels receive appropriate credit for initiating the journey.
Third, the fifth-ranked sequence — Video → Display → Search → Direct — involves four distinct channels and represents 8% of multi-channel conversions. This is not an outlier; it reflects the reality of high-consideration purchases where multiple impressions across multiple formats are required to move a customer from awareness to action.
Only 23% of conversions are single-channel events — one touchpoint, one conversion, one channel involved. The remaining 77% involve two or more channels. This means that any measurement system limited to single-channel reporting — including standard Google Analytics implementations — is fundamentally misattributing the majority of conversions. The customer journey is multi-channel by default. Measurement infrastructure must be multi-channel to match.
For marketers evaluating their tracking stack, this finding underscores the importance of investing in cross-channel tracking infrastructure that can stitch together touchpoints across channels, devices, and sessions into a unified conversion path. Without this capability, 77% of your conversions are being misattributed to some degree.
The modern conversion path does not stay on a single device. Across the AiTRK dataset, 58% of conversion paths begin on a mobile device — meaning the first tracked marketing touchpoint (whether an ad click, impression, or site visit) occurs on a smartphone or tablet. However, only 34% of conversions actually complete on mobile.
The 24-percentage-point gap between mobile initiation (58%) and mobile completion (34%) represents one of the most significant measurement challenges in digital marketing. These are users who discover a product or service on their phone — perhaps through a social media ad during their commute or a display ad while browsing — and later return on a desktop or laptop computer to complete the purchase. Without cross-device identity resolution, these two sessions appear as separate users, and the mobile marketing touchpoint that initiated the journey receives zero conversion credit.
The cross-device gap varies meaningfully across verticals, reflecting differences in purchase complexity, form factor preferences, and typical buying contexts:
| Vertical | Mobile Initiation | Mobile Completion | Cross-Device Gap |
|---|---|---|---|
| Healthcare | 67% | 22% | 45 pts |
| B2B SaaS | 62% | 28% | 34 pts |
| Financial Services | 55% | 29% | 26 pts |
| Home Services | 61% | 38% | 23 pts |
| E-commerce | 54% | 41% | 13 pts |
Median values across 79 client accounts, 2023–2025. “Mobile Initiation” = first touchpoint occurs on mobile device. “Mobile Completion” = conversion event occurs on mobile device.
Healthcare has the widest cross-device gap: 67% mobile initiation versus 22% mobile completion, a 45-point spread. This reflects the nature of healthcare decisions. Patients often encounter health-related advertising on their phones — through social media, search, or display — but complete sensitive actions like scheduling appointments, filling out intake forms, or researching providers on a desktop computer where they feel more comfortable sharing personal information and navigating complex forms.
E-commerce has the narrowest gap: 54% mobile initiation versus 41% mobile completion, a 13-point spread. This is driven by improvements in mobile checkout experiences, the prevalence of saved payment methods, and the lower average order values in e-commerce compared to other verticals. For transactions under $100, mobile completion rates approach 50% — but for transactions over $500, they drop below 25%, even in e-commerce.
Without cross-device identity resolution, every conversion path that spans devices creates a measurement gap. The mobile session and the desktop session appear as two different users. The mobile touchpoint — which may have been the critical awareness moment — receives zero conversion credit. The desktop session — which may have been a simple brand-name search or direct URL entry — receives 100% of the credit.
At the aggregate level, this means mobile marketing effectiveness is systematically understated in any measurement system that cannot connect mobile and desktop sessions to a single user. For the 24% of conversions in our dataset where the initiating device differs from the converting device, the true ROI of mobile advertising is invisible to single-device measurement approaches.
The AiTRK platform addresses this through deterministic identity resolution — connecting sessions across devices using first-party cookie architecture and authenticated user identifiers. This provides a single, unified view of the customer journey regardless of how many devices are involved. For clients in high-gap verticals like healthcare and B2B SaaS, enabling cross-device tracking typically reveals 30–40% more attributed mobile conversions than their previous measurement stack was reporting.
The ultimate test of any measurement system is whether it changes decisions. Across the AiTRK client base, advertisers using multi-touch attribution reallocate an average of 23% of their total media budget after gaining full-path visibility into their conversion data. This is not a minor adjustment. It represents a fundamental reassessment of channel value — one that the previous measurement system (typically last-click or platform-reported metrics) was unable to surface.
The 23% reallocation figure is a median; individual clients range from 8% (those already running sophisticated attribution models) to over 40% (those transitioning from pure last-click or single-platform reporting). The magnitude of reallocation correlates strongly with the complexity of the media mix: advertisers running five or more channels see larger reallocations than those running two or three, because more channels mean more opportunities for cross-channel assists to go undetected.
The most common reallocation pattern is remarkably consistent across the client base:
Median budget change within 90 days of implementing multi-touch attribution. Percentages are relative changes, not absolute points.
The pattern is clear: upper-funnel channels gain budget, and lower-funnel channels lose budget. Programmatic display receives the largest increase (+15%), while branded search sees the largest decrease (-11%). This is not because branded search is ineffective — it remains a critical conversion channel. It is because branded search was receiving disproportionate credit for conversions that were actually initiated by display, video, and social. Full-path attribution rebalances that credit to reflect the actual influence of each channel.
Retargeting also sees a significant decrease (-10%). Under last-click attribution, retargeting appears highly efficient because it targets users who have already demonstrated intent. But full-path data often reveals that retargeting is converting users who would have converted anyway — users who were already in the pipeline thanks to upper-funnel activity. Multi-touch attribution assigns retargeting appropriate fractional credit rather than full credit, revealing its true incremental contribution.
The average ROAS improvement after reallocation is 18% within 90 days. This is measured by comparing total attributed conversions per dollar spent in the 90 days before reallocation versus the 90 days after. The improvement comes not from spending more, but from spending more effectively — directing budget toward the channels and tactics that full-path data shows are actually driving conversions.
The 18% improvement is a median; top-quartile clients achieve 25%+ ROAS improvement, particularly those transitioning from pure last-click attribution and those with complex, multi-channel media mixes. The improvement tends to compound over time as marketers iterate on allocation based on ongoing attribution data rather than a single rebalancing event.
Clients who maintain last-click attribution miss this optimization entirely. They continue allocating budget based on an incomplete picture of channel performance, systematically overinvesting in lower-funnel channels and underinvesting in the upper-funnel activity that feeds the entire funnel. The 23% reallocation and 18% ROAS improvement are available to any advertiser willing to invest in full-path attribution infrastructure — but they require measurement data that last-click simply cannot provide.
The following benchmarks are derived from aggregate data across all 79 client accounts over the 2023–2025 analysis period. They provide a cross-industry reference point for channel-level performance metrics under multi-touch attribution. These figures represent medians, not averages, to reduce the influence of outliers. CPA figures are indexed (not absolute dollar values) to protect client confidentiality while preserving the relative cost structure across channels.
| Channel | Avg CPA (Indexed) | Click-Through Conv % | View-Through Conv % | Avg Assists per Conv |
|---|---|---|---|---|
| Programmatic Display | $42 | 69% | 31% | 1.8 |
| Paid Social | $38 | 78% | 22% | 1.4 |
| Paid Search | $29 | 94% | 6% | 0.8 |
| Video / CTV | $56 | 45% | 55% | 2.1 |
| Native Advertising | $35 | 72% | 28% | 1.2 |
Benchmarks represent median values across 79 client accounts, 2023–2025. CPA figures are indexed, not absolute. Click-Through Conv % + View-Through Conv % = 100% of attributed conversions for that channel.
The benchmark table reveals the fundamental character of each channel through two key metrics: the click-through/view-through split and the average assists per conversion.
Video/CTV is the most assist-heavy channel, with 2.1 assists per conversion and a 55% VTC rate. This confirms video’s role as a top-of-funnel awareness driver: more than half of its attributed conversions come from users who watched the ad but didn’t click it. Under click-only measurement, video appears to be the worst-performing channel. Under full-attribution measurement, it reveals itself as a critical initiator of conversion paths that other channels ultimately close.
Paid search is the most conversion-efficient channel, with the lowest indexed CPA ($29) and a 94% click-through conversion rate. But it also has the lowest assist rate (0.8), confirming that search primarily captures existing intent rather than creating it. Taken alone, search looks like the best-performing channel. Taken in context of the full funnel, it is a harvester that depends on upstream channels to generate the demand it captures.
Programmatic display occupies the middle ground, with a 69/31 click-through/view-through split and 1.8 assists per conversion. It plays both an awareness role (high assist count, significant VTC share) and a direct response role (69% click-through conversions). This dual nature is one reason display budget reallocation is the largest after implementing full-path attribution — display was being measured only by its direct response performance, missing its substantial awareness contribution.
Native advertising shows strong efficiency, with the second-lowest CPA ($35) and a balanced 72/28 click-through/view-through split. Native’s in-feed, content-aligned format drives both direct clicks and awareness-based view-through conversions, making it an efficient mid-funnel channel that often warrants more investment than last-click data would suggest.
These benchmarks are meant to be directional. Individual results vary significantly by vertical, creative quality, targeting strategy, and market conditions. Use them as a reference point for evaluating your own channel mix, not as absolute targets. For benchmarks specific to your vertical and media mix, the Atrilyx platform provides custom reporting across all integrated channels.
For over a decade, last-click attribution was the default measurement model in digital marketing. It was simple, deterministic, and easy to implement. It was also, as the findings in this report demonstrate, fundamentally misleading for any advertiser running a multi-channel media strategy.
The data shows that the industry is moving away from last-click, but the transition is still incomplete. Among AiTRK clients, only 12% still use last-click as their primary attribution model, down from 67% in 2021. The decline has been steady and accelerating:
Data-driven multi-touch attribution is now the dominant model, used by 48% of AiTRK clients. Data-driven models use algorithmic analysis of actual conversion path data to assign credit proportionally to each touchpoint based on its measured contribution. Unlike rule-based models (linear, time-decay, position-based), data-driven models adapt to the specific patterns in each advertiser’s data rather than applying a fixed formula.
Position-based (U-shaped) attribution ranks second at 28%. This model assigns 40% of credit to the first touchpoint, 40% to the last touchpoint, and distributes the remaining 20% evenly among middle touchpoints. It is popular among advertisers who want to give credit to both the awareness-driving first touch and the conversion-closing last touch while still acknowledging the contribution of mid-funnel interactions.
Time-decay attribution accounts for 12%. This model assigns increasing credit to touchpoints closer to the conversion event, with recent interactions receiving more weight than earlier ones. It is favored by advertisers with short purchase cycles who believe that recency is the strongest indicator of influence.
Last-click holds at 12% — but this number is declining every quarter. The remaining last-click holdouts tend to fall into two categories: (1) advertisers with very simple media mixes (e.g., search-only) where cross-channel attribution adds minimal value, and (2) advertisers who have not yet invested in the tracking infrastructure required for multi-touch attribution.
The shift away from last-click is not just a philosophical preference. It correlates with measurable performance improvements. Across the AiTRK client base, advertisers using data-driven multi-touch attribution show a 22% higher ROAS than those using last-click, after controlling for spend levels, verticals, and media mix complexity. This is not because the attribution model itself generates revenue — it is because better measurement leads to better allocation decisions, which lead to better outcomes.
The remaining 12% of advertisers still using last-click represent the largest untapped opportunity in the client base. Based on the reallocation patterns described in Finding 5, these advertisers are likely over-indexed on lower-funnel channels and underinvesting in the upper-funnel activity that full-path attribution would reveal as critical to their pipeline. The AiTRK glossary provides detailed definitions of each attribution model for teams evaluating their options.
The attribution landscape is being reshaped by two simultaneous forces: the increasing complexity of the customer journey (as documented in this report) and the accelerating rollout of privacy regulations, browser restrictions, and platform changes that constrain how that journey can be measured. The marketers who will succeed in this environment are those who invest in measurement infrastructure that is both comprehensive and privacy-compliant — not one at the expense of the other.
Third-party cookie deprecation is no longer a future concern — it is happening. Safari and Firefox have blocked third-party cookies for years. Chrome’s Privacy Sandbox initiatives are restructuring how cross-site tracking works in the world’s most popular browser. The result is clear: any measurement strategy built on third-party cookies is already losing data today and will lose more tomorrow.
Among AiTRK clients, 89% now use first-party cookie architecture (up from 54% in 2022). First-party cookies — set under the advertiser’s own domain through CNAME configuration — are treated as trusted by all major browsers. They are not subject to the same restrictions as third-party cookies and provide reliable, long-lived user identification for attribution purposes.
The shift to first-party data is not merely a technical change. It represents a philosophical shift in how advertisers approach measurement: from relying on platform-provided, walled-garden metrics to owning their own data and their own measurement infrastructure. AiTRK’s privacy architecture is designed for this reality, with first-party cookies as the default and server-side tracking available for environments requiring even greater control.
Consent rates average 72% across GDPR markets among AiTRK clients. This means that even in the strictest regulatory environments, the majority of users accept analytics tracking when presented with a clear, compliant consent mechanism. The 72% figure reflects properly implemented consent management — not dark patterns or manipulative design, but transparent opt-in requests that explain what data is collected and why.
For the 28% who decline consent, AiTRK provides cookieless measurement capabilities that respect the user’s choice while still capturing aggregate data for campaign optimization. This dual-mode approach — full attribution for consented users, privacy-safe aggregation for non-consented users — ensures that marketers maintain measurement coverage without compromising on compliance.
Server-side tracking adoption has increased 340% year-over-year among AiTRK clients. Server-side tracking moves data collection from the browser to the server, eliminating the impact of ad blockers, browser restrictions, and client-side JavaScript limitations on data accuracy. For advertisers in regulated industries (healthcare, financial services) or those with high ad-block rates, server-side tracking provides a significant improvement in data completeness.
The 340% growth rate reflects both the maturation of server-side tracking infrastructure and the increasing urgency created by browser privacy changes. Advertisers who relied on browser-based tracking are discovering data gaps as browsers tighten restrictions, and server-side tracking fills those gaps without requiring users to install or configure anything.
Based on the trends in this dataset and the broader industry trajectory, attribution in 2027 and beyond will require four foundational capabilities:
Advertisers who invest in these four pillars today will be well-positioned as the privacy landscape continues to evolve. Those who delay will face widening measurement gaps and increasingly unreliable data on which to base their most consequential marketing decisions.
The findings in this report converge on a clear set of actions for marketing leaders, media planners, and measurement teams. These recommendations are ordered by impact and urgency, based on the magnitude of the measurement gaps each addresses.
Last-click attribution leaves 22% of your conversion data invisible. Every cross-channel assist, every upper-funnel contribution, every view-through conversion disappears from your reporting. Multi-touch attribution — whether position-based, time-decay, or data-driven — provides the full-path visibility required to understand which marketing is actually driving your business. The data shows that advertisers who make this switch reallocate 23% of their budget and improve ROAS by 18% within 90 days. The cost of inaction is not zero — it is the ongoing misallocation of roughly one-quarter of your media spend.
Especially for display, video, and programmatic campaigns, VTC tracking is non-negotiable. These channels operate primarily as awareness drivers — users see ads but don’t click them, then convert later through other channels. Without VTC tracking, 31% of programmatic conversions are invisible, and your programmatic ROAS appears 2.1x lower than reality. If you are evaluating or cutting programmatic spend based on click-only data, you are almost certainly making the wrong decision. The AiTRK pixel captures both click-through and view-through conversions through impression-level integrations with 20+ ad networks.
24% of conversion paths span multiple devices. Without identity resolution, these paths break into disconnected sessions, and the mobile touchpoint that started the journey receives zero credit. Healthcare advertisers are particularly affected (45-point cross-device gap), but every vertical shows meaningful mobile-to-desktop handoff behavior. Deterministic identity resolution — using first-party cookies, authenticated sessions, and CNAME-based tracking — connects these fragmented paths into a single, accurate customer journey.
Before cutting upper-funnel spend, run the numbers with multi-touch attribution data. The most common mistake in digital marketing is cutting display or video budgets based on last-click performance, then watching overall conversion volume decline with no clear explanation. Full-path data typically reveals that branded search and direct conversions depend heavily on upstream display and social activity. The average advertiser reallocates 23% of budget after seeing full-path data for the first time. Review our case studies to see how similar businesses have benefited from attribution-informed budget reallocation.
First-party data is the foundation of future-proof attribution. With 89% of AiTRK clients already on first-party cookie architecture and server-side tracking growing 340% year-over-year, the industry has made its decision. Third-party cookies are a declining signal. Platform-reported metrics are walled-garden views. Owning your data — through first-party cookies, CNAME tracking, server-side collection, and direct ad network integrations — is the only way to maintain accurate, comprehensive attribution as the privacy landscape evolves. Start with a privacy-compliant tracking audit and build from there.
The State of Attribution 2026 was produced by AI Media Group using aggregate, anonymized data from the AiTRK tracking pixel and the Atrilyx attribution platform. The analysis covers January 2023 through December 2025, encompassing 79 active client accounts, $389M+ in cumulative tracked media spend, 4M+ programmatic view-through conversions, and 20+ integrated ad network data sources.
This is an annual report. Future editions will track the evolution of the metrics presented here, providing year-over-year comparisons and trend analysis as the attribution landscape continues to shift. If you would like to be notified when the 2027 edition is published, or if you have questions about the methodology, findings, or how these benchmarks apply to your business, we welcome the conversation.
For methodology details, vertical-specific benchmarks, or to discuss how these findings relate to your marketing measurement strategy, book a demo with the AiTRK team.
Citation: AI Media Group. “The State of Attribution 2026.” AiTRK Original Research. Published March 20, 2026. https://aitrk.com/state-of-attribution-2026
Related resources: AiTRK Features & Capabilities · Tracking Pixel Overview · Attribution Glossary · AiTRK vs GA4 · Case Studies
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