About This Report
This edition is written for marketing professionals, in-house marketing leaders, and agency practitioners. It covers the full strategic landscape: channel economics, measurement frameworks, AI governance, martech architecture, and case studies with quantified outcomes.
Data is drawn from primary sources: IAB/PwC, Gartner, OAAA, NIST, and peer-reviewed academic research, verified as of April 2026. All citations are numbered and listed at the end of the document.
Executive Summary
Marketing in 2026 operates in a tight loop of fragmented attention, privacy constraints, and accelerating automation. The result is a shift from channel management toward system management: building a data and measurement foundation that supports fast creative iteration across paid, owned, and earned channels while staying compliant and credible.
Key Data Points
U.S. digital advertising revenue reached $258.6 billion in 2024 (a 14.9% year-over-year increase) driven by search ($102.9B), social ($88.8B), retail media ($53.7B), digital video ($62.1B), and display ($74.3B).[2]
Average marketing budgets across large enterprises fell to 7.7% of company revenue in 2024, down from 9.1% in 2023. Paid media was protected while martech, labor, and agency spending were cut. Digital represents 57.1% of paid media spend. [3]
Note: The Gartner survey respondents have median annual revenues above $5.3 billion. Apply these benchmarks to enterprise clients; SMB clients will differ significantly.
U.S. out-of-home (OOH) advertising hit a record $9.46 billion in 2025 — its 19th consecutive quarter of growth — with digital OOH (DOOH) accounting for 36.3% of U.S. OOH revenue and growing 10.5% year-over-year. [14]
The Three Strategic Forces Reshaping Marketing
- Privacy as product constraint: California, Kentucky, Colorado, and the EU have enacted laws changing data collection, consent, targeting disclosure, and consumer rights. These are now operational requirements, not legal edge cases.
- Signal loss and measurement complexity: Apple ATT, the ongoing instability of Google’s third-party cookie approach, and platform-level modeled data mean last-click attribution is structurally unreliable. Modern measurement requires a portfolio of methods.
- AI from tool to operating system: Generative AI has moved from experimentation to production for creative, personalization, and measurement acceleration — but governance requirements are growing alongside capability.
Section 1: Strategic Framework
Foundational Definition
The American Marketing Association defines marketing as ‘the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.’ This end-to-end system framing — not a communications-only view — is the correct lens for modern marketing leadership. [17]
Key Strategic Shifts for 2026
Full-Funnel by Construction
Leaders must link spend to outcomes across brand, conversion, retention, and lifetime value simultaneously — while channel data becomes less granular. Nielsen’s Annual Marketing Research shows high confidence in ROI measurement among marketers, but much lower adoption of holistic practices that measure digital and traditional together. [19]
Audience Era → Context and Commerce Era
Retail media reached $53.7B in 2024 because it combines first-party commerce signals, high-intent moments, and closed-loop measurement. This represents the most durable structural advantage in the current ecosystem: platforms with large first-party datasets and native measurement are outpacing those reliant on third-party signals. [2]
Brand-Building Reasserts ROI
Multiple IPA Effectiveness Awards case summaries demonstrate measurable long-run gains from sustained brand advertising alongside performance activity, supported by econometrics. When targeting and attribution become less deterministic, brand equity becomes a more durable asset. [34,36]
Timeline: Major Changes Affecting Marketing Systems
| Year | Change | Strategic Implication |
| 2021 | Apple ATT — user permission required for cross-app tracking | Mobile measurement permanently changed; Meta ROAS reporting disrupted |
| 2022–23 | CPRA expanded CA rights; Colorado Privacy Act (July 2023) | Consent management and data rights handling now operational requirements |
| 2023 | Universal Analytics deprecated; GA4 becomes standard | Event-based measurement model; conversion data increasingly modeled |
| 2024 | EU DSA applied broadly; ban on targeting minors, sensitive data restrictions | EU ad targeting constraints; transparency requirements for all digital ads |
| 2024–25 | Google reverses cookie deprecation; moves to user-choice model | Third-party tracking instability continues; first-party data value rises |
| 2025 | $258.6B U.S. digital ad revenue; OOH hits record $9.46B; podcasts +26% | Retail media and streaming TV dominate growth; audio rebounds |
| Jan 1, 2026 | Kentucky KCDPA + California CPPA regulations take effect | New operational obligations for data collection, consent, and automated decisioning |
Sources: [1,2,3,5,6,7,9,10,11,14,28]
Section 2: Channel and Format Intelligence
U.S. Digital Ad Revenue by Channel
| Channel | 2023 | 2024 | YoY Growth | Strategic Notes |
| Search | $88.8B | $102.9B | +15.9% | Largest category (39.8% share); under pressure from AI-driven search disruption |
| Social | $64.9B | $88.8B | +36.7% | Rebound driven by creator economy and social commerce integration |
| Display | $66.1B | $74.3B | +12.4% | Programmatic buying dominant; curation trend reducing MFA allocation |
| Digital Video / CTV | $52.1B | $62.1B | +19.2% | 42% of 2023 video from CTV/OTT; streaming ad tiers expanding inventory |
| Retail Media | $43.7B | $53.7B | +23.0% | Fastest-growing new category; closed-loop measurement advantage |
| Podcasts / Audio | $7.0B | $2.43B (podcasts) | +26.4% (podcasts) | Podcast advertising rebounded from 5% growth in 2023 |
Source: [1,2] IAB/PwC Internet Advertising Revenue Reports, Full Year 2023 and 2024
Programmatic: From Scale to Quality
The programmatic conversation has shifted from scale and efficiency toward media quality, path transparency, viewability correlated with outcomes, and curated inventory. The ANA 2024 Programmatic Benchmark Study (summarized by WFA) reports improved ad spend efficiency, reduced MFA allocation, and fewer domains/apps used — signaling more curated buying becoming industry norm. [13,16]
Out-of-Home: Record Growth
U.S. OOH advertising revenue reached a record $9.46 billion in 2025, growing 3.6% year-over-year through 19 consecutive growth quarters. DOOH accounted for 36.3% of U.S. OOH and grew 10.5%. Globally, DOOH reached $17.9 billion — approximately 39% of all OOH revenues worldwide. [14,15]
The strategic implication: OOH is no longer evaluated separately from digital. Leading practices pair OOH with geo-matched market tests, QR/short-link tracking, and MMM integration to connect impression delivery to business outcomes.
Content and Creator Marketing
IAB revenue data explicitly links social’s rebound to creator marketing growth. The FTC updated Endorsement Guides in 2023 now govern influencer promotions at scale — including disclosure requirements, brand safety review obligations, and recordkeeping standards for all material commercial relationships. [2,12]
Operational implication: Creator programs require the same governance as any paid channel — contracts, disclosure templates, claims review, and performance measurement tied to business outcomes, not reach alone.
Section 3: Measurement Frameworks and Attribution
The Measurement Portfolio
Modern marketing measurement is a portfolio of methods, each answering a different question. No single method is sufficient; the goal is to combine them strategically.
| Method | Question It Answers | Best Use Cases | Key Failure Mode |
| Platform attribution (last-click/MTA) | What preceded conversion? | In-channel tactical optimization | Over-counts own contribution; misses offline; breaks under identity gaps |
| Incrementality experiments (A/B, geo) | Would results have happened anyway? | Budget decisions; new channel tests; publisher evaluation | Operational complexity; requires volume to detect difference |
| Media Mix Modeling (MMM) | What drove outcomes over time? | Cross-channel budget allocation; brand impact measurement | Slow feedback; requires data hygiene; history-dependent |
| Clean-room measurement | How did exposures relate to outcomes (privacy-safe)? | Retail media; large platform partnerships; closed-loop | Correlation ≠ causality risk; access dependent on platform |
| Brand/awareness lift surveys | Did perceptions and intent change? | Brand investment justification; upper-funnel measurement | Doesn’t always connect to sales; sampling bias risk |
| Direct response tracking | Which specific ad drove this customer? | Lead gen; phone-based businesses; promo code attribution | Requires discipline; incomplete if not applied consistently |
Sources: [19,20,21]
Academic Advances in Measurement
An arXiv paper on Predicted Incrementality by Experimentation demonstrates that randomized trials provide the most credible causal estimates but are costly to scale; the paper proposes training models on a large base of experiments to predict incrementality for untested campaigns — a direction now reflected in platform products. [20]
A paper in Marketing Science proposes a latent stratification method that reduces variance and decision errors in incrementality experiments by estimating effects across different customer strata — improving experiment efficiency without requiring larger sample sizes. [21]
The GA4 Transition and Event-Based Analytics
Google Universal Analytics stopped processing new data on July 1, 2023. GA4’s event-based model and modeled conversion concepts represent a structural shift in how web measurement works — and require new data governance practices around event taxonomy, conversion definitions, and data layer management. [11]
First-Party Data Strategy
As cross-site identifiers weaken, first-party data — CRM records, loyalty data, authenticated web activity, transaction history, call center events — becomes the most durable measurement and targeting asset. Businesses with strong first-party data have structural measurement advantages that compound over time.
Privacy-safe data collaboration tools (clean rooms) allow measurement and audience creation across partner datasets without raw data sharing. AWS Clean Rooms, Google BigQuery data clean rooms, and Amazon Marketing Cloud are the leading enterprise implementations.
Sources: [39,40]
Section 4: AI, Automation, and Personalization
What Has Genuinely Changed
Three developments represent real capability shifts, not relabeling of existing tools:
- Generative AI’s throughput and multi-modality (text, images, video) makes high-volume creative variant production and personalization operationally feasible at scale.
- Platform automation is progressing toward ‘agentic’ models — AI systems that manage campaigns end-to-end within guardrails, requiring less tactical human input per optimization cycle.
The value of personalization is rising while the operational cost of manual personalization becomes prohibitive at scale. McKinsey frames the next stage as combining AI-driven decisioning with generative AI for tailored message and creative production, supported by stronger underlying tech stacks and measurement layers. [22,23]
AI/ML Application Areas: Leader Map
| Application | Typical Outputs | Where It Works | Key Risks | Governance Controls |
| Creative generation | Copy, images, video scripts, product descriptions | High-volume variant testing; localization; content scaling | Factual errors; brand voice drift; IP risk | Human review for all claims; prompt/version control; brand voice libraries |
| Personalization / NBA | Dynamic offers, message sequencing, channel selection | Loyalty programs; lifecycle marketing; retail | Unfair targeting; privacy violations; consent gaps | Consent gating; segmentation audits; sensitive-data policies |
| Predictive scoring | Churn risk, propensity, LTV models | Retention; budget efficiency; high-value customer identification | Model drift; bias in training data | Monitoring and recalibration cycles; documented feature sets |
| Measurement acceleration | Modeled conversions; incrementality prediction | Where full experimentation is impractical | Miscalibrated causal inference | Keep experiments as anchor; regularly compare model vs. test outcomes |
| Customer feedback analysis | Theme extraction; complaint summarization; NPS drivers | Support insight; product feedback loops | Privacy and data leakage risk | Limit data exposure; retrieval controls; audit logs |
Sources: [22,23,24,25,41]
AI Governance Framework
The NIST AI Risk Management Framework provides a widely adopted baseline for managing AI risks across validity, safety, accountability, and transparency. For marketing-specific governance, translate this into: claim review processes, IP policies, dataset restrictions, automated decision disclosure practices, and documented approval chains for all customer-facing AI outputs.[41]
California’s CPPA 2026 regulations include specific rules around ‘automated decision making technology’ (covering profiling and personalization systems) with audit requirements. Governance built now will be compliant by design as regulatory scope expands.[6]
Section 5: Privacy, Regulation, and Platform Governance
U.S. Privacy Law: Current Landscape
| State / Jurisdiction | Law | Effective | Key Marketing Implications |
| California | CCPA/CPRA + CPPA Regulations | Ongoing; CPPA regs Jan 1, 2026 | Opt-out of sale/sharing; sensitive data use limits; automated decisioning disclosure; formal audit powers for CPPA |
| Kentucky | KCDPA (HB 15) | January 1, 2026 | Opt-out of targeted advertising; data rights (access, correct, delete); 30-day cure period; AG enforcement only |
| Colorado | Colorado Privacy Act (CPA) | July 1, 2023 | Opt-out of targeted advertising and profiling; universal opt-out mechanism required |
| Indiana / Rhode Island | INCDPA / RIDTPA | January 1, 2026 | Similar opt-out frameworks; targeted advertising restrictions; AG enforcement |
| EU (DSA) | Digital Services Act | Broadly Feb 2024 onward | Ban on targeting minors; ad labeling (who placed, why shown); no sensitive data targeting; fines up to % of global turnover |
Sources: [5,6,7,8,28,29,30]
Platform-Level Governance Changes
Apple App Tracking Transparency
ATT requires apps to request permission before tracking users across other companies’ apps and websites. Most users decline. The structural result: mobile conversion reporting on social platforms relies increasingly on modeled data, privacy-safe APIs (Conversions API, CAPI), and aggregated measurement — not individual-level tracking. [10]
Google’s Cookie Approach
Google moved from a planned third-party cookie deprecation to a user-choice model and subsequently restated its intent not to deprecate third-party cookies entirely. The UK CMA’s Privacy Sandbox case documents the evolving commitments and regulatory oversight. Regardless of Google’s final position, reliance on third-party cookies as a durable measurement foundation is structurally unsound. [9]
FTC Endorsement Guides
Updated in 2023, the FTC Endorsement Guides now address social media promotions, reviews, and influencer marketing explicitly. Material connections must be clearly disclosed. For agencies managing influencer programs: contract templates, disclosure review, claims oversight, and recordkeeping are now operational compliance requirements, not optional best practices. [12]
Section 6: Martech Stacks and Marketing Operating Models
The Martech Landscape: Rationalization Under Budget Pressure
The martech market has expanded dramatically over the past decade. Budget tightening — with martech investment now at its lowest level in a decade — is driving consolidation and rationalization. CMOs have diminishing control over enterprise technology decisions as IT and procurement take more control of martech budgets. [3,42]
Reference Martech Stack Architecture
| Layer | Function | Typical Components | Decision Criteria |
| Identity & Consent | Consent management; preference signals; identity resolution | CMPs; preference centers; identity resolution tools | Legal alignment; auditability; integration with downstream activation |
| Data Collection | Captures events from web, app, offline sources | Tag management; server-side pipelines; CRM/loyalty feeds | Event quality; data ownership; governance controls |
| Customer Data & Analytics | Unifies customer data; supports segmentation and insight | CDP; data warehouse; BI tooling | Single source of truth; data quality processes; business access patterns |
| Decisioning & Orchestration | Selects next message/offer/channel; coordinates workflows | Next-best-action engines; marketing automation; journey orchestration | Rules + model combination; iteration speed; controllability |
| Content System | Stores, approves, and distributes content variants | CMS; DAM; creative ops workflows; testing libraries | Version control; compliance review; asset reuse |
| Activation | Executes across paid, owned, and partner channels | Search/social/CTV buying; email/SMS; retail media | Measurement hooks; conversion quality; cost controls |
| Measurement & Learning | Quantifies impact; feeds learning into planning | Lift testing; MMM; clean rooms; attribution tooling | Incrementality discipline; cross-channel comparability |
Sources: [3,22,39,40,42]
Platform Comparison: Paid Environment Measurement Posture
| Environment | Best For | Measurement Strength | Key Consideration in 2026 |
| Search (Google, Bing) | High-intent capture; lower-funnel conversion | Strong within-channel conversion telemetry (with limits) | AI-driven search disruption may reduce query volume; Performance Max reduces transparency |
| Social + Creators | Demand creation; community-led conversion | Platform lift tools; creative iteration cadence | CAPI integration required for reliable measurement; creator disclosure compliance |
| Retail Media | Lower-funnel influence; closed-loop measurement | Strong linkage to commerce outcomes | Measurement standards vary by network; walled garden fragmentation |
| Streaming / CTV | Scaled video reach; brand influence | Improving but still fragmented cross-platform | Frequency management and deduplication across publishers unsolved |
| Out-of-Home (OOH/DOOH) | Broad reach; location salience; brand building | Geo tests + MMM + QR/short-link + retail outcomes | DOOH programmatic buying maturing; attention metrics emerging |
Sources: [1,2,14,15]
Operating Models: Key Trends
In-Housing Maturation
66% of major brands now have in-house agencies, with satisfaction improving as these functions mature. The trend continues, with brands building capability particularly in media buying, data and analytics, and content production. [31,43]
AI and Skill Mix
Gartner links GenAI investment to productivity expectations under constrained budgets. Deloitte’s workforce research describes AI shifting from tool to working teammate — implying role redesign and new training priorities rather than simple headcount reduction. [3,33]
Skills that matter most in 2026: measurement leadership (running incrementality tests, interpreting MMM, challenging platform reporting); data product thinking (event taxonomy, definition governance); creative operations for high-velocity testing; privacy and AI governance fluency.
Section 7: Case Studies with Quantified ROI
The IPA Effectiveness Awards case summaries provide unusually concrete financial metrics for marketing investments — making them valuable for building internal business cases and client presentations.
Brand Investment Reducing Price Sensitivity — McCain Foods
A long-run brand advertising commitment reduced price elasticity by 47%, raised base sales by 44%, and reached a profit ROI of £1.50 per £1 invested by the end of the case period. [34]
Application: Demonstrates brand activity in financial terms that resonate with finance partners (elasticity, base sales uplift) — not just awareness metrics.
SEO as a Business System — H&M
A multi-year search optimization strategy redesigned not just keywords but the entire content production and cross-functional process — generating an estimated £644M in incremental revenue and a £16 profit return per £1 invested. [35]
Application: Frames SEO as an operating model change, not a channel tactic — directly relevant when building client cases for content and technical investment.
Long-Run Brand Building with Econometric Proof — Yorkshire Tea
Econometric analysis (2019–2023) calculated £25.8M incremental revenue and a longer-term ROI of £1.60 per £1 invested, alongside market share growth and premium pricing maintenance against private-label pressure. [36]
Application: Compact example of ‘brand consistency + effectiveness measurement = durable share change’ — useful for clients considering cutting brand in favor of performance-only spending.
Proposition Reframing to Unlock New Demand — Specsavers
Repositioning the Home Visits service for prior rejecters generated an estimated £19.8M incremental profit, a 32% volume increase, and approximately 582,000 prior rejecters converted. [37]
Application: Shows how long-running brand memory structures can be leveraged for proposition expansion — directly applicable to clients with underperforming service lines.
Public-Purpose Campaign with Hard Outcome Metrics — Permian Strategic Partnership
A community vaccination campaign drove a 73% increase in vaccinations among undecided residents and 13% overall, with estimated lives saved and hospital cost avoidance quantified. [38]
Application: Demonstrates that persuasion campaigns — even in non-commercial contexts — can be evaluated against hard outcome metrics when measurement design is explicit upfront.
Section 8: Strategic Recommendations for 2026
Measurement
- Build a measurement portfolio: experiments as credibility anchor; MMM for budget allocation; platform reporting for in-channel optimization; clean-room collaboration for privacy-safe partner measurement.
- Make incrementality testing a standard practice for major budget decisions, not a one-off. Experiment design is improving — latent stratification methods can reduce required sample sizes.
- Treat GA4 governance as a data product — event taxonomy, conversion definitions, and data layer management require the same rigor as any business data system.
AI and Personalization
- Define use cases by business outcome before selecting tools. AI adoption as a goal produces activity, not results.
- Implement marketing-specific AI governance now: claim review, IP policies, dataset restrictions, automated decisioning disclosure, and documented approval chains.
- Build personalization as a product discipline: data → decisioning → design → distribution → measurement. Scale with AI only after the measurement loop is reliable.
Privacy and Compliance
- Treat Kentucky’s KCDPA and California’s CPPA regulations as operational requirements effective now — not future roadmap items. Consent management, data rights workflows, and automated decisioning disclosure are live obligations.
- Build first-party data collection as a strategic priority. It is the most durable response to signal loss and the foundation of compliant personalization.
- Audit any creator/influencer programs for FTC disclosure compliance. Documentation and claim review are required, not optional.
Org Design and Operating Model
- Rebalance around systems roles: measurement & experimentation, marketing data products, creative operations, privacy/consent, and platform operations.
- In-housing decisions should follow capability, not ideology. Build internal capability where institutional knowledge compounds (brand, audience data, measurement). Partner externally where specialist depth matters more than continuity.
- Invest in measurement literacy across the marketing team — the ability to set goals, design tests, and read results is now a core marketing skill, not a specialist function.
Sources and References
All sources verified as of April 2026.
[1] IAB/PwC Internet Advertising Revenue Report: Full Year 2023 — https://www.iab.com/news/2023-u-s-digital-advertising-industry-hits-new-record-according-to-iabs-annual-internet-advertising-revenue-report/ [2] IAB/PwC Internet Advertising Revenue Report: Full Year 2024 — https://www.iab.com/news/digital-ad-revenue-2024/ [3] Gartner CMO Spend Survey 2024 — https://www.gartner.com/en/newsroom/press-releases/2024-05-13-gartner-cmo-survey-reveals-marketing-budgets-have-dropped-to-seven-point-seven-percent-of-overall-company-revenue-in-2024 [5] California AG — CCPA Overview — https://oag.ca.gov/privacy/ccpa [6] CPPA — CCPA Regulations Effective January 1, 2026 — https://cppa.ca.gov/regulations/pdf/cppa_regs.pdf [7] EU Digital Services Act Overview — https://digital-strategy.ec.europa.eu/en/policies/digital-services-act [8] EU Commission — Two Years of the Digital Services Act (Feb 2026) — https://commission.europa.eu/news-and-media/news/two-years-digital-services-act-ensuring-safer-online-spaces-2026-02-17_en [9] UK CMA — Privacy Sandbox Case Timeline — https://www.gov.uk/cma-cases/investigation-into-googles-privacy-sandbox-browser-changes [10] Apple Developer News — App Tracking Transparency — https://developer.apple.com/news/?id=8h0btjq7 [11] Google Analytics — Universal Analytics to GA4 Transition — https://support.google.com/analytics/answer/10089681 [12] FTC — Updated Endorsement Guides (June 2023) — https://www.ftc.gov/news-events/news/press-releases/2023/06/federal-trade-commission-announces-updated-advertising-guides-combat-deceptive-reviews-endorsements [13] IAB Tech Lab — OpenRTB Standard — https://iabtechlab.com/standards/openrtb/ [14] OAAA — OOH Revenue Reaches Record $9.46 Billion (2025 Full Year, Released March 2026) — https://oaaa.org/news/out-of-home-advertising-revenue-reaches-record-9-46-billion/ [15] World Out of Home Organization — Global OOH Expenditure 2025 — https://www.worldooh.org/news/woo-global-expenditure-2025-released [16] WFA — ANA 2024 Programmatic Benchmark Study — https://wfanet.org/knowledge/item/2025/01/21/ana-s-2024-programmatic-benchmark-study-progress-but-challenges-remain [17] AMA — The Definition of Marketing — https://www.ama.org/the-definition-of-marketing-what-is-marketing/ [19] Nielsen Annual Marketing Report 2024 — https://youmark-images.b-cdn.net/wp-content/uploads/2024/05/AMR-2024_Local-EMEA_v1.pdf [20] arXiv — Predicted Incrementality by Experimentation — https://arxiv.org/html/2304.06828v2 [21] INFORMS Marketing Science — Latent Stratification for Incrementality — https://pubsonline.informs.org/doi/10.1287/mksc.2022.0297 [22] McKinsey — Unlocking the Next Frontier of Personalized Marketing — https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/unlocking-the-next-frontier-of-personalized-marketing [23] McKinsey — How Generative AI Can Boost Consumer Marketing — https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/how-generative-ai-can-boost-consumer-marketing [24] De Mauro, Sestino & Bacconi — ML/AI Use Cases in Marketing (Springer 2022) — https://link.springer.com/article/10.1007/s43039-022-00057-w [25] Springer JAMS — AI in Marketing: Academic & Practitioner Perspectives (2024) — https://link.springer.com/article/10.1007/s11747-024-01064-3 [28] Kentucky HB 15 — Kentucky Consumer Data Protection Act — https://apps.legislature.ky.gov/record/24rs/hb15.html [29] Colorado AG — Colorado Privacy Act — https://coag.gov/resources/colorado-privacy-act/ [30] CPPA — Regulations Hub — https://cppa.ca.gov/regulations/ [31] WFA — In-Housing at Major Multinationals (Dec 2023) — https://wfanet.org/knowledge/item/2023/12/20/In-housing-set-for-rapid-and-continued-growth-at-major-multinationals [33] Deloitte — Strategies for Workforce Evolution — https://www.deloitte.com/us/en/insights/topics/talent/strategies-for-workforce-evolution.html [34] IPA Effectiveness Awards 2024 — McCain — https://ipaeffectivenessawards2024.awardsengine.com/winners/view_awards_entry.cfm?id_entry=100163 [35] IPA Effectiveness Awards 2024 — H&M — https://ipaeffectivenessawards2024.awardsengine.com/winners/view_awards_entry.cfm?id_entry=100166 [36] IPA Effectiveness Awards 2024 — Yorkshire Tea — https://ipaeffectivenessawards2024.awardsengine.com/winners/view_awards_entry.cfm?id_entry=100079 [37] IPA Effectiveness Awards 2024 — Specsavers — https://ipaeffectivenessawards2024.awardsengine.com/winners/view_awards_entry.cfm?id_entry=100162 [38] IPA Effectiveness Awards 2024 — Permian Strategic Partnership — https://ipaeffectivenessawards2024.awardsengine.com/winners/view_awards_entry.cfm?id_entry=100219 [39] AWS Clean Rooms Overview — https://advertising.amazon.com/API/docs/en-us/guides/amazon-marketing-cloud/acr/1_overview [40] Google Cloud — BigQuery Data Clean Rooms — https://docs.cloud.google.com/bigquery/docs/data-clean-rooms [41] NIST AI Risk Management Framework — https://artificialintelligenceact.eu/ [42] chiefmartec.com — Marketing Technology Landscape — https://chiefmartec.com/ [43] Marketing Dive — In-House Agency Trends 2024 — https://www.marketingdive.com/news/in-house-agency-trends-media-buying-data-strategy-wfa/703212/Fastline Marketing Group | Buckner, Kentucky | fastlinemarketing.com
This report is produced for educational and informational purposes and does not constitute legal, financial, or regulatory advice.
