What is Real-Time Bidding (RTB): Definition, Benefits, and How It Works in 2026
December 19, 2025
14
minutes read
Real-time bidding (RTB) is the engine that powers the modern programmatic advertising ecosystem, handling billions of impression-level decisions every day. In 2026, more than 90% of global digital display spend runs through programmatic channels, with RTB responsible for the vast majority of real-time auction activity. What began as a simple automated alternative to manual media buying has evolved into an AI-driven system capable of evaluating each impression in milliseconds — analysing user intent, contextual signals, device data, and predicted outcomes before setting the perfect bid. RTB now allows brands to reach the right user, at the right moment, and at the most efficient price — automatically. With faster algorithms, richer real-time data, and cleaner supply paths, RTB in 2026 is the central engine powering scalable, intelligent, and performance-driven digital advertising across every channel.
Real-time bidding (RTB) has evolved from a niche auction mechanism into the central nervous system of modern programmatic advertising. By 2026, analysts project that 90 % of global display-ad budgets will flow through programmatic channels. Within Europe alone, the programmatic advertising market hit US$177.8 billion in 2023 and is on track for a compound annual growth rate of 23.1 % through 2030, with RTB already identified as the largest sub-segment in that region.
But what is RTB in essence? It’s the automated, impression-by-impression auction mechanism that underpins programmatic buying, allowing brands to bid for ad inventory in real time, and publishers to monetise each ad slot dynamically.
The term “real-time bidding” doesn’t just mean “fast”—it means leveraging data, algorithms and inventory optimisation to decide within milliseconds both who sees the ad and what they see. This article breaks down what real-time bidding is, how the RTB advertising ecosystem works, and why AI-driven programmatic buying has become essential for performance in 2026. It also explains how real-time bidding platforms, algorithms, and automation drive more efficient targeting, higher-quality impressions, and stronger results for both advertisers and publishers—setting the stage for the next era of real-time advertising.
What is real-time bidding (RTB)?
Real-time bidding (RTB) is an automated auction system that allows advertisers to buy digital ad impressions one at a time, in real time, as a user loads a webpage, opens an app, or streams content. Instead of purchasing ad inventory in bulk, RTB lets brands decide within 40–120 milliseconds how much each impression is worth based on data such as context, device, user behavior, predicted engagement, and campaign goals.
RTB sits at the center of programmatic advertising. According to industry projections, over 90% of global digital display spend in 2026 will occur through programmatic channels, with RTB remaining the fastest-growing buying method, particularly across mobile, CTV, and in-app inventory. This shift is driven by the increasing precision of machine-learning models, which now evaluate thousands of signals per auction to determine the optimal bid in real time.
In practice, the real-time bidding process involves three core components:
Real-time bidding platforms (DSPs) where advertisers set budgets, targeting, and bidding strategies.
Supply-side platforms that connect publisher inventory to RTB ad exchanges.
The RTB auction itself, where bids compete and the highest bid wins the impression—at the best achievable price.
RTB in advertising has become central to performance-driven marketing because it improves both efficiency and relevance. Advertisers gain more control over cost and audience quality, while publishers benefit from stronger competition, higher yield, and better monetization of premium inventory.
⚡️For a deeper comparison of RTB vs. other programmatic buying methods, see the full overview, which includes detailed breakdowns of open-auction RTB, private marketplaces (PMPs), programmatic guaranteed deals, and more.
💡Advanced real-time bidding algorithms now predict user value, optimize creative variations, and adapt bids dynamically. This evolution is why RTB ads consistently outperform traditional placements in both ROAS and cost efficiency, and why RTB remains the backbone of modern programmatic marketing.
How real-time bidding works (step-by-step)
RTB (real-time bidding) works like a lightning-fast marketplace that opens and closes every time a user lands on a page, scrolls a feed, or loads an app. In the split second it takes the content to appear, a full real-time bidding auction unfolds behind the scenes—data signals are gathered, the impression is evaluated, DSPs decide whether to compete, and bids are placed across multiple real-time bidding platforms.
1. A user visits a website or app
When a user loads a webpage, enters an app, or starts a video stream, an available ad slot becomes eligible for a real-time bidding auction. At this moment, the publisher’s site or app sends contextual information, such as page category, device type, screen size, and privacy-compliant user signals, into the RTB system. This is when bidding in digital marketing begins at the impression level.
2. Publisher sends an ad request via an SSP
The publisher’s supply-side platform (SSP) packages the impression and forwards it to multiple RTB ad exchanges. The SSP ensures the inventory is visible to demand sources and enforces rules such as floor prices, creative restrictions, brand-safety filters, and fraud checks. This step drives RTB demand by making each impression available to programmatic buyers in real time.
3. DSP evaluates available impressions and bids
Advertisers access inventory through demand-side platforms (DSPs), which use real-time bidding algorithms to decide whether to bid on the impression and at what price. Factors include audience targeting, contextual relevance, expected engagement, campaign pacing, and predicted conversion value.
💡Advanced DSPs now also use AI to forecast impression quality, adjust bidding time dynamically, and optimize for cross-channel placements, including mobile RTB and CTV.
4. The highest bidder wins the auction
Once the RTB auction closes—typically in under a tenth of a second—the highest bid that meets the publisher’s requirements wins. Most RTB advertising platforms now run first-price auctions, meaning the winning advertiser pays exactly what they bid. The shift from second-price to first-price auctions has pushed DSPs to rely more heavily on bid-shading and predictive RTB software to avoid overpaying.
5. The winning ad is served instantly
The SSP delivers the winning creative back to the publisher, and the ad appears on the user’s screen in real time. From the user’s perspective, this process is invisible—yet behind the scenes, multiple real-time bidding platforms, RTB software systems, and programmatic partners have evaluated the impression, placed bids, and selected the most relevant ad within milliseconds.
💡 This entire chain defines what RTB is in marketing: a fast, automated, data-driven ecosystem where each individual impression is priced and purchased in the moment. It’s the backbone of real-time advertising and remains the most efficient method for scalable, precise programmatic RTB buying.
RTB vs. programmatic buying
Real-time bidding (RTB) is a subset of programmatic buying—not the whole system. Programmatic advertising includes every method of automated media buying: preferred deals, private marketplaces, programmatic guaranteed, and fixed-price agreements. RTB, by contrast, refers specifically to auction-based buying, where each impression is evaluated and priced in real time.
Programmatic buying can therefore involve RTB auctions, but it can also use non-auction contracts with fixed CPMs or guaranteed volumes. Most advertisers blend both—using programmatic guaranteed for premium inventory, and RTB in marketing for scalable reach and real-time optimization.
⚡️ For a full breakdown of how real-time bidding compares to the broader programmatic ecosystem, including deal types, pricing structures, transparency levels, and when advertisers should choose one approach over the other—you can explore our complete guide on Programmatic vs. RTB.
Types of real-time bidding
Real-time bidding now operates across several auction models, each designed to balance scale, control, and price efficiency. In 2026, advertisers often use a mix of these RTB formats depending on campaign goals, audience quality, and inventory availability.
Open RTB
Open RTB takes place on public real-time bidding exchanges where any qualified buyer (via a DSP) can bid on available impressions. It offers maximum scale, competitive CPMs, and access to a wide range of inventory across display, video, mobile apps, and CTV. This model is ideal for performance-driven RTB buying, retargeting, and broad reach.
Private RTB (PMP)
Private marketplaces operate on an invite-only basis. Publishers allow select advertisers to access premium inventory before it reaches the open market. PMP deals provide more transparency, stronger brand safety, and higher viewability—making Private RTB popular for brand campaigns, premium publishers, and controlled supply paths.
Header bidding
Header bidding sends each impression to multiple SSPs and exchanges simultaneously, rather than relying on a traditional waterfall sequence. This creates more competition, increases publisher revenue, and gives advertisers fairer access to top-tier placements. Header bidding is now standard across major news sites, video platforms, and CTV apps.
First-price vs. second-price auctions
Most RTB ad exchanges have fully transitioned to first-price auctions, where the winning advertiser pays exactly what they bid. Second-price auctions—where the winner paid slightly above the second-highest bid—were once common, but they now exist mostly in legacy systems.
First-price auctions require DSPs to use smarter bidding algorithms and bid shading to avoid overspending, making RTB strategies more reliant on machine learning.
Hybrid auctions (emerging model)
Hybrid RTB auctions blend first-price mechanics with adaptive, AI-driven rules. These systems evaluate competition level, impression quality, and predicted ROI, then adjust pricing dynamically. Hybrid auctions are becoming more common in CTV and in-app environments where impression value fluctuates significantly.
Key components of RTB ecosystem
RTB works because several platforms connect advertisers, publishers, and data signals in real time. Each component plays a specific role in the real-time bidding process.
Supply-Side Platform (SSP)
SSPs manage publisher inventory and make impressions available for auction. They enforce floor prices, apply brand-safety filters, run fraud checks, and route impressions to RTB ad exchanges. SSPs ensure that each ad opportunity reaches the right buyers at the right moment.
Demand-Side Platform (DSP)
DSPs are where advertisers set campaign goals, budgets, targeting, and bidding strategies. The DSP evaluates each impression and uses real-time bidding algorithms to decide whether to bid—and at what price—based on contextual signals, audience fit, predicted conversion value, and pacing constraints.
💡Modern DSPs rely heavily on machine learning to process millions of bid requests per second, score impression quality, apply bid shading, and optimize spending across channels such as mobile, display, video, and CTV.
⚡️For a deeper look at how AI improves DSP accuracy and efficiency—including predictive bidding, creative optimization, automated pacing, and impression-level value scoring—you can explore the full breakdown here on AI in DSPs.
Ad exchange
The ad exchange is the central marketplace where SSPs and DSPs interact. It hosts the real-time bidding auctions, receives bids from multiple demand sources, evaluates them against publisher rules, and selects the winning advertiser in milliseconds. Ad exchanges sit at the heart of RTB, enabling the rapid matching of supply and demand across millions of impressions per second while maintaining auction logic, transparency rules, and fraud filtering between both sides of the ecosystem.
⚡️ For a deeper explanation of how ad exchanges differ from DSPs and SSPs, including their roles, data flows, and how each platform contributes to the RTB supply path, you can explore our full guide here on DSP vs. SSP vs. Ad Exchange.
Data Management Platform (DMP)
The DMP stores and organizes audience data—first-party, second-party, and third-party—used in RTB targeting. It helps advertisers build audience segments, enrich user profiles, and activate data across real-time bidding platforms. Although privacy regulations have changed how DMPs operate, they still play a major role in contextual, interest-based, and predictive audience modeling.
Top real-time bidding platforms
The table below highlights the major real-time bidding platforms shaping the programmatic ecosystem in 2026, outlining how each DSP or exchange contributes to scale, data quality, AI optimization, and cross-channel performance. These platforms, ranging from enterprise solutions like DV360 and The Trade Desk to powerful supply-side players such as Magnite and PubMatic, form the backbone of modern RTB buying across display, mobile, CTV, video, and commerce-driven environments.
Real-time bidding (RTB) has become a core part of programmatic advertising, shaping how brands buy media across web, in-app, CTV, and mobile RTB environments. With the rise of real-time bidding platforms and advanced RTB algorithms, advertisers rely on the RTB system to automate buying, refine targeting, reduce costs, and maintain transparency across every impression.
Automation and efficiency
RTB buying automates the entire real-time advertising workflow, allowing advertisers to evaluate and bid on individual impressions within milliseconds. This level of automation has scaled massively: by 2026, over 90% of global digital display spend is projected to run through programmatic channels, with RTB powering the majority of these transactions. With millions of bid requests processed per second, RTB software reduces manual work, speeds up optimization, and improves overall campaign performance.
Targeting precision
RTB in marketing stands out for its ability to match impressions with detailed audience data in real time. User behaviour, device type, context, and location all inform the bid decision during each real-time bidding auction. Research shows that synchronized data-sharing can raise impression value by 19%, highlighting how crucial high-quality data is in RTB advertising. As cookies fade, programmatic RTB and contextual models make targeting sharper—improving engagement and reducing wasted impressions.
Cost-effectiveness and ROI
Real-time ad bidding offers cost control because advertisers only bid when an impression meets their criteria. Programmatic and RTB channels—now representing 80–90% of all global display spending—are designed to optimize prices dynamically, helping brands avoid overspending. When paired with strong creative, first-party data, and proper KPI tracking, RTB ads consistently deliver higher ROI through lower CPA, better ROAS, and more efficient budget allocation.
As RTB demand rises, so does the need for supply-chain clarity. Modern RTB advertising platforms offer domain-level reporting, supply-path optimization, real-time metrics, and fraud-prevention tools. The European programmatic market—forecast to reach USD 203.18 billion by 2032—is shifting toward stricter transparency standards. This gives advertisers more control over where RTB ads appear, how much they pay, and which RTB ad exchanges or SSPs deliver the best performance.
Disadvantages and challenges of RTB
Real-time bidding (RTB) brings huge advantages in automation and efficiency, but it also introduces structural challenges that advertisers must manage carefully. As the RTB ecosystem becomes more complex—with more intermediaries, more data signals, stricter regulation, and higher competition—the risks increase. Understanding these challenges early helps advertisers build strategies, tools, and processes that stabilize performance and protect budgets.
Privacy and compliance (GDPR, CCPA, cookie deprecation)
Privacy regulations deeply affect how the RTB system works. Under GDPR and CCPA, every data point passed during real-time bidding auctions—device IDs, location data, audience segments—must be collected and shared legally. The challenge is that RTB in marketing involves multiple intermediaries. When a bid request is sent to an RTB ad exchange or SSP, the advertiser becomes responsible for ensuring that every partner handles data correctly. As cookies disappear and browsers block cross-site tracking, the traditional RTB definition of audience targeting becomes harder to execute. This weakens signal quality, increases acquisition costs, and affects how RTB algorithms evaluate users in milliseconds of bidding time.
Brand safety and ad fraud
Because RTB buying moves at extreme speed, the RTB system does not always filter out unsafe or fraudulent inventory in time. Real time bidding advertising can unintentionally place RTB ads on low-quality sites, misinformation pages, or environments that don’t align with a brand’s values. Ad fraud—bots, spoofed domains, invalid traffic—remains a major challenge for every RTB advertising platform and RTB software provider. Fraud distorts bidding time signals and makes advertisers pay for impressions that never reach real humans.
The solution is to build multi-layer protection: pre-bid brand-safety filters, supply-path optimization, fraud-prevention partners, and allowlists for trusted inventory. Examining log-level data helps detect anomalies like unusual click-through rates or suspicious bidding patterns. Choosing transparent real-time bidding platforms and reliable RTB ad exchange partners greatly reduces fraud exposure. While no RTB product can eliminate all risk, a strong verification framework keeps budget focused on high-quality impressions and protects your RTB in advertising strategy.
Latency and performance issues
The real time bidding process relies on ultra-fast response times. Every RTB ad exchange sends bid requests that must be evaluated in milliseconds; if the RTB algorithm responds late, the bid is ignored. High latency caused by slow servers, long supply paths, or overloaded DSPs—reduces win rates and raises CPMs. In periods of heavy RTB demand, performance issues disrupt pacing and reduce campaign reach, especially in mobile RTB environments where milliseconds matter most.
Improving performance requires strong infrastructure: server-side bidding, edge computing, geographically distributed data centers, and fewer intermediaries. Supply-path optimization shortens the route between SSPs and DSPs, lowering delays and improving real time advertising accuracy. Evaluating RTB software based on bid-response speed and reliability helps advertisers scale smoothly across formats. A robust, low-latency RTB system ensures that RTB buying remains efficient, competitive, and stable even during high-volume real-time bidding auctions.
No guaranteed placements
One structural challenge in real time bidding advertising is the absence of guaranteed impressions. RTB programmatic buying is fully auction-based: advertisers compete impression by impression, and nothing ensures they will win. When RTB demand spikes—during holidays or major events—premium inventory becomes more expensive, and smaller budgets may fail to win auctions. This unpredictability makes planning reach, frequency, and delivery more complicated for brands relying heavily on RTB in marketing.
How AI and automation enhance RTB
As real-time bidding (RTB) evolves, artificial intelligence has become the driving force behind modern programmatic RTB optimisation. The shift from manual bidding to AI-powered automation has transformed how advertisers evaluate impressions, model audiences, detect fraud, and manage overall campaign efficiency. Today’s RTB system no longer relies on human intuition—AI algorithms process billions of data points per day, making faster and more accurate decisions than any manual team ever could.
⚡️This evolution is part of a broader rethink happening across the programmatic ecosystem. AI Digital, a leader in programmatic strategy and real-time bidding innovation, explores this transformation in depth in its dedicated industry guide, Rethinking the Value Proposition of DSPs in Today’s Programmatic Landscape.
A core part of this transformation is Smart Supply, an AI-powered programmatic media buying and supply-path solution from AI Digital that delivers premium, outcome-based supply through transparent, high-quality inventory.
Outcome-based deal IDs: Every supply deal is optimised for your campaign KPIs.
Direct SSP access: 99.9% coverage from top-tier supply sources to ensure quality.
Real-time AI optimisation: Live traffic filtering, in-flight deal adjustments and full transparency.
DSP-agnostic execution: Works across platforms, eliminating bias and hidden mark-ups.
💡 Smart Supply matters because it solves some of the biggest inefficiencies in today’s fragmented programmatic ecosystem. When supply paths are cluttered with unnecessary intermediaries, hidden resellers, and low-quality inventory, media budgets are wasted on inflated bid streams and impressions that never deliver real value. Smart Supply eliminates these issues by providing unbiased, fully transparent supply routes that cut out redundant hops, lower CPMs, and redirect spend toward high-performance inventory.
Smart bidding algorithms
AI-powered bidding algorithms evaluate every impression in milliseconds, analysing user intent, contextual signals, device data, and historical performance to decide the optimal bid. Smart Supply strengthens this process by feeding the algorithm cleaner, higher-quality supply paths. When the system only receives verified, transparent, and fraud-free inventory, the bidding engine can make far more accurate decisions. Because Smart Supply removes inefficient hops, hidden-fee resellers, and low-quality SSPs, the bidding algorithm gets a clearer understanding of real impression value. This improves bid forecasting, reduces wasted spend, and increases the probability of winning premium placements at competitive prices. The result is an RTB environment where AI can operate with sharper signals, stronger data integrity, and higher bidding efficiency.
Predictive analytics and audience modeling
Predictive analytics and audience modeling rely on the quality of the underlying supply. AI models can only forecast engagement or conversion likelihood accurately when the impressions themselves come from clean, consistent, and transparent sources.
Smart Supply enhances this by prioritising inventory from SSPs and exchanges with strong user-quality and performance histories. With its filtering the supply chain, predictive systems work with more reliable data—cleaner user behaviour patterns, higher match rates, and more authentic engagement signals. This allows AI to model audiences with greater precision and forecast conversions more accurately. Smart Supply essentially removes “noise” from the dataset, giving predictive algorithms the clarity needed to target high-value impressions and build stronger, AI-driven RTB audiences.
Fraud detection and brand safety
AI-based fraud detection protects advertisers by identifying bots, spoofed domains, and invalid traffic before a bid is placed. Smart Supply amplifies these protections by cleaning the supply chain upstream, eliminating low-credibility sources before they even enter the auction. While AI guards the signal layer, Smart Supply reinforces the inventory layer—blocking unsafe or suspicious supply paths entirely.
This two-layer approach significantly reduces exposure to fraudulent impressions. By working only with vetted SSPs and exchanges, Smart Supply ensures that AI risk-scoring models evaluate impressions that already meet a higher baseline of authenticity. It also improves brand safety by removing inventory associated with harmful, sensitive, or unreliable environments, ensuring that RTB ads appear only in contexts that align with brand standards. Combined, AI and Smart Supply deliver a safer, more transparent, and more trustworthy RTB experience.
RTB pricing and CPM ranges
RTB pricing is determined by a mix of factors, including audience targeting depth, market competition, placement quality, supply-path efficiency, and the type of creative being used. CPMs can vary significantly across formats—display generally remains the most affordable, video commands higher prices due to stronger engagement, and CTV sits at the premium end of the spectrum because of its high-impact inventory and limited supply. Below is an approximate view of typical CPM ranges across RTB channels.
Conclusion: how RTB fits into the modern programmatic ecosystem
Real-time bidding has become the backbone of today’s programmatic ecosystem — fast, transparent, and increasingly shaped by AI-driven optimisation. RTB gives advertisers instant access to billions of impressions across display, mobile, video, audio, and CTV, while intelligent bidding algorithms assess each opportunity in milliseconds. But success in RTB doesn’t come from speed alone. It relies on aligning DSP capabilities, high-quality data, supply-path transparency, and consistently optimised creative. That’s where solutions like Smart Supply and Elevate play a transformative role: Smart Supply secures clean, premium, outcome-driven inventory across the supply chain, while Elevate enhances performance with AI-powered optimisation, audience intelligence, and cross-channel insights. Together, they streamline the entire programmatic workflow, giving advertisers a clearer path to stronger ROI across every channel.
For brands ready to strengthen their RTB strategy, here are four practical takeaways:
Prioritize supply quality, not just scale. Cleaner inventory and transparent supply paths improve bid efficiency, engagement, and overall performance.
Feed your DSP with high-quality data. First-party data, contextual insights, and privacy-safe identifiers give AI models the accuracy they need to optimise bids.
Optimise creative for real-time environments. Strong, adaptive creative drives better results when bidding algorithms can instantly match message to audience intent.
Combine automation with strategic oversight. AI handles bidding, pacing, and modelling, but human direction ensures alignment with brand goals, KPIs, and market conditions.
RTB is an intelligent engine at the centre of modern programmatic advertising. When supported by the right tools and supply infrastructure, it becomes one of the most powerful levers for scalable, sustainable growth in digital marketing.
Blind spot
Key issues
Business impact
AI Digital solution
Lack of transparency in AI models
• Platforms own AI models and train on proprietary data • Brands have little visibility into decision-making • "Walled gardens" restrict data access
• Inefficient ad spend • Limited strategic control • Eroded consumer trust • Potential budget mismanagement
Open Garden framework providing: • Complete transparency • DSP-agnostic execution • Cross-platform data & insights
Optimizing ads vs. optimizing impact
• AI excels at short-term metrics but may struggle with brand building • Consumers can detect AI-generated content • Efficiency might come at cost of authenticity
• Short-term gains at expense of brand health • Potential loss of authentic connection • Reduced effectiveness in storytelling
Smart Supply offering: • Human oversight of AI recommendations • Custom KPI alignment beyond clicks • Brand-safe inventory verification
The illusion of personalization
• Segment optimization rebranded as personalization • First-party data infrastructure challenges • Personalization vs. surveillance concerns
• Potential mismatch between promise and reality • Privacy concerns affecting consumer trust • Cost barriers for smaller businesses
Elevate platform features: • Real-time AI + human intelligence • First-party data activation • Ethical personalization strategies
AI-Driven efficiency vs. decision-making
• AI shifting from tool to decision-maker • Black box optimization like Google Performance Max • Human oversight limitations
• Strategic control loss • Difficulty questioning AI outputs • Inability to measure granular impact • Potential brand damage from mistakes
Managed Service with: • Human strategists overseeing AI • Custom KPI optimization • Complete campaign transparency
Fig. 1. Summary of AI blind spots in advertising
Dimension
Walled garden advantage
Walled garden limitation
Strategic impact
Audience access
Massive, engaged user bases
Limited visibility beyond platform
Reach without understanding
Data control
Sophisticated targeting tools
Data remains siloed within platform
Fragmented customer view
Measurement
Detailed in-platform metrics
Inconsistent cross-platform standards
Difficult performance comparison
Intelligence
Platform-specific insights
Limited data portability
Restricted strategic learning
Optimization
Powerful automated tools
Black-box algorithms
Reduced marketer control
Fig. 2. Strategic trade-offs in walled garden advertising.
Core issue
Platform priority
Walled garden limitation
Real-world example
Attribution opacity
Claiming maximum credit for conversions
Limited visibility into true conversion paths
Meta and TikTok's conflicting attribution models after iOS privacy updates
Data restrictions
Maintaining proprietary data control
Inability to combine platform data with other sources
Amazon DSP's limitations on detailed performance data exports
Cross-channel blindspots
Keeping advertisers within ecosystem
Fragmented view of customer journey
YouTube/DV360 campaigns lacking integration with non-Google platforms
Black box algorithms
Optimizing for platform revenue
Reduced control over campaign execution
Self-serve platforms using opaque ML models with little advertiser input
Performance reporting
Presenting platform in best light
Discrepancies between platform-reported and independently measured results
Consistently higher performance metrics in platform reports vs. third-party measurement
Fig. 1. The Walled garden misalignment: Platform interests vs. advertiser needs.
Key dimension
Challenge
Strategic imperative
ROAS volatility
Softer returns across digital channels
Shift from soft KPIs to measurable revenue impact
Media planning
Static plans no longer effective
Develop agile, modular approaches adaptable to changing conditions
Brand/performance
Traditional division dissolving
Create full-funnel strategies balancing long-term equity with short-term conversion
Capability
Key features
Benefits
Performance data
Elevate forecasting tool
• Vertical-specific insights • Historical data from past economic turbulence • "Cascade planning" functionality • Real-time adaptation
• Provides agility to adjust campaign strategy based on performance • Shows which media channels work best to drive efficient and effective performance • Confident budget reallocation • Reduces reaction time to market shifts
• Dataset from 10,000+ campaigns • Cuts response time from weeks to minutes
• Reaches people most likely to buy • Avoids wasted impressions and budgets on poor-performing placements • Context-aligned messaging
• 25+ billion bid requests analyzed daily • 18% improvement in working media efficiency • 26% increase in engagement during recessions
Full-funnel accountability
• Links awareness campaigns to lower funnel outcomes • Tests if ads actually drive new business • Measures brand perception changes • "Ask Elevate" AI Chat Assistant
• Upper-funnel to outcome connection • Sentiment shift tracking • Personalized messaging • Helps balance immediate sales vs. long-term brand building
• Natural language data queries • True business impact measurement
Open Garden approach
• Cross-platform and channel planning • Not locked into specific platforms • Unified cross-platform reach • Shows exactly where money is spent
• Reduces complexity across channels • Performance-based ad placement • Rapid budget reallocation • Eliminates platform-specific commitments and provides platform-based optimization and agility
• Coverage across all inventory sources • Provides full visibility into spending • Avoids the inability to pivot across platform as you’re not in a singular platform
Fig. 1. How AI Digital helps during economic uncertainty.
Trend
What it means for marketers
Supply & demand lines are blurring
Platforms from Google (P-Max) to Microsoft are merging optimization and inventory in one opaque box. Expect more bundled “best available” media where the algorithm, not the trader, decides channel and publisher mix.
Walled gardens get taller
Microsoft’s O&O set now spans Bing, Xbox, Outlook, Edge and LinkedIn, which just launched revenue-sharing video programs to lure creators and ad dollars. (Business Insider)
Retail & commerce media shape strategy
Microsoft’s Curate lets retailers and data owners package first-party segments, an echo of Amazon’s and Walmart’s approaches. Agencies must master seller-defined audiences as well as buyer-side tactics.
AI oversight becomes critical
Closed AI bidding means fewer levers for traders. Independent verification, incrementality testing and commercial guardrails rise in importance.
Fig. 1. Platform trends and their implications.
Metric
Connected TV (CTV)
Linear TV
Video Completion Rate
94.5%
70%
Purchase Rate After Ad
23%
12%
Ad Attention Rate
57% (prefer CTV ads)
54.5%
Viewer Reach (U.S.)
85% of households
228 million viewers
Retail Media Trends 2025
Access Complete consumer behaviour analyses and competitor benchmarks.
Identify and categorize audience groups based on behaviors, preferences, and characteristics
Michaels Stores: Implemented a genAI platform that increased email personalization from 20% to 95%, leading to a 41% boost in SMS click through rates and a 25% increase in engagement.
Estée Lauder: Partnered with Google Cloud to leverage genAI technologies for real-time consumer feedback monitoring and analyzing consumer sentiment across various channels.
High
Medium
Automated ad campaigns
Automate ad creation, placement, and optimization across various platforms
Showmax: Partnered with AI firms toautomate ad creation and testing, reducing production time by 70% while streamlining their quality assurance process.
Headway: Employed AI tools for ad creation and optimization, boosting performance by 40% and reaching 3.3 billion impressions while incorporating AI-generated content in 20% of their paid campaigns.
High
High
Brand sentiment tracking
Monitor and analyze public opinion about a brand across multiple channels in real time
L’Oréal: Analyzed millions of online comments, images, and videos to identify potential product innovation opportunities, effectively tracking brand sentiment and consumer trends.
Kellogg Company: Used AI to scan trending recipes featuring cereal, leveraging this data to launch targeted social campaigns that capitalize on positive brand sentiment and culinary trends.
High
Low
Campaign strategy optimization
Analyze data to predict optimal campaign approaches, channels, and timing
DoorDash: Leveraged Google’s AI-powered Demand Gen tool, which boosted its conversion rate by 15 times and improved cost per action efficiency by 50% compared with previous campaigns.
Kitsch: Employed Meta’s Advantage+ shopping campaigns with AI-powered tools to optimize campaigns, identifying and delivering top-performing ads to high-value consumers.
High
High
Content strategy
Generate content ideas, predict performance, and optimize distribution strategies
JPMorgan Chase: Collaborated with Persado to develop LLMs for marketing copy, achieving up to 450% higher clickthrough rates compared with human-written ads in pilot tests.
Hotel Chocolat: Employed genAI for concept development and production of its Velvetiser TV ad, which earned the highest-ever System1 score for adomestic appliance commercial.
High
High
Personalization strategy development
Create tailored messaging and experiences for consumers at scale
Stitch Fix: Uses genAI to help stylists interpret customer feedback and provide product recommendations, effectively personalizing shopping experiences.
Instacart: Uses genAI to offer customers personalized recipes, mealplanning ideas, and shopping lists based on individual preferences and habits.
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Questions? We have answers
What is the full form of RTB?
RTB stands for Real-Time Bidding. It refers to the automated process where advertisers bid on individual ad impressions in real time, typically within milliseconds, through programmatic platforms.
What is an example of real-time bidding?
A common example is when a user opens a webpage or mobile app, and an ad slot becomes available. A DSP instantly evaluates the user’s data (context, device, behaviour) and submits a bid in a live auction. If the bid wins, the advertiser’s ad appears — all within the time it takes the page to load.
What is the difference between an auction and real-time bidding?
An auction is a broad concept describing any competitive bidding process.
Real-time bidding, however, is a specific type of automated, impression-level auction that happens in milliseconds within the programmatic ad ecosystem. Unlike traditional auctions, RTB evaluates and prices each impression individually, based on real-time data.
What is the real-time bidding protocol?
The real-time bidding protocol defines how DSPs, SSPs, and ad exchanges communicate during an RTB transaction — including bid requests, responses, user data, creative requirements, pricing, and time limits. The most widely used standard is OpenRTB, which provides the technical rules that allow programmatic platforms to run synchronized, real-time auctions at scale.
How much does RTB cost per CPM?
RTB CPMs vary by format, targeting depth, and competition, but typical ranges are:
Display: $1–$3
Mobile In-App: $2–$5
Video: $6–$12
Audio: $4–$8
Connected TV (CTV): $20–$40+
Premium targeting, high-value audiences, and brand-safe inventory can push CPMs higher.
How do CTV platforms enable real-time bidding?
Connected TV (CTV) platforms integrate with SSPs and programmatic exchanges that support live, impression-level bidding. When a viewer starts a CTV stream, each ad break triggers real-time auctions where DSPs bid for the impression using data such as household demographics, device type, content genre, and viewing history. This allows advertisers to buy TV inventory with the same precision and automation found in digital RTB environments.
Is RTB still relevant in the age of AI automation?
Yes — RTB is even more relevant today. AI doesn’t replace RTB; it supercharges it. Modern RTB relies on AI to optimise bids, predict audience behaviour, filter fraud, and make real-time decisions far faster than humans ever could. AI enhances the RTB engine, enabling advertisers to achieve better performance, stronger ROI, and more precise targeting across channels, including CTV and mobile.
Have other questions?
If you have more questions, contact us so we can help.
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