Adtech explained: definition, ecosystem, benefits, and trends in 2026

Adtech (advertising technology) is the collection of software platforms and tools that enable advertisers, agencies, and publishers to plan, buy, sell, optimize, and measure digital advertising campaigns at scale. Where traditional advertising relied on direct negotiations and bulk media buys, adtech platforms automate these processes using real-time data, machine learning algorithms, and programmatic auctions that execute in fractions of a second.

By 2026, this ecosystem has become essential infrastructure for digital marketing. The numbers prove it: Verified Market Research values the global adtech market at $783.46 billion in 2024, with projections showing growth to $2.55 trillion by 2032 at a 14.3% CAGR. This expansion reflects fundamental shifts in how advertising works. Privacy regulations like GDPR and CCPA have forced the industry to rethink targeting. Third-party cookies are disappearing. AI now powers everything from audience segmentation to creative optimization.

Yet growth alone doesn't explain why adtech matters. The real value lies in what these platforms enable: reaching billions of consumers across connected TV, mobile apps, digital out-of-home screens, streaming audio, and traditional web properties. Doing this efficiently, measuring what works, and protecting brand safety in an environment where ad fraud costs reach $41.4 billion annually requires sophisticated technology.

Publishers need adtech solutions to monetize content and compete for advertiser dollars. Advertisers need them to find the right audiences without wasting spend. Agencies use adtech platforms to manage campaigns across dozens of clients and hundreds of publishers. This interdependence creates an ecosystem where demand-side platforms talk to supply-side platforms through ad exchanges, all while data management platforms and customer data platforms feed audience intelligence into the bidding process.

Understanding this ecosystem helps you make better investment decisions, avoid common pitfalls like made-for-advertising sites, and take advantage of emerging opportunities in retail media networks, connected TV, and privacy-safe targeting solutions.

What is adtech?

Adtech encompasses the software, platforms, and services that power digital advertising transactions. At its core, adtech technology solves a coordination problem: connecting advertisers who want to reach specific audiences with publishers who have inventory (ad space) to sell.

Traditional advertising required direct relationships, manual insertion orders, and limited targeting capabilities. You bought ad space in bulk, hoped your message reached the right people, and measured results weeks later through indirect proxies. Advertising technology changed this by introducing automation, real-time bidding, granular targeting, and immediate measurement. Modern adtech stacks include:

• Platforms for buying ads (demand-side platforms that let advertisers bid on inventory across thousands of sites and apps)
• Platforms for selling inventory (supply-side platforms that help publishers maximize revenue through automated auctions)
• Marketplaces (ad exchanges where buyers and sellers transact)
• Data systems (DMPs and CDPs that collect, organize, and activate audience data)
• Infrastructure (ad servers that decide which ads to show, track impressions, and manage creative assets)
• Specialized services (verification vendors that detect fraud, measure viewability, and ensure brand safety)

These components work together to execute what used to take days or weeks in milliseconds: 

• When someone visits a website or opens an app, the publisher's ad server generates a bid request containing information about the impression, user signals, and contextual data. 
• This request goes to multiple ad exchanges simultaneously. 
• Demand-side platforms evaluate it against their campaign parameters and place bids. 
• The highest bid wins, the ad serves, and performance data flows back to optimize future bids. All of this happens faster than a page loads. 

We’ll walk you through the entire process in the following sections.

Adtech vs. martech

The line between adtech and martech (marketing technology) blurs more each year, but understanding the distinction helps clarify how different tools serve different purposes.

Ad technologies focuse on paid media. These platforms help you buy advertising inventory, deliver ads to target audiences, and measure campaign performance across third-party publishers. When you use a DSP to run display campaigns across the open web, you're using adtech. These systems optimize for metrics like impressions, click-through rates, cost per acquisition, and return on ad spend. They typically rely on anonymous identifiers (cookies, device IDs, contextual signals) and probabilistic data from exchanges.

Martech handles owned and earned media. These tools manage customer relationships, personalize experiences on your own properties, and coordinate marketing across email, social media, content management, and analytics. CRM systems, marketing automation platforms, A/B testing tools, and web analytics all fall under martech. They work with first-party data from customers who've opted in and track engagement, lead generation, lifetime value, and conversion paths within your controlled ecosystem.

Three key distinctions matter:

• Data sources differ fundamentally. Adtech platforms aggregate anonymous signals from bid streams and third-party data providers. Martech systems build persistent profiles from known customers who've given you their information directly.
• Measurement goals diverge. Adtech optimizes media efficiency (reaching the right people at the right cost). Martech optimizes customer relationships (nurturing leads, increasing retention, maximizing lifetime value).
• Control varies. With adtech, you're buying access to audiences on someone else's property. With martech, you're managing experiences on channels you own.

That said, convergence is happening. Customer data platforms now unify first-party data for both advertising and marketing use cases. Retail media networks blend commerce data with programmatic buying. Agency trading desks coordinate paid and owned channel strategies. The future belongs to integrated approaches that use adtech for acquisition and martech for retention, with shared data and measurement frameworks connecting both.

💡 For a deeper exploration of how these technologies complement each other, see our guides on MarTech: Complete Guide to Marketing Technology in 2026 and Adtech vs. Martech: Understanding the Differences and Synergy.

Why adtech matters in 2026

Several forces make adtech solutions more critical now than at any previous point.

• Programmatic dominance continues. eMarketer forecasts that programmatic advertisers will spend more than $200 billion by 2026, representing 92.6% of US display ad spending. Globally, programmatic ad spend reached $595 billion in 2024 and is expected to approach $800 billion by 2028, with about 90% of display buying happening programmatically. Automation isn't the future; it's the present.
• Connected TV is reshaping video advertising. The IAB predicts digital video will capture nearly 60% of total TV/video ad spend in 2025, up from roughly 30% five years earlier. CTV ad spend is projected to grow from $20.3 billion in 2024 to $26.6 billion in 2025. GroupM expects streaming TV revenue growth of 12.9% in 2025 and forecasts CTV will overtake linear TV revenue by 2029. This shift requires programmatic infrastructure that didn't exist a decade ago.
• Commerce media networks are booming. McKinsey estimates U.S. commerce-media networks could exceed $100 billion in ad spending by 2026, growing at a 21% CAGR from 2023-2027. Surveys show nearly 70% of advertisers see better performance in retail media networks, and 82% plan to increase spending. Retailers like Amazon, Walmart, and Target now operate sophisticated adtech platforms that rival traditional publishers.
• Privacy regulations are forcing adaptation. GDPR, CCPA, and similar laws tightened rules around user data collection. Google's delays in deprecating cookies don't change the underlying trajectory: Safari and Firefox already block them, and cookies appear on only about 33% of bid requests as of mid-2024. Publishers are pivoting to authenticated first-party data. Digiday research shows 71% of publishers in Q1 2025 said first-party data drives positive ad results, up from 64% in 2024, and 85% expect its role to increase further in 2026.
• Digital channels keep expanding. Beyond display and video, programmatic is spreading to digital out-of-home (projected to reach $3 billion by 2025, with programmatic DOOH exceeding $1.25 billion by 2026), digital audio (expected to hit $12.16 billion in 2025 and $14.84 billion in 2029), gaming, and immersive formats. Each channel requires specialized adtech marketing capabilities.

⚡ Adtech isn't just about automation anymore. It's about solving for privacy, fraud, and fragmentation while scaling personalized experiences across every screen consumers use.

The bottom line: reaching audiences at scale while respecting privacy, avoiding fraud, measuring accurately, and optimizing budgets requires sophisticated advertising technology. Companies that understand how these systems work have a decisive advantage.

💡 To understand how AI is reshaping targeting capabilities, see What is AI targeted advertising and why it’s changing everything.

Core components of Adtech

The adtech ecosystem consists of specialized platforms that handle different parts of the advertising transaction. Each component solves specific problems, but they all interconnect to enable programmatic advertising at scale.

Demand-side platforms (DSPs)

A demand-side platform is software that allows advertisers and agencies to purchase digital ad inventory across multiple ad exchanges and supply-side platforms through real-time bidding. Instead of negotiating with individual publishers or buying bulk packages, DSPs enable media buyers to purchase impressions on a per-impression basis.

DSPs automate and optimize ad buying using data about demographics, behavior, and location. Advertisers can scale campaigns across display, video, and mobile at lower costs while maintaining control over targeting, frequency, and creative delivery.

The core benefits include:

• Automation and scalability: DSPs execute bids in milliseconds across thousands of publishers. Media buyers gain immediate access to global inventory and can launch cross-channel campaigns without building individual publisher relationships.
• Cost efficiency and ROI optimization: Advertisers pay only for impressions that meet their criteria. Machine learning algorithms continuously optimize bids and targeting to maximize conversions while minimizing waste.
• Advanced targeting capabilities: DSPs ingest audience segments from DMPs and CDPs to deliver hyper-targeted ads based on demographics, browsing behavior, purchase intent, location, or contextual signals.
• Centralized management: Instead of logging into multiple publisher dashboards, buyers control all campaigns from a single interface with unified reporting and budget allocation.

Major DSPs include The Trade Desk, Google Display & Video 360, Amazon DSP, Verizon Media DSP, and MediaMath. Each offers different inventory sources, data integrations, and optimization features.

Supply-side platforms (SSPs)

Supply-side platforms help publishers manage and sell their ad inventory across ad exchanges and ad networks. SSPs provide yield management, pricing controls, and audience insights, allowing publishers to maximize revenue while giving advertisers access to quality inventory.

When a user visits a publisher's site or app, the SSP packages information about that impression and sends bid requests to multiple DSPs simultaneously. It then selects the highest bid and returns the winning creative to the publisher's ad server. This process, known as header bidding, ensures publishers get the best possible price for each impression by creating competition among demand sources.

SSPs have evolved beyond simple auction management. Leading platforms now focus on three priorities:

• Quality and transparency. Major SSPs have removed made-for-advertising (MFA) sites from their exchanges. Index Exchange reported that after eliminating MFA inventory, the top 10 buyers who previously spent on MFA sites increased their spend 39% year-over-year as budgets reallocated to legitimate publishers.
• Curated marketplaces. Rather than offering undifferentiated open auctions, SSPs are creating curated deals that bundle premium inventory with specific data, measurement, or creative capabilities. Index Exchange's Marketplaces beta launched over 60 partner marketplaces in 2024, enabling buyers to activate solutions at the supply layer.
• Publisher controls. SSPs give publishers tools to set price floors, block unwanted advertisers, manage ad quality, and control how their inventory appears in bid requests. Better controls improve the experience for both publishers and advertisers.

Key SSPs include Google Ad Manager, PubMatic, Magnite, OpenX, and Index Exchange.

Data management platforms (DMPs) and CDPs

Data platforms collect, organize, and activate audience information. Two types dominate the adtech industry: DMPs and CDPs.

• Data management platforms (DMPs) house audience and campaign data from various sources including websites, apps, cookies, and mobile IDs. A DMP helps marketers create audience segments for targeting in digital advertising campaigns. DMPs aggregate anonymous data from multiple sources, apply machine learning to identify patterns, and build targetable segments. DMPs "talk" to DSPs by passing segment data for bidding decisions, then ingesting performance data to refine those segments over time. They excel at third-party data integration and probabilistic matching but struggle with persistent identity as cookies disappear.

• Customer data platforms (CDPs) collect and normalize customer data (both known and anonymous) across channels into a persistent, unified database. CDPs provide marketer-controlled segmentation and orchestration, enabling cross-device identity resolution and improved personalization. Unlike DMPs, which focus on campaign data for advertising, CDPs unify all customer interactions for both marketing and advertising use cases.

The key difference: DMPs optimize for campaign reach using anonymous signals; CDPs build persistent customer profiles using first-party data. As privacy regulations tighten, CDPs are becoming more important for advertisers who need durable identity solutions.

Ad servers

An ad server is the central platform that decides which ads to display, serves the creative, and tracks impressions, clicks, and conversions. Ad servers allow marketers to upload campaigns and manage how, when, and where ads appear.

Both advertisers and publishers use ad servers, but for different purposes:

• Advertiser ad servers store creative assets, track campaign performance across multiple publishers, manage frequency capping (limiting how often someone sees the same ad), and provide centralized reporting.
• Publisher ad servers manage available inventory, rotate ads based on rules or priorities, ensure contractual obligations are met, and optimize yield by balancing direct-sold campaigns with programmatic demand.

Ad servers provide the measurement infrastructure for digital advertising. When an ad serves, the ad server records an impression. When someone clicks, it tracks the click. When they convert, it attributes that conversion back to the right campaign. This data feeds back into optimization algorithms that improve future performance.

Ad exchanges

Ad exchanges are digital marketplaces where DSPs and SSPs transact in real time. Ad exchanges act as matchmakers that connect buyers and sellers and facilitate transparent auctions.

When a bid request arrives, the exchange broadcasts it to multiple DSPs simultaneously. Each DSP evaluates the opportunity based on the advertiser's targeting criteria, budget, and bidding strategy. They submit bids, and the exchange rewards the ad space to the highest bidder through real-time bidding. The entire process happens in under 100 milliseconds.

Exchanges operate different auction types:

• Open auctions let any buyer bid on inventory. These maximize competition and often deliver the highest CPMs for publishers, but quality control can be challenging.
• Private marketplaces (PMPs) restrict bidding to invited buyers. Publishers use PMPs to offer premium inventory to select advertisers at negotiated floor prices.
• Programmatic guaranteed combines the automation of programmatic with the certainty of direct deals. Buyers and sellers agree on price and volume upfront, then execute through programmatic pipes.

Major ad exchanges include Google Ad Exchange (AdX), OpenX Exchange, Magnite, and Index Exchange.

Agency trading desks (ATDs)

Agency trading desks are specialized programmatic buying units within advertising agencies. An ATD is a technology or service that media agencies provide to plan, buy, and manage advertising campaigns.

ATDs sit between advertisers and DSPs, using proprietary technology and specialized expertise to optimize programmatic buying. They allow advertisers to purchase media at scale without building in-house programmatic capabilities and often negotiate better rates through aggregated buying power.

The value proposition includes access to multiple DSPs and private marketplaces, proprietary data partnerships, advanced analytics and attribution modeling, and dedicated teams that stay current with programmatic innovations.

For a detailed comparison of how these platforms interact, see DSP vs. SSP vs. Ad Exchange: understanding the differences in programmatic advertising.

How the adtech ecosystem works

Think of the ecosystem as a fast, repeatable loop. A team defines goals and audiences, supply packages opportunities, buyers compete in auctions, and the winning creative is served and measured. Each step produces data that improves the next cycle, so accuracy and consent handling matter as much as media price.

Step 1: Campaign creation and targeting

Everything starts with intent. The advertiser (or agency) sets objectives (reach, CPA, ROAS, incremental sales), budgets and pacing, and guardrails (brand safety, geo, contexts to include or exclude). They then assemble the inputs a DSP needs to buy well.

• Audiences: consented first-party segments from a CDP, contextual categories, lookalikes, and retail/partner signals from clean rooms where appropriate.
• Creative: sizes and formats for display, video, CTV, audio, and DOOH, with variations for testing and dynamic elements (e.g., product feeds).
• Controls: frequency caps, day-parting, bid ceilings, supply preferences (e.g., direct paths, preferred marketplaces), and measurement tags.
• KPIs & attribution plan: how success will be judged (view-through rules, experiments, matched-market tests, or clean-room measurement).

Before moving on, the team validates consent handling, confirms data contracts with partners, and aligns on how conversions will be captured (pixels, server-to-server, or both). Getting this right prevents wasted spend and disputed results later.

Step 2: Bid request and inventory management

With the campaign ready, the supply side prepares opportunities. A publisher ad server decides when a programmatic call should happen, then an SSP packages the impression and sends a bid request through the exchange to eligible buyers.

• What’s inside a bid request (subject to privacy rules): placement metadata (size, position), content/app context, device and connection details, ad format, basic geo, user signals where permitted, and any deal ID for PMPs or programmatic guaranteed.
• How supply stays clean: ads.txt/sellers.json alignment, supply-chain objects (schain) for path transparency, traffic-quality filters, brand-safety labels, and floor-price rules.
• Publisher yield choices: which demand partners to call, when to favour direct deals, how to balance user experience, and whether to use header bidding to widen competition.

The outcome of this step is a stream of valid, well-described opportunities that buyers can appraize quickly and fairly.

Step 3: Real-time bidding (RTB) and auctions

Each qualifying DSP receives the request and decides whether to bid, how much to bid, and which creative to serve if it wins. This evaluation happens in milliseconds.

• Scoring the opportunity: the DSP matches the request to the advertiser’s audiences and contexts, checks frequency and recency, forecasts incremental value, and applies pacing.
• Bid calculation: models weigh win probability against expected outcome (e.g., conversion or attention), apply bid caps, and respect any deal terms.
• Auction mechanics: most exchanges now run first-price auctions with soft floors; ties are broken by priority rules. Private marketplaces can change priority or fees.

Header bidding and unification: multiple demand sources compete in a unified auction so the highest valid bid wins, improving price discovery.

💡 For background on how RTB differs from broader programmatic buying, see Programmatic vs RTB

Step 4: Ad delivery and measurement

When a bid wins, the exchange returns the decision to the publisher ad server, which retrieves the creative from the advertiser ad server or CDN and renders it in the page, app, TV stream, audio slot, or DOOH screen.

• Serving and quality checks: viewability measurement starts, brand-safety and fraud detection run, and any verification tags fire.
• Conversion capture: pixels or server-to-server events record outcomes such as purchases, sign-ups, or store visits. On CTV and some retail environments, conversions are often matched in clean rooms to protect identity.
• Attribution and incrementality: teams compare exposed vs control groups, run geo or time-based experiments, or use multi-touch models to estimate contribution.
• Feedback loop: the ad server and DSP logs feed performance back into bidding, frequency, and creative decisions so the next impression is priced and selected more intelligently.

That closes the loop. A well-run ecosystem keeps signals flowing, honours consent, and uses every impression to learn—so planning, buying, delivery, and measurement continually sharpen each other.

Benefits of adtech

Adtech pays off when each component is clear on its job and the data between them is dependable. The gains come from scale, precision, and the ability to value every impression against an outcome that matters to the business. Below are the practical benefits teams see when the stack is configured well.

Scalable reach

Programmatic marketplaces aggregate supply from thousands of publishers and apps, so a single plan can reach qualified audiences at national or international scale. 

⚡Scale without frequency control becomes duplication.‍

The DSP evaluates each impression in real time, letting you concentrate spend where attention is available and waste is low. 

As you grow, private marketplaces and programmatic guaranteed deals add quality and predictability without losing the efficiency of automation. 

Crucially, frequency controls and suppression lists keep scale from turning into duplication, so additional budget delivers incremental reach rather than the same users again.

Advanced targeting & personalisation

Modern targeting blends what you know with what the impression can tell you. 

First-party segments from your CDP activate through the DSP, clean rooms enable privacy-safe joins with publishers or retailers, and contextual signals add meaning where IDs are limited. 

Lookalike or predictive models can extend reach while staying inside consent boundaries. 

On the creative side, dynamic templates adapt copy, visuals, or product sets to the context or audience, so the message matches the moment without creating manual work for every variation.

Budget efficiency & ROI optimisation

Adtech treats every opportunity as an investment decision. 

⚡ The cheapest path isn’t always the lowest cost—fees hide in the hops.‍

Bid strategies weigh win-rate, expected value, and price floors to avoid overpaying. 

Pacing and day-parting smooth delivery across the flight so spend lands where response is strongest. 

Supply path optimisation favours direct, transparent routes that reduce fees and fraud risk. 

On the back end, verified conversions and incrementality tests show which media actually moves outcomes, so budgets shift from cost-per-something to proven business impact.

Cross-channel advertising (CTV, DOOH, in-app)

People move between screens, so your plan should too. 

A unified stack coordinates reach and frequency across web, apps, connected TV, audio, and digital out-of-home. That makes sequential storytelling possible—introduce the idea on CTV, reinforce with short mobile video, and close with a high-intent display or retail media touch. Triggers such as location, weather, or store hours can drive timely DOOH and mobile combinations, while SDK-level in-app signals help refine attention and fraud controls. 

⚡ One plan across screens beats five siloed plans every time.

💡 For a deeper dive on television specifically, see Connected TV advertising.

Analytics & measurement

An ad server provides the canonical log of impressions and clicks; verification tools measure viewability, invalid traffic, and brand safety; and clean rooms or server-to-server setups link exposure to outcomes without leaking identities. 

⚡ If it isn’t logged in the ad server, it didn’t happen.

With that foundation, you can run experiments, compare exposed vs control groups, and combine short-term attribution with longer-horizon mix models. 

The real advantage is feedback: performance data flows back into bidding, audience selection, and creative rotation so the system gets sharper each week, not just each campaign.

⚡ The best Adtech strategies don't just optimize individual channels. They unify measurement across every touchpoint to understand the complete customer journey.

Challenges in adtech

Adtech delivers reach and control, but it also inherits the industry’s toughest problems: shifting privacy rules, invalid traffic and brand suitability threats, complex stacks that don’t always talk to each other, and competition that pushes up prices for quality attention. Below is a clear view of what to expect and how to respond.

Cookieless future & privacy regulations

As mentioned earlier—after years of signalling a full phase-out, Google said on 22 April 2025 that third-party cookies will remain in Chrome, pivoting to a user-choice approach rather than a hard deprecation timeline. That change reduces immediate shock for buyers, but it doesn’t roll back signal loss across Safari, Firefox and mobile ecosystems—or the need for strong consent and data minimisation. Privacy-first activation, first-party data, clean rooms and contextual intelligence remain essential.

⚡ Cookies may linger, but consented identity is the durable asset.

Regulators are still watching closely. The UK Competition and Markets Authority has kept “concerns” on Google’s ad-privacy approach and competition effects, signalling that governance—and therefore technical change—may continue. Teams should plan for variability by keeping identity-agnostic tactics ready and ensuring consent records and data contracts are auditable.

Industry guidance mirrors this path: IAB’s 2025 State of Data highlights the shift to first-party data, alternative IDs and data clean rooms as the durable response to signal deprecation. Treat these as core capabilities, not add-ons.

💡 Further reading: In a cookie-less world: new challenges and opportunities

Ad fraud and brand safety risks

Fraud and low-quality supply drain budgets and distort reporting. The ANA’s 2025 Programmatic Transparency work found $26.8B in global media value lost to programmatic inefficiencies, with strong recommendations to tighten supply paths and avoid made-for-advertising (MFA) inventory. In parallel, ANA benchmarking shows MFA impressions fell sharply since 2023 as buyers cleaned up supply—proof that active governance works.

⚡ Eliminate MFA and low-quality paths before you optimize bids.

Independent estimates put fraud’s global cost in the tens of billions annually; while numbers vary, the pattern is consistent and warrants continuous verification, pre-bid filtering, strict allow-lists, experiment-based validation and log-level audits with partners.

Complexity of integrations & fragmentation

Verified Market Research observes that the adtech market has become saturated with numerous vendors offering overlapping services, creating confusion for buyers and compressing margins. Programmatic supply chains remain opaque, with significant portions of ad spend lost to fees and inefficiencies.

Integrating disparate systems and data sources proves difficult. Only 15% of publishers report effectively reaching users across browsers, and fewer advertisers have the technical resources to unify data across walled gardens like Google, Meta, and Amazon. Complex ad stacks also increase operational costs, requiring specialized teams to manage.

⚡ Standardize taxonomies now; integration pain shrinks later.

Rising costs and competition

Competition for high-quality, brand-safe inventory—especially premium video and CTV—pushes prices up, even as overall market growth moderates. Forecasts for 2025 still point to digital capturing ~73% of global ad revenue, which concentrates demand in digital channels and intensifies auctions for top-tier placements. 

Expect more pressure to prove incrementality, prioritize direct, transparent supply paths and negotiate PMPs or programmatic guaranteed for predictability.

👉 What to do next: keep a dual-track plan. Maintain cookie-compatible tactics where allowed, but invest in first-party data, clean rooms and contextual models so performance doesn’t depend on any single identifier. Pair that with supply-path optimisation, verification and controlled tests to defend ROI in a market where quality attention has become scarcer—and pricier.

Examples of adtech in action

Real value shows up when the components you’ve just met work together. Below are three recent, concrete adtech examples—one from the buy side (DSP), one from the sell side (SSP), and one from the marketplace (exchange)—that illustrate how decisions, data, and contracts translate into outcomes.

Example 1: How DSPs optimize cross-channel campaigns

A leading healthcare agency ran a controlled, head-to-head test across two DSPs to see whether consolidating CTV, online video, and display into a single buying plan with shared frequency controls would cut waste and lift reach. Using DeepIntent, they tightened CTV frequency, coordinated creative across channels, and executed programmatic-guaranteed deals with premium CTV partners.

Mechanics that mattered:

• One DSP controlled pacing and frequency across CTV, OLV, and display—reducing overlap and duplicative reach.
• The plan mixed open-auction with programmatic guaranteed for quality CTV supply, keeping delivery predictable while still optimizing price.
• Verified health audiences ensured spend reached the intended patient cohorts.

Results: The team reinvested 23% of its media budget through cost savings and engaged 71,000 additional verified patients without increasing spend; they also closed multi-million-dollar PG deals with Hulu, Paramount, and Roku, alongside display and OLV buys.

Example 2: SSPs helping publishers maximize yield

Publisher group Inspira Grupa worked with Setupad to integrate an agency SSP (Adform SSP) directly into their Prebid stack. This placed the agency’s demand in live competition with existing SSPs and resellers in the publisher’s header bidding setup.

Mechanics that mattered:

• Setupad enabled the publisher’s direct SSP connections alongside 15 reseller SSPs, increasing bid density and creating more price competition.
• With the agency SSP live, Adform SSP became the highest bidder in the stack, forcing other SSPs to raise bids dynamically.
• The relationship expanded into preferred deals for premium campaigns once the value was proven, further improving predictability and price.

Outcomes observed: Setupad reports that revenue and eCPM rose across channels after the agency SSP integration; their case study includes time-series graphs showing total SSP revenue and eCPM climbing after Adform SSP joined the auction.

Example 3: Ad exchanges enabling programmatic scale

Index Exchange removed all made-for-advertising (MFA) supply from its exchange in 2024, then expanded curated marketplaces to concentrate quality at scale.

Mechanics that mattered:

• Exchange-level policy (zero-tolerance for MFA) simplified supply-path optimisation for buyers and reduced low-quality impressions.
• Curated marketplaces leveraged unthrottled access to supply while letting marketers activate data and measurement partners at the exchange layer—efficient scale without adding hops.

Results: After eliminating MFA, spend reallocated to legitimate publishers; the top 10 buyers who had previously spent on MFA increased their spend by 39% YoY. Index also reported rapid growth in its curated Marketplaces, citing efficiency and lower costs from activating data closer to supply.

💡 Related reading for applications in commerce and omnichannel planning: Retail digital marketing strategies for 2026: driving growth in a digital-first world.

Adtech trends and the future in 2026

The near-term direction of adtech is shaped by three forces: smarter automation, channel shifts (especially CTV and retail media), and stricter data rules. Below, we unpack what each means for planning, execution, and measurement—plus where to invest now so 2026 performance improves, not just spend.

AI-driven programmatic advertising

AI is already inside bidding, pacing, and creative selection. What’s changing is fidelity and coverage. Models that once optimized toward proxy clicks are learning from richer outcomes (sales, lifetime value, attention) and applying those learnings across formats and channels. In practice, that means more accurate bid prices, tighter frequency control, and creative that adapts to context without manual versioning. 

PwC’s global outlook notes AI is cutting cost and time in content creation and—combined with more accurate TV measurement—helping smaller advertisers access connected TV (CTV) inventory once out of reach.
💡 For background on automation fundamentals, see Programmatic advertising

Growth of CTV and retail media networks

CTV continues to absorb brand budgets as measurement improves and inventory scales. PwC tracks CTV’s share of traditional broadcast TV ad revenue rising from 5.9% in 2020 to 21.5% in 2024, on course to 44.7% by 2029. 

Industry forecasting covered by AdExchanger points to double-digit streaming TV ad growth in 2025 and nine US streaming services each surpassing $1B in ad revenue by 2026—a sign of breadth, not just depth, in the market.

Retail media is the other major growth engine. McKinsey projected retail/commerce media to reach $100B+ by 2026, with 70% of advertisers reporting better performance than other channels and 82% planning to increase spend—driven by high-quality, near-purchase signals and closed-loop measurement. 

💡 Read next: What is a retail media network: definition, trends, insights.

Privacy-first adtech (first-party data, clean rooms)

Signal loss hasn’t stopped; it has shifted. The durable playbook pairs consented first-party data with clean rooms to join with publisher or retailer signals, then activates via DSPs without exposing identities. 

PwC also flags the regulatory pressure around AI and data use—clarity is still evolving, so consent capture, purpose limitation, and audit trails remain non-negotiable. 

Expect more value to migrate from third-party cookies to publisher APIs, retail audiences, and contextual models that are measurable and privacy-safe.

Omnichannel strategies (DOOH, audio, gaming)

Attention is fragmented across environments that never relied on cookies: programmatic DOOH, digital audio, CTV, and gaming. The near-term advantage comes from coordinating these in one plan so frequency and creative sequencing work together. 

Typical patterns: CTV for storytelling, mobile video for reinforcement, DOOH for location-timed prompts, and audio for habit-based reach. 

The operational shift is to treat these channels as first-class citizens in your DSP, with their own brand-safety, fraud, and measurement setups rather than bolt-ons.

Convergence of adtech and martech → hybrid strategies

Boundaries are softening. CDPs feed the DSP with consented audiences and suppressions; ad servers return exposure logs to owned-channel analytics; clean rooms reconcile media touchpoints with CRM outcomes. 

The winners are building a hybrid operating model: marketing ops governs data and permissions, while media ops governs supply paths and measurement. Shared taxonomies, shared experimentation frameworks, and a single place to resolve identity and consent keep this scalable.

AI-driven creative optimisation & automation

Creative is becoming data-literate. Templates ingest product feeds and contextual signals; models pick the right combination of message, image, and format; and experiments run continuously so the best variant serves more often. 

PwC’s analysis of AI’s cost and time savings in content creation—and its role in making CTV more accessible—underscores why creative automation is moving from “nice to have” to core workflow.

💡 Explore our approach: Smart Supply.

Deeper integration with CTV, OTT, and streaming platforms

Expect tighter technical and commercial integrations: more programmatic guaranteed inventory, unified ad pods, and cleaner APIs for measurement. As streaming services scale, their ad businesses behave more like premium publishers, with private marketplaces and curated supply to maintain quality. Use direct, transparent paths where possible and coordinate creative and frequency across TV and digital video so each impression adds incremental value. 

💡 For channel primers, see What is OTT advertising and Streaming TV advertising: 

👉 What this means for your 2026 plan: invest in AI-assisted execution where you can audit outcomes; shift identity work to consented first-party data and interoperable clean rooms; treat CTV and retail media as core, not experimental; and design campaigns to travel across formats with one measurement spine.

Conclusion: How to choose the right adtech advertising strategy

If you’ve read this far, you’ve seen how the moving parts fit together: data governs who you reach, marketplaces decide what it costs, and measurement closes the loop. The “right” strategy is the one that matches your goals, data maturity, and channel mix today while building the capabilities you’ll need six months from now.

Five practical moves to put on your 2026 plan:

1. Invest in platforms when the job is clear.
   
   • DSP for cross-channel scale, frequency control, and outcome optimisation.
   • CDP + clean room when you have consented first-party data to activate and need privacy-safe joins.
   • Advertiser ad server if you need a single audit trail and creative decisioning across partners.
   • SSP and publisher ad server investments are for sell-side teams; buyers should prioritize clean supply paths and high-quality PMPs/PG deals.
2. Adopt a hybrid operating model (adtech + martech): Create one shared data spine (taxonomy, identity, consent) across marketing ops and media ops. Let the CDP govern permissions and audience definitions, and let the DSP execute with clear guardrails. Use clean rooms at the seams so results flow back into owned-channel analytics without exposing identities.
3. Make measurement multi-method, not single metric: Pair short-term attribution with experiments and matched-market tests. Use the ad server as your canonical log, verification for quality, and incrementality tests to decide where the next dollar should go. This keeps budgets moving toward proven impact rather than chasing surface-level metrics.
4. Tighten supply and creative where it counts: Prefer direct, transparent supply paths; set allow-lists; use private marketplaces for premium video and CTV. On the creative side, standardize templates, feed in product and context signals, and run continuous experiments so the best variant serves more often.
5. Plan for identity variability: Keep cookie-compatible tactics where they’re permitted, but build durability with first-party audiences, publisher and retail signals, and contextual models. Treat privacy and consent records as product features, not paperwork.

If you’d like a hands-on plan for your team, reach out to AI Digital. We’ll map your current stack, identify the quickest wins, and design a test-and-learn roadmap that proves value before you scale.