| Advertise
What is AdTech?
A practical guide to understanding the fundamentals of advertising technology.
Today, marketing teams face a perfect storm: navigating complex advertising platforms while being pressured to deliver results with shrinking budgets. As privacy regulations continue to evolve and third-party cookies phase out, advertisers find themselves struggling to effectively reach their target audiences.
While many professionals recognize their current advertising approach isn't working optimally, they hesitate to venture into the seemingly overwhelming world of advertising technology solutions.
Without a strategic AdTech framework, campaigns risk wasting valuable resources on irrelevant impressions instead of connecting with high-value prospects who are ready to engage.
This article cuts through the complexity of AdTech. Learn how advertising technology simplifies the buying and selling process, and adapts to the new privacy-focused reality.
Plus, discover practical strategies for optimizing your ad spend, and leveraging AI advancements to help you stay ahead in an increasingly competitive marketplace.
What is AdTech?
AdTech refers to the tools and systems that connect advertisers with publishers. This enables efficient ad buying and selling across online channels like social media, websites, and streaming platforms.
The purpose is to help businesses reach the right audience with the right message while optimizing costs and outcomes. It helps create a sophisticated ecosystem that automates previously manual processes and enables precise targeting that traditional advertising methods can't match.
Key players in the digital advertising technology ecosystem include:
- Demand Side Platforms (DSPs): Tools that allow advertisers to bid for ad space programmatically from websites, apps or other online channels (e.g., buying banner ads on a website in real-time).
- Supply Side Platforms (SSPs): Platforms for publishers to manage and sell their ad inventory (e.g., auctioning ad spots during a popular streaming show).
- Ad Networks: Companies that connect advertisers with websites or apps to display ads (e.g., a network that handles ads across mobile games).
- Ad Servers: Technology that delivers ads to audiences and tracks performance (e.g., ensuring a specific ad plays before a YouTube video).
The AdTech stack: A complete ecosystem
The advertising technology stack functions as an interconnected ecosystem. Multiple platforms and tools work together to streamline the entire advertising process from planning to execution to measurement.
Think of the AdTech stack as a factory assembly line: DSPs are the machines buying materials, SSPs are the suppliers, DMPs are the managers organizing production schedules, and ad servers are the delivery trucks getting products to customers.
Each component plays a critical role in ensuring ads reach the right people at the right time while maximizing value for both advertisers and publishers.
Here's how each component works within this ecosystem:
- DSPs (Demand Side Platforms): DSPs leverage real-time bidding and machine learning to instantly identify optimal ad placements matching advertiser criteria. Unlike walled gardens (Google, Meta, Amazon), DSPs provide access to the entire open internet, expanding reach across diverse digital properties.
- SSPs (Supply Side Platforms): Used by online publishers and digital media owners, to manage, sell, and optimize their ad inventory. SSPs automatically connect publisher inventory to multiple ad exchanges and networks, creating competition among buyers that drives higher revenue for the owners.
- DMPs (Data Management Platforms): Collect and organize data to help marketers understand who their audience is and how to target them effectively. The focus is on building comprehensive user profiles. They bring together first-party data (from company’s own websites and apps), second-party data (from partners), and third-party data (from external providers) to build comprehensive audience profiles.
- Ad Servers: Deliver ads and track how well they perform in real-time. They store the creative assets, determine which ads to show based on targeting parameters, and gather performance metrics like impressions, clicks, and conversions.
When these components work together seamlessly, that is where the real magic happens! Advertisers precisely target specific audiences across multiple channels while publishers maximize the value of their inventory. This helps create a more efficient marketplace for digital advertising.
How AI and machine learning are transforming AdTech
Today, 35% of B2B marketers say AI implementation is their biggest priority this year. At the juncture of rapidly evolving technology and the need to prove ROI, marketing teams have begun to rely on AI and machine learning as a potential solution. Here’s how AI and ML are fundamentally transforming three critical areas of online advertising:
Automated bidding optimization
AI uses real-time data to bid on ad spaces where audiences are most likely to convert. These systems analyze thousands of signals in milliseconds to determine the optimal bid amount for each impression opportunity. For instance, AI might recognize that showing a shoe ad to someone browsing fashion sites on weekday evenings results in higher conversion rates. Then, it automatically adjusts bidding strategies to prioritize these placements.
Advanced bidding algorithms can now factor in demographic information, behavioral patterns, device usage, and weather conditions. This helps determine the perfect moment and price point for ad display, capabilities that were impossible with manual bidding approaches.
Predictive analytics for audience insights
Machine learning predicts what customers will want based on their past behavior, allowing advertisers to anticipate needs. These systems identify subtle correlations between seemingly unrelated behaviors that indicate purchase intent.
For example, predictive models might recognize that customers purchasing home office equipment will likely be interested in productivity software within the next 30 days. This enables advertisers to proactively recommend new products to frequent online shoppers before they've actively started searching for them.
Dynamic creative optimization (DCO)
AI adapts ads in real time to match the viewer's preferences, creating personalized experiences at scale. Instead of creating dozens of ad variations manually, advertisers develop component libraries (images, headlines, CTAs) that AI systems assemble based on what will most likely resonate with each viewer.
This technology enables tailoring a travel ad to show Paris for one person and Tokyo for another based on their search history. DCO helps adjust the messaging, imagery, and offers based on where a customer sits in their buyer's journey. The most sophisticated DCO systems can generate thousands of creative variations and continuously optimize based on performance data, essentially running thousands of A/B tests simultaneously.
Navigating privacy-first advertising
While AI and ML could have set the stage for personalization and targeting at scale, the digital advertising landscape is undergoing a fundamental transformation. Third-party cookies, the backbone of online targeting and measurement, are being phased out across major browsers. This shift stems from increasing consumer privacy concerns, regulatory pressure from legislation like GDPR and CCPA, and tech platform policies that prioritize user privacy.
AppTrackingTransparency (ATT) is a privacy framework Apple released in April 2021 that requires apps to request permission to track a user across other apps on iOS 14.5+ devices. ATT posed yet another hit to third-party identifiers, specifically Meta’s Pixel.
For advertisers who have relied on cookies to track user behavior across the web, this change creates significant challenges in targeting, personalization, and attribution. Campaigns that once depended on detailed cross-site tracking must now find new approaches to reach relevant audiences without the same level of individual tracking.
Several privacy-friendly alternatives are emerging to fill this gap:
- Contextual Advertising: Matching ads to the content a person is viewing rather than their personal profile. For example, placing an ad for running shoes on a fitness blog targets users based on their current interests rather than past behavior. This approach respects privacy while still delivering relevant ads by focusing on content alignment instead of user tracking.
- First-Party Data Strategies: A study by LinkedIn and Boston Consulting Group found that more than 90% of marketers believe greater use of first-party data will play either a critical or important role in response to privacy changes. This includes information collected directly from a company's own websites or apps with user consent such as site registrations, purchase history, email subscriptions, and direct customer interactions.
As you invest in your first-party data, build audience trust with clear and transparent messaging around the data value exchange (how do users benefit by sharing their info?), as well as providing straightforward opt-in and opt-out opportunities.
Within your organization:
1. Consider creating a data center of excellence:
This means breaking down intern al data silos, engaging data leadership stakeholders, merging disparate datasets, and resolving identity. This also means ensuring your organization has a plan to manage compliance and consent regulations for using customer data across the regions you may be operating in.
2. Determine an optinal martech stack:
This could include a customer data platform or a data clean room to support the management and activation of your first-party datasets ensuring anonymity of user data, as well as measuring return on spend.
3. Work with partners who have quality first-party data:
Marketers who have traditionally relied on third-party data should take stock of how those datasets will be impacted by privacy changes and ensure they are working with publishers and platforms that offer quality first-party data that will be more resilient.
Within your customers:
1. Trust above all else:
Brands need to establish a clear value exchange, ensure transparency as to how data will be used and provide clear opt-ins and outs.
"Even as privacy concerns mount, about
30%
of respondents said they are willing to share their email address with a given company for no incentive.
90%
are willing to share that data when presented with the right value exchange.
To own your data game, leverage insights tools such as Audience Insights and shape your target audiences through Matched Audiences. At launch, start broad and consider a plan to A/B test the same ad creative with different audiences to ensure your message is resonating.
Privacy-preserving measurement techniques
As individual tracking becomes more restricted, advertisers are adopting new measurement approaches that balance performance insights with privacy protection:
- Aggregated measurement: Instead of tracking individual user journeys, these techniques analyze performance data at a group level. For instance, rather than seeing that a specific person clicked an ad and made a purchase, advertisers can see that 100 people saw the ad and approximately 5 purchases resulted—providing statistical insights without personal tracking.
- Data clean rooms: Secure environments where multiple parties can analyze combined datasets without exposing individual user data. For example, a retailer might bring their first-party customer data into a clean room provided by a media platform, allowing both parties to identify valuable audience segments and measure campaign impact without either side gaining access to each other's raw user data. In fact 60% of the marketers who have adopted data clean rooms, have seen an improvement in advertising and marketing ROI.
- Probabilistic attribution models: These use statistical modeling and machine learning to estimate campaign impact rather than directly tracking user conversion paths. By analyzing patterns in anonymous data, these models can predict which channels and touchpoints are most influential in driving conversions.
As these privacy-preserving approaches mature, they're creating a more sustainable balance between effective advertising and consumer privacy. This has resulted in more respectful, consent-based marketing practices.
Last click performance metrics overstate the role of activities such as search and display by 2- 10x. Choose to avoid them for correct analysis of your data.
Getting started with AdTech
With the rapid innovation in advertising, algorithms, laws and technology, many professionals feel overwhelmed by the array of platforms and tools, unsure where to begin or how to build a coherent strategy. Rather than allowing these challenges to create paralysis, a structured approach can transform this complexity into a competitive advantage.
This practical roadmap helps organizations of any size navigate the AdTech landscape effectively:
- Define objectives: Align campaigns with business goals, such as driving website traffic or increasing sales. Start by identifying what success looks like for your organization—whether that's building brand awareness, driving conversions, or increasing customer lifetime value. These objectives will determine which metrics matter most and which advertising technologies you'll need to deploy.
- Choose platforms: Decide whether to use DSPs, SSPs, or ad networks depending on your goals and audience. For advertisers looking to reach specific audiences across multiple publishers, a DSP offers the most control and targeting precision. Publishers seeking to maximize revenue from their digital properties will benefit from SSP integration. Smaller businesses with limited resources might start with self-serve ad platforms or networks that offer simplified buying processes before advancing to more sophisticated technologies. Platforms like LinkedIn offer robust AdTech capabilities, including Sponsored Content and Lead Gen Forms, to help marketers efficiently reach professional audiences and capture qualified leads.
- Understand your audience: Use tools like DMPs to identify your ideal customer and tailor your approach. Gather and analyze audience data from your CRM, website analytics, social media, and other channels to build detailed profiles of your target customers. This information helps you create more relevant campaigns and avoid wasting budget on unqualified prospects. Consider what demographic, behavioral, and contextual signals will help you identify your highest-value audience segments.
Gather and analyze audience data from your CRM, website analytics, social media, and other channels to build detailed profiles of your target customers. LinkedIn’s Matched Audiences feature allows you to integrate this data for precise B2B targeting across professional segments.
- Set budgets and KPIs: Plan ad spend and establish metrics like clicks, conversions, or ROI to measure success. Determine how much you're willing to invest in your advertising efforts. Plus identify how you'll allocate that budget across different channels and campaigns. Establish clear key performance indicators that align with your objectives, keeping in mind that different campaign types may require different success metrics—from cost per click for awareness campaigns to return on ad spend for direct response efforts.
- Launch and optimize: Continuously refine campaigns based on real-time data from ad servers. AdTech’s greatest advantage is the ability to measure performance and make adjustments in real-time. Implement a testing strategy that allows you to compare different audience segments, creative approaches, and bidding strategies. Use the insights gained to continuously improve campaign performance, shifting budget toward what works and away from what doesn't.
Measuring success in digital advertising campaigns
Marketing leaders often struggle to translate complex metrics into meaningful insights that demonstrate true business impact.
With dozens of data points available across multiple platforms—teams find themselves either drowning in numbers or focusing on vanity metrics that don’t capture actual contribution to business growth. By identifying the right KPIs and understanding how they connect to business objectives, advertisers can cut through the noise and build campaigns that deliver measurable returns.
Here are the essential key performance indicators that provide clarity on campaign performance:
- Reach: How many unique people saw your ad. This metric helps you understand the scale of your campaign and whether you're successfully connecting with your target audience. High reach doesn't guarantee success. However, insufficient reach means your message isn't being seen by enough potential customers to drive meaningful results.
- Engagement: Actions people took, like clicks, shares, comments, or time spent viewing content. Engagement metrics indicate whether your creative and messaging resonate with audiences. Strong engagement suggests your content is relevant and compelling. While low engagement may signal a disconnect between your ads and audience interests or needs.
- Conversions: Sales, sign-ups, or other outcomes driven by the ad. These metrics directly connect advertising to business results. Conversion metrics should align with your campaign objectives, whether that's generating leads, driving purchases, or encouraging app downloads.
- Ad Spend Efficiency: ROI from impressions, clicks, and conversions compared to your budget. These metrics—including cost per click (CPC), cost per acquisition (CPA), and return on ad spend (ROAS)—show how efficiently your campaigns deliver results. They help you compare performance across different channels, campaigns, and creative approaches to optimize your advertising investment.
For B2B marketers specifically, LinkedIn offers specialized measurement capabilities designed for longer, more complex buying cycles. Their suite includes tools for tracking engagement across the entire funnel—from awareness to consideration to decision.
With features like the Revenue Attribution Report and Conversions API, LinkedIn helps marketers connect advertising efforts directly to business outcomes. In fact, Bhanu Chawla, Global Head of Demand Gen & Growth at Tractable says, “LinkedIn’s Revenue Attribution Report tool has opened up new levels of granularity for monitoring funnel metrics and understanding the overall impact of LinkedIn ads. Being able to now leverage this information has made it super helpful for analyzing LinkedIn’s end-to-end influence as a channel.”
Case study: Cognism and Revenue Attribution
The marketing team of Cognism knew that campaigns were influencing sales pipeline. However, their traditional last touch attribution, reliant on click-based UTMs, made it difficult to connect marketing spend to business outcome. After connecting their CRM to LinkedIn’s Business Manager to access the Revenue Attribution Report, Cognism learned that LinkedIn-influenced deals had:
• 1% shorter deal cycles
• 14% larger deal sizes
• 8% higher likelihood to close.
To this effect, Cognism has seen a 2x increase in ROAS from LinkedIn.
Advantages and challenges of advertising technology
While AdTech offers powerful capabilities that were unimaginable just a decade ago, implementing these solutions effectively requires navigating complex technical, strategic, and ethical considerations.
Understanding both sides of this equation helps marketers develop realistic expectations and build campaigns that maximize advantages while mitigating potential pitfalls.
Advantages
- Highly targeted advertising tailored to specific audiences. AdTech allows marketers to define audiences with remarkable precision. It helps companies reach people based on demographics, interests, behaviors, and even intent signals. This ensures positive ROI on ads, dramatically increasing efficiency compared to traditional media's broader approach.
- Real-time feedback to improve campaigns quickly. Unlike traditional advertising that might take weeks or months to measure, digital campaigns provide immediate performance data. This instant feedback loop enables marketers to optimize messaging, creative elements, and targeting parameters, rather than long periods.
- Scalability to reach audiences across multiple channels. AdTech solutions enable brands to deploy consistent messaging across websites, social platforms, mobile apps, and connected TV simultaneously. As campaigns prove successful, they can be scaled up instantly by increasing budgets. Additionally, companies could expand into new channels without the production delays associated with traditional media.
- Cost efficiency with flexible budget controls. AdTech allows for precise budget management with options to set daily, weekly, or lifetime spending caps. This flexibility enables marketers to test campaigns with minimal investment, making it accessible to businesses of all sizes.
Challenges
- Difficulty tracking performance across platforms. The fragmented nature of digital media consumption makes it challenging to follow customer journeys that span multiple devices and platforms. When a prospect sees an ad on social media, researches on mobile, but converts on desktop, attributing that conversion to the correct touchpoint becomes complex.
Solution: Implement cross-device tracking solutions and multi-touch attribution models that provide a more complete view of the customer journey.
- Ad fraud risks, like bots clicking on ads instead of real people. Bots drain advertising budgets without delivering real human engagement. This includes bot traffic, click farms, domain spoofing, and invisible ad placements.
Solution: Brands in the Web2 world have begun to use traffic filtering and machine learning to detect anomalies and ensure ad interactions are legitimate. While in Web3, further advancements in this field have led to the introduction of tamper-proof ledger for ad interactions, enhancing transparency and security.
- Privacy regulations impacting targeting capabilities. Evolving privacy laws like GDPR, CCPA, and the phasing out of third-party cookies are fundamentally changing how advertisers can collect and use consumer data, creating challenges for personalization and measurement.
Solution: Develop first-party data strategies, implement proper consent management, and explore privacy-preserving targeting alternatives like contextual advertising.
AdTech has fundamentally transformed how businesses connect with customers in the online world. By leveraging AdTech solutions, marketers gain unprecedented abilities to target specific audiences that impact the bottomline, while maintaining spends.
As we move forward, embracing AI-powered solutions will become increasingly critical for competitive advantage. Machine learning algorithms that can optimize bidding strategies, predict audience behavior, and personalize creative content at scale will separate leading marketers from those falling behind.
Similarly, developing privacy-first measurement approaches that respect consumer preferences will be essential as regulatory landscapes continue to evolve.
The time to build your AdTech strategy is now. By understanding the fundamentals of the different types of digital advertising and related technology, you can create more efficient, effective, and measurable campaigns that drive real business results.
Get started with LinkedIn Ads
