• 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).

  • 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.

  • 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.

 

 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.
  • 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.

 

  • 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.

 

  • 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.

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.