Probabilistic Identity Resolution uses machine learning to infer connections between devices, sessions, and behaviors that likely belong to the same user. It relies on patterns such as IP address ranges, device types, and browsing behavior rather than personally identifiable information.

For example, if a user visits a website on mobile and later completes a purchase on desktop from the same network, probabilistic identity graphs can link those actions with high confidence.

This technology is crucial in a privacy-first world, where deterministic identifiers like cookies or device IDs are increasingly restricted. It enables accurate cross-device tracking, frequency management, and attribution without violating privacy laws.

Predictive Bidding uses machine learning models to forecast the likelihood that a specific impression will lead to a conversion. By analyzing historical data, user signals, time of day, and device context, the system determines how much an advertiser should bid in real time.

For example, a DSP may recognize that users browsing fashion sites at night on mobile devices are 30% more likely to convert. The algorithm automatically increases bids for these impressions while reducing spend on less valuable ones.

This AI-driven approach eliminates guesswork, improves efficiency, and ensures that every euro invested contributes to measurable performance gains. Predictive bidding is one of the key applications of AI in programmatic advertising today.

Programmatic Advertising is the automated process of buying and selling digital ad inventory using software and data-driven algorithms. It replaces manual negotiations with real-time auctions where advertisers bid on individual impressions based on user data and targeting parameters.

The programmatic ecosystem includes several core components: demand-side platforms (DSPs) for buyers, supply-side platforms (SSPs) for sellers, ad exchanges, and data management platforms (DMPs) for audience segmentation.

For example, when a user visits a website, a programmatic auction takes place within milliseconds, determining which ad will appear. This process maximizes efficiency, improves targeting precision, and delivers measurable results for both advertisers and publishers.

Programmatic advertising is now the dominant model for digital media trading—driven by automation, transparency, and real-time performance optimization.

Programmatic Guaranteed is a form of programmatic buying where advertisers purchase a set amount of impressions or inventory at a fixed price, with guaranteed delivery. Unlike open or private auctions, there’s no bidding involved—terms are negotiated upfront, but delivery and reporting are handled automatically through the programmatic infrastructure.

This model benefits advertisers seeking predictable outcomes and premium placements. For example, a global brand launching a new product might reserve homepage takeovers on top-tier publishers through a programmatic guaranteed deal.

Publishers benefit from revenue stability, while advertisers gain access to trusted inventory with full transparency and automation. It’s the digital equivalent of a direct media buy—modernized for the programmatic era.

Programmatic Direct refers to the automated process of purchasing ad inventory directly from publishers without an auction. Both parties agree on pricing, volume, and placement in advance, but transactions are executed through programmatic technology for efficiency and reporting accuracy.

This approach blends the reliability of traditional media buying with the convenience of automation. For example, an advertiser can secure 1 million guaranteed impressions on a publisher’s website for a fixed CPM, while still managing delivery and tracking through a DSP.

Programmatic Direct ensures consistent delivery, predictable costs, and real-time analytics, making it especially valuable for high-budget brand campaigns that require both control and scale.

A Private Marketplace (PMP) is an invitation-only programmatic environment where publishers sell premium ad inventory to selected advertisers under negotiated terms. Unlike open auctions, PMPs provide greater transparency, brand safety, and pricing stability.

In a PMP deal, the publisher defines rules for pricing, ad formats, and buyer access, while advertisers benefit from preferred placement and higher-quality traffic. For example, a major news publisher may reserve its homepage inventory for verified advertisers through PMP deals.

PMPs combine the automation of real-time bidding with the control of direct sales, making them ideal for brands seeking to protect their reputation while maintaining programmatic efficiency.

A Prelander is an intermediary webpage that users visit immediately after clicking an ad but before arriving at the main landing page. Its purpose is to pre-qualify and engage users, providing context or additional motivation that increases the likelihood of conversion.

For instance, an advertiser promoting a health supplement might use a prelander that includes testimonials or a short educational video before directing users to the purchase page. This strategy filters out uninterested traffic and improves conversion efficiency.

Prelanders are common in performance marketing and native advertising, where storytelling and trust-building significantly influence buying decisions. Optimized prelanders enhance the user journey, reduce bounce rates, and boost ROI.

Post-Back Tracking is a method of reporting conversions between an advertiser’s system and a traffic source using server-to-server (S2S) communication. Instead of relying on browser cookies, the advertiser’s server sends a confirmation (postback) to the ad platform once a specific event, such as a sale or registration, occurs.

This technique provides superior accuracy, speed, and privacy compliance compared to traditional client-side tracking. For example, when a user installs a mobile app, the attribution platform sends a postback to the DSP to confirm the source of that conversion.

Post-back tracking ensures that every campaign event is verified directly, preventing duplication and improving reporting precision—especially important in a cookieless advertising environment.

A Pixel, also called a tracking pixel, is a small snippet of code placed on a webpage that collects data when a user visits the site or performs a specific action. It’s invisible to users but invaluable for advertisers seeking to measure performance and understand audience behavior.

When a user views or clicks an ad, the pixel fires and sends information such as page visits, conversions, or time spent to the advertiser’s analytics platform. For instance, a pixel might track when a user completes a purchase after clicking a display ad, confirming campaign success.

In programmatic ecosystems like TwinRed, pixels power retargeting, attribution modeling, and performance optimization. They are fundamental to data-driven decision-making and accurate ROI tracking.

Paid Traffic refers to visitors who arrive at a website as a result of paid advertising campaigns rather than organic search or direct access. It includes traffic from programmatic display ads, paid search (PPC), native placements, and social media promotions.

In programmatic advertising, paid traffic is generated through real-time bidding on ad exchanges, allowing advertisers to target specific audiences based on demographics, interests, and intent signals. For example, a skincare brand might use paid traffic campaigns to reach women aged 25–40 who have recently browsed beauty content.

The quality of paid traffic is measured by engagement rates, conversion rates, and ROI. Continuous optimization ensures that each click or impression contributes meaningfully to campaign goals and long-term business growth.