Programmatic Direct

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.

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.