Observed Signal · May 29, 2026 · Interview · Source: t3n · Impact: 2/5 · Sentiment: Positive
Deep Learning and LLMs Improve Programmatic Targeting
t3n published an interview (May 29, 2026) with Daniel Volož, Managing Director DACH at programmatic advertising provider RTB House, about using deep learning and large language models (LLMs) to improve audience targeting. Volož explains that neural networks and LLMs can identify purchase intent in real time across the open web, and he emphasises that companies’ own first‑party customer data is a key competitive advantage for campaign performance. The article frames AI-powered models as necessary responses to fragmented digital customer journeys where traditional algorithms struggle.
Provides industry-relevant practitioner insight on applying deep learning and LLMs to programmatic targeting and the role of first‑party data, but is an interview rather than a major platform policy or technical release.
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Key Takeaways & Evidence Grounding
- Article published by t3n on 2026-05-29.
- Interviewee: Daniel Volož, Managing Director DACH at RTB House.
- RTB House uses neural networks and Large Language Models (LLMs) to identify purchase intent in real time for targeting on the open web.
- The piece highlights the strategic importance of first‑party customer data for campaign success.
Connected Companies & Entities
1 Entity mappedRelated Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
AI Transforms Affiliate Marketing: Data Ownership is Key
In an interview with ADZINE, Marcel Schöne discusses how large language models (LLMs) and generative AI are reshaping affiliate and partner marketing. He warns that AI adoption has created urgency and uncertainty among advertisers, and stresses that good outcomes require robust data governance, clean tracking data, first‑party data and server‑side tracking. Schöne argues last‑click attribution is widely used but inadequate, and that meaningful attribution is currently impossible when transactions occur entirely inside assistants. He highlights the need for deeper partner relationships (fewer, stronger partners), standardized ways to push structured publisher content into LLMs (Model Context Protocol/MCP), and preparing systems to be “AI Commerce Ready” so transactions can be handled without frontend visits. The interview emphasizes partnerships, data ownership, and foundational data quality as prerequisites for effective AI-driven optimisation.
LLMs Become Default Answer in Ad Tech
Shirley Marschall published an opinion column on 2026-04-29 arguing that large language models (LLMs) have become the default solution across many ad‑tech functions — from creative to media planning — often displacing more specialised, interpretable systems. The column cites major platform products and trends (e.g., Google Performance Max & Gemini, The Trade Desk’s Koa AI and Agents, Meta Advantage+, TikTok Smart+, Pinterest Performance+, LinkedIn Accelerate) and warns of a self‑reinforcing 'mirror room' dynamic where outputs feed new inputs. It also notes OpenAI’s move toward an ad manager and partnerships with ad‑tech vendors (Criteo confirmed; The Trade Desk rumoured) and describes publisher pushback (blocking crawlers, restricting access) as tensions over content ingestion and monetisation rise.
Myths About AI Visibility in LLMs Debunked
Digiday interviewed agency experts who debunk common misconceptions about brands’ visibility in large language models (LLMs). Experts say AI visibility is largely an evolution of SEO — many traditional SEO principles (backlinks, quality content, site health) still matter — but new priorities (earned media, citations, cross-channel signals) are critical. Agencies are experimenting with structured data, earned media and links between paid social and AI visibility. The piece warns there are no silver-bullet hacks, that LLMs draw from many web sources beyond brand sites, and that measurement should shift from click-through metrics to share-of-voice and context. Contributors include executives from RPA, Go Fish Digital, VML and Markacy. A Brainlabs report cited in the article estimates 30% of shoppers now use AI for product research.
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