Attribution modelling lies at the heart of marketing effectiveness, yet most existing approaches depend on user-level path data, which are increasingly inaccessible due to privacy regulations and platform restrictions. This paper introduces a Causal-Driven Attribution (CDA) framework that infers channel influence using only aggregated impression-level data, avoiding any reliance on user identifiers or click-path tracking. CDA integrates temporal causal discovery (using PCMCI) with causal effect estimation via a Structural Causal Model to recover directional channel relationships and quantify their contributions to conversions. Using large-scale synthetic data designed to replicate real marketing dynamics, we show that CDA achieves an average relative RMSE of 9.50% when given the true causal graph, and 24.23% when using the predicted graph, demonstrating strong accuracy under correct structure and meaningful signal recovery even under structural uncertainty. CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.

In e-commerce private-domain channels such as instant messaging and e-mail, merchants engage customers directly as part of their Customer Relationship Management (CRM) programmes to drive retention and conversion. While a few top performers excel at crafting outbound messages, most merchants struggle to write persuasive copy because they lack both expertise and scalable tools. We introduce CRMAgent, a multi-agent system built on large language models (LLMs) that generates high-quality message templates and actionable writing guidance through three complementary modes. First, group-based learning enables the agent to learn from a merchant's own top-performing messages within the same audience segment and rewrite low-performing ones. Second, retrieval-and-adaptation fetches templates that share the same audience segment and exhibit high similarity in voucher type and product category, learns their successful patterns, and adapts them to the current campaign. Third, a rule-based fallback provides a lightweight zero-shot rewrite when no suitable references are available. Extensive experiments show that CRMAgent consistently outperforms merchants' original templates, delivering significant gains in both audience-match and marketing-effectiveness metrics.

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders. NVIDIA is a multinational company known for its computing hardware, especially its graphics processing units (GPUs) and systems on chip units (SoCs) for mobile devices. The company went public on January 22, 1999. While the company remains focused on hardware production, it has implemented deep learning and AI into its GPUs and specific software, such as its autonomous driving platform. The company trades on the NASDAQ (symbol: NVDA) with a market cap of just above $418 billion and employs approximately 23,000 globally.

Recent research (here if you are interested) has shown that people retain information they read in an actual book better than when they read it on a screen. So here are a few book suggestions, a marketing effectiveness reading list for those of you who'd like to get your head around the subject matter, but would like a break from the white papers and infographics. Something to curl up with and really get your head into. Patrick Barwise is speaking at Effectiveness Week in a session called "Beyond the Marketing Budget". So it's no surprise that one of the key powers he and Thomas Barta identify in great marketing leaders is the ability to get out of the marketing silo and work with others in the company who are influencing customer experience.

Data-driven digital marketing is big business. According to eMarketer some 55% of all digital advertising dollars will be driven by programmatic initiatives in 2015 where computer speed and machine learning take precedence over human guess work. By 2016 that number is expected to rise to 63% representing over $20 billion in programmatic ad buys. Legions of data scientists and math Ph.Ds have taken over the digital advertising business. They are serving to enhance efficiency for the notoriously inefficient business of marketing.