The future of digital marketing lies in the convergence of human creativity and generative AI, where insight, strategy, and storytelling are co-authored by intelligent systems. We present MindFuse, a brave new explainable generative AI framework designed to act as a strategic partner in the marketing process. Unlike conventional LLM applications that stop at content generation, MindFuse fuses CTR-based content AI-guided co-creation with large language models to extract, interpret, and iterate on communication narratives grounded in real advertising data. MindFuse operates across the full marketing lifecycle: from distilling content pillars and customer personas from competitor campaigns to recommending in-flight optimizations based on live performance telemetry. It uses attention-based explainability to diagnose ad effectiveness and guide content iteration, while aligning messaging with strategic goals through dynamic narrative construction and storytelling. We introduce a new paradigm in GenAI for marketing, where LLMs not only generate content but reason through it, adapt campaigns in real time, and learn from audience engagement patterns. Our results, validated in agency deployments, demonstrate up to 12 times efficiency gains, setting the stage for future integration with empirical audience data (e.g., GWI, Nielsen) and full-funnel attribution modeling. MindFuse redefines AI not just as a tool, but as a collaborative agent in the creative and strategic fabric of modern marketing.

The opaqueness of modern digital advertising, exemplified by large platforms such as Meta Ads , raises concerns regarding their control over audience targeting, pricing structures, and ad relevancy assessments. Locked in place by network effects, these natural monopolies attract countless advertisers who rely on subjective intuition, with billions of dollars lost on ineffective social media advertisements. The platforms’ algorithms rely on huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque too, so advertisers often cannot make informed decisions. To promote transparency and help individual advertisers, we first propose novel ways to optimize advertising strategies, predicting click-through rates of novel advertising content based on the content itself. However, advertisers face both opaqueness and a vast abundance of data: a large platform has so many competitor ads that it is hard to derive meaningful insights. Drawing inspiration from the success of Large Language Models (LLM), we propose a system that merges multimodal LLMs and pretrained AI models with an emphasis on digital marketing and advertising data analysis. Leveraging the capabilities of LLMs and incorporating explainability features, including modern text-image models, we aim to improve efficiency and produce synergy between human marketers and AI systems.

The opaqueness of modern digital advertising, exemplified by platforms such as Meta Ads, raises concerns regarding their autonomous control over audience targeting, pricing structures, and ad relevancy assessments. Locked in their leading positions by network effects, "Metas and Googles of the world" attract countless advertisers who rely on intuition, with billions of dollars lost on ineffective social media ads. The platforms' algorithms use huge amounts of data unavailable to advertisers, and the algorithms themselves are opaque as well. This lack of transparency hinders the advertisers' ability to make informed decisions and necessitates efforts to promote transparency, standardize industry metrics, and strengthen regulatory frameworks. In this work, we propose novel ways to assist marketers in optimizing their advertising strategies via machine learning techniques designed to analyze and evaluate content, in particular, predict the click-through rates (CTR) of novel advertising content. Another important problem is that large volumes of data available in the competitive landscape, e.g., competitors' ads, impede the ability of marketers to derive meaningful insights. This leads to a pressing need for a novel approach that would allow us to summarize and comprehend complex data. Inspired by the success of ChatGPT in bridging the gap between large language models (LLMs) and a broader non-technical audience, we propose a novel system that facilitates marketers in data interpretation, called SODA, that merges LLMs with explainable AI, enabling better human-AI collaboration with an emphasis on the domain of digital marketing and advertising. By combining LLMs and explainability features, in particular modern text-image models, we aim to improve the synergy between human marketers and AI systems.

In this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore.

In this technical demonstration, we present SMILEY, a voice-guided virtual assistant. The system utilizes a deep neural architecture ContraCLIP to manipulate facial attributes using voice instructions, allowing for deeper speaker engagement and smoother customer experience when being used in the "virtual concierge" scenario. We validate the effectiveness of SMILEY and ContraCLIP via a successful real-world case study in Singapore and a large-scale quantitative evaluation.

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. Still, McKinsey-style manual categorization is a very labour-intensive task that is probably impractical in a real-world scenario, so automated incorporation of audience behaviour and personality mining into industrial applications is necessary. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Even worse, there is no dataset available for the research community to serve as a benchmark and drive further research in this direction. The present study is one of the first attempts to bridge this important industrial gap, contributing not just a novel personality-driven content recommendation approach and dataset, but also facilitating a real-world ready solution which is scalable and sufficiently accurate to be applied in real-world settings. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations, which when deployed are able to improve digital advertising efficiency by over 420% as compared to the original human-guided approach.

In this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin.ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform can be used for enhancement of the social networking user experience as well as for content marketing routines. Guided by the MBTI personality type, automatically derived from a user social network content, SoMin.ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns. The real-time user feedback to the platform via the platform's GUI fine-tunes the content generation model and the evaluation results demonstrate the promising performance of the proposed multi-view personality profiling framework when being applied in the content generation scenario. By leveraging content generation at a large scale, marketers will be able to execute more effective digital marketing campaigns at a lower cost.