We explore how Vision-Language Models (VLMs) are revolutionizing ad click prediction by processing both ad images and detailed user personas. It explains the architecture of VLMs, highlighting the dual-encoder structure and the importance of a shared embedding space and attention mechanisms in understanding the interplay between visual and textual information. The text discusses key VLM models like CLIP, ALIGN, Flamingo, BLIP-2, LLaVA, GPT-4V, and Gemini, outlining their innovations. Ultimately, it describes how VLMs use the persona as a "lens" to personalize understanding and predict click likelihood, emphasizing the impact on personalized marketing, the associated challenges, and the exciting future directions of this technology.