In the context of digital transformation empowering growth marketing, marketing data analysis integration and real-time display strategies need to break through the limitations of traditional tools to achieve efficient decision support. The HAIChart system proposed in this study constructs a "generation feedback optimization" closed loop through a reinforcement learning framework, integrating artificial intelligence computing power with user intent insight to address the dual pain points of time-consuming interactive tool operations and lack of understanding of automatic tool intent. The system adopts a dual module design of offline learning and online recommendation, explores the visualization space using Monte Carlo graph search algorithm, and dynamically adapts to requirements by combining composite reward functions (integrating data characteristics, visualization rules, and user preferences in three dimensions); By using a visual prompt mechanism to guide user feedback, multiple rounds of iterative optimization of recommendation quality can be achieved. Experimental verification shows that in the VizML dataset Hit@1 The indicator reached 79. 3%, and the P @ 10 of KaggleBench data query task increased to 79. 2% in the third round. User satisfaction was significantly better than tools such as Voyager2. This achievement constructs a growth oriented marketing intelligent visual recommendation system, which realizes real-time integration of multi-source marketing data and dynamic visualization of core indicators, deeply integrates user intentions to enhance strategic accuracy. In the future, we will expand multimodal interaction and dynamic data support, strengthen decision interpretability and real-time performance, and promote the dual improvement of marketing efficiency and growth potential.

Xiangping Yu. Digital Transformation Empowers Growth Marketing with Marketing Data Analysis Integration and Real-Time Display Strategy. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 119-126. https://doi.org/10.38007/SESR.2025.060212.

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