In this paper, we propose a novel user representation methodology called Multi Datasource LTV User Representation (MDLUR). Our model aims to establish a universal user embedding for downstream tasks, specifically lifetime value (LTV) prediction on specific days after installation. MDLUR uses a combination of various data sources, including user information, portrait, and behavior data from the first n days after installation of the social casino game "Club Vegas Slots" developed by Bagelcode. This model overcomes the limitation of conventional approaches that struggle with effectively utilizing various data sources or accurately capturing interactions in sparse datasets. MDLUR adopts unique model architectures tailored to each data source. Coupled with robust dimensionality reduction techniques, this model succeeds in the effective integration of insights from various data sources. Comprehensive experiments on real-world industrial data demonstrate the superiority of the proposed methods compared to SOTA baselines including Two-Stage XGBoost, WhalesDector, MSDMT, and BST. Not only did it outperform these models, but it has also been efficiently deployed and tested in a live environment using MLOps demonstrating its maintainability. The representation may potentially be applied to a wide range of downstream tasks, including conversion, churn, and retention prediction, as well as user segmentation and item recommendation.