In today's increasingly competitive gaming industry, continuous product iteration has become an effective method to maintain user stickiness and extend the lifecycle. However, excessive product iteration frequency often leads to problems such as confused goals, unstable performance, and delayed response, seriously hindering the gradual improvement of game quality. This article mainly focuses on the utilization of data analysis and performance evaluation in game product iteration. This article systematically elaborates on the role and impact of user behavior, performance indicators, and collaborative methods in product optimization and improvement processes. It deeply analyzes the main problems such as lack of data fusion, incomplete performance measurement system, and unscientific collaborative strategies. Measures such as establishing a unified database system, improving performance monitoring network, and building comprehensive and diverse collaborative strategies are proposed to provide practical guidance and achieve good suggestions and scientific theories for high-quality game product iteration, and to move towards the transformation goal of the entire industry from experience driven to big data-driven.

Xia Hua. Data Analysis and Performance Evaluation in Game Product Iteration. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 45-51. https://doi.org/10.38007/IJBDIT.2026.070106.

[1] Clayton R. Efficient instruction scheduling using real-time load delay tracking. Computing reviews, 2023, 64(12):302-302.

[2] Blevins J R. Leveraging Uniformization and Sparsity for Computation of Continuous Time Dynamic Discrete Choice Games. 2024.

[3] Hyyti E, Righter R. Optimal Size-Aware Dispatching Rules via Value Iteration and Some Numerical Investigations. 2024.

[4] Nakhleh K, Eksin C, Ekin S. Simulation-Based Optimistic Policy Iteration For Multi-Agent MDPs with Kullback-Leibler Control Cost. 2024.

[5] Gargiani M, Sieber R, Balta E, et al. Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes. 2024.

[6] Lu, Z. (2025). Design and Practice of AI Intelligent Mentor System for DevOps Education. European Journal of Education Science, 1(3), 25-31.

[7] Yu, X. (2025). Application Analysis of User Behavior Segmentation in Enhancing Customer Lifetime Value. Journal of Humanities, Arts and Social Science, 9(10).

[8] Zheng, H. (2025). Research on Lifecycle Configuration and Reclamation Strategies for Edge Nodes Based on Microservice Architectures. Advances in Computer and Communication, 6(5).

[9] Dingyuan Liu. The Relationship between Household Consumption Pattern Changes under Disasters and the Recovery of Business Ecosystems. Academic Journal of Business & Management (2025), Vol. 7, Issue 12: 151-156.

[10] Zhen Zhong. Big Data Engineering and Intelligent Analysis Framework for Compliance Investigation. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 11: 107-115

[11] Li, J. (2025). The Impact of Distributed Data Query Optimization on Large-Scale Data Processing.

[12] Lu, Z. (2025). AI-Driven Cross-Cloud Operations Language Standardisation and Knowledge Sharing System. European Journal of AI, Computing & Informatics, 1(4), 43-50.