陆港电商城管理数据

园区管理者通过对入驻企业的租赁楼宇类型、租赁面积等信息，可以了解到企业更倾向于租赁的楼宇类型，并方便衡量企业的营收、规模。这有助于园区在后续的招商过程中，更精准地定位目标企业，制定更有针对性的招商策略，进而提升整个园区的竞争力。数据来源：采集企业入驻时签订的合同信息数据；
数据处理：去除源数据错误、不完整的信息，对隐私数据进行脱敏处理，确保数据的准确性和一致性；
算法处理：企业评分 = sum（合同租赁面积 * 合同租赁时间 * 租赁楼宇类型权重），租赁楼宇类型权重会根据当前运营策略和政策会进行调整，对评分划分为A、B、C、D四级。通过对用户行为的分析有助于理解的当前市场需求，并方便对用户精准服务，制定未来运营方案。

Park administrators can gain insights into the preferred leasing building types of settled enterprises and conveniently measure the revenue and scale of these enterprises by utilizing information such as the leased building type and rental area of the settled enterprises. This enables the park to accurately target prospective enterprises during subsequent investment promotion, formulate more targeted investment strategies, and ultimately enhance the overall competitiveness of the park.
Data Source: Contract information data collected when enterprises sign contracts upon settling in the park;
Data Processing: Erroneous and incomplete information in the source data will be removed, and desensitization processing will be conducted on private sensitive data to ensure data accuracy and consistency;
Algorithm Processing: Enterprise Score = sum (contract rental area × contract lease term × weight of leased building type). The weight of leased building types will be adjusted based on current operational strategies and policies, and the scores are classified into four levels: A, B, C, and D. Analysis of user behavior helps understand current market demands, facilitate precise services for users, and formulate future operational plans.