Research on Optimizing and Systematizing Internet Bidding Advertising for Local Lifestyle Services

Driven by the digital economy, the local service industry is accelerating digital transformation, with internet bidding ads becoming core connectors between merchants and consumers through precise targeting and measurable outcomes. This study focuses on ad optimization and system architecture, proposing strategic frameworks to overcome industry bottlenecks.Analyzing mobile internet's 98.3% penetration and China's 3,530 billion yuan O2O market (2019-2023), we identify three challenges in bidding ads: performance volatility, rising costs, and experience fragmentation. Using 120 million ad records from Meituan and Douyin, our multi-factor CTR/CVR models reveal creative quality impacts 37.2% of click-through rates, while landing page experience determines 51.8% of conversions. Experiments show: emotional creatives boost CTR by 42.3%, 10% geo-targeting precision reduces CAC by ¥6.8, and reinforcement learning increases ROI 29.7%.
To address algorithm homogeneity and data silos, we propose a Dual-Cycle Optimization Model: inner-cycle optimizes creatives via LSTM and dynamic bidding; outer-cycle establishes full-chain systems through data platforms. Implemented at Hema Fresh, this reduced CAC by 18.4% and increased in-store conversion 26.9%. A four-dimensional framework includes: 1) knowledge graph-driven creative automation; 2) federated learning cutting budget waste by 32.7%; 3) cross-channel monitoring; 4) adaptive anti-fraud systems.
Case studies show 23.6% lower ad spend with 41.8% order growth for F&B chains, and CAC reduction from ¥58 to ¥32 for home-service platforms. This research pioneers service-scenario parameterization algorithms and closed-loop optimization systems, paving the way for 5G/AR immersive ads and AI-driven neuromarketing.