Understanding user behavior is essential for improving digital experiences, optimizing business conversions, and mitigating threats like account takeovers, fraud, and bot attacks. Most platforms separate product analytics and security, creating fragmented visibility and delayed threat detection. Trackly, a scalable SaaS platform, unifies comprehensive user behavior analytics with real time, rule based anomaly detection. It tracks sessions, IP based geo location, device browser fingerprints, and granular events such as page views, add to cart, and checkouts. Suspicious activities logins from new devices or locations, impossible travel (Haversine formula), rapid bot like actions, VPN proxy usage, or multiple accounts per IP are flagged via configurable rules with weighted risk scoring, enabling transparent, explainable decisions. A real time dashboard provides global session maps, DAU MAU, bounce rates, and session durations. Integration is simplified with a lightweight JavaScript SDK and secure REST APIs. Implemented on a multi tenant microservices stack (ASP.NET Core, MongoDB, RabbitMQ, Next.js), Trackly achieved 98.1% accuracy, 97.7% precision, and 2.25% false positives on synthetic datasets, proving its efficiency for SMEs and ecommerce.

翻译：理解用户行为对于改善数字体验、优化商业转化以及缓解账户接管、欺诈和机器人攻击等威胁至关重要。当前多数平台将产品分析与安全防护分离，导致可视性碎片化且威胁检测延迟。Trackly作为一个可扩展的SaaS平台，将全面的用户行为分析与基于规则的实时异常检测相统一。该平台追踪会话、基于IP的地理定位、设备浏览器指纹以及细粒度事件（如页面浏览、加入购物车和结算）。通过可配置规则与加权风险评分，系统可标记可疑活动——包括来自新设备或位置的登录、不可能行程（Haversine公式）、快速的类机器人操作、VPN/代理使用或单IP多账户行为，从而实现透明可解释的决策。实时仪表板提供全局会话地图、日活跃用户/月活跃用户、跳出率及会话时长等指标。平台通过轻量级JavaScript SDK与安全REST API简化集成流程。基于多租户微服务架构（ASP.NET Core, MongoDB, RabbitMQ, Next.js）实现的Trackly，在合成数据集上达到98.1%的准确率、97.7%的精确度及2.25%的误报率，证明了其对中小型企业及电子商务场景的有效性。