Customer Service Automation · Marketing Personalization
China Mobile Online needed faster and deeper customer-intent insight than manual sampling and call listening could provide so it could support precision marketing, real-time service intervention, and low-threshold model reuse across business teams.
Reported impact
China Mobile Online gained faster response to operational signals, better customer-intent capture, higher script coverage, and a reusable model-management foundation that improves both service operations and targeted marketing.
Baidu AI Cloud built a real-time intelligent-insight platform for China Mobile Online to analyze service interactions at scale and turn those signals into operational actions. The platform consolidates underlying modeling and analytics capabilities so business teams can create, manage, and iterate insight models without rebuilding data labeling and algorithm components every time. Customer, agent, and scenario data are transformed into intelligent profiles that support intent prediction, complaint handling, script mining, and priority routing across online marketing and service flows. On top of this shared base, the system provides self-service modeling, centralized model management, and real-time knowledge-push functions that surface scripts to agents during calls. The architecture shifts China Mobile Online away from delayed sample analysis toward minute-level warnings, faster clustering, and reusable model services that can be applied across at least ten core production scenarios.
Call and online-interaction data is processed into customer, agent, and scenario profiles on the shared insight platform. Models predict intent, complaints, and routing needs in real time, and the resulting insights are sent into script-push, priority-transfer, clustering, and warning workflows that support marketing and customer-service operations.
Baidu built a real-time intelligent insight platform that helps China Mobile Online capture customer intent precisely, with model accuracy above 80% and script coverage at 85% (original: 百度智能云构建实时智能洞察平台…模型准确率超80%、话术覆盖比率达85%).
Response speed for intelligent-analysis demand should improve 50%, and clustering timeliness should be reduced to under one hour (original: 支撑各单位智能化分析需求响应速度提升50%…聚类分析平均时效性缩减至1小时以内).
The platform aims to form intelligent portrait capabilities for customers, seats, and business scenarios and support at least 10 core scenarios (original: 面向客户、坐席、业务场景，形成智能画像能力…完成不少于10个核心场景的洞察能力建设).
The system supports real-time intent mining and script push during live agent conversations (original: 支持坐席话中实时意图话术挖掘与推送).
Traditional hotline analysis relied on recording samples and manual listening, which could not support precise marketing services (original: 传统热线运营分析基于大批量录音抽样和人工测听…无法支撑精准营销服务).
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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