Baidu AI Cloud Logistics China Translated from Chinese

Deppon Express

Customer Service Automation

As order volume and service complexity grew, Deppon Express needed to lower communication cost, improve response speed, and deliver consistent service across many digital channels without overloading human agents.

The platform connects more than 10 service entrances.
The transfer-to-human rate was reduced to 10%.

Reported impact

Business outcome

Deppon Express created a consistent omnichannel service model for logistics support, reduced manual handoffs, and improved customer-response efficiency while continuing to route complex cases to live agents.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

Deppon Express built an online customer-service platform with Baidu AI Cloud to create a dual-track service model that combines automation with human escalation. The implementation paired Baidu Intelligent Customer Service, Baidu Keyue, and Deppon's online customer-service IM system so the company could standardize logistics-domain scripts, accumulate reusable Q&A knowledge, and expose the same service logic across more than ten customer touchpoints. Incoming questions from mini programs, the website, the mobile app, Alipay, Douyin, and other channels flow into the intelligent layer first, where routine intents are answered automatically and unresolved issues are handed to live agents with a shared knowledge context. The setup gives Deppon one service backbone for consistent logistics answers, faster response times, and lower transfer-to-human rates while preserving human support for complex requests.

Data flow

Questions from WeChat mini programs, the website, the app, Alipay, Douyin, and other service entries are routed into the intelligent customer-service layer. Routine intents are answered from Deppon's logistics knowledge base, while harder cases are transferred into the IM system for human follow-up with the same script and context foundation.

Solution architecture

4 components · 2 layers
  1. Storage
    • Logistics Knowledge Base Stores the domain-specific scripts and Q&A knowledge built from Deppon's logistics service scenarios.
  2. Serving
    • Baidu Intelligent Customer Service Handles first-line automated responses for routine logistics inquiries across digital channels.
    • Baidu Keyue Provides the conversational layer that helps structure Deppon's intelligent online service workflows.
    • Online Customer Service IM System Hosts Deppon's live-service interactions and receives escalations from the AI layer when human support is required.

Architecture clues

  • Deppon built a logistics-specific script system so the same service language can be reused across channels.
  • The deployment uses an 'AI + human' dual-track service model rather than AI-only automation.
  • The knowledge base grows over time as Deppon accumulates logistics Q&A data.

Evidence from the source

Deppon deployed Baidu's intelligent customer-service system to build an 'AI + human' dual-track service model (original: 通过部署百度智能客服系统,构建"AI+人工"双轨服务体系).
The intelligent platform combines Baidu Keyue with Deppon's online customer-service IM system (original: 德邦快递通过建设智能客服平台,搭配百度客悦与在线客服IM系统).
The project built a logistics-industry-specific script system and connected more than ten service entrances including WeChat, Alipay, and Douyin (original: 搭建物流行业专属话术体系,统一对接微信/支付宝/抖音等10余个服务入口).
The transfer-to-human rate was reduced to 10% (original: 转人工率降低至10%).
Transparency Evidence & methodology 90/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Deppon Express customer story

    First-party
    https://cloud.baidu.com/customer/case/deppon-express.html
    Source type
    Vendor-published customer story
    Published
    Jul 7, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 7, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.