How Big Data Drives Intelligent Outbound Calls and AI Customer Service

The presentation, originally delivered at Tencent Cloud+ Community’s technical salon, outlines how big‑data techniques are applied to intelligent outbound call systems and AI customer service, focusing on the education sector but also relevant to finance, real‑estate, and automotive industries.

Business Motivation

Marketing suffers from information asymmetry, leading to winner‑takes‑all effects where high‑visibility products dominate despite comparable or superior alternatives. The data team aims to use communication and big‑data technologies to provide digital, intelligent services that increase marketing ROI by identifying high‑value prospects and reducing churn.

Pre‑sale Behavior Prediction

Two prediction modes are used: lead scoring based on phone numbers collected during offline/online campaigns, and audience expansion from existing customer seeds. Scoring evaluates conversion likelihood using big‑data features; audience expansion extracts similar users from the broader market, employing tag‑based sampling and a “Pulearing” process that refines seed users with negative samples before building predictive models.

Data Processing and Modeling

Raw data undergoes extensive preprocessing, followed by experimentation with various algorithms (CNN, GBDT, etc.) and hyper‑parameter tuning. Models are industry‑specific, requiring separate training, online debugging, and deployment, which incurs significant time and resource costs.

TI One Big‑Data Platform

The solution runs on Tencent Cloud’s TI One platform, which supports multiple machine‑learning frameworks. Users can chain tasks into workflows, such as training a model then evaluating its performance, enabling decoupled development and easier integration into production pipelines.

Model Performance and Use Cases

Across four industries, the solution achieves high matching rates (e.g., 93% in education, 87% in real‑estate) when processing a million‑phone‑number dataset. High‑intent leads are prioritized for SMS and outbound calls, improving conversion efficiency.

SMS Marketing Workflow

SMS campaigns are split into sub‑segments (e.g., a 420k high‑intent batch divided into three smaller groups) to test different timing and content strategies. Real‑time monitoring tracks conversion steps, allowing dynamic adjustment of marketing tactics.

Outbound Call Framework

The backend integrates a soft‑switch server (FS) and supports multi‑channel outreach (web, app, WeChat, etc.). Calls can identify user attributes such as gender, education, income, and interests, enabling personalized scripts and dynamic routing.

AI Customer Service Architecture

The AI客服 system combines robot and human agents. It supports single‑turn, multi‑turn, task‑oriented, and chit‑chat bots, leveraging knowledge graphs and data‑analysis tools. A websocket‑based real‑time channel connects web/H5/WeChat front‑ends to the backend, with asynchronous message handling for high reliability.

Algorithmic Details

Incoming queries undergo typo correction, tokenization, and retrieval‑based similarity matching. Initially, a feature‑based similarity score was used; later, deep‑learning CNN models improved accuracy. The system also maintains a 7×24 h support guarantee, with proactive outreach based on detected user interest.

Key Takeaways

Integrating big‑data pipelines with AI enables end‑to‑end marketing automation from lead scoring to post‑sale support.

Industry‑specific models and flexible workflow tools (TI One) reduce time‑to‑value despite heavy preprocessing overhead.

Combining SMS, voice calls, and AI chatbots creates a multi‑channel engagement strategy that improves conversion and customer satisfaction.