How Intelligent Marketing Leverages AI and Big Data to Boost Conversion Rates

01 Introduction

Marketing is the process of discovering consumer needs, informing them about a product, and encouraging purchase. The purpose of marketing is to match product offerings with customer demand, as higher demand correlates with higher purchase probability. Precise demand discovery is essential to avoid wasteful marketing.

02 Drivers

Traditional marketing relies on manual strategy creation, which is time‑consuming and often based on intuition. Intelligent marketing uses extensive databases and user‑centered analysis to accurately profile and target users at the right time and channel, thereby meeting their needs more precisely.

03 Architecture Evolution

Accurate multi‑dimensional user data capture underpins precise operations. Early intelligent marketing used user‑profile tags and operational models to improve conversion and reduce acquisition cost. AB testing showed that automated strategies increased conversion rates significantly compared with manual approaches.

To maximize ROI, the system evolved into a platform with three layers: data, algorithm/strategy, and application.

Data layer includes business data and user behavior logs stored in HDFS (offline) and Kudu (real‑time).

Algorithm strategy layer processes offline and real‑time data, builds user profiles, and applies machine‑learning models (LR, XGBoost, GBDT+LR) for prediction, intelligent reach, and distribution. It also handles lead and seat ranking and automated lead allocation.

Application layer exposes RESTful APIs to deliver model predictions as user‑profile tags or to push leads directly to sales systems.

04 Algorithm Analysis

Feature extraction from massive historical data focuses on recent user activity and marketing interactions. Pre‑processing includes missing‑value imputation and normalization. Feature selection uses variance, correlation, and single‑feature AUC filters. Models are trained, tuned, and deployed online with monitoring.

Logistic Regression (LR) is widely used for conversion prediction due to its scalability, but its linear nature limits learning capacity, requiring extensive feature engineering. Combining Gradient Boosted Decision Trees (GBDT) with LR (GBDT+LR) automatically generates effective feature combinations, improving performance as demonstrated in industry practice.

05 Summary & Outlook

Qudian's marketing business is rapidly growing; precise demand discovery and intelligent marketing are key. Future work includes automated personalized copy generation and incorporating deep‑learning algorithms to further enhance model performance.