Social Growth Engine

智能社交增长引擎：自动化与用户获取优化

Data-Driven Automation & Organic User Acquisition

Data Processing & Distribution Pipeline with Feedback Loop
Project Type

Growth Infrastructure

B2B Service

Platform

Xiaohongshu/LinkedIn

My Role

Product Engineer & Growth Lead

Engineer

Tech Stack

Python, n8n, SQL, Headless CMS Workflow, A/B Testing Framework

Programming

1. The Challenge

背景与挑战

Goal

To To architect a scalable, zero-ad-spend growth engine for B2B lead generation on Xiaohongshu (Red Note) and LinkedIn without increasing manual headcount.

Pain Points

  • Operational Bottleneck: Content distribution was manual and unscalable, consuming 15+ hours/week of human resources.
  • Data-Blind Strategy: Decision-making relied on subjective intuition rather than objective metrics, leading to volatile engagement rates.
  • Unsustainable CAC: High effort-to-lead ratio made organic acquisition costly and unpredictable.

2. My Role

我的角色

Product Growth Lead & Engineer

Led the end-to-end development of automation infrastructure and data-driven optimization strategies.

Core Focus
  • Full-Cycle Automationn
  • AHypothesis-Driven Testing
  • Agile Growth Iteration
Tech Stack
Python n8n SQL Data Analysis
Technical Implementation

Instead of a rigid script, I built a modular Python system that acts as a "digital employee."

Configurable Personas

I defined Python dictionaries to manage different brand voices (Role A vs. Role B), allowing the system to switch strategies instantly.

Weighted Randomness

The system doesn't just loop; it probabilistically selects content topics (e.g., 50% Case Studies, 50% Methodology) based on the account's growth stage.

Human-in-the-Loop

The script integrates with Feishu/Lark Webhooks, pushing generated drafts to my phone for a one-click copy/approval, ensuring AI safety.

# ==========================================
#  STRATEGIC CONFIGURATION
#  Translating Marketing Personas into Code
# ==========================================
ROLE_CONFIG = {
    "A": {
        "name": "Official/Business Account",
        "persona": "Senior expert, authoritative, data-driven.",
        "mode_weights": {
            "case_study": 50,    # Focus on building authority
            "methodology": 50,   # Focus on sharing SOPs
            "avoidance": 0,      # Not suitable for brand image
        },
        "card_color": "blue",    # Professionalism
    },
    "B": {
        "name": "Operations/Traffic Account",
        "persona": "Insider, sharp criticism, emotional resonance.",
        "mode_weights": {
            "avoidance": 50,     # High engagement potential
            "trends": 50,        # Gossip & Hot topics
        },
        "card_color": "orange",  # Eye-catching
    }
}
def job(role=None):
    """
    Main Workflow:
    1. Identifies current role (A/B)
    2. Selects content strategy based on weights
    3. Fetches trends & data
    4. Generates content via AI
    5. Pushes to Feishu/Lark for approval
    """
    # ... (Setup code omitted) ...

    # 1. Strategy Selection (Weighted Random)
    mode = random.choices(modes, weights=weights)[0]

    # 2. Dynamic Context Injection
    if mode == "avoidance":
        target_item, prompt_context = prepare_avoidance_mode(history, role)
    elif mode == "trends":
        target_item, prompt_context = prepare_trends_mode(role)

    # 3. AI Generation (DeepSeek/OpenAI)
    # Uses specific prompts based on the Persona defined in Config
    full_text = generate_final_post(
        target_item,
        prompt_context["data_input"],
        role=role
    )

    # 4. Human-in-the-loop (Feishu Push)
    if full_text:
        push_feishu_card(token, title, body, role=role)
        print(f"✅ Content generated for {role} in {mode} mode.")

3. The Solution

解决方案 - 核心部分

A dual-track approach: Engineering a scalable automation pipeline while running high-velocity experiments to crack the growth code. (双轨制：一边造流水线，一边做高速实验).

Phase 1: Automation Infrastructure

I architected an end-to-end automation pipeline to replace manual operations:

Headless Publishing Pipeline

Engineered Python scripts + n8n to decouple content creation from distribution, enabling "set-and-forget" scheduling.

Context-Aware Assembly

Programmed dynamic CTA injection based on account logic (Business vs. Personal IP) to optimize conversion paths.

Impact

Reduced manual operational load by 50%, shifting team focus from "execution" to "strategy."

Phase 2: Agile A/B Testing & Optimization

Instead of guessing, I applied an agile testing framework to validate content performance across 3 key dimensions:

Experiment #01: Visual Psychology

Hypothesis: Do professionals prefer aesthetic vibes or information density?

Result::"Structured Knowledge" covers outperformed artistic ones by 2x during peak business hours (12:00 PM).

Experiment #02: Content Granularity

Hypothesis: Broad tags vs. Vertical SOPs.

Result:Deep-dive SOPs posted at 23:00 achieved highest "Save Rate," validating the "Anxious Professional" persona.

Experiment #03: Value Proposition

Hypothesis: Tools vs. Insights.

Result: Tool-based posts drove Leads (DMs); Intel-based posts drove Authority (Follows).

A/B Testing Log

Some Test Logs I Used for A/B Testing

Automation Web App

Design a Web App for Friendlier Use

4. The Results

成果 - 用数据说话

By combining automation efficiency with data-driven creative adjustments, I achieved:

60%
Reduction in CAC

Achieved through purely organic traffic growth and automated workflows.

35%
Increase in CVR

Lift in "View-to-Lead" conversion via optimized CTA placement and timing.

50%
Time Saved

Reduction in daily operational time through automation

Scalable System

From "Intuition" to "Algorithm". Transformed a chaotic manual process into a predictable, code-based growth machine that scales linearly.

Key Takeaways

"Build the car while driving it." This project proved that technical leverage (Automation) combined with scientific marketing (A/B Testing) is the fastest path to product-market fit. It wasn't just about saving time; it was about operationalizing creativity.