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Solo AI-powered companion influencer marketing system making 20k RMB monthly

Workflow: At 9:00 AM every day, input the brand's product category, unit price, target ROI, and budget range. The system automatic

AGENT

Key Fields

FIELD STAMPS

IndustryMarketing / Advertising

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

At 9:00 AM every day, input the brand's product category, unit price, target ROI, and budget range. The system automatically crawls historical sales data, audience overlap, and engagement rates of vertical KOLs/KOCs from public data sources on Douyin, Xiaohongshu, and Bilibili, predicts the expected conversion rate of single content pieces through large models, and generates personalized outreach scripts. Humans focus on reviewing high-match influencer lists and script rationality. Upon confirmation, the system batch-sends cooperation invitation direct messages, automatically tracks replies, and collects influencer quotes and media kits. Every night, a placement suggestion report containing real influencer data, expected ROI ranges, and cooperation quote comparisons is delivered to the client, while updating the influencer cooperation database for subsequent reuse.

🛠 Setup Requirements

Requires basic operations of agent building platforms like Coze and Dify, secondary local development relying on open-source project frameworks like AiToEarn, and familiarity with public data interface calling rules and prompt engineering of social media platforms. The technical barrier is medium, with basic Python scripting capability sufficient. Setting up the first complete closed-loop system including data crawling, ROI prediction, and direct message reach takes about two to three weeks. The first two weeks require accumulating at least 100 vertical influencer sample data points to fine-tune the prediction model's accuracy.

🧰 Toolchain

🔧 Coze platform
🔧 Large Language Model API
🔧 AiToEarn open-source project
🔧 Douyin/Xiaohongshu public data crawling plugin

💰 Revenue

The primary monetization model involves charging brands a monthly influencer marketing outsourcing service fee, taking a 10%-15% commission on placement amounts, or charging a fixed single-project service fee of 3,000 to 8,000 RMB. After stable personal operations, managing 2-3 small and medium-sized beauty, apparel, and food brand clients per month yields a monthly income of approximately 20,000 to 35,000 RMB, which can exceed 40,000 RMB in peak months when handling 4 or more clients.

💸 Cost

Main costs include large model API call fees (approx. 200-400 RMB/month), data crawling plugin subscription fees (approx. 100-200 RMB/month), and influencer data platform basic membership fees (approx. 200 RMB/month), totaling about 500-800 RMB per month with almost no other fixed expenses.

⏱ Time Investment

Dedicate about three to four hours daily: 30 minutes in the morning to review the day's influencer matching results and outreach scripts pushed by the system, 1 hour to follow up on communication and quote collection with high-intent influencers, 1.5 hours to adjust and optimize system prompts and data models, and the remaining time to handle client feedback and deliver reports.

🚀 Getting Started

First step for beginners: bootstrap and validate the minimum viable closed-loop with self-funding by picking low-risk categories with unit prices of 50-100 RMB (such as snacks, daily general merchandise), crawling historical sales data of 100 micro-influencers with under 10k followers on Xiaohongshu/Douyin using the system, and generating an influencer placement ROI prediction report after manual verification. Second step: use this report as a case study to contact local small and medium-sized e-commerce brands or white-label merchants, offering 1 free trial placement. After validating actual conversion results, convert them into paid long-term clients.

🔑 Keys to Success

✅ Accumulate real influencer cooperation and conversion data in vertical categories, continuously fine-tuning the ROI prediction model's accuracy to avoid matching deviations caused by model hallucinations.
✅ Manual quality control must be strictly enforced. All system-generated outreach scripts and influencer lists must be manually reviewed before sending to prevent triggering platform anti-spam restrictions that lead to account bans.
✅ Build a tiered influencer cooperation database, distinguishing cooperation schedules and quotes of top-tier, mid-tier, and micro-influencers to enhance the professionalism of client proposal delivery.
✅ Bind 1-2 supply chain resources in vertical categories to additionally provide clients with a one-stop service combining influencers and product inventory, increasing the unit transaction value.

⚠️ 风险

⚠️ Upgrades in social media platform risk control rules for batch direct message outreach may lead to temporary or permanent account bans; preparation of multi-account rotation outreach plans is necessary.
⚠️ When the actual sales conversion rate of influencers deviates significantly from model predictions, it may trigger client complaints or refund requests; expected ROI should be explicitly stated in contracts as a reference value rather than a guarantee.
⚠️ If homogenized competition intensifies, service prices may be driven down, requiring continuous accumulation of vertical category data barriers and influencer resource barriers.

📌 Real Cases

📌 Tezign's launched 3KGen proactive expert agent enables KOLs/KOCs/KOSs to operate automatically and proactively, achieving a 10x efficiency boost in influencer marketing processes, and has served multiple international beauty brands.
📌 TuringMarket's influencer marketing agent launched as an AI-driven influencer outreach manager, having helped over 200 small and medium-sized brands reduce influencer placement communication costs by over 60%.
📌 An influencer marketing SaaS tool built by a domestic independent developer based on the AiToEarn open-source framework currently serves 37 local white-label merchants, stably generating 28,000 RMB in monthly service revenue.

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