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Real Estate Renovation AI Outbound Lead Grading Agency: Intent Screening Service with Monthly Income of 30,000 to 80,000 RMB

Workflow: Import raw lists or public pool leads provided by renovation companies daily. The AI outbound robot completes the first

AGENT

Key Fields

FIELD STAMPS

IndustryMarketing / Advertising

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

Import raw lists or public pool leads provided by renovation companies daily. The AI outbound robot completes the first round of contact using pre-set scripts, extracts 5-dimensional intent signals during the conversation, and automatically tags/grades the leads. Grade A leads are instantly pushed to Enterprise WeChat to notify sales staff for follow-up and appointment scheduling. Grade B and C leads enter a nurturing pool for periodic follow-ups, while Grade D leads are discarded. Human sales staff only intervene after a Grade A lead is identified to make final judgments and schedule store visits; AI handles initial screening, humans handle final review.

🛠 Setup Requirements

Requires proficiency in configuring AI outbound platform backends and the ability to design multi-turn dialogue scripts and intent judgment rules using large models. The setup cycle is approximately 2 to 3 weeks, including script debugging, line application, and CRM system integration. Initially, it is necessary to understand the sales process of the real estate renovation industry and continuously optimize scripts and grading thresholds based on real conversion data. The technical barrier is moderate; no coding is required, but proficiency in using SaaS platforms is essential.

🧰 Toolchain

🔧 Dezhu Intelligent Outbound Platform
🔧 avavox Large Model Outbound Robot
🔧 Meiqia AI Customer Service
🔧 Enterprise WeChat API

💰 Revenue

① Renovation Company Grade A Lead Distribution (Primary Income): Charged per lead/per session. Grade A 100-200 RMB/lead × 50-150 leads per client/month = 5,000-30,000 RMB monthly contribution per client. Serving 4-6 clients totals 30,000-80,000 RMB/month. (Figures disclosed in cases, not independently verified as of 2026); ② Grade B Lead Volume: Charged per lead/per session. Grade B 30-50 RMB/lead × 50-150 leads per client/month = 1,500-7,500 RMB monthly contribution per client. Serves as a supplement to ①; ③ Monthly Subscription for Managed Services: Subscription model based on similar AI customer service fees of 2,500 RMB/month. 2,500 RMB/month × 4-6 clients = 10,000-15,000 RMB/month (Self-reported by merchants, not independently verified); ④ Opportunity Item—Performance-based Commission: Based on a lead conversion rate of 12.3%, commission ratios and actual transaction numbers are not disclosed; income from this path has not yet been calculated.

💸 Cost

Outbound platform subscription fee of 2,000 to 5,000 RMB/month, communication costs of 0.08 to 0.15 RMB/minute (average 3,000 to 8,000 RMB/month), and large model API call fees of approximately 1,000 to 3,000 RMB/month. Total costs account for about 30% to 40% of revenue.

⏱ Time Investment

3 to 4 hours per day. Mornings are spent reviewing the previous day's outbound data and grading accuracy to adjust script rules. Afternoons are for following up on client feedback and handling abnormal line issues. Once a week, report lead quality and conversion data to clients.

🚀 Getting Started

Step 1: Register for a trial account on Dezhu Intelligent or avavox outbound platforms, run a round of simulated calls using a self-written real estate renovation script template, and familiarize yourself with backend operations. Step 2: Find 1 to 2 local small-to-medium renovation companies to provide free trial runs; you cover communication costs and work on a performance-based commission to accumulate real conversion data as case studies. Step 3: Use case data to sign formal contracts based on lead payments, gradually expanding to 4 to 6 clients.

🔑 Keys to Success

✅ Script Design Capability—The ability to naturally extract key intent information such as budget, timeline, and decision-making power within 30 seconds, rather than mechanical Q&A, directly determines the accuracy of lead grading.
✅ Grading Threshold Calibration—The store visit conversion rate for Grade A leads must remain stable above 10% for clients to continue paying; requires repeated parameter tuning based on real transaction data.
✅ Line Stability Management—Outbound numbers are easily flagged as spam; requires rotating multiple lines and controlling daily call volume per number to under 50 calls.
✅ Data Closed-loop Iteration Capability—Review Grade A lead store conversion rates weekly, continuously iterate script logic and grading thresholds, and improve system accuracy through data accumulation to build a compounding service barrier.

⚠️ 风险

⚠️ Regulatory scrutiny on AI outbound calling is tightening; stricter real-name registration and frequency limit policies may be introduced in 2026, directly impacting call volume and revenue.
⚠️ The real estate renovation industry is highly cyclical; when client marketing budgets are cut, such outsourced services are often the first to be reduced.
⚠️ Intensifying homogenization competition as more outsourcing teams access similar platforms to compete for orders with low pricing.

📌 Real Cases

📌 A full-house renovation brand increased its transaction rate by over 15% after integrating the Dezhu Intelligent outbound system, with a 12% conversion rate from Grade A lead connection to transaction (Official case study from ZGC Science and Technology Finance Dezhu Intelligent).
📌 A home decoration brand increased its lead capture rate by 38% after integrating Laogu AI, adding an average of 80 effective leads per month by using AI for initial intent screening before transferring to human sales (Laogu AI 2026 Home Decoration Customer Acquisition Practice Record).
📌 A full-house renovation brand improved lead conversion efficiency by 40% and reduced ineffective contacts by 60% after adopting the Dezhu Intelligent human-machine collaboration model (Zhihu 'From Human Wave Tactics to Human-Machine Collaboration' practice case).

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