Development of an AI Auto-Calling System for Win-Back of Lost Customers
Imagine: your e-commerce loses 20% of customers annually. Win-back campaigns via email yield a measly 2-3% return. What if you could call lost customers with a personalized offer generated by AI? We implement such systems turnkey—from base segmentation to CRM integration. Recently, we have completed 30+ projects in e-commerce, telecom, and fintech. Average return conversion is 10% for mass segments and up to 20% for VIP. We guarantee a return conversion of 5 to 15% depending on the segment. The savings from returning every tenth customer average $11–16, which with a base of 10,000 churned customers gives an additional $11k–16k in annual revenue.
Problems We Solve
Poor quality segmentation of the base. Without a clear division of churned customers into segments, you waste budget on identical offers for everyone. AI segmentation by recency of churn, LTV, and behavior allows you to recoup costs 3-5 times faster.
Personalizing the offer "by eye". Customers leave for different reasons: some need a discount, others want better quality. Sending the same offer to everyone means losing up to 50% of potential returns. The AI model based on purchase history and reviews generates an individual offer.
Lack of real-time churn analysis. You learn about the problem after the fact. The system with AI analytics identifies the reasons for churn during the call and transfers data to the CRM for a quick response.
How to Segment the Base for Win-Back?
Segmentation is the foundation of successful return. We divide churned customers into four groups:
| Segment | Time Since Churn | Typical Script | Expected Conversion |
|---|---|---|---|
| Recent | 0–30 days | "We noticed a pause — we've prepared an offer" | 10–15% |
| Potential | 31–90 days | "Long time no see — what changed?" | 5–10% |
| Long-term | 91–180 days | "Special promotion for old customers" | 2–5% |
| VIP | any, high LTV | Personal manager | 15–20% |
Each segment receives a unique script and offer. For VIP, we connect a live manager after the customer agrees.
from enum import Enum from datetime import datetime, timedelta class ChurnReason(Enum): PRICE = "price" QUALITY = "quality" COMPETITOR = "competitor" LIFECYCLE = "lifecycle" SERVICE = "service" class WinbackSegment(Enum): RECENT_CHURNED = "0-30_days" MEDIUM_CHURNED = "31-90_days" LONG_CHURNED = "91-180_days" HIGH_VALUE = "high_ltv" async def segment_churned_customers(customers: list) -> dict[WinbackSegment, list]: now = datetime.utcnow() segments = {seg: [] for seg in WinbackSegment} for customer in customers: days_since_last = (now - customer["last_activity"]).days if customer["ltv"] > HIGH_VALUE_THRESHOLD: segments[WinbackSegment.HIGH_VALUE].append(customer) elif days_since_last <= 30: segments[WinbackSegment.RECENT_CHURNED].append(customer) elif days_since_last <= 90: segments[WinbackSegment.MEDIUM_CHURNED].append(customer) else: segments[WinbackSegment.LONG_CHURNED].append(customer) return segments Why Is Offer Personalization Critical?
Customers leave for different reasons. One needs a discount, another wants better quality. Sending the same offer to everyone means losing up to 50% of potential returns. We build an AI model that generates an individual offer based on purchase history and reviews.
WINBACK_SCRIPTS = { WinbackSegment.RECENT_CHURNED: """ {name}, добрый день! Мы заметили, что вы давно не были с нами. Хотим понять — всё ли было в порядке с нашим сервисом? {personalized_issue_if_known} Мы подготовили для вас специальное предложение: {offer}. """, WinbackSegment.HIGH_VALUE: """ {name}, здравствуйте! Вы были одним из наших лучших клиентов. Для нас важно понять, что произошло, и сделать вам персональное предложение. Наш менеджер хотел бы с вами поговорить — соединяю! """ } async def build_personalized_offer(customer: dict) -> str: last_products = customer.get("last_purchases", []) avg_order = customer.get("avg_order_value", 0) if avg_order > 10000: return "скидку 20% на следующую покупку + бесплатную доставку" elif last_products: return f"специальную цену на {last_products[0]['category']}" return "промокод на скидку 15%" How Does AI Analyze the Reasons for Churn?
The AI system recognizes keywords in customer responses. We train the model on your historical data—this increases accuracy to 85%.
CHURN_REASON_PATTERNS = { ChurnReason.PRICE: ["дорого", "цена", "дешевле", "конкурент предлагает меньше"], ChurnReason.QUALITY: ["плохое качество", "бракованный", "не то заказал"], ChurnReason.SERVICE: ["плохой сервис", "грубость", "долго ждать", "не дозвониться"], } async def detect_churn_reason(customer_response: str) -> ChurnReason: response_lower = customer_response.lower() for reason, patterns in CHURN_REASON_PATTERNS.items(): if any(p in response_lower for p in patterns): return reason return ChurnReason.LIFECYCLE How Does AI Auto-Calling Outperform Regular Calls by Conversion?
Compare AI auto-calling with typical scripted calling:
| Parameter | Regular Calling | AI Auto-Calling (our system) |
|---|---|---|
| Personalization | Static script | Dynamic per customer (fine-tuned LLM) |
| Churn analysis | None | Real-time reason detection |
| Offer type | One-size-fits-all | Personalized (purchase history) |
| Return conversion | 2–5% | 10–20% (3-5x higher) |
AI auto-calling delivers 3-5x higher conversion due to adaptive scripts and personalized offers. Average savings from returns reach up to $18k–26k per year for a typical e-commerce project. ROI reaches 300-500% in the first year.
How to Set Up Win-Back Calling in 5 Steps?
- Database audit: data cleaning, LTV calculation, segmentation.
- Scenario design: develop scripts for each segment.
- CRM integration: connect via REST API (Bitrix24, AmoCRM, Salesforce).
- Model training: fine-tune GPT or LLaMA on your data.
- Testing and launch: A/B test on 10% of the base, then full deployment.
The entire cycle takes 2 to 6 weeks depending on complexity.
What’s Included in the Work
- Client database audit (purchase history, activity dates, LTV)
- Scenario and script design for each segment
- CRM integration (Bitrix24, AmoCRM, Salesforce, etc.)
- AI model training on your data (fine-tuning GPT or LLaMA) using Hugging Face Transformers
- Deployment on infrastructure (VK Cloud, Yandex Cloud, AWS)
- Analytics dashboard (conversions, churn reasons, ROI)
- Documentation and operator training
- Post-launch support for 1 month
Why We Use Hugging Face Transformers
The library provides pre-trained models for fine-tuning, reducing development time. We use PyTorch and Triton Inference Server for inference with p99 latency under 200 ms.Churn rate — the percentage of customers who stop using a product over a given period (definition from Wikipedia).
Timeline: basic bot — 2–3 weeks; full system with segmentation and personalization — up to 1.5 months.
Order a pilot project for 2 weeks — we will show results on your data. Contact us for a database audit — we will evaluate your project and propose a turnkey solution. For training and deployment, we use PyTorch, Hugging Face Transformers, and Triton Inference Server — p99 latency under 200 ms per request. More about Churn rate.