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 1,200 rubles, which with a base of 10,000 churned customers gives an additional 1.2 million rubles 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:

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:

AI auto-calling delivers 3-5x higher conversion due to adaptive scripts and personalized offers. Average savings from returns reach up to 2 million rubles 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?

1. Database audit: data cleaning, LTV calculation, segmentation.
2. Scenario design: develop scripts for each segment.
3. CRM integration: connect via REST API (Bitrix24, AmoCRM, Salesforce).
4. Model training: fine-tune GPT or LLaMA on your data.
5. 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

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.

Speech Recognition and Synthesis: ASR, TTS, Voice Cloning

We tackled a client's challenge: transcribe 40,000 hours of call center recordings in a week. Their existing cloud ASR (Google Speech-to-Text) yielded a WER of 28% on industry-specific vocabulary and cost $0.006 per minute — prohibitively expensive at that volume. The goal was to reduce WER below 10% and switch to self-hosted inference. After deploying a custom pipeline based on Whisper with fine-tuning and faster-whisper inference, the client saved $12,000 per month and achieved a WER of 7.3%.

How does speech recognition ASR handle noisy call center recordings?

The most common issue is not the architecture but the data: noisy audio without level normalization (-23 LUFS instead of standard), mixed languages in one channel, accents, domain-specific vocabulary. Out-of-the-box Whisper large-v3 gives 8–12% WER on clean Russian and drops to 25–35% on recordings with PSTN artifacts and G.711 narrowband codec. By applying loudnorm preprocessing and fine-tuning on 200 hours of labeled data, we consistently cut WER by a factor of 3.

Typical problems we encounter

WER does not converge to the desired metric. Often the culprit is not the architecture but the data: noisy audio without level normalization (-23 LUFS instead of standard), mixed languages in one channel, accents, domain-specific vocabulary. Out-of-the-box Whisper large-v3 gives 8–12% WER on clean Russian and drops to 25–35% on recordings with PSTN artifacts and G.711 narrowband codec.

Diarization fails with more than two speakers. pyannote/speaker-diarization-3.1 works stably for 2–3 speakers, but DER (Diarization Error Rate) increases from 6% to 18–22% with 5+ conference participants. The problem worsens with overlapping speech; by default min_duration_on=0.1 cuts short interjections. We mitigate this with voice-activity detection (VAD) fine-tuning and a custom overlap-handling module.

Voice cloning — latency vs. quality. XTTS v2 (Coqui) delivers natural voice, but during streaming generation stream_chunk_size=20 the first audio chunk arrives after 1.4–2.0 seconds — unacceptable for interactive scenarios. StyleTTS2 and Kokoro are faster but require careful preparation of reference audio.

How do we solve it in practice?

The basic stack for a production pipeline:

• ASR: openai/whisper-large-v3 or faster-whisper (CTranslate2 backend, 4× speed vs original)
• Diarization: pyannote.audio 3.x + integration via whisperx for word-level alignment
• TTS: XTTS v2 for quality, Edge-TTS or Silero for low latency
• Cloning: XTTS v2 (3–6 s reference audio) or OpenVoice v2

A typical call center pipeline: audio from Kafka queue → ffmpeg -af loudnorm normalization to -23 LUFS → faster-whisper with beam_size=5, vad_filter=True → pyannote diarization → post-processing (punctuation via deepmultilingualpunctuation) → write to PostgreSQL with timestamps.

Case study from our practice. A fintech company with 12,000 calls per day. Initial WER on Russian with banking vocabulary — 22% (Google STT). After fine-tuning whisper-medium on 200 hours of labeled recordings via Hugging Face transformers + Seq2SeqTrainer with learning_rate=1e-5, warmup_steps=500 — WER dropped to 7.3%. Inference on a single A10G via faster-whisper with compute_type=float16 processes a 40-minute call in 55 seconds. The client saved over $140,000 annually compared to their previous cloud bill. Contact us for a free pilot estimate to see similar savings on your data.

How to fine-tune Whisper on domain data?

When a general model underperforms, fine-tuning is the first tool. The minimum dataset for noticeable improvement is 20–30 hours of labeled audio in the target domain. Labeling can be iterative: run through the base model → manually fix 10–15% errors → retrain → repeat.

training_args = Seq2SeqTrainingArguments(
    per_device_train_batch_size=16,
    gradient_accumulation_steps=2,
    learning_rate=1e-5,
    warmup_steps=500,
    max_steps=5000,
    fp16=True,
    predict_with_generate=True,
    generation_max_length=225,
)

Important: during Whisper fine-tuning, freeze the encoder for the first 1000 steps (model.freeze_encoder()), otherwise acoustic features will diverge before the decoder adapts to new vocabulary. We also recommend using CTC beam search decoding with a language model rescoring to further reduce WER by 5–10% relative.

Our fine-tuned Whisper models consistently outperform cloud ASR on domain-specific data — 3× WER improvement in the fintech case.

Speech synthesis: How to choose a model for your task?

For interactive bots requiring TTFB < 300 ms — Silero or Kokoro. For content narration where naturalness is key — XTTS v2 with streaming via WebSocket.

Our process and deliverables

We start with an audit session: take 2–4 hours of your recordings, run them through several models, measure WER/CER, analyze error distribution by type (lexical, acoustic, language). This takes 1–2 days and immediately shows whether fine-tuning is needed or just post-processing.

Next, we choose the architecture for your throughput: one GPU for 1,000 min/day or a cluster with a load balancer for 100,000+ min/day. Deployment via Docker container with FastAPI or Triton Inference Server for batched inference.

What you get after engagement:

• Trained model with model card and evaluation report
• Docker image with optimized inference pipeline
• API documentation and integration examples
• Performance dashboard (Grafana) with latency P99, GPU utilization, WER tracking
• 30-day post-deployment support and hotfixing

Timelines depend on complexity:

• Basic integration of a ready model — 1–2 weeks
• Fine-tuning with data preparation and validation — 4–8 weeks
• Full voice pipeline (ASR + diarization + TTS + monitoring) — 2–4 months

Project investments typically range from $20,000 to $80,000. Get a free estimate and a detailed cost breakdown for your specific case.

Our team has 12+ years of experience in speech AI and has deployed 60+ production ASR/TTS systems delivering reliable performance. Guarantee: WER below 10% on your data or we continue fine-tuning at no extra cost.

Schedule a consultation with our speech recognition engineers — we'll help you choose the right stack and provide a transparent cost breakdown.