The Problem
Brand: Mid-size fashion D2C brand, 300+ orders/day, selling across Shopify + Amazon.
Support load: 500+ customer queries/day across WhatsApp, email, and Instagram DMs.
Team: 4 full-time support agents (₹18K/month each = ₹72K/month)
Average response time: 3-4 hours. Customer satisfaction: 3.2/5.
The Solution: AI Chatbot + Human Hybrid
Tool Selected: Interakt (WhatsApp) + Tidio (Website)
Why this combination: Interakt handles WhatsApp (70% of queries), Tidio handles website chat (20%), email remains human-managed (10%).
Implementation Timeline: 3 Weeks
Week 1: Data preparation
- Exported 3 months of support conversations (15,000+ queries)
- Categorized by type: order tracking (38%), returns/exchange (22%), sizing (15%), product queries (12%), complaints (8%), other (5%)
- Created FAQ document: 50 questions with detailed answers
- Mapped common conversation flows for each category
Week 2: Bot setup and training
- Configured Interakt chatbot with the FAQ knowledge base
- Built automated flows: order tracking (connects to courier API), return initiation, size recommendation
- Set up escalation rules: transfer to human if bot confidence is low, or customer asks for human
- Tested with team members pretending to be customers — fixed edge cases
Week 3: Gradual rollout
- Day 1-3: Bot handles 20% of queries (random routing). Humans monitor all bot responses.
- Day 4-7: Bot handles 50%. Human review on bot-resolved conversations to catch errors.
- Day 8-14: Bot handles 80%. Humans only get escalated queries.
- Day 15+: Full deployment. Bot as first point of contact for ALL queries.
What AI Handles vs What Humans Handle
| Query Type | AI Handles? | How |
|---|---|---|
| ‘Where is my order?’ | Yes (100%) | Bot asks for order number → fetches tracking from API → sends status |
| ‘How to return?’ | Yes (90%) | Bot collects return reason → checks eligibility → initiates return ticket |
| ‘What size should I order?’ | Yes (85%) | Bot asks height/weight/usual size → recommends based on size chart |
| ‘Do you have X in blue?’ | Yes (80%) | Bot searches catalog → shows available options |
| ‘Product is damaged’ | Partial | Bot collects photos and details → creates ticket → routes to human for resolution |
| Payment/refund issues | No | Immediately routes to human agent |
| Angry/escalated customer | No | Bot detects negative sentiment → immediate human handoff |
The Results (After 3 Months)
| Metric | Before AI | After AI | Change |
|---|---|---|---|
| Queries handled by humans | 500/day | 180/day | -64% |
| Average response time | 3-4 hours | Under 2 minutes | -98% |
| Support team size | 4 agents | 2 agents (₹36K/month saved) | -50% |
| Customer satisfaction | 3.2/5 | 4.1/5 | +28% |
| Monthly support cost | ₹72K | ₹28K (agents) + ₹5K (tools) | -54% |
| Resolution rate (first contact) | 65% | 82% | +26% |
Key Lessons
- AI handles volume, humans handle complexity — The goal isn’t replacing humans, it’s freeing them for high-value interactions.
- Training data quality matters more than AI model quality — A well-trained simple bot outperforms a poorly trained advanced one.
- Always offer human handoff — The #1 customer complaint with chatbots is being stuck without a way to reach a person.
- Monitor weekly — Review bot conversations weekly. Customers ask new questions. Keep updating the knowledge base.
- Hindi/Hinglish support is mandatory — 45% of queries came in Hindi or Hinglish. The bot needed to understand both.
Want to Implement AI Support?
At Growww Tech, we implement AI chatbots for D2C brands. Let’s reduce your support costs.
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