Product recommendations shouldn’t feel like spam. Done well, they feel like a helpful shop attendant: “If you like this, you’ll probably love that.” In chat-based commerce, recommendations can drive higher conversion rates and increase average order value (AOV) with fewer messages.
Why recommendations work in chat
- Customers ask for guidance: “Which one is best?” “What goes with this?”
- Context is rich: you can learn budget, size, color, urgency, and use-case in minutes.
- Friction is low: customers can confirm quickly and move to payment.
Three recommendation moments to implement
1) Before purchase (help choose)
Ask 2–3 qualifying questions (budget, preference, usage) and show 3 options with clear differences.
2) At checkout (bundle)
Suggest complementary items: batteries for devices, a case for phones, matching accessories, etc.
3) After purchase (reorder/upsell)
Send a follow-up that’s genuinely useful: “How did it fit?” “Want care tips?” Then offer the next logical item.
How AI helps (without the hype)
AI can turn messy chat messages into structured intent: product type, variant, constraints, and confidence. That means you can:
- Respond faster with relevant options
- Reduce back-and-forth
- Standardize quality even as volume grows
Bottom line: recommendations increase revenue when they’re based on intent and constraints—not random upsells.