1. Product Recommendations
What It Does:
AI recommends relevant products to customers based on:
- Browsing/purchase history
- Similar customer behavior
- Demographic or contextual data (e.g., time, location, weather)
Techniques Used:
- Collaborative Filtering: “Users who bought X also bought Y.”
- Content-Based Filtering: Recommends similar items based on features (color, brand, style).
- Hybrid Models: Combine both to improve accuracy.
- Deep Learning (CNNs for images, RNNs for sequences) to understand user preferences over time.
Implementation Example:
- Amazon: Uses a hybrid model with personalized “Recommended for you” and “Frequently bought together.”
- Fashion Retailers: Suggest coordinated outfits based on a single product view.
Business Impact:
- ↑ Average Order Value (AOV)
- ↑ Conversion rates
- ↑ Customer retention
2. 📧 Personalized Marketing Campaigns
What It Does:
AI tailors email, push notifications, ads, and SMS campaigns to:
- Customer preferences
- Lifecycle stage (e.g., welcome series, re-engagement)
- Behavioral triggers (cart abandonment, product views)
Techniques Used:
- Segmentation via Clustering (e.g., K-Means): Groups similar user personas.
- Propensity Modeling: Predicts likelihood of actions (e.g., purchase, churn).
- A/B/n Testing + Reinforcement Learning: Optimizes message, timing, and channel.
- LLMs (e.g., GPT): Generate hyper-personalized content at scale.
Implementation Example:
- Sephora: Sends personalized emails based on past purchases and beauty profiles.
- Spotify: Uses behavioral segmentation for playlist and campaign targeting.
Business Impact:
- ↑ Email open & click-through rates
- ↑ Re-engagement and LTV
- ↓ Marketing waste
3. Virtual Shopping Assistants / Chatbots
What It Does:
AI-powered assistants help with:
- Product discovery
- Order tracking
- Size or fit help
- Returns or FAQs
Techniques Used:
- Natural Language Understanding (NLU): Understands customer queries.
- Dialog Management Systems: Maintains context in multi-turn conversations.
- LLMs (ChatGPT-level): Conversational commerce & natural recommendations.
- Voice AI: Voice-activated shopping on apps or kiosks.
Implementation Example:
- H&M Chatbot: Helps users browse by style preference.
- IKEA’s “Ask Anna”: Assists customers in product finding and services.
Business Impact:
- ↓ Call center costs
- ↑ Customer satisfaction
- 24/7 self-service = ↑ conversions
4. Visual Search
What It Does:
Customers upload an image to find the most visually similar products in the catalog.
Techniques Used:
- Convolutional Neural Networks (CNNs): Extract image features.
- Similarity Matching: Finds matches in a vector space of product images.
- Semantic Tagging: AI labels items (e.g., “blue, polka-dot, midi-dress”) for better filtering.
Implementation Example:
- ASOS Style Match: Scan a look to find similar items.
- Home Depot: Find matching tools or home decor from photos.
Business Impact:
- ↓ Search friction
- ↑ Discovery of hard-to-find items
- ↑ Engagement via visual experiences
Bonus: Combine These Use Cases
Omnichannel Personalization Engine:
AI integrates all the above touchpoints—site, app, email, chatbot—to deliver a unified, personalized experience.
- If a user browses red sneakers:
- AI chatbot recommends sizes
- Email follow-up shows matching activewear
- Home page highlights promotions on sneakers
