Using AI Product Recommendations to Boost Ecommerce Sales

How AI recommendations differ from 'you might also like'

'You might also like' sections are everywhere in ecommerce — and they mostly don't work. The typical algorithm shows products from the same category or items other customers also bought. It has no idea what this specific customer is thinking, what question they just asked, what problem they're trying to solve, or what they explicitly told you they don't want.

AI recommendations are different in one fundamental way: they're contextual. They factor in the customer's live behavior, their explicit input (questions, searches, stated preferences), their purchase and browsing history, and current inventory availability. The result is a recommendation that feels like advice from a knowledgeable salesperson — not a popularity chart.

Where to deploy AI recommendations

In-chat recommendations stand out because of the context available: you know exactly what question the customer just asked, what they're looking at, and what they're unsure about. That information makes for dramatically more relevant recommendations than any passive placement.

In-chat product recommendations

When a customer asks a question in chat — 'do you have anything warmer than this jacket?' or 'what size should I get for a 5-year-old?' — the AI agent has everything it needs to recommend with confidence: the customer's question, the product they're looking at, your full catalog, and your inventory in real time.

This is where conversational AI outperforms any on-page widget. The recommendation isn't a pre-computed list — it's generated from the conversation. If a customer says they need something for a gift for their partner who runs cold, the agent narrows the recommendation to insulating layers in the right size range, checks stock, and links directly to them.

Bookbag's chat agent handles this natively — it's trained on your product catalog and can recommend specific SKUs with real-time stock checks, size guidance, and compatibility information. Recommendations made in this context (as answers to real questions) see conversion rates 3–5x higher than passive on-page widgets.

• Question-driven: recommends based on what the customer explicitly asked, not a popularity algorithm.
• Real-time inventory: doesn't recommend out-of-stock items; flags low-stock to create urgency.
• Size and compatibility aware: factors in what the customer told you about their needs.
• Cross-sell in context: if a customer asks about a product, the agent can suggest a complementary item naturally in the same conversation.

Personalization signals AI recommendations use

The richer the input signals, the better the recommendation. AI recommendation systems draw from several data layers:

1. 1Explicit input — what the customer typed: 'something for cold weather,' 'gift for a 30-year-old woman,' 'runs true to size?'. This is the highest-signal input and unique to conversational contexts.
2. 2Browsing behavior — which products they've looked at in this session and historically, category preferences, time spent on product pages.
3. 3Purchase history — what they've bought before, how frequently, and in what categories. Returning customers get more personalized recommendations than first-time visitors.
4. 4Cart contents — what's already in the cart informs complementary item suggestions and avoids recommending things that conflict.
5. 5Inventory and business rules — your AI agent should always check live inventory before recommending and can apply margin or clearance preferences to prioritize certain products.

Revenue impact benchmarks

The caveat: these numbers assume relevant recommendations. A recommendation engine that suggests the same 10 'bestsellers' to everyone drives near-zero lift. The value is in relevance, and relevance comes from signal richness and real-time context.

• Average order value (AOV) typically increases 15–25% when AI recommendations are part of the checkout experience.
• Conversion rate lift from personalized recommendations on product pages is typically 10–30% vs. non-personalized.
• In-chat recommendations show the highest per-session impact — 20–40% conversion on sessions where a recommendation was given and a question was answered.
• Repeat purchase rates increase 15–20% when post-purchase recommendation emails are personalized to what the customer actually bought.
• Amazon's famous statistic — that 35% of revenue is driven by recommendations — is often cited; for smaller DTC brands, the figure is typically lower (10–20%) but still material.

Getting started with AI recommendations on Shopify

For Shopify stores that want to get meaningful recommendations running quickly, the priority stack looks like this:

1. 1Deploy a chat agent with product knowledge: connect your catalog to an AI agent like Bookbag, which can answer product questions and recommend alternatives in real time. This gives you the highest-ROI recommendation channel (conversational) from day one.
2. 2Improve your on-page recommendations: if you're using a basic 'frequently bought together' widget, consider upgrading to a recommendation app with behavioral personalization (LimeSpot, Wiser, or Rebuy are the main Shopify options).
3. 3Add cart-level upsell/cross-sell: Rebuy's smart cart is well-regarded for Shopify; it personalizes add-on suggestions based on cart contents and customer history.
4. 4Build post-purchase recommendation emails: use Klaviyo's product recommendation blocks with behavioral data, not just 'category bestsellers.'
5. 5Measure incrementally: A/B test recommendations vs. no recommendations on product pages and in post-purchase flows to confirm lift before scaling.