Product Recommendation Chatbot: How to Help Shoppers Choose

What is a product recommendation chatbot?

A product recommendation chatbot uses a conversation to understand a shopper’s needs and suggest matching items from a store’s catalog. For this guide, a useful recommendation includes the relevant variant, an explanation of fit, any important tradeoff, and a product link.

Conversation adds an opportunity to clarify intent. “For travel” might mean a short commute or several days of luggage. Conversational product recommendations should turn that ambiguity into explicit selection criteria before presenting options.

Recommendation widgets serve a different interaction. Shopify’s recommendation documentation describes product-page sections for related products and complementary items, customizable through Search & Discovery. Such a section can offer alternatives or additions without asking the visitor questions. A merchant does not need to replace it to test conversation.

AWS’s conversational recommendation example shows a chatbot asking about gift preferences, querying a product catalog through an API, and returning matching items. This illustrates the gather, retrieve, recommend pattern; it does not establish a conversion improvement.

A hypothetical shopper conversation

The following conversation uses invented products, prices, and specifications. It demonstrates a response pattern, not a real store interaction or a Cortian feature demonstration.

Shopper: I need a backpack for cycling to work and occasional weekends away. My maximum is €100. I prefer blue.

Assistant: What are your laptop’s dimensions, and do you need protection from light showers or prolonged heavy rain?

Shopper: It measures 33 × 23 × 2 cm. Mostly light showers. I would like room for a change of clothes.

Assistant: These two options fit your stated laptop dimensions and budget. The Metro 24 in blue is €79, with a 35 × 25 × 3 cm laptop sleeve and 24 litres of capacity. Its listed water-resistant fabric suits your light-shower preference; it is not listed as waterproof. The Weekender 28 in charcoal is €95, with the same sleeve dimensions and more packing space. Its color differs from your preference. Both variants are available in this example catalog. Would you prioritize blue or extra space?

Shopper: Blue matters more.

Assistant: The blue Metro 24 is the closer match. Check its product page for the full specifications and current price before buying.

In a real implementation, show a verified product link beside each option. Notice the distinction between the budget ceiling and the color preference. The assistant asks about consequential unknowns, explains its choices, and avoids promising protection the specifications do not support.

Catalog readiness checklist

Make the product facts usable

Audit the category from a shopper’s perspective. If customers ask whether a bag fits a laptop, a description saying “ideal for work” is insufficient. Record the compartment dimensions and measurement convention. Normalize centimetres and inches before comparing sizes, and preserve the original specification for review.

Treat an unknown value separately from a confirmed mismatch. Missing waterproofing information does not mean waterproof, and a product-level stock label does not verify every color. Decide how quickly prices and inventory must refresh, and what the assistant should do when that check fails.

Roll out an ecommerce recommendation chatbot in five steps

The following sequence is an editorial starting point. Adapt the scope and acceptance criteria to your category rather than enabling recommendations across the whole store immediately.

1. Choose one category and shopping task. Start with a decision your staff can explain, such as choosing a commuting backpack. Write down common requirements and reasons to reject a product. Keep ordinary search and filters available so shoppers can choose their route.
2. Prepare the category’s data. Complete the checklist, remove broken links, and identify the authoritative source for changing fields. Give someone responsibility for resolving missing specifications. Review sample records with a person who knows the products before connecting them to the assistant.
3. Define recommendation rules. Separate hard constraints, such as a maximum price or required compatibility, from preferences such as color. Filter on hard constraints before ranking suitable candidates. Limit the initial shortlist and require an explanation based on retrieved attributes. Ask permission before relaxing a constraint.
4. Test complete conversations. Include clear requests, ambiguous wording, changed preferences, incompatible requirements, and missing data. Write down acceptable products and rejection reasons for each scenario. Inspect the selected variant, facts, price, links, and follow-up question; a fluent answer alone should not pass.
5. Pilot with a limited audience. Set the exposure, review schedule, and stop conditions before launch. Pause recommendations if stock checks fail or compatibility claims become unreliable. Review unsuccessful conversations with merchandising and support staff, fix their causes, and expand only after the category meets your agreed criteria.

When evaluating Cortian’s e-commerce assistant for this workflow, use the same sample catalog and test conversations you would use for any implementation. Request a demonstration of the specific data checks and fallback behavior your category needs.

Handle missing facts and unsuitable products

No suitable product: State which requirement excludes the available options. Ask whether the shopper wants to change it, such as considering another color. Do not silently raise their budget or present a partial match as suitable.

Missing specifications: Ask a question if the missing information belongs to the shopper. If the catalog lacks the specification, offer staff help or explain that fit cannot be confirmed. More conversation cannot establish a fact your store has not recorded.

Unavailable variants: Check the exact size or color before recommending it. Offer an available alternative with its difference explained. Mention a restock option only if the store actually provides one; do not invent an arrival date.

Changing prices: Refresh the selected variant’s price before displaying it and before a cart action where supported. If the price exceeds the budget, reconsider the shortlist. Explain any discrepancy and let the shopper decide whether to continue.

Involve staff when compatibility remains uncertain, sources conflict, or the customer requests human advice. Pass along the requirements, options already considered, and unresolved question so the shopper can continue without repeating everything.

Measure recommendation quality before claiming sales lift

Use consistent definitions for AI product recommendations across the pilot. The following are suggested operating measures, not universal industry benchmarks:

• Recommendation accuracy: Review a sample against the shopper’s hard constraints and verified catalog facts. Divide passing recommendations by all reviewed recommendations. Track unsupported claims separately; review correct no-match responses too.
• Product clicks: Count recommendation sessions with a product-link click against sessions where recommendations were shown. Keep individual link clicks available for diagnosing weak options.
• Cart additions: Track sessions that add a recommended variant, using stable item identifiers. Distinguish a recommendation being displayed from that item actually entering the cart.
• Purchases: Record completed orders containing recommended items within a defined attribution window. Deduplicate orders and report cancellations or returns consistently.

Attributed sales are orders your reporting rule connects to recommendations. They do not establish how many extra orders the chatbot caused. Shoppers who choose chat may already differ in purchase intent from visitors who ignore it.

Microsoft’s research on online experimentation describes controlled experiments as a way to evaluate changes to websites and services. For this pilot, randomly assign eligible visitors to assistant access or the existing experience, keep assignment consistent, and compare purchases across all assigned visitors. Define the main metric, duration, and required sample before launch; report uncertainty. If traffic cannot support a reliable experiment, describe the results as observational rather than demonstrated incremental lift.

Frequently asked questions