How to Set Up ChatLab for Your E‑Commerce Store

How to Set Up ChatLab for Your E‑Commerce Store

In the competitive e‑commerce environment, properly configuring ChatLab is key to ensuring your chatbot delivers precise product information and real‑time order updates. Understanding the different types of chatbots helps you choose the right approach. With the right setup, ChatLab can answer detailed product queries, fetch order statuses, and assist customers around the clock, delivering significant benefits of chatbots for customer service. This guide walks you through the steps of training, integrating, and optimizing ChatLab so it becomes an indispensable tool for product support and order management.

What Is Important in a Chatbot for E‑Commerce

• Accurate product information - ensure customers receive correct details about specifications and offers.
• Clear presentation of offers - highlight discounts, bundles, and promotions effectively.
• Up-to-date data - use supported integrations to retrieve prices, stock levels, and order status. Freshness depends on the data source and its refresh process.
• Order tracking & user data - allow customers to check order details, shipping status, and personalized recommendations when integrated.

Picture ChatLab as your always‑ready storefront assistant, combining precision, clarity, and live insights to instill confidence and drive sales.

How to Provide Product Information to ChatLab

Before teaching ChatLab about your store, decide how it will ingest product data:

• Website Scan: ChatLab crawls and indexes your product pages - descriptions, specifications, and images - into a static knowledge base using RAG technology. Ideal for rich context and detailed answers when your catalogue is relatively stable.
• API Integration: Connect a supported platform such as Shopify or WooCommerce and enable the actions you need. Product search, order lookup, and add-to-cart support vary by platform; connecting an order integration alone does not enable product discovery or cart actions. AI Search uses an indexed catalog for discovery, while supported API integrations can retrieve current product details. XML feed results use the last indexed feed data rather than a live store lookup.
• Combine Both: Use a scan for detailed contextual knowledge and supported integrations for store data. Keep website training and catalog indexing current.

Scanning vs Integration: Which Is Better?

Website Scan

• Pros:
  • Full context AI research, rich answers
• Cons:
  • Requires manual or scheduled re-training to capture updated content. Select your chatbot and open Training > Links to retrain sources. Available schedules depend on your account's features; check the monthly allowance in Training > Limits > Re-trainings. See Re-training.
  • May hit training-character quota when indexing large catalogs - requires scanning optimization, more below.
  • Potential misinterpretation of detailed specs  - may miss real-time availability
  • Incomplete structured data parsing  - may fail to extract information from tables or parameter lists

API Integration

• Pros:

  • Current prices and stock where supported via store API calls; indexed sources need refreshing
  • Order lookups where supported for tracking status using the configured verification requirements

• Cons:

  • Search capabilities vary by integration; AI Search needs a supported, indexed catalog for semantic discovery
  • Dependent on API availability and rate limits, which can introduce latency or downtime
  • Initial setup complexity, requiring proper credentials and configuration
  • Test your selected model with real store scenarios - check product queries, tool calls, and order lookup before publishing. Compare response quality and message-credit use; a higher-cost model does not guarantee better results for your setup.

The Winning Formula: Combine Scan & Integration

Combining a website scan with a supported integration gives ChatLab both descriptive context and access to store data. It does not guarantee that every price or stock figure is current: scanned pages and indexed feeds reflect their last update, and live requests depend on the store API. Keep training and indexing current, and test the enabled actions. Under Bot Settings ▸ Role & Behavior, you can add the following instructions:

When a user asks about products or categories, use the available product-search tools. Use scanned content for contextual detail, not as confirmation of current price or availability. If current data cannot be verified, explain that limitation and direct the customer to the product page.

This combined approach can provide richer answers, but it still requires testing and regular data updates.

How to Properly Scan Your E‑Commerce Website

Before you scan, plan your approach to ensure efficiency and relevance:

1. Define proper scanning scope - use URL exclusion filter:

• Include only URLs for product and category pages (e.g., /products/ and /collections/).
• Exclude low-value paths like /blog/, /tags/, and archive pages using the URL Exclusion filter.
• For multilingual stores, restrict scan to a primary language

2.Exclude elements that are repeating on each page - ie header, footer, sidebar - use Html Element Exclusion filter.

3.Include only relevant content - use CSS Selector Inclusion to focus on product descriptions, specifications while excluding reviews, ratings, similar products sections.

4.Use Sitemap if you have one - it helps to identify all product and category pages efficiently, much better than scanning

You can read more about optimizing your website scan here:  How to reduce training characters when scanning a website

Additional features worth enabling

See product discovery in a conversation

Product recommendations and order lookup use different data. A connected, indexed catalog supports discovery; checking an individual order requires an enabled order integration.

For the configuration steps and limitations, read AI Search and Checking order status.

• Offer Cards: Select your chatbot and open Settings > Model & Advanced, then turn on Enable Offer Cards to display product names, images, and prices as cards. Availability depends on your account's features and a compatible model. Cards present the data supplied by the connected source; they do not make indexed data live. See Offer Cards.
• Suggested questions: Add preset conversation starters, such as shipping, returns, and opening hours, under Settings > Chat Conversation > Welcome & suggestions. These are separate from Dynamic suggested follow-ups, which generate new buttons from the conversation context. To use those, open Conversation behavior on the same page, keep Hide suggested questions after first message off, and enable Dynamic suggested follow-ups with a compatible model. See Chat Conversation settings and Dynamic suggested follow-ups.
• Human hand‑off: Activate the Contact Human form and provide your support email for escalations.
• Conversation & Client Summaries: When enabled, ChatLab automatically generates a summary for each conversation and maintains an up-to-date profile summary for every returning customer. This powers conversational AI for customer service and helps your team quickly understand past interactions and tailor their responses accordingly.
• Treat customer context separately from order verification. Passing a user ID, email, or security token through the Chat frontend API does not by itself authorize access to order history. The default order lookup requires an order number and the matching email address; any alternative lookup mode must be explicitly configured where supported. Test the verification flow before using customer context for a more personalized customer experience.

Language setup

ChatLab supports 90+ languages. Read more about proper language configuration  /help/chatbot-language-setup

In case you use integration with Shopify, WooCommerce or other e-commerce platform, always consider what is the language in which your system returns the products. As the users may ask questions in any language, add following instructions to your Role & Behavior settings:

When searching for products use [shop language] product and category names in singular form.

Monitor & Iterate Daily

To ensure ChatLab remains accurate and effective, implement a daily review routine:

• Review conversation logs regularly. Fine‑tune answers or add content where the bot hesitates.
• Select your chatbot and review Analytics > AI Insights. Reports are generated automatically when the feature is available and there is sufficient conversation activity; if it is not included, the panel shows a sample preview with an upgrade prompt. See AI Conversation Insights.
• Leverage the automated Daily Activity Report 

By making daily monitoring a habit, you’ll keep ChatLab precise, responsive, and aligned with your evolving store needs.

Optimizing Chatbot Responses

Even well-configured chatbots can benefit from regular fine-tuning. Analyze conversation logs to identify weak spots, misinterpreted queries, or repetitive fallback answers. Refine prompts, update training material, and experiment with system instructions to improve how the bot handles nuanced cases.

For in-depth guidance, visit: /help/how-to-improve-chatbot-responses

Ready to Get Started?

Setting up ChatLab for your e-commerce store is just the first step. To maximize your chatbot's potential, explore how to build your own AI chatbot or view all features and pricing plans to find the perfect solution for your business.