Ecommerce chatbots have become an important conversion tool for websites and online sales platforms. These assistants are conversational agents that typically use generative or hybrid artificial intelligence (AI). They provide an accessible interface to answer visitor queries and assist them with the shopping experience. Today, 94% of French businesses say they use at least one generative AI solution.

For online retailers, ecommerce chatbots have become a central tool for enhancing sales and customer interactions amid a booming ecommerce market (nearly €200 billion in revenue in 2025).

Learn what ecommerce chatbots are, how they operate in France, the regulations that apply to them, their pros and cons, and how to evaluate their performance.

Key takeaways

• An ecommerce chatbot is a conversational assistant built into an online shop that talks to visitors to answer questions, guide the shopping journey, and intervene during or after the order to improve the customer’s experience.
  
• There are many types of chatbots available on the market: decision-tree models, natural language processing (NLP) models, generative AI models, and hybrid models that combine both types of logic depending on the sensitivity of the subject.
  
• In France, chatbots are subject to two regulatory frameworks: the General Data Protection Regulation (GDPR) for personal data and the European Union AI Act, which requires businesses to inform users that they are interacting with AI or face fines.
  
• When used correctly during the shopping experience, ecommerce chatbots are available at all times, cut customer service costs, increase conversion rates by 15% to 35%, and reduce abandoned carts.
  

What is an ecommerce chatbot?

An ecommerce chatbot is a conversational agent built into a seller’s webpage or mobile application. It interacts with visitors using natural language to answer questions, guide the shopping journey, recommend items, track orders, and process after-sales service requests—automatically and 24 hours a day, 7 days a week.

Also called virtual assistants, ecommerce chatbots act as virtual salespeople, always available. They typically appear as a chat window in an online shop and could be triggered by the visitor or activated automatically, depending on the site’s settings (e.g., time spent on a product page, mouse movement to close the window, cart abandonment).

Recently, ecommerce chatbots have begun using natural language. Early “rules-based” systems worked via preprogrammed decision trees and were limited to defined scenarios. Most chatbots today use large language models (LLMs) such as ChatGPT. LLMs enable these virtual assistants to understand conversation context, interpret ambiguous queries, and generate relevant responses based on the specific content of the business’s website (e.g., catalog, FAQs, general terms and conditions of sale, return policy).

France has numerous chatbot publishers, including several specializing in ecommerce, such as Dydu (used by Cdiscount), Botmind (used by Selency), and iAdvize (used by Fnac).

What is the difference between ecommerce chatbots and autonomous agents?

An ecommerce chatbot is first and foremost a conversational interface that talks with, informs, and guides users, but does not make autonomous decisions. Autonomous agents, on the other hand, can perform actions without human intervention, such as finalizing an order, reordering inventory, or changing prices.

Example: A customer receives a pair of shoes they purchased online, but they are the wrong size.

With an ecommerce chatbot

The customer opens the chat window on the webpage. The chatbot requests the order number, informs the buyer that they have 30 days to return the item, and sends a link to the return form. They then fill out the form, print the mailing label, mail the package, wait for it to be received, wait for it to be verified, and wait for the refund.

With an autonomous agent

The customer opens the same chat window, and an autonomous agent automatically finds the order in the customer relationship management (CRM) software, performs a brief photo verification, immediately generates a return label, schedules the package for pickup, issues a refund to the bank card after visual verification, and updates inventory records in advance to show the item is on its way back into stock. All of these steps happen in a single conversation without manual assistance.

Most businesses use ecommerce chatbots, and autonomous agents are becoming more popular. Although in 2026 just 30% of French people said they would interact with a business’s autonomous agent, the market for virtual sales assistants could reach $2.9 trillion in transaction volume by 2030, representing 29% of ecommerce worldwide.

What are the different types of ecommerce chatbots?

There are several main types of ecommerce chatbots: rules-based, NLP, generative AI LLMs, and hybrid ones that combine rules- and AI-based approaches.

Rules-based chatbots

Rules-based chatbots operate using decision trees and preprogrammed scenarios. The user chooses from a list of options, typically with buttons. These tools are more visible and easier to manage, but they don’t understand natural language. They work best for simple FAQs, standardized order tracking processes, and lead qualification.

NLP chatbots

This type of ecommerce chatbot understands natural language through automated language processing models but relies on predefined scenarios. It’s best suited to large, structured knowledge bases such as product FAQs or shipping policies.

Generative AI chatbots

Generative AI chatbots run on LLMs, such as ChatGPT and Claude. They interpret context, engage in nuanced exchanges, and generate answers based on a business website’s content. They learn from catalogs, item descriptions, FAQs, and general terms of sale. They are becoming the standard for advanced ecommerce use cases.

Hybrid chatbots

Hybrid chatbots combine deterministic logic (for sensitive processes such as invoicing or retention) and generative AI (for open conversations). Hybrid ecommerce chatbots switch automatically between both modes depending on the question asked, using rules for legally sensitive subjects (e.g., prices, returns, warranties) and generative AI for interactions where nuance and context are important (e.g., product recommendations, rephrasing).

How are ecommerce chatbots regulated in France?

In France, two main regulations govern ecommerce chatbots: the General Data Protection Regulation (GDPR) on personal information and the EU AI Act of 2024, which mandates transparency.

GDPR applies to all ecommerce chatbots. The National Information Privacy Commission (CNIL) has published dedicated recommendations for chatbots and generative AI.

Article 5(d) of the GDPR requires personal data to be accurate (valid and up to date) and stipulates reasonable measures for correcting or deleting inaccurate records. The CNIL emphasizes that generative AI systems might produce “hallucinations,” results that sound plausible but are incorrect. In practice, online businesses must ensure their chatbots don’t return false information about identified shoppers and put in place processes to correct data.

Article 22 of the GDPR prohibits fully automated decision-making on matters of legal consequence or with significant impact on the person. According to the CNIL, a conversation with a chatbot without human intervention cannot, by itself, be used to refuse credit, charge higher rates, or refuse service. These types of decisions still require human involvement.

The AI Act (Regulation EU 2024/1689) is the first law in the world to restrict the use of AI. It entered into force on August 1, 2024, and lawmakers will phase in its provisions over the coming years. The regulation classifies chatbots as “limited risk” AI systems and imposes specific transparency obligations under Article 50. Users must be clearly informed when they are interacting with AI, and providers must visibly label AI-generated content—including text, images, audio, and video.

The AI Act’s sanctions, stipulated in Article 99, are substantial. Ecommerce chatbots that violate Article 50 could result in fines of up to €15 million or 3% of global annual revenue.

How to use ecommerce chatbots

Ecommerce chatbots can support customers throughout the shopping journey: on the homepage to guide visitors, on product pages to address queries and minimize uncertainty, during checkout to help reduce cart abandonment, and after purchase to track orders and provide post-sale support.

Learn when to use ecommerce chatbots:

Exploration phase

When a visitor arrives at a website, a chatbot can proactively offer assistance: “Are you looking for a specific product?” Such an approach works well on sites with large catalogs and a more complex browsing experience. Chatbots can ask questions (e.g., budget, use, size, occasion) to filter results and direct customers to relevant selections.

Product searches and recommendations

Chatbots act as virtual sales assistants. They can connect to catalogs and behavioral insights to recommend complementary products (e.g., cross-selling) or higher-end alternatives (e.g., upselling). This approach can help increase revenue. Conversational recommendations occur when visitors are making a decision.

Product descriptions

Chatbots answer targeted queries, including those related to materials, size, availability, shipping times, compatibility, and warranties. They eliminate friction just before an item is added to the cart, when conversion benefits are highest.

Payment funnel

Chatbots can intervene to reassure customers (e.g., regarding payment security, return policy, or shipping methods), apply a promo code, or remind them about abandoned carts by offering contextual help.

Order tracking and logistics

Once someone places an order, ecommerce chatbots can answer common questions such as “Where’s my order?”, “When will my package arrive?”, and “How can I change the delivery address?”

After-sales service and returns

Chatbots guide customers through the return process by generating return labels, automatically sending refunds in simple cases, and evaluating disputes before forwarding them to a human.

Customer loyalty and retention

Chatbots provide information on loyalty programs, offer personalized content, and inform inactive buyers of targeted offers.

In addition to websites, ecommerce chatbots can operate across messaging channels such as WhatsApp, Messenger, Instagram, and other marketplaces. Chatbots answer product questions, track orders, process returns, and remind customers about abandoned carts, all directly in their preferred communication channel, without redirecting them back to the site.

What are the benefits of ecommerce chatbots?

Ecommerce chatbots offer many benefits for both businesses (e.g., lower customer service costs, higher conversion rates, and the collection of qualified data) and shoppers (e.g., 24-hour availability, instant responses, personalized help, and improved shopping experiences).

The main benefits of ecommerce chatbots are as follows:

• 24/7 availability: Chatbots are immediately available, any time, without human intervention—on nights, weekends, and holidays.
  
• Instant assistance: Chatbots answer any questions visitors have while shopping (e.g., size availability, shipping costs, accepted payment methods) in seconds, creating a frictionless experience.
  
• Lower customer service costs: Chatbots automatically answer many frequently asked questions (e.g., order status, delivery times, returns), reducing customer service expenses for tasks that do not require staff involvement.
  
• Higher conversion rates: Conversational AI agents can boost conversion rates by 15% to 35% and lower abandoned cart rates by 25% by overcoming obstacles in real time. At the moment, potential customers are making a decision.
  
• Immediate scaling: Ecommerce chatbots can process thousands of conversations simultaneously, making them particularly useful during seasonal rushes (e.g., sales, holidays), when demand is much higher.
  
• Collecting qualified data: Every conversation produces information about the customer (e.g., intentions, friction, common questions), allowing businesses to refine their catalog, product descriptions, FAQs, and shopping experiences.
  
• Large-scale personalization: When combined with CRM software and purchase history, chatbots can recommend highly curated products or services, increasing the average basket size.
  
• Multilingual support: Modern ecommerce chatbots can respond instantly in multiple languages without human intervention, making it easier to enter European and international markets without corresponding cost.
  
• Better use of human labor: By filtering out simple questions, chatbots allow employees to focus on complex cases (e.g., quotes, chargebacks) and high-stakes situations, improving employee satisfaction.
  

What are the limits of ecommerce chatbots?

Ecommerce chatbots also have real limits, including the risk of hallucinations in generative AI virtual assistants, an inability to handle emotionally complex cases, user frustration with unsatisfactory responses, dependence on the quality of the knowledge base, and GDPR/AI Act requirements.

The main limits of ecommerce chatbots follow:

• Hallucinations and factual errors: LLM-based chatbots might generate false responses and present them with confidence. With more sensitive topics (e.g.,return policies, warranties, and pricing), false responses could lead to legal issues for the business.
  
• Limits in complex or emotional cases: For cases requiring human discernment (e.g., supplier disputes or special requests for long-standing customers), a poorly calibrated chatbot could increase frustration by leading customers in circles without resolution.
  
• User frustration due to poor comprehension: If an ecommerce chatbot doesn’t understand a question or answer it directly, the customer experience might quickly become unsatisfactory.
  
• Dependence on source content quality: A generative AI chatbot is as reliable as the knowledge base that feeds it. If FAQs are outdated, product descriptions are incomplete, or terms of sale are contradictory, the answers provided might reproduce these inconsistencies.
  
• Setup and maintenance costs: While rules-based chatbots require just a medium investment, generative AI solutions customized to a business’s CRM, inventory, and payment processes could cost much more and might not be right for every merchant.
  
• GDPR and AI Act requirements: Violations of the AI Act could result in substantial fines.
  
• Brand dilution: An overly standardized experience with no personality can erode customer brand loyalty.
  

How to measure ecommerce chatbot performance

There are three main indicators for measuring ecommerce chatbot performance: conversational (e.g., autonomous resolution rate, user satisfaction), sales (e.g., conversion rates, average basket size, cart abandonment), and operational (e.g., volumes processed, savings generated, response time). Track these indicators over time and compare them against predeployment benchmarks to assess performance.

The main key performance indicators (KPIs) to track are as follows:

• Autonomous resolution rate: This is the percentage of conversations resolved by the ecommerce chatbot without transferring the customer to a human.
  
• Escalation rate: This is the percentage of exchanges transferred to a human agent. Transferring a conversation to a human is not necessarily a failure. Rapid transfers of complex, well-qualified cases are successes. Indicators must distinguish legitimate escalations from failures to comprehend an issue.
  
• Abandonment rate: This is the percentage of exchanges abandoned without resolution. A high chatbot abandonment rate is a sign of poor conversation quality.
  
• User satisfaction: Measure user satisfaction with a brief survey at the end of the conversation.
  
• Conversion rate attributed to the chatbot: This is the percentage of chatbot interactions that result in an order, compared to sessions without this interaction. The difference indicates the chatbot’s actual contribution to conversion.
  
• Impact on average basket size: This indicator helps businesses compare the average basket size of visitors who interacted with a chatbot to the average basket size of visitors who did not.
  
• Average resolution time: This is the time between the first question and full resolution.
  
• Cost per interaction: The total cost of the chatbot divided by the number of conversations handled, compared to the equivalent cost of a human addressing them.
  
• Compliance and quality: This indicator helps businesses track signs of factual errors, transparency complaints (e.g., a user who didn’t know they were talking to AI), and potential GDPR incidents.
  

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