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Chatbots can make routine customer support easier to access and help online shoppers get product and policy answers quickly. Their value depends on whether they can draw on accurate, current information, handle the question at hand, and hand unresolved issues to a person without making customers start over. They are not a guaranteed way to raise sales or a substitute for human service in every situation.
What customer-support chatbots do well
The strongest use case is a recurring question with a reliable, documented answer. A chatbot can respond to requests about order status, shipping, returns, or product details without requiring a customer to wait for an agent. That can make help available outside staffed hours and give support teams more room to handle issues that need judgment.
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Those benefits depend on the bot having access to the right information. A chatbot that cannot see current order data, or that relies on outdated policies, may answer quickly but still leave the customer without a solution. Speed is useful only when the answer is accurate and the next step is clear.
Fast access is not the same as proven cost savings
Automation may reduce the number of routine conversations agents need to handle, but that alone does not establish lower operating costs. Businesses also need to account for the work involved in maintaining answers, connecting relevant data, monitoring failures, and resolving escalations. A deflected conversation is not a successful outcome if the customer has to contact support again.
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How chatbots can help ecommerce shoppers
During a purchase, customers may need quick clarification about a product, delivery, returns, or store policies. A chatbot can make those answers easier to find while the shopper is deciding whether to buy. This is most useful when answers are specific, consistent with the store’s current policies, and grounded in accurate product and order information.
That is a plausible way for chatbots to support shopping, not proof that they generally increase conversion. A 2025 article discusses purchase assistance and conversion as areas of chatbot impact, but the evidence summarized there does not establish a universal causal sales lift European Economic Letters (2025). A retailer’s reported sales result or a survey response should not be treated as a forecast for another store.
Keep the answer within the bot’s knowledge
A bot should not invent product details, promise a delivery date it cannot confirm, or present a policy exception as certain. When it lacks reliable information, it should say so and offer a route to a person. The same principle applies to privacy: ecommerce chatbot use can raise data-privacy concerns, but the available article abstract does not provide a detailed risk assessment, so it does not support specific legal or security conclusions European Economic Letters (2025).
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What the evidence says—and what it does not
Results from a retailer, a vendor’s platform data, a customer study, and a randomized workplace experiment answer different questions. They should not be collapsed into one promise about chatbot performance.
| Evidence | Reported finding | How to interpret it |
|---|---|---|
| Solo Brands retailer case study, Gartner abstract (2024) | Gartner says the retailer’s generative AI chatbot resolved 75% of customer interactions, up from a 40% resolution rate. | A named case-study result, not an independent controlled estimate or a forecast for other retailers. Gartner case-study abstract |
| Three online-panel studies on customer trust (2024) | In the study setting, human service produced higher satisfaction, repatronage intentions, and recommendation acceptance than chatbot service. Chatbot service paired with light-touch human intervention matched the more costly human-only interaction. | Evidence that customer outcomes depend on service design and context; it does not show that bots should replace agents broadly. Journal of Retailing and Consumer Services (2024) |
| Randomized field experiment at a meal-delivery company (2025) | AI-generated suggestions helped human agents respond faster, engage customers more deeply, and improve customer sentiment. Effects varied by conversation type and were weakest for repeat complaints tied to systemic issues. | This tested AI assistance for human agents, not autonomous customer-facing chatbot performance. INFORMS Management Science (2025) |
| Freshworks benchmark study (2024) | Freshworks reported up to 38% shorter resolution time and up to 6% improvement in customer satisfaction scores, based on anonymized data from 19 million Freshdesk tickets and 37 million Freshchat conversations across 25+ industries from January 2023 through April 2024. | Vendor-published benchmark based on usage of the vendor’s own products, not a controlled estimate that applies to every support operation. Freshworks benchmark study |
| Liquid Web proprietary ecommerce survey (publication year not clearly stated) | Liquid Web reports that among ecommerce businesses using AI chatbots, 75% reported an average sales or lead increase of 20%. | Business-reported survey results, not evidence that chatbot adoption caused those increases or that other businesses should expect them. Liquid Web survey |
Where chatbot service can fall short
Complex, sensitive, or unresolved problems
Customers may need empathy, judgment, or an exception when a payment, delivery, return, or account problem goes beyond a standard answer. A bot that keeps repeating a script or makes a human difficult to reach can turn a small issue into a frustrating one. The trust studies’ stronger outcomes for human service—and the finding that light-touch human intervention could match human-only service—support keeping a usable human route available Journal of Retailing and Consumer Services (2024).
Repeat complaints caused by a wider problem
Better wording cannot fix a delayed shipment pattern, a faulty product, or another systemic issue. In the meal-delivery experiment, AI suggestions were least effective for repeat complaints tied to systemic issues. That is a warning against treating every negative conversation as a response-generation problem; some require an operational fix as well as a customer reply INFORMS Management Science (2025).
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Wrong or stale information
Answers about policies, inventory, orders, and delivery can become incorrect as the business changes. If the bot cannot access current information, it should avoid guessing. A reliable escalation path matters because a fast incorrect answer can create repeat contacts and weaken customer trust.
Choose the right kind of support automation
Businesses generally face a choice among customer-facing automation, AI assistance for human agents, and human-only service for a particular interaction. The best fit depends on the work rather than on a blanket preference for automation.
| Approach | Best fit | Main trade-off |
|---|---|---|
| Customer-facing chatbot | Frequent, well-documented questions where the bot can access accurate, current answers. | Offers quick access for routine work, but can frustrate customers when it misunderstands or cannot resolve a problem. |
| AI suggestions for human agents | Agent-handled conversations where suggested replies can help staff respond, while people retain control. | A field experiment found faster responses and improved sentiment, but benefits varied by conversation type and were weakest for repeat complaints tied to systemic issues. INFORMS Management Science (2025) |
| Human-only service | Issues requiring judgment, empathy, exceptions, or investigation. | In the trust studies’ setting, human service achieved stronger customer outcomes than chatbot-only service, though the study also found a light-touch human intervention could match human-only service. Journal of Retailing and Consumer Services (2024) |
How to evaluate a chatbot without overclaiming
Set a baseline before launch and evaluate defined tasks rather than relying on a single deflection rate. A bot may close simple requests successfully while performing poorly on returns, delivery problems, or other categories. Measurement should include customer outcomes and the consequences of failed or repeated interactions.
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- Choose a bounded set of tasks. Start with recurring questions that have stable, documented answers, such as order status or a clearly stated return policy. Do not assume a bot is suitable for every contact.
- Check the information the bot can use. Confirm that answers reflect current policies and, where needed, current order or product information. Record cases where the bot lacks the data required to answer accurately.
- Make escalation part of the workflow. Define when a conversation should reach a human, and preserve the customer’s context so the person can continue without asking for the same information again.
- Compare with a baseline. Track performance for the selected tasks before and after launch, using comparable periods and categories. Separate chatbot-only interactions from conversations where agents use AI suggestions.
- Review a balanced set of outcomes. Measure task-specific resolution, customer satisfaction, escalation, repeat contact, and—if the goal is ecommerce support—relevant sales outcomes. A change in one metric does not establish success across the others.
- Look for failure patterns and adjust the scope. Review misanswers, unresolved contacts, and repeat complaints. If a recurring failure reflects a broken policy or operational issue, fix that underlying problem instead of asking the bot to paper over it.
This evaluation approach follows from the different outcomes found across the trust studies and the variation by conversation type in the agent-assistance experiment Journal of Retailing and Consumer Services (2024) INFORMS Management Science (2025).
Frequently Asked Questions
Can chatbot customer service match human agents on customer satisfaction?
It depends on the service design and study setting. In three 2024 online-panel studies, human service led to higher satisfaction than chatbot service, while chatbot service with light-touch human intervention matched the more costly human-only interaction Journal of Retailing and Consumer Services (2024).
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The evidence summarized here does not establish a general causal conversion increase. Purchase assistance is a potential use, but Liquid Web’s reported sales figures are proprietary survey results rather than proof that chatbots caused the reported gains European Economic Letters (2025) Liquid Web survey.
What customer-service questions are good candidates for a chatbot?
Recurring questions with stable, documented answers—such as order status, shipping, returns, and product information—are sensible candidates when the bot can access accurate, current details. Questions that need judgment or remain unresolved should have a human route.
Does AI assistance for support agents prove that autonomous chatbots work?
No. The 2025 randomized field experiment tested AI-generated suggestions used by human agents at a meal-delivery company. It found faster responses and improved customer sentiment, with weaker effects for repeat complaints involving systemic issues; those findings are about agent assistance, not autonomous chatbot service INFORMS Management Science (2025).
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