Google Review Response Examples

How to use these review response examples

Use each example as a structure, not a finished script. Replace placeholders, verify the location and facts, and adapt the tone to the customer’s review and your brand. A response should never claim details that the review or business records do not support.

The strongest replies acknowledge the main point, add relevant context, provide a next step when one is needed, and close naturally. Short reviews usually need short responses; complaints that require investigation should move to a private channel.

For the complete decision process, read how to respond to Google reviews. This page focuses on what good responses look like in common situations.

• Keep only details that are accurate for the customer and location.
• Match the length and tone to the review.
• Remove placeholders before publishing.
• Use approved contact information for private recovery.
• Escalate sensitive cases instead of forcing them into a template.

Examples for employee, product, and service praise

Customer praises an employee: “Thank you for recognizing Alex. We’re glad they made your visit helpful and welcoming, and we’ll share your feedback with the store team.”

Why it works: it acknowledges the named employee and makes a realistic commitment to share the compliment without overstating what happened.

Product praise: “Thank you for the kind review. We’re happy the [product] worked well for your needs and that the team helped you find the right option.”

Why it works: it uses the product and assistance mentioned by the customer. The placeholder must be replaced with the exact verified product.

Service praise: “We appreciate your feedback, Jordan. It’s great to hear that booking was straightforward and the service was completed on time.”

Why it works: it reflects the specific parts of the service journey instead of offering a generic thank-you.

Neutral and mixed review examples

Three-star review: “Thank you for your feedback. We’re glad you enjoyed [positive detail], and we’re sorry that [issue] did not meet expectations. We’ve shared your comments with the location team.”

Why it works: it reflects both sides of the review rather than treating a mixed experience as entirely positive or negative.

Positive experience with a complaint: “We’re glad the team was helpful, and we appreciate you flagging the delay at checkout. Your feedback has been shared with the store manager so the team can review that part of the visit.”

Why it works: it does not let the compliment erase the complaint. It names a credible internal next step without promising a specific outcome.

Mixed product and service feedback: “Thank you for the detailed review. We’re pleased the service was attentive, and we’re sorry the product did not meet expectations. Please contact us at [approved contact] if you would like the team to review the purchase with you.”

Why it works: it separates the two dimensions of the experience and moves transaction-specific follow-up to a private channel.

Service, staff, and wait-time complaint examples

Service complaint: “Thank you for bringing this to our attention. We’re sorry the service did not meet expectations. Please contact [manager or team] at [approved contact] with your visit details so we can review the experience.”

Why it works: it acknowledges the problem and names the owner of the next step without admitting facts that have not been investigated.

Staff complaint: “We’re concerned to hear about the interaction you described. We would like to understand it fully and review it with the appropriate team. Please contact [approved owner] at [approved contact].”

Why it works: it treats an employee allegation seriously while keeping personnel details and investigation out of the public thread.

Wait-time complaint: “We’re sorry about the wait during your visit. We know delays can disrupt the experience, and we’ve shared your feedback with the location team. If you would like a direct follow-up, please contact us at [approved contact].”

Why it works: it recognizes the effect of the delay and offers follow-up without making an unsupported explanation.

Restaurant review response examples

Positive restaurant review: “Thank you, Chris. We’re glad you enjoyed the pasta and that the Harbor location team made dinner feel welcoming. We appreciate the recommendation.”

Why it works: it uses the dish and location from the review and avoids adding unrelated menu promotion.

Restaurant complaint: “We’re sorry the order and wait time did not meet expectations. Please contact the restaurant manager at [approved contact] with the date and order details so the team can review the visit privately.”

Why it works: it identifies the correct recovery owner and requests operational details outside the public response.

Franchise and service-business response examples

Franchise location: “Thank you for sharing this feedback about our [city] location. We’ve sent your comments to the local operator and regional team. Please contact [approved contact] if you would like a direct follow-up.”

Why it works: it identifies the location and shows a clear ownership path without implying that every franchise issue is handled centrally.

Service business: “We’re sorry the appointment did not go as planned. Please contact our service team at [approved contact] with the appointment date so we can identify the job and follow up privately.”

Why it works: it requests only the context needed to locate the service and avoids discussing customer records publicly.

Generate responses consistently at scale

Examples and templates help teams establish a standard, but manually selecting and adapting them becomes difficult across many locations. AI can generate a contextual draft from the rating, review text, language, business instructions, and location information.

Learn how to design Google review response automation with automatic, hybrid, or approval workflows.

For organizations that need to operate this process across locations, Cacao’s automated AI review response software combines configurable instructions, location context, approvals, and direct Google publishing.