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How to turn reviews into marketing leverage: key facts + practical solutions

How to turn reviews into a powerful marketing lever

Table of contents

    For many companies, reviews are essentialThe aim is both to drive sales and to enhance brand reputation.

    However, in the face of a forthcoming scenario in which AI will be able to be used to get "fictitious" reviews, brand owners should be alert to this possible paradigm shift.

   That's why, in this article, we reveal some very important details about what kind of reviews should be achieved by customers and where we should focus as companies.

Google's new anonymous reviews

Attention: Google has just implemented a rather significant change:

users can now leave reviews under a pseudonym or a custom display name instead of your actual profile name.

 This means that some people who have been hesitant to leave comments due to privacy concerns may finally speak up, and that opens up a real opportunity for businesses like yours.

Why should you care?

  • Reviewers can now choose a custom name and image to display in Google Maps (and in normal search), not only for new reviews, but for all contributions past and future.

  • This makes leaving reviews much less intimidating for customers concerned about privacy (think: healthcare, legal services, financial services, therapy, elderly care, etc.).

  • More reviews = more social proof, which can increase both search visibility and conversion potential for businesses that spread the word.

1. Why is "positive reviews" not enough?

Having a lot of recent reviews alone helps (volume and freshness) for improve your local positioning. But if you get "powerful reviews" with certain additional elements, you can gain real advantages:

    • The reviews include images or videos tend to remain visible in the featured reviews section for the longest time.

    • Reviews with specific language about products, services or features allow Google to extract local justifications (local justifications) in the results.

    • If many customers mention a particular aspect of the business (e.g. "fast service" or "carpet cleaning"), that topic can become a "place topic"- i.e. a filter that search engines can apply to see reviews of that specific aspect.

    • Reviews that contain detailed customer descriptions increase confidence in the authenticity in ~33 %.

    • In a future scenario, with the proliferation of AI-generated content, authentic reviews will be even more valuableIf many platforms start to fill up with artificial reviews, they will trust less those that appear generic or automated.

Conclusion: It is not enough to ask for reviews ("what did you think?"). If you guide the customer to include certain elements, you increase the strategic value of the review.

2. Recommended (and adaptable) templates for requesting powerful reviews

In the Local SEO Agency Grupo Eiji we propose three common scenarios - with their email and SMS templates - which you can adapt to English or to your sector.

Scenario A: requesting a review of a specific product

Suggested email (adapted version):

Hello [client's name],

I am [your name and position] from [company name]. I am writing to find out if you are satisfied with your purchase of [product]. I was wondering if you would be willing to leave us your opinion in a review on [link to Google Business Profile].

I've attached a picture of [product] for you to use if you don't have your own, and I'd love to hear from you:

 

    1. What features of the product most caught your attention?

    1. What you like or dislike about it

    1. How you have been using it since you bought it

If there is anything you think we could improve, you can contact us directly at [phone or feedback link]. We want to make sure you are completely happy with your purchase.

[Button/link "Write review"].

Thank you very much for your time,
[Your name, position and company]

Suggested SMS (adapted version):

[Customer name], we would like to know what you think of your new [product]: [link to review]. Attached is an image for you to use if you don't have one of your own. thanks!

Scenario B: request a review of a service provided

Suggested email (adapted):

Hello [client name],
I am [your name and title] from [company]. It was a pleasure for us to [service provided]. I would like to know if the outcome met your expectations, and if you would be willing to share your experience in a review on [link to review].

I am sending you a photo of the service we performed (or of the result) in case you want to use it. I would like you to tell me about it:

 

    1. Whether the service met expectations

    1. What aspects did you like/dislike?

    1. How was our customer service

If you think something could have been done better, write to us directly at [phone or form]. We want to make sure you are completely satisfied.

[Button/link "Leave review"].

Thank you for your trust,
Your name / position / company] [Your name / position / company

Suggested SMS (adapted):

[Customer name], did we meet your expectations today? Here you have a photo and the link to leave us your opinion: [review link]. Thank you very much.

Scenario C: when you don't know exactly what he bought or contracted

Here you ask for a more general review, without assuming the product or service.

Suggested email:

Hello [client name],
I'm [your name and title] from [company]. We'd love to hear your feedback on your overall experience with us, if you'd like to share it in a review at [link to review].

I have attached a photo of our facilities / shop / premises in case you need it. If you can, let us know:

    1. If our customer service was helpful

    1. What did/didn't you like about our shop/venue?

    1. Why you chose us

If you think we can do something better, please contact us at [phone or link]. We want your experience to be excellent.

[Button/link "Leave review"].

Thank you very much for your time,
[Your name, position, company]

Suggested SMS:

[Customer name], we would love to know why you chose us. Here is a photo + link to leave us your opinion: [review link]. Thank you for your support.

3. Additional good ideas to encourage detailed reviews

   Beyond the message, there are tactics that help your customers respond with valuable content:

Action What to do specifically Expected benefit
Include your own images in your applications Send photos of your product, service or premises for the customer to use in the review if they do not have their own. If the client does not have images of himself, he already has a "seed" to include media.
Empower your team Have your staff identify satisfied customers and request reviews personally, just when they deliver the product/complete the service. Ideal timing (delivery) usually increases the likelihood of response.
Create photo ups at your venue Have visually attractive corners, with signs inviting photos to be taken and shared. It makes it easier for customers to generate images that they can then include in reviews or social media.
Send images/videos of work done For services (renovations, installation, etc.), ask for permission to capture photos or video, and send them for the customer to use in their review. Make it easy for the review to include relevant visual content.
Include printed reminders Add on tickets, menus, flyers or receipts a note inviting to leave a review with a QR code. Reach customers who may not open the email or SMS.
Make it part of your launch or campaign When you launch a new product or service, in your request messages emphasise that you are interested in feedback on that launch. Reviews of new developments can generate additional interest.
Respond to all reviews Showing that you read reviews inspires confidence and motivates reviewers to expect an active response. It strengthens the relationship with the customer and can encourage more reviews.

4. Future plan: resisting the AI-generated review boom

The article warns that by the end of 2026If we do, we could see a lot of reviews generated by artificial intelligence. This poses risks of audience distrust of generic or artificial reviews.

Recommended preventive strategies:

    • Generate first hand" reviews on your own website or platform, where your customers write them directly. These first-hand reviews can be considered more trustworthy than third-party content.

    • Encourages the mouth to mouth real and verbal testimonials - people recommending your business in their circle. This minimises reliance on external reviews.

    • Ask your clients to include specific details of your personal experience (context, emotions, problems solved). The more personal it is, the more difficult it is for a generic AI to replicate.

    • Post photos, videos and actual visual content accompanying the review (or short interviews), to strengthen authenticity.

5. How to implement this approach in your business (suggested steps)

  1. Define your most common scenarios (product, service, general customer) and adapt the templates to your language and style.
  2. Prepare the visual material (photos, images of the premises, examples of products) to accompany your applications.
  3. Automate shipping (email / SMS) after a purchase or service, with clear triggers (e.g. 2 days later).
  4. Train your team to identify, in direct contact with the customer, the right moment to ask for a review.
  5. Monitor the reviews receivedNote whether they include the desired elements (images, details, mention of attributes).
  6. Adjust your applications depending on the results: if few photos are used, emphasise more on asking for visual support; if customers do not comment on certain aspects, guide them more.
  7. Always respond all reviews with thanks and, if appropriate, request for further context or follow-up.

Major recent studies and research

  1. "AI vs. human: A large-scale analysis of AI-generated fake reviews, human-generated fake reviews and authentic reviews". (2025)

    • The following were analysed 714,016 reviewsincluding reviews generated by IAThe reviews are not only fake reviews made by humans, but also authentic reviews.

    • Linguistic differences were found: AI-generated reviews tended to be more comprehensible but with less specificityThe most important thing to note is that the reviews are more generalisations and sometimes carelessness in details, compared to real reviews or fake ones made by humans.

  2. MAiDE-up: Multilingual Deception Detection of GPT-generated Hotel Reviews (2024)

    • Dataset with real reviews and fake reviews generated by GPTcovering 10 languages.

    • Linguistic study to see which features help distinguish authentic reviews from AI-generated reviews. Performance of the models varies according to language, location, type of sentiment of the review.

  3. Explainable Deep Learning Model for ChatGPT-Rephrased Fake Review Detection Using DistilBERT

    • This work focuses more on reviews than on have been rewritten or paraphrased by ChatGPT, not necessarily generated from scratch.

    • It employs techniques such as POS tagging, lemmatisation, etc., and transformer models such as DistilBERT, together with local explanations (LIME, SHAP) to understand which features of the text lead the model to "say it is false".

  4. Improving trust in online reviews: a machine learning approach to detecting artificially intelligence-generated reviews (2025)

    • Applied to the hotel sector. A special dataset with AI-generated reviews was built to train models that detect "non-genuine" reviews.

    • The combination of textual features (how the text is written), vectorisation, linguistic analysis, etc., is very important in order to improving reliability.

  5. Large Language Models as 'Hidden Persuaders': Fake Product Reviews are Indistinguishable to Humans and Machines (2025)

    • One of the most worrying studies. Several experiments were done that show that human can no longer distinguish consistently between real reviews and reviews generated by large AI models, with an average accuracy of ~50,8 % - which is practically random.

    • AI models also have similar problems in distinguishing between them.

  6. Fake Google restaurant reviews and the implications for consumers and restaurants

    • Concrete study about restaurants and fake reviews on Googlesome of them generated by AI. An experiment was conducted with participants evaluating different scenarios: mixtures of real and fake reviews, with different proportions.

    • It was observed that consumers are often confused when there are fake reviews generated by IA, and they may make poor decisions (for example, choosing a restaurant based on reviews they believe to be genuine but are not).

  7. Consumer reactions to perceived undisclosed ChatGPT usage in an online review context

    • This study investigated what happens when consumers believe that a review was generated by ChatGPT vs. if they think it's human.

    • It was found that when the review is perceived as being generated by ChatGPT, people consider it to be less useful, less reliable, less authentic. This happens regardless of whether the review is positive or negative.

    • In addition, "perceived authenticity"mediates the relationship between whether they think the review is ChatGPT vs. human and how trustworthy or useful they consider it to be. That is, if the review seems authentic, its credibility goes up; if not, it goes down.

  8. What Google does to detect fake reviews (or AI-generated/influenced content)

    • Google Maps has implemented AI-based systems (e.g. Gemini AI) to identify suspicious reviews, fake business profiles, and remove reviews that violate policies.

    • By 2024, according to Google, more than 240 million of reviews or edits that violated policies.

Conclusion

The reviews play a key role in building trust and attracting new customers to your local business. However, they are only one part of the local business landscape. local marketing.

   If you want to know the health of your brand at a local level, request a local SEO audit. 

Do you want to know how to improve your visibility in search results? Contact Eiji Group

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