AI Guide for Growth Companies: Visibility, Trust and Reviews

Artificial intelligence has changed marketing forever. Until recently, AI was mainly an efficiency tool for content production, but now it is the new middleman between businesses and customers. Increasingly, the first point of contact with a brand is not a Google search or a web page, but an AI-assisted response, summary or recommendation.

For growth companies, this means one thing: visibility and trust are created differently than before. AI will not judge companies on advertising promises, but on their actual digital footprint. Customer reviews, feedback and experience data have taken centre stage.

This article gives you an overview of the most important topics that growth companies should know about AI.

What is AI from a marketing perspective?

Artificial intelligence (AI) is the ability of a computer program to perform functions considered intelligent, such as learning, reasoning, observing and problem solving.

In marketing, AI is no longer a single tool, but part of the digital marketing infrastructure. It enables marketing tools that improve efficiency, analyse data, identify patterns and support decision-making.

For many, the most common example is certainly content creation: texts, images and even videos are easily created by prompting. But this is just a glimpse. AI can be used to analyse customer data, to help with strategy work, or even to build entire products.

In addition, AI is a whole new channel for customer acquisition. ChatGPT, Gemini, Perplexity and Google's AI Mode are some examples of AI tools that are acting as search engines for an increasing number of consumers.

For a growing company, AI offers the opportunity to scale marketing without a corresponding increase in resources, make more efficient use of customer data, and improve the relevance and timing of messages.

But AI is no substitute for strategy or customer insight. It strengthens those companies that have a clear understanding of their customers.

Generative AI and language models

To generate means to create something new. Generative AI is therefore a type of AI that does not just recognise or analyse, but creates something new: text, images, videos, and so on.

Large language models (LLMs) are generative artificial intelligence (AI) that can understand and produce text, summarise information and answer questions in natural language.

If AI is a toolbox, then a language model is a single tool, like a hammer or a screwdriver – although much more complicated than that.

Popular AI tools that use language models include ChatGPT, Gemini, and Perplexity. These can also be described as "answer machines" that are fed a question (a prompt) which is then answered by a language model.

In marketing, this can be seen, for example:

• generating content ideas and drafts
• analysing customer feedback
• answering frequently asked questions

It should be understood that despite the name "AI", the language model is not supernaturally intelligent and its answers should not be taken as absolute truth.

Language models do not have access to information that humans do not have. It relies on educational data and information available online to calculate the probabilities of word occurrence.

The quality of the answers therefore depends entirely on the quality of the training data, and the AI cannot necessarily judge what is correct and what is incorrect. Sure, it can analyse the data and find a consensus, but the less data available, the more likely the language model will pick up a single piece of data and present it as truth.

However, AI has superior processing speed compared to the human brain. It can analyse large amounts of data and search for information faster than humans.

AI visibility and organic visibility online

Many of you may have noticed a decrease in organic traffic to your website. This phenomenon is largely due to the increase in so-called "zero-click content". Users no longer need to click on a link to read the answer, as Google's AI summary or ChatGPT chat provides the answer right out of the box.

However, this does not mean that organic visibility is a thing of the past. Channels have diversified and new ways of measuring visibility are being invented.

In addition to search engines, visibility now also means how a company appears:

• AI-assisted searches (e.g. Google's AI Mode)
• chat-based answers (ChatGPT, Gemini, Perplexity)
• recommendations and summaries (Google AI summaries and featured snippets)

AI looks for answers from multiple sources, with an emphasis on clarity, consistency and reliability.

Companies with a strong and coherent digital footprint are more likely to be featured. This is not achieved through a single optimisation trick, but through long-term content and customer experience.

The biggest challenge today is how to measure AI visibility. You can see website visitors coming through language templates, but pure visibility without clicks is a more challenging equation.

You can see for yourself if ChatGPT or another tool recommends your brand by asking relevant questions related to your industry. But keep in mind that the answers are personalized, so the answer you get may be completely different from the one a real customer gets.

There are tools that test dozens or hundreds of prompts in AI conversations, and use them to form an idea of visibility. But it's impossible to know which prompts the right customers will use to search and find information about your brand.

One way to measure your visibility is to see if bots from AI tools are visiting your pages. This can be an indication that the AI might be mentioning your brand in its response.

AI SEO, GEO and LLM optimisation

Traditional search engine optimisation is not dead, but it has been replaced by new concepts such as AI SEO, GEO (Generative Engine Optimisation) and LLM optimisation.

They all have the same basic point:

• how to structure content so that AI can understand it
• how the company is presented as a knowledgeable and trustworthy source in ChatGPT and other AI tools
• how to answer the right questions, not just keywords

Structured content, clear answers and real customer data are key signals for AI.

Some of the same strategies apply to both traditional SEO and its new forms: respond to search intelligence, help the reader by providing good content, gather trust signals from other sites and take care of the technical functioning of the website.

However, the biggest difference between SEO and GEO is probably the way people search. The language models ask longer and more complex questions, to which more specific answers are sought that are appropriate to the limited context. Google, on the other hand, emphasises shorter searches, although this difference is narrowing. Question and answer searches have also become more common in traditional search engines.

As noted earlier, it is difficult to know what kind of "keywords" or prompts people use in AI chats. Therefore, the key is to describe services and products in as many ways as possible, so that the AI has a good overall understanding of them and can describe them in its responses.

In the future, we are unlikely to need a distinction between SEO and GEO as AI becomes more commonplace and more involved in traditional search.

Below is a brief summary of how SEO and GEO differ.

The impact of reviews on AI visibility

Customer reviews are one of the strongest signals of trust in the age of AI. AI uses user-generated content to understand what a business is really like.

Ratings influence which companies are highlighted by AI, how the brand is described in the responses, and how the overall customer experience is perceived.

The more genuine, timely and multi-channel reviews a company has, the stronger its AI visibility will be. This is especially true for Google reviews and customer feedback on websites.

External channels such as the Trustmary profile page also increase brand visibility and at the same time change the nature of the responses: instead of just basic information, language models talk about companies in a more service-oriented and recommendable way when customer reviews are available.

Language models can also provide better and more relevant answers to the user based on the reviews. In our own tests, we found, for example, that when a user asked the language model about renovating a sloping plot, the language model picked up a single review that mentioned the word sloping plot.

It is no wonder that a review from one genuine customer becomes the answer to a question from another genuine (potential) customer. Real people say exactly the things that other people are interested in - unlike the communication that the company itself makes.

Please note that reviews up to 3 months old are reliable. So it's not enough to have a few reviews from a couple of years ago, you should have a lot of them and preferably as recent as possible.

Brand reliability in the age of artificial intelligence

Reliability is a key criterion for evaluating AI. AI seeks to minimise misleading information and favours sources based on real experience.

A trusted brand from an AI perspective:

• does not hide feedback
• shows a complete picture of the customer experience
• base their message on what customers say, not just on promises

For a growth company, this is an opportunity to differentiate itself. Trust does not require a massive budget, but the systematic collection and use of feedback.

Reliability is also built by getting mentions on other sites. The more places your business is mentioned, the more the AI will begin to understand its context and legitimacy.

This is why, for example, taking control of different review platforms will benefit AI visibility.

Customer data and feedback to fuel AI

Without high-quality data, AI will not provide a competitive advantage. Customer feedback and reviews are often undervalued data that could be very useful both internally and in marketing.

Customer data in internal development

It would take hours for people to go through a large amount of feedback and criticism and then draw up sensible follow-up actions.

AI, on the other hand, can analyse large amounts of open feedback in seconds to identify recurring themes, highlight areas for improvement and support decision-making.

And AI is impartial, unlike humans, who may even resent critical feedback and would prefer to sweep it under the carpet.

With Trustmary, you can compile an unbiased, real-time report on the feedback you receive, providing both numerical and qualitative insights into your customer experience.

Customer data as marketing material

The same feedback also serves as effective marketing content. Traditional methods include adding reviews to the website, sharing them on social media, and presenting relevant testimonials in sales materials.

In the AI era, reviews act as trust signals for language models and provide material to cite in responses. In particular, reviews on third-party sites, such as the Trustmary profile page, are particularly trustworthy and are more likely to appear in searches.

But reviews can also be used as a marketing tool at a more strategic level.

Have you tried feeding AI with all the feedback you've received from customers, and using it to look for ideas and themes for marketing communications, new advertising campaigns, or website development?

An easy way to boost AI visibility

In the midst of all this information, one question arises from the perspective of a small or medium-sized growth company: where do I start, and how do I get AI visibility up quickly?

We have a solution that requires an hour or two of active time, and within a few weeks you will start to see results.

The Trustmary profile page is a quick and easy way to boost your AI visibility. It allows you to present your entire customer experience in a reliable and transparent way.

Why it works:

• Genuine customer reviews build trust and help language models to recommend your company.
• Trustmary is a reliable source of reviews, which is why it ranks well in both traditional and AI searches.
• You'll get more eyeballs on your brand, and your profile page will redirect you directly to your company's website.
• You will catch keywords such as "[your company] experiences, [your company] reviews", which would otherwise probably lead to discussion forums.
• AI can lift your customers' comments or star ratings directly into their responses to increase trust.

If you've already collected customer feedback and reviews, your page will be up and running in just a few minutes. But if you haven't yet, don't worry: start right away with a bulk collection of dozens or hundreds of feedbacks, depending on the size of your customer base. Then automate the ongoing collection so that you receive new reviews on a regular basis.

Book a demo with us and we'll show you how it works and what kind of results your business can expect.