Conducting an AI Sentiment Audit for Your Brand

Why AI Is Now Interpreting Your Brand Reputation

Traditionally, brand reputation is determined by what customers say about brands in reviews and articles and what people discuss in public conversations. Companies have always tried to keep track of public perceptions of their brands through reviews and social media conversations. For instance, if customers have had a good experience with a brand, they would share it in public conversations. On the flip side, if something goes wrong with a brand, people would voice their complaints in public conversations.

However, with the advent of generative AI, there is another level of determining brand reputation. For instance, AI tools like ChatGPT and Perplexity AI often answer questions related to brands and companies by analyzing patterns in reviews and public conversations.

For instance, a user may ask an AI system a question like, "What are the pros and cons of a specific brand?" The AI system will then create a summary of the data based on what is available on the internet. This implies that your brand reputation is not only represented by customer reviews but is also interpreted by AI systems.

This change in brand reputation representation by AI systems requires a new way of monitoring brand reputation, known as an AI sentiment audit. An AI sentiment audit is a way of understanding how AI represents a brand and whether it is positive, negative, or neutral.

What Is an AI Sentiment Audit?

An AI sentiment audit is the process of assessing how artificial intelligence platforms report their findings regarding opinions about your brand. This is different from other methods of sentiment analysis, where you would look at reviews from individual customers. Instead, this method looks at how AI platforms report their findings.

Large language models are trained using vast amounts of data, including articles, discussions, and other publicly available information. When users ask questions about a company, the AI platforms use this data to generate answers to the questions.

While other platforms may use sentiment analysis to determine whether reviews are negative or positive, AI platforms tend to report balanced results, including both negative and positive aspects of the brand. For example, the AI platform may report to users that the brand has excellent customer service but has some negative reviews regarding its pricing.

The AI sentiment audit helps marketers to view this report and understand the narrative presented to users regarding their brand.

Why AI Sentiment Matters in the Era of Generative Search

The value of AI sentiment is rising, particularly because many people are relying on AI to help them learn about companies or products of their choice. Instead of searching for information from different websites, many people are asking AI assistants questions about companies.

For instance, if an AI assistant offers an overview of a company, many people are likely to take this information as factual. This implies that the sentiment of AI assistants plays an essential role in shaping first impressions, especially for many consumers.

Positive AI sentiment can be essential in shaping perceptions about companies. When AI assistants describe companies as trustworthy, innovative, or popular, many people may feel encouraged to do business with such companies. However, negative AI sentiments may discourage people from using certain companies, even if the negative sentiments are based on complaints from a few people.

For this reason, AI sentiment has become an essential aspect of Generative Engine Optimization.

How AI Models Form Opinions About Your Brand

To form opinions about your brand, AI models use their sentiment-based summary, which is created by analyzing all the publicly available information on the internet.

Customer reviews are one of the most impactful sources of information for AI models. Customer review websites may contain detailed information about product quality, service quality, and customer satisfaction.

Articles and blogs written about the brand are another source of information for AI models.

Community discussions are also an important factor for AI models. They contain honest opinions about the brand from actual users. This information is important for AI models to understand the context of how people perceive the company in their daily lives.

For instance, organizations such as OpenAI and Google, which are developing advanced AI systems, are training their AI systems to learn and recognize these patterns. When the same positive or negative themes are consistently repeated across different sources, the AI can learn to use these themes in its responses. This process is closely related to entity authority, where AI systems evaluate whether a brand is consistently recognized as credible across the web.

This helps businesses understand the reason behind the presence of certain strengths or weaknesses in AI-generated summaries.

Step 1: Identify Key Reputation Queries

The first step in conducting an AI sentiment audit is to identify the questions users are likely to ask about your brand. This should be based on actual research questions asked by potential customers.

Common reputation queries may include questions about strengths, weaknesses, and customer experiences. Users may ask, for instance, what people like most about the company or what people complain about most.

Other prompts may ask about the trustworthiness of the brand, the reliability of the products, or even comparisons with competitors. These are all aimed at stimulating the research process that customers perform on the company.

Through these prompts, marketers are able to see the manner in which the AI system is summarizing their brand's reputation.

Step 2: Test the Prompts on Multiple AI Systems

After marketers are able to determine the queries for the company's reputation, they should then proceed to test these queries on multiple AI systems. This is aimed at helping marketers understand the manner in which different AI systems are interpreting the company's information.

Using AI systems such as ChatGPT and Perplexity AI, marketers are able to compare the results and determine the common themes that are presented. While each system may present the results differently, the sentiment is often similar.

Marketers should pay attention to the results and note the common factors that are presented.

Step 3: Analyze Positive, Neutral, and Negative Themes

Once the AI responses have been gathered, it is essential to analyze the sentiment expressed in those responses. The aim is to spot patterns expressed across multiple sources and prompts.

Positive themes can be related to strengths such as quality, customer service, and expertise. Neutral themes can be related to factual statements regarding the business's offerings and market presence.

Similarly, negative themes can be related to issues and limitations expressed by customers, indicating areas of dissatisfaction. These are often the key issues expressed by AI systems regarding businesses.

This is essential as it can help businesses understand how AI systems view them.

Step 4: Identify Reputation Risks and Opportunities

An AI sentiment audit can provide businesses with risks and opportunities. For example, there are issues that are repeatedly brought up by customers, and this can be identified through an AI sentiment audit.

On the other hand, there are strengths identified through the responses provided to businesses, and this can be used as part of their marketing strategy. For example, if innovation is repeatedly identified as part of an AI sentiment audit, this can provide businesses with an opportunity to market this as part of their strengths.

Step 5: Improve Your Brand Sentiment Signals

To start improving AI sentiment, it is necessary to first work on improving the signals that are being sent to AI and that impact the summaries it creates. For example, satisfied customers can be encouraged to leave positive reviews, which can offset negative reviews. Transparency in communication can also help build trust.

Another strategy is to deal with negative issues in a timely and constructive manner. This can create a sense of responsibility and hence improve sentiment.

Creating high-quality content is another strategy that can be adopted. The content should be able to provide positive signals to AI, which can then be referenced while creating summaries.

The Significance of Periodically Checking AI Sentiment

The AI sentiment audit should not be a one-time process. This is because brand reputation is constantly changing with the appearance of fresh content in the form of articles and conversations online.

The AI system is constantly changing and may interpret data in a different way in the future. Periodically conducting an audit helps businesses monitor the change in reputation in AI-generated answers. This helps in maintaining a positive reputation in AI-driven search platforms.

Conclusion: Managing Your Brand's Reputation in the AI Era

Artificial intelligence is revolutionizing the way people are researching brands and consuming information to make a buying decision. Rather than perusing a multitude of sources, people are increasingly relying on AI-generated content that aggregates data from all over the internet.

In that sense, conducting an AI sentiment audit for your brand is a critical part of reputation management in the AI era. By asking AI tools a series of questions about your brand, you can get a sense of how your brand is represented to potential customers.

Businesses that are constantly working to improve their AI sentiment are more likely to build credibility and trust in a world where AI is revolutionizing the way people are researching brands.