Unlocking Stakeholder Perception of your Brand Using Google Search Data and AI

Communications and PR professionals often rely on social media and earned media to gauge public perception of their brands or those of their clients. However, there’s a wealth of information hiding in plain sight that can provide even deeper insights into what people really think about your brand and your competitors: Google search results data.

By analyzing the search results for your brand and your competitors, you can uncover patterns of thought, and identify the questions and concerns that your stakeholders have. This is because Google’s algorithm depends on satisfying as many of the searchers as possible. This means that Google is already working to understand what people think and what they want to know. Tapping into this information can be invaluable in making decisions that shape your online presence and addressing potential challenges head-on.

One of the most prominent examples of AI in action on Google’s search results page is the People Also Ask feature. This section typically includes four questions and answers that Google deems most relevant to the search query. By examining these questions across multiple brands in your industry, you can gain a general understanding of what people are thinking and what they want to know about your brand and your competitors.

To demonstrate the power of this approach, we used Five Blocks IMPACT™, our tracking and analytics platform, to analyze the search results for several outdoor sportswear brands like North Face, Columbia, and Patagonia on March 31st in North America. We then used an AI  to identify patterns in the People Also Ask sections and summarize how each brand is perceived, as well as any obvious challenges and potential lessons from their peers.

 

The People Also Ask Questions and Answers collected automatically by Five Blocks IMPACT and then used for this analysis.

 

The analysis revealed some surprising insights. For example, searchers seemed particularly concerned with where each company is based. But the AI went much further, characterizing how each brand is seen by stakeholders, based on the questions asked and providing recommendations for what they could learn from their competitors. While AI models can make mistakes, we found this to be a fascinating instantaneous analysis.

 

A visualization of search results for various sportswear brands as seen in Five Blocks IMPACT and used for this analysis.

 

Here are the conclusions that the AI model provided:

Canada Goose

  • Seen as an expensive but high-quality and durable brand, known for its premium materials.
  • Can learn from Patagonia’s focus on sustainability and ethical sourcing.

Arc’teryx

  • Perceived as a popular and functional outdoor brand, with some questions around its Chinese ownership.
  • Can learn from Canada Goose’s reputation for premium materials and durability, and from Patagonia’s environmental initiatives.

Burberry

  • Firmly established as a luxury fashion brand, with questions about its history and store locations.
  • Can learn from Arc’teryx’s technical expertise in outdoor apparel and from Patagonia’s commitment to sustainability.


Columbia Sportswear

  • Seen as a good mid-range outdoor brand, often compared to The North Face.
  • Can learn from Arc’teryx’s focus on technical performance and innovation, and from Patagonia’s sustainability efforts and brand purpose.

Moncler

  • Perceived as a high-end luxury brand, known for its expensive but high-quality winter wear.
  • Can learn from Patagonia’s transparency and ethical practices, and from Arc’teryx’s technical expertise in outdoor gear.

Patagonia

  • Widely recognized for its sustainability efforts and commitment to environmental causes.
  • Can learn from Canada Goose’s reputation for premium materials and durability, and from Arc’teryx’s technical innovation in outdoor apparel.

REI

  • Seen as a reputable outdoor co-op.
  • Can learn from Patagonia’s strong brand purpose and environmental initiatives, and from Arc’teryx’s technical expertise in outdoor gear.

The North Face

  • Perceived as an expensive but high-quality outdoor brand, with some questions about its popularity and branding.
  • Can learn from Patagonia’s sustainability efforts and brand purpose, and from Arc’teryx’s technical innovation in outdoor apparel.

 

Using only Google Results of peers, we can then paint a picture of how Google, and perhaps their searchers, see each brand. 

A visualization of what we learned using this Data and AI analysis of the Google Results.

 

Using AI-powered search data analysis, brand and communications directors can identify areas where their brand’s image might not align with their goals and adjust their digital reputation management strategy accordingly. This data serves as a kind of  insightful preliminary focus group, providing valuable insights that can be tracked over time.

In today’s fast-paced digital landscape, staying on top of how your brand is perceived is more important than ever. Leveraging the power of AI and search data analysis, you can uncover hidden insights, address potential challenges, and ensure that your brand is resonating with your target audiences.

 

Five Blocks specializes in digital reputation management, combining cutting-edge technology and personalized service to help our clients overcome digital reputation challenges. Our advanced data analysis and AI-powered insights allow us to identify the root causes of various issues and uncover overlooked opportunities for improvement. We work closely with your communications team to develop and implement sustainable solutions that deliver long-lasting results. For more information or to see what we can do with your brand’s data, contact us.

 

 

The Future of Wikipedia in the Age of AI

As the use of AI models increases, the way users seek information is evolving. Queries are becoming more complex and conversational, and results are typically based on a much larger body of data, rather than a specific source or page. 

As these models become increasingly integrated into our daily lives, the importance of Wikipedia in shaping brand reputation cannot be overstated, since it is a major source for training AIs.

Importance of Wikipedia in AI Training
According to The New York Times, “Wikipedia is probably the most important single source in the training of AI models.” The platform’s vast trove of crowdsourced knowledge, covering a wide range of topics, provides invaluable data for AI models to learn from. Without access to this information, the development of current generative AI capabilities might not have even been possible. (Here’s some additional information on how AIs/LLMs/Chatbots are trained.

Impact on Brand Reputation
With AI models like ChatGPT, Claude AI, and Gemini having been trained on Wikipedia, inaccurate or biased information on the site can lead to negative or incorrect information about a brand, potentially harming its reputation. With so much riding on the underlying information in Wikipedia, ensuring the positivity and accuracy of a brand’s Wikipedia presence has become more important than ever.

Recommendations
Given Wikipedia’s elevated status, our recommendations for companies, brands, and individuals are to work within the Wikipedia guidelines to do the following:

  1. Maintain: Create and/or maintain a well-structured, robust Wikipedia page for your brand or personal profile.
  2. Update Accurately: Make sure the page remains updated and accurate with current facts, figures, and noteworthy achievements.
  3. Include more sources: Since LLMs utilize all of the content, include as many relevant, verifiable sources, as appropriate – these should only help the AI training.
  4. Go Multilingual: Consider developing a presence across multiple language editions of Wikipedia. LLMs often learn from content in various languages, and the more you play an active role, the better. Also, consider that English is often the hardest language version of Wikipedia to impact, and other language versions can be very easy to edit.
  5. Other Wiki pages: LLMs can learn about your brand and industry from any Wikipedia article, so consider getting relevant information added to relevant industry articles, not just the ones about your brand.
  6. Talk Pages: Leverage Wikipedia’s “Talk” pages to include additional relevant information, as LLMs may also use these for training.
  7. Images: Consider submitting relevant images via Wikimedia Commons to enhance your Wikipedia page and improve AI model understanding.
  8. Categorize: Utilize Wikipedia’s category system to ensure your page is properly categorized and connected to the ideal topics.
  9. Monitor: Monitor your Wikipedia presence for edits that may introduce inaccuracies, outdated information, or bias; address issues appropriately and promptly. Do the same for other relevant pages related to your company or brand. Our free WikiAlerts service provides tracking of Wikipedia and Talk pages.
  10. Wikidata: Beyond Wikipedia, leverage Wikidata, Wikipedia’s sister project, a powerful database of community-contributed structured data that LLMs will increasingly use to verify facts.

 

Do We Even Need Wikipedia in a World of AI?
An interesting question that has been raised recently is whether there is even a need for Wikipedia. Since the content is taken from various third-party sources, and the LLMs presumably have access to the sources and probably many more, why can’t an AI produce Wikipedia content that would be as good or better than content created by Wikipedia editors?

To answer this question there have been various attempts to utilize AI to write sections of Wikipedia pages, but so far, despite the great capabilities of AI, they have not been proven to produce content that is up to par. It is possible that this will change at some time in the future, but for now there still seems to be tremendous benefit derived from the human (crowdsourced) process that helps create a Wikipedia page. Perhaps AIs that are trained on this process will eventually produce content that is recognized to be of high enough quality. 

Conclusion
Ongoing tracking of how AI models represent your brand, and what role Wikipedia may be playing, can help you identify areas for improvement within Wikipedia and beyond. 

As the use of Wikipedia in AI training continues to grow, we believe that the future of brand reputation management will be even more closely tied to Wikipedia. By actively managing their Wikipedia presence, companies can ensure that AI models have access to an important trusted source of accurate and up-to-date information, ultimately leading to a more positive online reputation.

 

Five Blocks specializes in digital reputation management for platforms including Google and Wikipedia, combining cutting-edge technology and personalized service to help our clients overcome digital reputation challenges. Our advanced data analysis and AI-powered insights allow us to identify the root causes of digital reputation issues and uncover overlooked opportunities for improvement. We work closely with your communications team to develop and implement sustainable solutions that deliver long-lasting results.

For more information or to see what we can do with your brand’s data contact us.

 

AI and the Future of Digital Reputation

Over the past month or so, the internet has been buzzing about the new ChatGPT bot by OpenAI. This moment has been coming for a while, in which AI seems almost ready to take a seat at the human table. 

And now the humans are excited. I spent way too many hours asking the chatbot to write sonnets for my kids and sitcom scripts (including a scene from The Good Doctor in which he has to treat a marshmallow who has been badly burned in a fire; At one point the marshmallow actually says to Dr. Murphy, “But I’m a marshmallow!”) All this is making many of us a bit nervous. What does this new technology mean for jobs, education, and relationships? What does it mean for human intelligence? 

From a business perspective, one of the questions that interests me the most is: How will a new, pervasive reliance on AI potentially impact the digital reputations of brands and individuals? 

There have already been numerous articles written on the subject, many of them with doomsday predictions about the coming irrelevance of everything we once knew. In particular, the New York Times raised a series of challenges that this new technology would pose to Google’s revenue and ethics models, as the company evolves its AI strategy.

As usual, I am more optimistic about our capability to incorporate this new technology wisely.

Google vs OpenAI

Right now, when we want to know about a person or a company we Google it, and we see a list of results that the algorithm thinks (based on various factors) will satisfy the searcher. Deciding which of these results to read (or scrolling on) is up to the searcher, as is constructing a conclusion on their basis. 

The search page gives us pieces of information to choose from, but we do the work of picking which ones to read, and analyzing what we read. Searching the way we do now gives us an opportunity to consider: Is that what I really wanted to know? Is there important context available that I might be missing? Do those sources look reliable? Is there bias I am missing?

ChatGPT makes the leap from providing information to performing analysis and stating conclusions. Like Google, it makes some algorithmic decisions about which information to use in its analysis (though less transparently, since it does not typically share sources), and then does its own thinking and analysis in order to provide a cogent answer – one that requires very little work from the searcher. And one that may seem satisfying, in easily accessible language. 

Take for example the question: “Is Tesla a good company to work for?”

When I asked OpenAI, I got this back: 

Yes, Tesla is a good company to work for. The company has consistently been ranked as one of the top companies to work for and has recently been named one of the best places to work for in 2021. Tesla provides competitive salaries and benefits, a great work environment, and the opportunity to work on innovative projects.

When I typed the same thing into Google, I got much more complicated and thought-provoking results.

  • Empire Resume told me it’s a valuable company and has many perks.
  • Google then suggested some questions and answers: 
    • How is pay? Good, according to Zippia
    • How hard is it to get a job? Really hard, according to Zippia
    • How stressful will it be? Very, according to Business Insider
    • How are the benefits? Great, according to Tesla.com

After that, you get to the Indeed.com and Glassdoor.com review sites, where you can see star ratings and read what could be actual reviews from employees. There’s a YouTube video with more information.

You get the idea.

Getting to know the searcher 

So what’s the right answer to the question about Tesla? As a human (and one who has spent 18 years focused on search) I think the answer is “it depends.” If the AI understands the searcher’s specific needs, in some cases it will be able to weigh various factors and make better decisions. Google knows a lot about you – where you are, the types of sites you frequent, your interests – and yet its personalization feels very incomplete. AI will hopefully be able to synthesize the facts about you and better predict what you care about.   

Of course, much of the burden will fall on the searchers themselves. Just as it took many years to get smart about how to use Google, there will definitely be a learning curve as we learn how to ask AI to help us with complex questions. When search was new, many people clicked on the top results almost blindly, but now most of us have better ways to get to the information we trust. Searchers are likely to use AI in the same way, and they will learn to ask for sources. I can imagine something like Google results alongside the AI results. In fact – a new plug in is piloting just this functionality, albeit in a very cursory way.

As I mentioned above, knowing about the searcher would be invaluable, and would make AI that much more useful as a provider of both information and analysis. If I ask AI for dinner suggestions, it would be good if it knew what ingredients are available in my area (or even in my house) and that my family keeps kosher. While it may sound scary, if it knows that we ate pasta yesterday, and that we are trying to watch our carbs, it will be more likely to suggest roasted salmon with broccoli – not a bad decision. 

Where does this leave reputation management?

I believe that in the not-so-distant future, AI will be able to helpfully synthesize a lot of information about brands and executives. This could actually be a great development for brands – assuming that robust, accurate information is available, and that AI is seeing and understanding it. 

As its use in search develops, AI will likely be better at ignoring transient negative news cycles, despite their high clickability on Google, especially when in the overall context they are not that relevant to the searcher. My sense is that we are moving to a place where companies will need to make even more efforts to communicate holistically, as they will need to ensure that humans, computers, and now AI, all get a holistic picture of who they are. The rise of AI will make it even more important to carefully curate your digital presence. 

This development will be bad news for those who are not working to deliberately plan their online presence, or those who have relied on tricks and manipulations to control their online presence. These companies will  now find themselves at the mercy of automated processes which play by different rules. 

Google and other search engines are already using AI and smart algorithms in order to choose sources to display, and it is likely that the search of the future will have elements of Google search (providing key sources and context) as well as elements of AI – providing analysis and cogent answers in language we understand. In the meantime, we humans will need to make sure we are firmly in the driver’s seat when it comes to how we want ourselves and our companies to be perceived.