Mar
Brand Health Tracking with LLM Equity (Part 3)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
What Is An AI Trust Infrastructure?
In the second blog of our three-part series, we discussed the benefits of tracking brand health to form brand strategies that help improve how AI describe and surface your brand. But aside from understanding the dimensions of brand health and the metrics from which brand messaging can be measured, there is another layer that you would need to consider when building your brand strategies. Sure, your brand is now being represented in AI search results and recommendations, but have you set up your brand to not just catch attention but also gain consumer trust?
We’re in the early days of the AI-driven shopping with brands experimenting on how to best connect with customers and compete in this new landscape. While impressive and promising, consumers are approaching this emerging new shopping experience not without caution and circumspection. PwC’s 2025 Future of Consumer Shopping Survey has 64% of its respondents expressed that it would help them trust AI assistants to shop in their behalf if at least one safeguard is in place. These safeguards include but are not limited to approving all purchases before completion, money-back guarantees, turning off access anytime as well as setting strict spending limits.
This echoes back to the early stages of e-commerce with customers exercising prudence when providing credit card information on websites. The implementation of safeguards like SSL encryption and fraud protection subsequently enabled e-commerce to gain consumer confidence and scale for mass adoption.
Once AI-assisted shopping starts to scale, brands that have incorporated an AI trust infrastructure in their strategies would most likely thrive and surface better than those that don’t. But an AI trust infrastructure goes beyond just implementing safeguards for purchases.
Image: www.kaboompics.com
Building An AI Trust Infrastructure
There are at least a couple of things that could go wrong with AI assistants making your purchases. It could overspend or make unexpected or unauthorized decisions. It could buy the wrong selection because it misinterpreted products. That misunderstanding could be a result of outdated or inaccurate product information, or even an instance of AI hallucination when it had to guess because it has inadequate or misaligned data to work with.
While safeguards like spending limits and final customer approval could circumvent the abovementioned situations, what about for errors it commits that a customer is unable to fix because they don’t know what went wrong or how to resolve it? Now these are just a few examples of how consumer trust could be broken, but from these challenges a brand can base on and build their AI trust infrastructure.
Nowadays, product content are mostly structured to capture human attention and rank favorably with search engine optimization (SEO); with the rise of AI agent shopping, content needs to be just as friendly with generative engine optimization (GEO) by including product data optimized in a machine-readable format. In other words, brand content should start speaking to both customer and AI, with consumer terms mapped into specific attributes to help improve precise product matches.
Brands would also need to constantly monitor the accuracy of their product information and how they show up in AI search results to make corrections or adjustments whenever necessary.
Expanding into the concept of purchasing safeguards, perhaps an even greater degree of trust can be earned if consumers understand the scope of delegating to AI assistants through a clear, accessible and easily configurable presentation of the AI-assisted shopping process. In addition to limits and conditions on the purchasing decisions AI is allowed to make, this could include requiring customer approval under certain parameters, mapping and tracing every decision and action the AI makes throughout the shopping process, as well as the abilities to dispute and/or reverse results. Brands can also explore the option to collaborate with popular AI platforms to extend their suite of purchasing controls and safeguards to customers who prefer to shop in those third-party platforms over purchasing directly at their website.
There is also the question about how sensitive customer data is protected. In the coming age of AI-assisted shopping, this won’t be limited to just payment details but also include contextual data such as preferences, constraints, and intent. Understanding how that data is used, remembered, or protected could help customers make that leap into delegating shopping to an AI agent. This includes what data is being shared and who or which other platforms or companies it’s being shared with.
Brands can offer options to minimize the data being retained or limit the amount of time that information is kept, or even present the choice for guest or one-time shopping where no transaction details are ultimately stored. Customer should feel empowered when it comes to their privacy choices by being presented with clear, visible and configurable options.
And despite the gradual transition to an automated shopping experience, brands shouldn’t forget the value of being able to reach a human representative, especially when things escalate. Customers could feel lost, powerless and frustrated in a situation that could’ve been salvaged with intervention by another human.
Image: Cup of Couple
The Future of Brand Health Tracking
The concept of brand health has been around for more than just three decades but how it’s being tracked moving forward is being rewritten. Just as Generative AI has caught the world’s attention and fascination, LLM equity is quickly gaining steamed across various industries in just these last few years. While AI has a democratizing effect of leveling the field for players of all sizes, companies who are able to understand and leverage brand health tracking with LLM equity would likely emerge as leaders in their sectors.
Brands might not have full control over how they’re described or surfaced by AI, but they could strongly influence how they’re represented by developing coherent and consistent brand messaging reinforced by consumer-earned content built on trust and loyalty.
Image: MrWashingt0n
Featured Image: www.kaboompics.com
Top Image: Sagar Soneji
Mar
Brand Health Tracking with LLM Equity (Part 2)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
Is AI Surfacing Your Brand?
In the first blog of our three-part series, we touched on how AI is reshaping the shopping process from the searching for products up to completing the purchase in the customer’s behalf, and what Large Language Models (LLM) equity means for brand health. To illustrate, when a consumer asks an AI agent like ChatGPT for recommendations on clothing brands, does your clothing line show up? And if it does, how what image is being surfaced for your brand?
By tracking brand health, brands are able to learn not only whether their marketing strategies and creative directions are converting into market share, but also determine performance drivers per platform and digital metric, understand which themes or aspects of their brand resonate with consumers, and assess their “piece” of the LLM pie- or how often LLMs surface or recommend their brand.
Image: Julio Lopez
How Is AI Surfacing Your Brand?
There are several dimensions to brand health which includes the strength of your brand to be picked up and recommended by algorithms, the main themes and imagery associated with your brand by consumers and AI, how often consumers and AI share your content or recommend your brand, how likely your consumers would convert into advocates for your brand, and the perceived value of your brand in terms of pricing, quality and worthiness across different media. These can be boiled down into three main dimensions: brand awareness, brand associations, and brand loyalty. From these three main dimensions, a company can form and anchor their LLM equity strategies for visibility, communication, differentiation or positioning, engagement, attracting potential customers, optimizing marketing spend, as well as pivoting or responding to the competition or other emerging challenges.
The effectivity of brand strategies can be measured in three metrics: alignment, engagement, and intent. Alignment refers to how clearly and consistently your brand messaging, themes and values are being represented and communicated to and by your consumers, engagement is concerned with how customers are interacting with your brand in different media and platforms, while intent looks at how your brand moves audiences to search and look up your products and services. All three consider the strength of your brand messaging and values as reflected through consumer-earned content and digital footprints.
With these sets of brand health dimension and key metrics forming the backbone of brand marketing strategies, brands are not only able to catch attention but also earn consumer trust; not just reach audiences but also influence customer decisions; become not only “first to mind” to consumers but also strongly and coherently presented by LLM platforms as we gradually move into an AI-driven shopping landscape.
Image: Atlantic Ambience
Featured Image: TungArt7
Top Image: Anna Shvets
Mar
Brand Health Tracking with LLM Equity (Part 1)
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World
AI Is Disrupting The Shopper’s Experience
There’s a paradigm shift in the shopping process and AI is the driving force behind this change. Shoppers are no longer just searching online or scrolling through websites; they’ve now taken advantage of AI platforms to discover, compare, and even buy products in their behalf.
According to generative engine optimization (GEO) firm The Rank Collective, their analysis of cross-platform AI visibility data revealed that 64% of consumers are now using AI tools to discover and learn about new products, with frequent online shoppers increasing that share to 66%. ChatGPT serves as a starting point for 34% of these high-intent users.
Another study based on two multi-market surveys of 5,000 consumers aged 18-67 comprised of US, UK, Canadian and Australian residents reported that 41% of consumers trust Gen AI search results more than paid search results. That same study- the 2025 Consumer Adoption of AI Report- also found that only 15% trust AI less than search ads.
Additionally, Adyen’s Retail Report shared that 51% of shoppers are open to AI making purchases in their behalf. It also noted that the number of US shoppers using AI assistants rose from 12% to 35%. With these encouraging figures, 88% of retailers are considering adopting AI to handle the entire shopping process in the shopper’s behalf, with 56% of them prioritizing this technology for 2026.
Image: Google DeepMind
LLM Equity and Brand Building
AI has opened up a new world of fast and frictionless shopping experience. While still in its early stages of adoption, companies have begun exploring this new space to understand what challenges it would need to address in order to compete and thrive.
Perhaps a good starting point is understanding Large Language Models (LLM) equity. LLM equity generally refers to ensuring that AI models are fair, unbiased, and accessible across diverse populations, preventing the reinforcement of existing disparities. It requires addressing algorithmic bias in training data specifically with race, gender, and socioeconomic status, especially in the field of healthcare. It’s also concerned with expanding access and at the same time, performing in non-English languages and low-resource settings.
For brand building, LLM equity is more concerned with whether your brand shows up in Gen AI search results and how it’s being represented. What theme or themes are being represented by your brand? Are those themes coherently represented in your social media? Is your current brand representation connecting and engaging with your audience? Is that connection strong enough to not only move consumers to purchase your product but also engage with your content? Is your brand content strong enough to capture the interest and be remembered by prospective consumers?
In other words, understanding LLM equity in brand building is understanding and tracking your brand health.
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Featured Image: Shoper.pl
Top Image: Nataliya Vaitkevich
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