Who speaks to Machines? Optimizing for LLM-Based Platforms: Demographic Insights for FMCG Brands
The rapid adoption of AI assistants and large language models (LLMs) is reshaping how consumers discover and decide on products. From open-ended chatbots like ChatGPT to retailer-specific shopping aides like Amazon’s Rufus, each platform comes with its own audience profile. For fast-moving consumer goods (FMCG) brand strategists and digital marketers, understanding who uses these AI platforms – and how that aligns with target customer segments – is crucial. This essay analyzes the core demographics and psychographics of top users across major LLM-driven platforms (ChatGPT, Google’s Gemini AI Overviews, Perplexity, Amazon “Rufus,” Walmart “Sparky,” and Target “Bullseye”) and offers guidance on where to prioritize optimization efforts. The aim is to ensure your brand’s content and strategy meet consumers on the AI interfaces they actually use.
User Demographics of Key AI Platforms
Each AI platform attracts a somewhat different user base. Below we break down the audience characteristics of general-purpose AI assistants versus retailer-specific shopping assistants.
General AI Assistants: ChatGPT, Google Gemini, and Perplexity
ChatGPT (OpenAI): ChatGPT has achieved mass popularity, especially among younger, tech-savvy users. Demographically, the user base skews young and slightly male-oriented. Figure: Example breakdown of ChatGPT’s user demographics by age and gender (Semrush data, Feb 2025). According to recent analytics, about 64% of ChatGPT users are male and 36% female, and nearly half of users are under 25 years oldexplodingtopics.com. In fact, an estimated 63% of users fall in the 18–34 range – essentially Gen Z and younger Millennialsbankmycell.com. This reflects ChatGPT’s origin as a viral tech phenomenon embraced first by students, developers, and early adopters. However, usage is broadening: by early 2024, 23% of all U.S. adults had tried ChatGPTbacklinko.com, indicating older professionals and even parents are experimenting with it. Psychographically, ChatGPT’s core users are curious self-educators and productivity hackers. They turn to the chatbot for instant information, creative inspiration, and advice – from researching school topics to brainstorming recipes or gift ideas. This “early adopter” audience is comfortable with AI and often seeks efficient, conversational answers in place of traditional search. Many use it for general knowledge or work tasks, but these same users may also ask product-related questions (“What’s a good budget shampoo for oily hair?”) as part of their discovery process. The upshot for FMCG brands is that ChatGPT is a prime channel for reaching Gen Z and Millennials during the awareness and consideration stages of shopping. If your target includes digital-native young adults or globally dispersed audiences, ChatGPT is likely a platform to watch.
Google Gemini (AI Search Overviews): Google’s generative AI, known through features like Search “AI Overviews” and the Gemini chatbot (formerly Bard), has rapidly gained a massive user base by piggybacking on Google’s ubiquitous search engine. Sundar Pichai noted that Google’s AI Search Overviews now serve around 2 billion users per monthtechcrunch.com – essentially reaching a broad mainstream audience. Because it is integrated into search results for users in many countries, the demographic spread is wide, spanning all typical Google users (teens doing homework, millennials researching purchases, Gen X and Boomers asking practical questions, etc.). That said, early adopters of the dedicated Gemini chatbot skew somewhat younger: roughly 58.5% of Gemini’s users are maledemandsage.com, and the largest age segment is 25–34 (31% of users), closely followed by 18–24-year-oldsdemandsage.com. In other words, millennial users dominate the Gemini/Bard chatbot, with strong representation from Gen Z as well. Google’s AI appeals to those who want quick, authoritative answers backed by Google’s vast index – which includes everything from academic facts to product info. Psychographically, Gemini’s users value convenience and trust Google’s brand. They might use AI Overviews to save time (for example, getting a summary of “best protein powders for women” without clicking multiple links). Many are information-gatherers by habit – the same people who habitually “Google” everything are now letting Google’s AI summarize it for them. For FMCG brands, Google’s AI integration means your content can be surfaced to virtually any segment if their query triggers an AI summary. This platform isn’t confined to a niche; it’s an AI channel to the mass market, from Gen Z snack enthusiasts to Gen X suburban moms searching for quick answers about products.
Perplexity AI: Perplexity is a newer AI-driven “answer engine” known for combining LLM answers with live web search and source citationsfrancescatabor.com. It has seen impressive growth among a niche of power-users: as of 2025 it boasts ~30 million monthly active users and hundreds of millions of queries, albeit far less than ChatGPTindex.devindex.dev. Demographically, Perplexity’s audience is very similar to ChatGPT’s in age and gender. Over 53% of its users are 18–34 (with roughly 20% in 18–24 and 33% in 25–34)index.dev, and about 60% are maleindex.dev. This reflects a young, tech-focused crowd – students, developers, and knowledge workers – who appreciate Perplexity’s emphasis on accuracy and transparency. Psychographically, Perplexity users tend to be research-oriented and skeptical; they want clear sources and often dig deeper into the citations provided. For example, a user might query, “What are the top-rated infant car seats under $200?” and expect Perplexity to not only answer but show which review sites or experts back up the recommendation. This platform likely attracts high-information shoppers and professionals (think of a diligent consumer reading Consumer Reports – that mindset translates here). It may also attract early adopters in high-income urban segments who are willing to try new tech. For FMCG brands, while Perplexity’s user base is smaller, it’s influential: these users are often trendsetters or advisors within their circles. If your brand targets the type of consumer who reads labels, compares ingredients, or loves to be the one recommending products to friends, a presence in Perplexity’s answers (via strong web content that the AI can cite) can be valuable.
Retailer AI Shopping Assistants: Amazon Rufus, Walmart Sparky, Target Bullseye
Amazon’s “Rufus”: Amazon’s generative AI shopping assistant – code-named Rufus – launched in late 2023/early 2024 and is embedded in the Amazon mobile app and website. Its rollout gives it instant potential reach to Amazon’s enormous customer base, but adoption is still in early stages. New consumer research shows that about 14% of shoppers have tried Amazon’s Rufus as of mid-2025retailmediabreakfastclub.com. This indicates growing awareness, especially considering that represents millions of users, but it’s far from universal among Amazon’s customers. Who are these early Rufus users? Likely, they skew toward Amazon’s most engaged and digital-savvy shoppers – for example, Prime members in their 20s, 30s, and 40s who shop frequently and don’t shy away from new features. Amazon’s overall customer demographics are broad (spanning all incomes and ages), but early Rufus adopters probably include busy young parents, deal-hunters, and tech-friendly professionals who welcome any help to make buying easier. Psychographically, Rufus users value convenience and informed decisions. Many are the kind of shoppers who would normally read through dozens of reviews or spend time comparing specs; now they let the AI do that heavy lifting. Amazon reports that customers ask Rufus detailed product questions – e.g. “Is this coffee maker easy to clean?” or “What’s the best pool umbrella for Florida weather?” – and receive answers synthesized from product listings and reviewsaboutamazon.comaboutamazon.com. In essence, Rufus’s top users are purchase-driven (often with intent to buy), but discerning – they want to ensure they pick the right product. This aligns well with typical FMCG segments like time-starved parents (needing quick, reliable answers on household products) or urban professionals (who want efficient, data-backed recommendations). For brands, optimizing for Rufus means optimizing your Amazon presence: rich product content, clear benefits, and robust positive reviews, since the AI uses those to answer shopper queries.
Walmart’s “Sparky”: In mid-2025, Walmart introduced Sparky, a generative AI assistant in its mobile app (via an “Ask Walmart” or “Ask Sparky” button)francescatabor.com. Walmart’s huge customer base (over 240 million weekly shoppers worldwide) means Sparky could eventually influence a vast swath of consumers. Presently, about 15% of shoppers have experimented with Walmart’s Sparkyretailmediabreakfastclub.com – a similar penetration to Amazon’s AI. Walmart’s core customers are often described as middle-income, value-conscious families across suburban and rural Americabusinessmodelanalyst.combusinessmodelanalyst.com. These include a lot of “everyday” shoppers: suburban moms and dads, budget-conscious young adults, seniors on fixed incomes, etc. The subset trying Sparky likely leans toward the younger and middle-aged Walmart shoppers who use the app regularly for groceries or general merchandise. Notably, Walmart integrated Sparky prominently in the app’s interface (the assistant is “unavoidable” on the home screen)retailmediabreakfastclub.com, so many users may tap it out of curiosity while doing their usual shopping. Psychographically, Sparky’s early users value practical help and savings. They’re asking things like product comparisons (“difference between gas vs. electric mower?”) or seeking recommendations (“What do I need for a kids’ pizza party?”). Walmart has showcased that Sparky can even handle contextual questions – e.g., checking weather or sports schedules to suggest related purchasesfrancescatabor.comfrancescatabor.com. This hints that its users include planners (e.g., a parent planning for an event or season) and bargain hunters looking for the best choice at the best price. In FMCG terms, think of a Sparky user as the mom planning a weekly family menu on a budget, or the college student furnishing an apartment cheaply – traditionally they might browse Walmart aisles; now they can ask the AI. For brands, aligning with Sparky means ensuring your product data (on Walmart’s online listings) answers common questions and that your value proposition (price, quality) is clear so the AI can pick it up when asked for “affordable but good [product category]”.
Target’s “Bullseye” (Gift Finder & Shopping Assistant): Target has been more targeted in its GenAI rollout, testing specific use-cases rather than a universal chat on the home screen. In late 2024, Target launched the Bullseye Gift Finder, an AI tool on its website/app focused on holiday toy recommendationscorporate.target.comcorporate.target.com. It asks for a child’s age, interests, etc., and then suggests toys – essentially a personalized gift concierge for the holidays. Target also piloted a “Shopping Assistant” chatbot on select product pages (under “About this item”) to answer detailed questions about those products (e.g., “Will this shirt shrink in the wash?”)corporate.target.comcorporate.target.com. Because these tools were initially limited in scope, user adoption data isn’t widely reported. However, Target’s typical shopper demographics give clues: Target’s appeal is strongest among 25–44 year-olds (younger Millennials and older Gen Z), often suburban women (especially moms) with moderate to high incomesinspiramarketing.cominspiramarketing.com. This group is sometimes playfully dubbed “the Target moms” – busy parents who enjoy Target’s mix of value and style. Those who tried the Bullseye Gift Finder in 2024 were likely parents and gift-givers in that demographic, eager for quick help finding the “perfect toy.” The psychographic profile here is experience-oriented and trend-conscious shoppers – people who love finding just the right item and appreciate Target’s curated product selection. They may not be hardcore tech early adopters, but they respond to easy-to-use, fun digital experiences (a whimsical holiday gift finder fits well). As Target expands AI features, one can expect its top users to be loyal Target customers who value convenience but also curation – e.g., a style-savvy Millennial mom asking the assistant if a certain throw pillow matches a decor trend, or checking if a skincare product contains a specific ingredient. For FMCG brands (especially those in categories like toys, apparel, beauty, home goods that Target emphasizes), Bullseye’s AI hints at a future of highly personalized shopping advice. Optimizing for it means making sure your product attributes and differentiators (sustainability, ingredients, style notes) are well-represented in Target’s product content so the AI can highlight them in responses.
Aligning AI Platforms with FMCG Customer Segments
Understanding these user profiles is only useful if we connect them to typical FMCG customer segments. Below, we interpret how each platform’s audience aligns (or not) with key segments such as Gen Z, Millennials, suburban moms, bargain hunters, and high-income urban professionals:
Gen Z (Teens & Young Adults): Gen Z shoppers (late teens to mid-20s) are heavy users of general AI chat platforms. They grew up with instant answers and have readily embraced ChatGPT – indeed, a large chunk of ChatGPT’s users are under 25explodingtopics.com. Gen Z consumers are likely to ask AI for product advice in conversational ways, like chatting about makeup tips or snack recommendations they saw on TikTok. They are also more likely to trust peer content, so an AI that summarizes “what’s popular” resonates. Platforms to prioritize for Gen Z: ChatGPT and Google’s AI (which they use via Search) should be top considerations. Perplexity might also capture the more academic Gen Z users (students looking up everything). Retailer AIs (Amazon/Walmart) play a role if the Gen Z consumer is actively shopping – e.g. college-age individuals using Amazon for convenience – but Gen Z might still do a lot of research in ChatGPT first. FMCG brands targeting Gen Z (think: trendy beverages, snack foods, budget cosmetics) should ensure they are visible in those chat-oriented platforms. That could mean your brand’s story and reviews are present in the online content that ChatGPT or Google AI trains on or retrieves. Also note, Gen Z highly values authenticity and social impact – while an AI might not convey “cool factor” directly, ensure that factual queries (like “Is Brand X cruelty-free?”) yield favorable answers. Gen Z are experimenters, so hooking them via AI suggestions can feed into social media chatter as well.
Millennials (Young to Mid-Life Adults): Millennials (mid-20s to about 40) are a broad group, now encompassing new parents, established professionals, and everything in between. They are power users of multiple AI platforms. Millennials constitute the largest share of Google’s Gemini AI usersdemandsage.com, meaning they’re heavily using AI in search for research and shopping. They also make up a big portion of ChatGPT and Perplexity users, given their 25–34 age strengthindex.dev. Many Millennials are comfortable with e-commerce and likely to try Amazon’s Rufus or Walmart’s Sparky if it promises to save time. Sub-segments matter: older Millennials in their late 30s (often parents) might lean more on retailer assistants to streamline grocery and household shopping, whereas younger Millennials might be more prone to chat with AI for product discovery or use Perplexity to deeply research a big purchase. Platforms to prioritize for Millennials: Essentially all of the above – but in practice, consider the context. For discretionary FMCGs (like beauty, specialty foods, gadgets), Millennials might start on Google or ChatGPT to read reviews or get ideas, then move to Amazon or Target to purchase. For everyday staples, they might go straight to Walmart’s app or Amazon. Millennials famously value efficiency and reviews from fellow consumers, so an AI that combines those (like Rufus summarizing reviews, or Sparky giving tips) is appealing. Brands targeting Millennials should ensure strong SEO presence for informational queries (to feed Google/ChatGPT answers) and robust product listings on retail sites (to satisfy AI assistants like Rufus). Also, Millennials appreciate personalization – Target’s gift finder is squarely aimed at millennial parents and aunts/uncles shopping for kids. If Millennials are your key demo, don’t neglect platforms like Target’s AI experiments that align with life moments (holidays, back-to-school, etc.) central to this group.
Suburban Moms (and Dads): This segment – often Gen X or older Millennial parents managing a household – is crucial for many FMCG categories (food, household cleaners, diapers, etc.). Traditionally, they rely on large retailers and value a mix of price and quality. These consumers may not be early adopters of ChatGPT for fun, but they will use an embedded AI if it makes shopping easier. For example, a busy mom might not visit Perplexity.ai, but if Walmart’s app suggests “Ask Sparky” to create a shopping list for a birthday party, she’ll try it. Likewise, if Amazon’s Rufus can compare two car seats in chat, a parent will use it rather than read 100 reviews. Platforms to prioritize for suburban family shoppers: Walmart Sparky and Amazon Rufus are top, given Walmart’s focus on families and Amazon’s one-stop convenience. Target’s AI tools are also relevant, since Target is popular among suburban moms (known for its stylish yet affordable product mix). Google’s AI in search is important for top-of-funnel queries like “best stain remover for baby clothes” – many parents still start on Google – but the conversion likely happens on a retailer site or store. ChatGPT might see use in this segment more for personal productivity (like meal planning or kid’s homework help) than direct product search, but it’s not out of the question that some parents will ask ChatGPT for, say, a quick weeknight recipe and then rely on the suggested ingredients/brands. Key alignment: Suburban parents are often value-driven but pressed for time. They align with AI that is practical and trustworthy. Walmart’s positioning of Sparky as a “trusted partner” for planning and eventually automating chores fits this wellfrancescatabor.com. FMCG brands targeting this group should ensure that their products are recommended when users ask these assistants about family needs – which comes down to having the right keywords (e.g., “easy to clean,” “kids’ favorite”) in your product descriptions and perhaps strong ratings that the AI can cite. Also, consider that this segment will follow AI recommendations if they seem to match real user feedback; highlighting positive family-oriented reviews will help both AI and the end-user trust your product.
Bargain Shoppers: Bargain hunters span age groups but share a psychographic focus on getting the best deal. They flock to sales, use coupons, and compare prices closely. Currently, retailer AIs like Sparky and Rufus are not explicitly price-comparison tools (they tend to recommend “best” or “suitable” products rather than cheapest outright), but this segment is likely to ask AI “...under $X” type questions. We can expect queries like “What’s the best shampoo under $10?” or “Cheapest store-brand alternatives for Tide detergent” to become common. Indeed, generative AI is shifting search behavior towards natural language such as “best X for budget Y”retailmediabreakfastclub.com. Platforms to prioritize for bargain-focused consumers: Walmart is a natural fit – its entire brand appeals to the budget-conscious, and Sparky will likely be tuned to value propositions. (Walmart’s own segmentation notes they prioritize price-sensitive shoppersbusinessmodelanalyst.com.) Amazon also serves deal-hunters (Prime Day, anyone?), so Rufus may be asked to find “great deals on [item]”. Google’s AI Overviews might present buying guides that include budget picks. Bargain shoppers might not be heavy ChatGPT users unless they are particularly tech-savvy, but some could use ChatGPT or Perplexity to scour for coupon codes or hacks (e.g., using plugins or browsing capability – beyond standard usage). Key alignment: Brands with a value proposition should ensure AI-visible content about affordability. For example, if you offer a cheaper alternative to a national brand, make sure that gets mentioned in comparison articles or Q&A that AI might scrape. If a shopper asks Sparky for “cheap but reliable laundry detergent,” and your brand is known for low price, the AI should have data (from reviews or descriptions) to include it. Being highly rated also matters to AI – bargain shoppers don’t want “cheap and nasty,” they want best bang for buck. So focus on maintaining good reviews and star ratings, which these assistants often factor into answers.
High-Income Urban Professionals: This segment (think young professionals or affluent city-dwellers in their 30s and 40s) values convenience, quality, and often has the disposable income to choose premium brands. They are frequently early adopters of new tech – many had smart speakers early on, for instance – and are likely over-represented among ChatGPT Plus subscribers and Perplexity’s power users. They also rely heavily on e-commerce due to busy lifestyles. Platforms to prioritize: ChatGPT (and GPT-4 plugins) is popular with professionals for productivity; these same users may leverage it for shopping advice, especially for complex or high-end purchases (e.g., asking for a comparison of gourmet coffee makers or organic skincare products). Perplexity appeals to the analytically minded subset – those who read Wirecutter or detailed reviews before buying. Google’s AI is a given, since this group Googles everything from “best running shoes for marathons” to “reviews of [premium blender]”. Amazon Rufus will be used by high-income users who are already Amazon devotees – for instance, an urban professional might ask Rufus to narrow down best noise-cancelling headphones during a quick lunch break. Walmart might be less frequented by the affluent urban set (as Walmart’s footprint is more suburban/rural), except for those who love a good deal or use Walmart.com for staples. Target could go either way – many urban professionals prefer Target for nicer design and brands, so if Target’s AI evolves, it could serve premium store-brand recommendations (Target’s customer base includes a fair share of higher-income shoppers in metro areas). Psychographic alignment: This segment wants efficiency and expertise. They are likely to trust AI recommendations more than generic marketing, which is supported by data – globally, GenAI users prefer AI recommendations over brands’ own sites or apps for purchase adviceemarketer.com. For a premium FMCG brand, this means you want to be the “expert’s choice” when an AI is asked for the best. Ensure that expert reviews, credible articles, or any data point that positions your brand as top quality is available for AI to ingest or cite. Also, high-income users often value personalization – they might expect AI to tailor answers (like knowing their past purchases or preferences). Amazon and others are working on that personalization. If your target customers are in this group, your strategy should include presence on general AI (for discovery) and seamless integration into e-commerce AI (for decision and purchase). For instance, a premium organic food brand would want to appear in a ChatGPT answer about “healthy lunch ideas for professionals” and also be highlighted by Amazon’s AI when someone asks “what are high-quality organic snack bars?”.
Strategic Guidance for FMCG Brands: Prioritize and Optimize
Given these insights, how should FMCG brands allocate their efforts across AI platforms? Here are practical recommendations:
Match Platform to Segment Priorities: Identify which AI platforms overlap most with your brand’s target demographics. If you sell a teen-centric snack or a Gen Z cosmetic line, focus on ChatGPT and TikTok-influenced channels (ChatGPT usage is huge among under-25sexplodingtopics.com) while maintaining a strong Google search presence for when they seek validation of trends. On the other hand, a household cleaning brand aimed at families should prioritize Amazon and Walmart’s AI assistants, since parents in the shopping mindset will use those for convenience. In short, meet your customers where they are adopting AI. A quick cheat-sheet might be: ChatGPT/Perplexity for Gen Z–Millennial discovery; Google’s AI for broad reach across ages; Amazon/Walmart AI for family and mainstream shoppers at point-of-sale; Target’s budding AI for millennial moms and curated product seekers. Use this to guide where you put your optimization dollars.
Ensure Your Content is AI-Friendly: Large language models draw on what they can read. That means your brand’s information online needs to be structured and prominent. For general AI platforms, invest in high-quality, search-engine-optimized content that answers common consumer questions about your category. For example, if you make plant-based protein shakes, have content on your site (or collaborate on articles) about “best post-workout shakes for vegans” – something an AI might pick up and include in an answer. Because Perplexity and Google Gemini cite sources, getting your brand mentioned in credible sources (reviews, comparison articles, expert blogs) can directly lead to AI recommending your product with a citationfrancescatabor.comfrancescatabor.com. For retailer AIs, the data is your product listing and related info: fill every field with relevant details (dimensions, materials, allergen info, etc.), and write the description in a natural language that might mirror a question. Think: a customer might ask Sparky, “Does this laundry detergent work for sensitive skin?” – if your listing explicitly says “ideal for sensitive skin” and reviews reinforce that, the AI will likely relay it. Amazon’s Rufus, for instance, draws on listing details, customer reviews, and Q&Aaboutamazon.com – so keep those sections robust and up-to-date. Anticipate questions and make sure the answers are either in the product info or have been asked/answered in the community Q&A.
Leverage AI-Specific Optimization Tactics: Just as SEO became a discipline for Google Search, we are entering the era of “AI Visibility” or LLM optimization. Some emerging tactics:
Conversational Keywords: People use natural, full-sentence queries with AI (“What’s the best baby formula for colic?”). Incorporate likely Q&A phrasing into your content. FAQs on your website, written in a Q&A format, can be gold – they might be lifted directly into an AI’s answer snippet.
Schema Markup & Structured Data: Using structured data on your site (for product info, ratings, etc.) can help ensure AI models ingest accurate details. Google’s AI, for example, will rely on structured data for things like pricing or availability when providing shopping overviews.
Third-Party Validation: Encourage satisfied customers and experts to talk about your product online. The more high-quality discourse about your brand, the more material AI has to potentially recommend it. This could mean seeding products with reviewers or engaging in communities where your target demo asks for advice – those dialogues might later inform AI answers.
Monitor AI Mentions: Start “listening” to what these AIs say about your brand. Just as brands monitor social media, you can pose typical consumer questions to ChatGPT, Bard/Gemini, etc., and see what comes up. If the AI’s answer is inaccurate or your brand is absent, that’s a gap to address either with content or, if needed, with direct collaboration (some brands are beginning to feed data to AI platforms via plugins or APIs).
Optimize for Conversion in AI Shopping Assistants: When dealing with retailer AIs, think of them as a new kind of search engine on the storefront. Content that might have lived in product titles or bullets now needs to also work conversationally. For example, a user might tell Walmart’s Sparky, “I need an outdoor rug that won’t fade in sun.” If you sell outdoor rugs, ensure your product listing mentions “UV-resistant” and “won’t fade” explicitly – if not, the AI may skip over your item. Likewise, if Amazon’s Rufus is asked, “What’s the difference between Brand A and Brand B coffee makers?”, and your Brand B has a clear spec sheet or comparison info available, Rufus can pull that in. In the Forbes retail media analysis, it’s noted that these assistants will likely incorporate “add to cart” and other shopping actions in their answersfrancescatabor.com. Make sure your product’s digital shelf is ready for that era: have enticing images (since AI might show product thumbnails), competitive pricing, and consider enrolling in programs that ensure your product is recommended (e.g., Amazon’s “Editor’s Choice” or Walmart’s verified brand content, as applicable).
Plan for Paid Visibility and New Metrics: As AI assistants become gatekeepers to product discovery, expect new advertising opportunities to emerge. Industry experts predict “sponsored recommendations” will start appearing within AI assistant answersretailmediabreakfastclub.com. Amazon and Google have already experimented with ads in conversational AI results. Brands should budget for AI placement much as they do for search ads or retail media today. This might take the form of bidding to be the recommended product when a user asks “What’s the best toothpaste?” on a platform, or paying for higher visibility in an AI-curated list. Keep an eye on retailer announcements – for example, if Walmart offers a way to promote your product in Sparky’s suggestions, test it early. Additionally, prepare for new metrics: instead of clicks and impressions, you might be looking at “AI recommendation share” or conversion from AI-driven sessions. Start discussions with your analytics teams now on how to capture when users come via AI (some retailers may tag these sessions).
Maintain Trust and Authenticity: Consumers tend to trust the neutral, advice-like tone of AI recommendations – often, they see it as more objective than a sales clerk. But that trust can be lost if the advice feels sponsored or if it leads to poor experiences. It’s important for brands not to try to “trick” the AI with misinformation or overhype. Focus on factual accuracy and genuine benefits so that when the AI conveys info about your product, it rings true. Also, consider that if your product has any controversies or common questions (say, about an ingredient or sustainability), those will surface in AI answers. Proactively address these in your content. The goal is to have the AI answer in a way that builds confidence in your brand. For instance, if you sell a herbal supplement and some users worry about safety, ensure the AI finds “brand X has been tested and is certified by Y” in the source material.
Stay Agile as the Landscape Evolves: The AI platform landscape by late 2025 is dynamic – new entrants (e.g., rumored offerings from other retailers or an upgraded GPT-5 in ChatGPT) can shift user bases quickly. And features are evolving (Google expanding AI to more countries, Target adding AI beyond the holidays, etc.). As a brand strategist, continuously monitor where your target consumers are migrating. Perhaps Gen Z will move from ChatGPT to a new AI integrated in Instagram or Snapchat; maybe large retailers will federate their assistants into voice apps, etc. The key is to build a capability within your team to keep up with AI trends and user adoption data. By 2029, nearly half the US population is projected to be regular GenAI users in some formemarketer.com – essentially, AI-based touchpoints will be everywhere. Don’t consider this a one-off project; make AI platform optimization a persistent part of your marketing strategy. Engage in knowledge sharing – for example, participate in beta programs that OpenAI, Google, or retail partners offer for brands to feed data or test integrations.
Educate and Empower Your Team: Finally, ensure your marketing and content teams understand these differences in platforms and user behavior. The tone and design of content might differ for each. A straightforward, fact-filled approach might work best for Perplexity (where the audience wants depthindex.dev), whereas a more inspirational or lifestyle angle could be effective on a Target AI that’s helping someone find gift ideas. By appreciating the nuances – e.g., that Walmart’s Sparky users may value no-nonsense advice and savings, while Target’s AI users might respond to style cues and trendiness – your team can tailor messages appropriately. In all cases, keep the tone helpful and relevant because AI assistants relay information without the human nuance; whatever you put in is exactly what comes out to the customer.
Conclusion
The rise of AI assistants in consumer life represents both a challenge and an opportunity for FMCG brands. On one hand, the fragmentation of platforms – from ChatGPT to retailer-specific AIs – means brands must juggle multiple optimization efforts. On the other, each platform offers a chance to connect with consumers in a more personalized, conversational way than ever before. The key is to align your strategy with the demographics and psychographics each AI platform attracts. Gen Z and Millennials might discover your product through an open-ended chat; busy parents might rely on a store’s AI to recommend your brand on the spot. By understanding these audience profiles and by proactively shaping your content and data for AI consumption, you can ensure your brands become the trusted answers that these systems deliver. In the evolving AI-driven marketplace, those who adapt early – placing the right information in the right AI channels – will win the attention and wallets of tomorrow’s consumers. The bottom line for marketing decision-makers: know your customer, know their AI. Optimize accordingly, and your brand will be the one these intelligent platforms serve up as the solution for shoppers’ needs – no matter how they ask the question.