People are increasingly turning to AI models for practical advice, like how to spend less on everyday necessities like groceries. What we found is that the answers provided were unlikely to begin and end with a single store.

Instead, AI responses combine retailer recommendations with advice about making lists, buying in bulk, choosing store brands, using digital coupons, earning cash back, shopping sales, and avoiding impulse purchases. Grocery chains are competing for visibility not only against one another, but also against the apps, cards, products, and behaviors associated with saving money.

We used GenAI Lens, Meltwater’s AI visibility tracking capability, to examine how eight AI systems answered two grocery-savings prompts over a two-month period. The analysis reveals a closely contested retailer landscape, significant differences between models, and a source ecosystem where established financial publishers sit alongside social platforms and smaller lifestyle sites.

The results reveal a highly competitive AI visibility landscape that consistently emphasizes the tactics consumers can use to reduce grocery costs, rather than rewarding overall brand recognition alone.

No grocery retailer owns the default AI answer

Which grocery savings tips do AI models recommend most?

Why do apps, cards, and store brands outperform some retailers?

Which AI models are most likely to name grocery brands?

What language dominates AI grocery-savings advice?

Which sources shape AI grocery-savings answers?

How Meltwater GenAI Lens helps brands compete in AI-driven grocery-savings discovery

FAQ: AI visibility in grocery-savings recommendations

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    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold; width:30%;">Capability</td>
      <td style="padding:12px; border:1px solid #ccc;">GenAI Lens</td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Analysis window</td>
      <td style="padding:12px; border:1px solid #ccc;">June 1 – July 31, 2026</td>
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      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Data sources</td>
      <td style="padding:12px; border:1px solid #ccc;">
        Anthropic Claude, ChatGPT, DeepSeek, Google AI Mode, Google AI Overviews, Meta Llama, Perplexity, and xAI Grok
      </td>
    </tr>
    <tr>
      <td style="padding:12px; border:1px solid #ccc; font-weight:bold;">Prompts</td>
      <td style="padding:12px; border:1px solid #ccc;">“How do I save money while grocery shopping?”, “How to save money on groceries”</td>
    </tr>
  </tbody>
</table>


The analysis examined how eight generative AI systems responded to grocery-savings questions. Because the prompts asked how to save money, rather than asking which retailer is best, the results include shopping tactics, tools, cards, apps, and private-label products alongside grocery store recommendations. 

Download a one-page PDF summarizing the findings of this analysis.

Leading retailers in AI grocery-savings answers

Consumers asking AI how to save money on groceries receive recommendations built around savings strategies, not just grocery chains. Across the two-month analysis period, no retailer emerged as a clear frontrunner recommendation. Instead, AI responses consistently tied retailers with the specific ways they help shoppers reduce costs, creating a competitive landscape where visibility depends on a brand's association with a particular savings tactic rather than overall market recognition.

Among the retailers analyzed, Aldi achieved the highest AI Visibility Score at 27%, followed by Lidl (19%), Costco (15%), Walmart (14%), and Kroger (14%). While Aldi leads the field, the relatively small gap between the top retailers reinforces the fact that grocery savings remains a highly competitive AI category, not dominated by a single brand.

Instead, retailer visibility depends heavily on whether the brand fits naturally within a specific piece of savings advice. Aldi and Lidl are associated with discount pricing. Costco appears in recommendations about buying in bulk. Walmart, Kroger, and regional grocers enter the conversation through their apps, coupons, and loyalty programs.

So, when it comes to this topic, AI is not simply ranking stores. It is answering a behavioral question. Consumers are asking how to reduce grocery costs, and LLMs are prioritizing examples that illustrate specific strategies. Retailers are a factor supporting those strategies rather than the primary focus of the recommendation.

Why this matters: The grocery-savings category remains open and contestable. Instead of pursuing generic recognition as the best grocery store, retailers can build a more defensible AI presence by becoming strongly associated with one recognizable savings behavior, such as discount pricing, bulk purchasing, digital coupons, or rewards.

The strongest retailer visibility is usually connected to a clear and repeatable reason to save.

Aldi and Lidl are the two most-visible retailers because their established discount positioning fits the prompt without requiring much additional explanation. The underlying AI content also groups them with Trader Joe’s as stores consumers can consider for lower-priced basics.

Their visibility shows the advantage of having a brand identity that already functions as a savings tactic. When the recommendation is simply to switch to a discount retailer, Aldi and Lidl are natural examples.

However, Aldi’s eight-point visibility lead over Lidl remains modest enough that the category cannot be considered settled.

Costco’s visibility is tied more directly to a particular shopping behavior.

AI responses regularly connect the retailer to buying non-perishable products and household staples in bulk. Sam’s Club appears in similar advice about reducing per-unit costs through warehouse-club shopping, even though it falls outside the top-10 mentions table.

This gives warehouse retailers a defined place within AI answers: they are not presented as the right option for every purchase, but as a specific solution for products that can be bought in larger quantities.

Apps and loyalty programs create another route to visibility

Walmart, Kroger, Safeway, and regional grocers appear in connection with digital coupons, store apps, and rewards programs.

In the underlying analysis, regional retailers including H-E-B and Randalls are named alongside Walmart as examples of stores offering digital coupons and rewards through their apps. The recommendation is not simply to shop at those retailers. It is to use a particular part of their savings infrastructure.

Why this matters: Brands need a specific answer to the question, “How does shopping with us help someone save?” A clear, well-documented tactic gives AI systems a reason to include the brand in an otherwise broad piece of advice.

In AI grocery-savings advice, the tool used to save can become more prominent than the store where the purchase happens.

Looking from a sentiment point of view rather than mention volume, the featured brands change significantly and different entities rise to the top. This view highlights the brands AI most strongly associates with helping consumers save money—notably it includes some non-store names. 

Regional grocers such as Raley's, Randalls, and Save Mart rise to the top in this view, alongside American Express, Kirkland, and Great Value. With AI Visibility Scores ranging from 55% to 80%, these brands outperform many of the retailers in the previous ranking—not because they're mentioned more often, but because they're repeatedly associated with practical ways to save.

The reasons for their visibility are highly specific:

Regional grocers appear as examples of loyalty-app and digital-coupon programs.

American Express is connected to a supermarket cash-back benefit.

Kirkland and Great Value appear as lower-priced alternatives to national name brands.

Ibotta and Rakuten function as direct savings mechanisms rather than shopping destinations.

In each case, the brand provides a worked example of a tactic. AI can therefore treat a cash-back card, coupon platform, private-label product, or regional chain as part of the answer to the same grocery-savings question.

That distinction reinforces a broader theme: AI isn’t treating “grocery savings” as a competition between retailers. It builds recommendations around the strategies that consumers can use to spend less and save more, rewarding visibility to any brand that helps illustrate those strategies. It’s a wide ecosystem rather than a narrow retail-only category.

Why this matters: "Grocery savings" represents an AI-discovery opportunity for a much wider group than national supermarket chains. Fintech companies, loyalty platforms, private-label brands, and smaller regional retailers can all earn significant visibility when their offering is closely tied to a concrete savings mechanism.

DeepSeek names the widest range of retailers

DeepSeek had the highest combined score across the 10 tracked grocery brands, approximately 26% ahead of the next-highest model.

Its strongest retailer scores included Aldi at 71%, Lidl at 66%, Kroger at 41%, Safeway at 33%, and Walmart at 27%. DeepSeek was particularly assertive about discount retailers, creating a significant portion of Aldi’s and Lidl’s overall advantage.

Grok ranked second across the tracked brand set, but it led individual retailers including Costco, Walmart, and Target, as well as savings platform Ibotta. Its Costco score of 44% was more than twice DeepSeek’s score for the retailer.

Google AI Overviews occupied a more moderate position across most brands but produced the highest score for Sam’s Club. Google AI Mode generally named retailers at low-to-moderate levels.

Claude and ChatGPT rarely recommend specific retailers

Claude and ChatGPT were the clearest outliers.

ChatGPT scored 0% for eight of the 10 tracked retailers. Costco, with a score of 5%, was its only meaningful retailer result in the set. Claude scored 0% for seven retailers, with only Aldi, Costco, and Sam’s Club registering.

These systems still provided grocery-savings advice, but their answers relied much more heavily on general behaviors and shopping principles than on brand recommendations.

Taken together, these differences underscore an important reality of LLM visibility strategy: AI visibility depends on which model a consumer uses. A retailer that appears prominently in one LLM may receive little or no visibility in another, even when the prompt is exactly the same. 

Why this matters: Consumers don't all use the same AI assistant, and different AI assistants don’t generate the same recommendations. Brands need model-level analysis to distinguish a general narrative gap from a platform-specific behavior.

Planning before entering the store is the most common theme in AI grocery-savings responses.

“Items” was the most prominent tracked term, with 1,900 mentions and 72% visibility. “Store” followed with 1,600 mentions and 66% visibility. “List” and “shopping” each generated approximately 1,300 mentions.

Together, these terms show that AI systems frequently begin with basic preparation: decide what to buy, make a shopping list, and approach the store with a plan.

More specific behaviors appear further down the ranking:

“Money on groceries” had the highest mention-rate score of any tracked phrase, at 56%. Although it had lower raw volume than the broad planning terms, it was used consistently when it appeared. “Impulse” was the smallest tracked topic but remained present throughout the two-month analysis window, indicating that avoiding unplanned purchases is a durable part of the AI savings narrative.

The language also varied by model. ChatGPT leaned most heavily into broad terms such as “store,” “items,” and “list.” Meta Llama placed more emphasis on behavior-specific terms including “money,” “stores,” and “bulk.” DeepSeek combined high usage of general shopping language with unusually strong volumes for “shopping” and “impulse.”

Why this matters: Grocery-savings content that clearly addresses shopping behaviors, such as buying in bulk, planning meals, or eliminating impulse purchases, may help brands align better with the language AI models consistently use when answering savings-related questions — thus increasing visibility and brand awareness.

AI grocery-savings advice draws on a mixed source ecosystem spanning major publishers, social platforms, aggregators, and smaller lifestyle sites.

Of the approximately 6,400 citation sources in the dataset:

44.9% were classified as Other, including aggregators and personal-finance or lifestyle blogs.

Interestingly, review sites represented a negligible share.

The mix reflects the nature of the question. Saving money on groceries sits at the intersection of personal finance, shopping, household management, and lived consumer experience. AI systems therefore draw from both professionally reported advice and community-led content.

The most-cited sources for grocery-savings advice

YouTube led with 539 citations, followed by NerdWallet with 324 and Facebook with 311.

The top YouTube video by citations (94) is titled “How to Save Money on Groceries (No Coupons Needed!)”, from an account with 226K subscribers. 

How to Save Money on Groceries (No Coupons Needed!)

The second highest video by citations (70) is a clip from a Good Morning America segment. 

Both videos share recency and while the account with the top video has fewer subscribers than GMA, their engagement is far stronger with around 80 comments and 16K views.

Meltwater's GenAI Lens analysis reveals how AI models recommend grocery savings.

How AI Recommends Saving Money on Groceries: Analysis of 8 Leading AI Models

The World Artificial Intelligence Conference (WAIC) is an annual conference held in Shanghai that brings together governments, AI companies, research institutions and policymakers to showcase advances in artificial intelligence, explore emerging technologies and discuss the future of global AI governance. Since its launch in 2018, WAIC has grown in scale, combining a major technology exhibition with high-level policy discussions and academic exchange.

Key announcements from WAIC 2026 included

World AI Cooperation Organisation (WAICO) 

The 2026 edition carried added significance as a platform for international AI governance, marked by the founding of the World AI Cooperation Organisation (WAICO), a new intergovernmental AI cooperation body headquartered in Shanghai, established when 29 countries signed its charter on 16 July, a day ahead of the conference's opening keynote. The WAICO launch and Xi Jinping's keynote address were the two biggest drivers of conversation, as the conference generated 204K mentions and 1.74 million engagements, and 1.3B estimated views between 15 and 20 July.

The opening keynote generated the highest share of discussion, though the wider conversation extended to international AI policy, core technologies, robotics and commercial applications. The following breakdown outlines the themes, narratives and channels that shaped the event.

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  <tbody>
    <tr style="background-color:#28BBBB;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold; width:30%; color:#FFFFFF;">Metric</td>
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold; color:#FFFFFF;">Value</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Mentions analyzed</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">204,000</td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Engagement</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">1.74M</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Estimated reach</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">1.3B</td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Largest storyline</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">Opening keynote</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Biggest policy announcement</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">WAICO</td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Dominant market</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">China (94%)</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Main technology theme</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">Core AI infrastructure</td>
    </tr>
  </tbody>
</table>

The keynote that defined the news cycle 

Policy narratives shaped discussion beyond the keynote

Technology and commercial applications dominated the conversation

A conversation concentrated on Chinese platforms

From insight to intelligence: how Meltwater Social Listening Platform helps

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  <tbody>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold; width:30%;">Capability</td>
      <td style="padding:12px; border:1px solid #E1EFF0;"><a href="https://www.meltwater.com/en/capabilities/social-listening">Meltwater Social Listening Platform</a></td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Analysis window</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">15-20 July 2026</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Data sources</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">30 sources, including broadcast television and radio, online and print news, blogs, comments, reviews, forums, podcasts, TikTok, Instagram, Reddit, Facebook, Pinterest, X, Douyin, Red and YouTube</td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Regions</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">Regional analysis focused on China and four major Western markets to compare domestic and international media coverage of WAIC 2026.</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Language</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">English and Chinese</td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Definitions</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">Mentions (individual pieces of content referencing WAIC 2026 keyphrases); Engagement (likes, shares, comments and reactions summed across tracked platforms); Estimated Views (modelled potential audience reach across broadcast and online sources)</td>
    </tr>
  </tbody>
</table>

The opening keynote was the single largest driver of conversation at WAIC 2026.  Keynote-related coverage generated 40.1K mentions, accounting for close to one-fifth of the full total and more than any other individual theme, and drove the conference's busiest day on 17 July with 49.3K mentions and 575K engagements.  While the keynote drew a lot of coverage, product and exhibitions, humanoid robots, general AI hype, and product and chip showcases combined accounted for a larger share of overall volume (50.7%).

Conversation built steadily into the conference opening before reaching its peak on 17 July with 49.3K mentions and 575K engagements. Momentum began building a day earlier with the signing of the World AI Cooperation Organisation (WAICO), before the opening keynote and a series of major policy and technology announcements, including China's AI Cooperation and Development Action Plan, Huawei's Atlas 950 SuperPoD AI computing cluster and Moonshot AI's Kimi K3 model, drove the conference's busiest news cycle. Daily conversation remained above pre-conference levels through to 20 July, sustained by continued discussion of the keynote alongside product launches and exhibition activity.

Why it matters: Major technology conferences are increasingly driven by multiple parallel news cycles. While opening-day announcements create the largest spikes in attention, product launches, technology developments and exhibition activity continue to shape the conversation throughout the event. For communications teams, understanding how these storylines interact is just as important as monitoring the headline moments.

Policy narratives shaped discussion beyond the keynote 

While the opening keynote generated WAIC's largest spike in attention, its influence extended well beyond the speech itself. Discussion continued around three closely connected policy narratives: responsible AI development, international cooperation and the establishment of new multilateral institutions. Together, these themes positioned policy as a parallel conversation to the conference's technology announcements, accounting for 38.7% of total discussion across the analysis period. 

Responsible AI development remained a central theme

Discussion consistently focused on balancing AI innovation with security, openness and inclusiveness. Keyphrases relating to governance challenges (10,028 mentions) and regulation and technology monitoring (9,962 mentions) reflected continued attention on how AI should be developed responsibly alongside rapid technological progress. The keynote framed these principles through openness, risk-awareness, inclusiveness and solidarity, establishing a recurring reference point for subsequent media coverage.

International cooperation became a defining policy narrative

International cooperation emerged as one of WAIC's defining policy narratives, reinforced by both the keynote and a series of practical initiatives announced throughout the conference. President Xi's call for global cooperation around AI became a recurring message across conference coverage. The theme was supported by announcements including 5,000 AI training opportunities for developing countries and new AI cooperation centres targeting ASEAN, the Arab League, the African Union, CELAC, the SCO and BRICS. The prominence of related keyphrases—including ASEAN (13,435 mentions), the African Union (12,490 mentions) and the Arab League (12,013 mentions)—demonstrates that these initiatives became an integral part of the wider conference discussion.

The development of artificial intelligence should not be a solo performance by any one country, but a symphony of global cooperation.

- President Xi Jinping, 2026 World Artificial Intelligence Conference (Source)

The World AI Cooperation Organisation (WAICO) reflected a longer-term institutional ambition

The launch of the World AI Cooperation Organisation (WAICO) generated 15,022 mentions, making it one of the conference's most prominent policy developments. Signed by 29 countries the day before WAIC officially opened, the organisation represented the progression of an initiative first proposed at WAIC 2025 into a formal international institution. Its timing also helped build momentum ahead of the conference, reinforcing WAIC's role as both a technology showcase and a platform for international AI dialogue. 

Why it matters: As AI continues to evolve, so too do the policies, governance frameworks and international cooperation shaping its development. Major AI conferences increasingly generate parallel conversations around both technological innovation and the frameworks that enable its adoption.

While policy remained a significant part of WAIC, technology and application-related themes collectively accounted for 50.7% of total conversation, making them the dominant focus of the conference. Within this broader discussion, core technologies generated the highest directional volume (104,644 mentions), while commercial applications across transport, manufacturing, healthcare and consumer devices demonstrated how AI is increasingly being discussed through real-world deployment rather than future potential.

Core technologies generated by far the highest volume of discussion (104,644 mentions), reinforcing that the race to build faster chips, more capable models and greater computing infrastructure remains central to AI development. Announcements such as Huawei's Atlas 950 SuperPoD and Moonshot AI's Kimi K3 reflected continued industry focus on the capabilities underpinning next-generation AI.

While core technologies generated the highest directional volume, discussion also spanned a broad range of AI applications, including intelligent driving (17,638 mentions), manufacturing (17,341), smart terminals (17,272), smart healthcare (5,517) and embodied intelligence (3,780). Rather than centring on a single breakthrough, WAIC reflected an AI ecosystem advancing across infrastructure, enterprise and consumer applications simultaneously.

Why it matters: Narratives at WAIC 2026 suggest the AI conversation is becoming increasingly layered. While foundational technologies continue to dominate industry attention, organisations are placing growing emphasis on how those capabilities translate into commercial products, enterprise adoption and everyday experiences. Understanding both the technologies enabling AI and the industries adopting it provides a more complete view of where the market is heading. 

Although WAIC attracted international speakers, exhibitors and media attention, the conversation remained overwhelmingly concentrated within China. 94% of mentions originated in China, with the four Western markets analysed (US, UK, Germany, France) accounting for under 6% combined. This highlights the extent to which WAIC's media impact was shaped by China's domestic news and social ecosystem.

Chinese platforms played a central role in driving visibility throughout the conference. Online news was the largest single platform at 70.2K mentions (34.6%), followed by WeChat (65.8K, 32.2%), Weibo (34.4K, 16.9%) and Red (26.6K, 13.0%). Together, these channels accounted for the overwhelming majority of conversation, reflecting the importance of domestic media and social platforms in shaping WAIC's narrative.

In contrast, coverage in the four Western countries remained fairly limited and was concentrated in online news at 80.7%, with X/Twitter a distant second at 13.4%. Discussion focused largely on the opening keynote, with specific coverage around 29 countries signing the WAICO agreement, China's NDRC releasing its AI Cooperation and Development Action Plan, and Xi calling for global cooperation on AI development. 

Why it matters: Major technology events can have global significance while their media conversation remains geographically concentrated. Organisations monitoring these events should understand where discussion is actually taking place, rather than assuming international attention is evenly distributed across markets and platforms.

Use case 1: Track multiple narratives as they emerge 

The problem — Conferences rarely revolve around a single announcement. Policy developments, product launches and commercial applications evolve simultaneously, making it difficult to identify which narratives are gaining momentum. 

Monitor policy, technology and industry themes in a single dashboard.

Track volume, engagement and sentiment as narratives evolve.

Set alerts for emerging topics rather than individual keywords.

Business outcome — Communications and insights teams can identify which stories are shaping the conversation in real time and decide where to participate before narratives become established. 

Use case 2: Understand where conversations are actually happening

The problem — Events do not always generate global conversations. As WAIC demonstrated, discussion may be concentrated in specific languages, regions and platforms.

Track native Chinese and English keyphrases side by side.

Analyse media and social platforms within a single workflow, including regional platforms like WeChat, Weibo and RedNote, in a single dashboard.

Compare how narratives differ across markets and channels.

Business outcome — Teams gain a more representative view of how an event is being discussed, rather than relying on a single market or platform. 

WAIC 2026 demonstrated that AI conversations are not defined by a single announcement or technology. While the opening keynote generated the event's largest spike in attention, policy, core technologies and commercial AI applications evolved as parallel narratives throughout the conference. At the same time, with 94% of conversation originating in China, the event highlighted how globally significant AI discussions can remain concentrated within domestic media ecosystems. For organisations tracking AI, understanding both what is shaping the conversation and where it is taking place will be critical to interpreting future industry developments. 

What was the biggest driver of World Artificial Intelligence Conference (WAIC) 2026 media coverage?

The opening keynote, given by Chinese President Xi Jinping, was the single biggest media driver. According to Meltwater's social listening platform, it accounted for 40.1K of the 204K total mentions tracked between 15 and 20 July 2026.

What is the World AI Cooperation Organisation (WAICO)?

WAICO is a new intergovernmental AI cooperation body headquartered in Shanghai. It was founded when 29 countries signed its charter on 16 July 2026, the day before Xi's keynote, generating 15,022 mentions in WAICO-tagged coverage on Meltwater's social listening platform. 

How positive was sentiment around the World Artificial Intelligence Conference (WAIC) 2026?

Sentiment stayed overwhelmingly healthy. Meltwater's social listening platform recorded 65% neutral and 34% positive sentiment, with under 1% negative, across the full analysis window. 

Which platform carried the most conversation from the World Artificial Intelligence Conference (WAIC) 2026?

Online news led at 70.2K mentions (34.6%), followed by WeChat at 65.8K mentions (32.2%), according to Meltwater's social listening platform data. X/Twitter was the largest non-Chinese social platform at just 3.04K mentions (1.5%). 

An analysis of the themes, narratives and media channels that shaped conversation around the conference, from policy and governance to product and robotics.

The World Artificial Intelligence Conference (WAIC) 2026: The Conversation Split Between Policy, Technology and AI

The main finding: trust depends on post-purchase control

Why Reddit is useful for consumer insight

The category pattern: trust builders, recurring friction, and control events

Battery life and accuracy remain persistent barriers

App and software decisions can create sudden trust risk

Positive whitespace exists in design, value, and comfort

Brand-level analysis changes the category read

What smartwatch marketers can do with these insights

How Meltwater turns Reddit conversation into marketing intelligence

When I was eight years old I thought the Casio Databank watch was just about the coolest thing I’d ever seen - a computer you could wear! If you’d told me back then that eventually wrist watches would pack enough processing horsepower to make every supercomputer of the 1980s look like an abacus, and everybody would be wearing one, my mind would have been blown. 

But that’s where we are today - smartwatches have become mainstream consumer gadgets, and people the world over use them for navigation, communication, health and fitness, and any number of daily tasks. 

The smartwatch market continues to grow, with industry analysts highlighting a 4% year on year growth for global shipments in Q1 2026. As existing users look to upgrade their devices to more recent models, and new buyers enter the market for the first time, they are highly likely to read, or even participate in, one of the many Reddit threads exploring the merits of different smartwatch brands. 

What will they find there? Let’s dig into the data.  

We used Meltwater Explore to analyze 502.8K Reddit mentions about smartwatches from Jan. 1 to Jun. 30, 2026. Our research covered broad smartwatch conversation, brand-specific discussion, and targeted communities including r/AppleWatch, r/GarminWatches, r/GalaxyWatch, r/PixelWatch, r/fitbit, r/whoop, r/amazfit, and others. 

Volume, engagement, and sentiment were analyzed across the full query, while theme and brand findings came from an approximately 4,000-post sample that combined the top 2,000 most-engaging posts with a randomized 2,000-post sample.

This analysis gives marketing teams a practical example of what social listening can provide beyond topline sentiment. Reddit conversation shows what consumers value, what frustrates them, how they compare brands, and which product decisions create reputational risk.

Smartwatch buyers compare specifications, but their Reddit conversations show how quickly feature debates become discussions about trust. Battery life, health tracking, comfort, price, and app experience all factor into purchase decisions; after purchase, users also want confidence that brands will preserve the product experience they bought into, or explain major changes clearly.

From January through April, smartwatch conversation followed a relatively stable pattern. Consumers discussed reliability, fitness credibility, comfort, and perceived price fairness as reasons to stay with a product or recommend it to others. These themes formed the category’s baseline, with users trading advice and comparing lived experience rather than reacting to major controversy.

In May and June, the conversation took a different shape. Mentions rose 20.3% in May, driven by backlash around Fitbit and Google’s forced Google Health migration. Negative sentiment reached 21.2% in June, as Reddit users reacted to Apple’s watchOS 27 support cutoff. As the clear market leader, Apple Watch’s 39.4% post share creates a large enough footprint for Apple-specific controversies to influence the entire category analysis.

Those two incidents involved different brands and different product decisions, but Reddit users interpreted them through the same lens: the terms of the product relationship had changed after purchase. For marketers, that is the insight worth acting on. A forced app migration, support cutoff, subscription change, warranty decision, or ecosystem requirement can become a brand trust issue when customers feel locked out of the decision.

Simply, the product they purchased in good faith no longer behaves the way they expect it to, for reasons beyond their control. 

Reddit is especially valuable because consumers explain their reasoning in one of the internet’s most candid environments. They compare products, challenge each other’s assumptions, ask for recommendations, troubleshoot problems, and describe how a device performs in everyday use.

That’s the difference between Reddit and a review site, or a simple rating system. A one-star review can show dissatisfaction, while a Reddit thread can show why the dissatisfaction exists, how other users interpret it, and whether the issue is isolated or spreading through the community.

For marketers, this kind of context can be valuable. For example, a battery complaint may reveal a mismatch between advertised battery life and real-world usage, or a support cutoff thread may reveal anger about communication, not only the loss of functionality. It’s hard to find such granular detail about customer pain-points anywhere else. 

Meltwater helps teams organize those signals into usable consumer intelligence. Instead of treating Reddit as a stream of disconnected posts, marketers can analyze volume, engagement, sentiment, themes, brand mentions, and recurring language to see which conversations deserve action. 

Our research found that smartwatch discussion breaks into three useful categories.

The first category is trust builders. Reliability, fitness credibility, comfort, and price fairness help create baseline goodwill. These are the reasons people keep wearing a smartwatch, recommend it to others, or stay within a brand ecosystem.

The second category is recurring friction. Battery life, health-metric accuracy, durability, screen issues, software bugs, and customer service concerns appear across the category. These issues may not always create dramatic spikes, but they influence day-to-day satisfaction and purchase consideration.

The third category is control events. These occur when a company changes the software, app, support terms, or access model after customers have already bought the device. Control events can create faster reputational impact because they change how consumers interpret the brand relationship.

Our analysis shows why marketers need to separate these categories: 

Recurring friction calls for expectation-setting, product investment, and customer education. 

Control events require communication planning, scenario monitoring, and cross-functional coordination before the rollout reaches customers.

Battery life represented 6.5% of posts in the thematic sample and had the strongest negative skew among major concerns. The discussion was not limited to charging frequency; Reddit users connected battery performance to reliability, sleep tracking, travel, workouts, and daily convenience. A watch that needs charging at the wrong moment can fail in the exact use case that made the buyer want a wearable in the first place.

This is a messaging challenge for smartwatch brands. “All-day battery” may be technically accurate under one set of conditions and still feel misleading to a user who tracks sleep, uses GPS workouts, turns on always-on display, and expects detailed health monitoring. Social listening can show which usage scenarios create friction and how consumers describe the gap between claims and reality.

Health-metric accuracy is another durable barrier. Heart rate and health-metric accuracy represented 3.3% of sample posts and had the second-highest negative skew among major concerns. Accuracy concerns hit close to the category’s core promise because many consumers buy smartwatches for fitness, health, and wellness tracking.

Brands can improve trust by explaining how metrics are measured, where readings may vary, and how users should interpret the data. Clear education can reduce confusion, especially when consumers are comparing smartwatch data with chest straps, medical devices, gym equipment, or competing wearables.

App and ecosystem friction was the highest-volume barrier in the sample, accounting for 11.3% of posts. That category includes routine software annoyances, companion app questions, integration issues, and setup friction. It also captured the sharper reaction to Fitbit and Google’s forced Google Health migration.

Software bugs and updates represented 5.1% of posts and spiked sharply around the June watchOS 27 support controversy. The key issue was the perceived loss of agency: users felt that support decisions affected devices they had already paid for, and the explanation did not satisfy the community.

This is where marketing, communications, product, and customer experience teams need to work from the same playbook. A forced migration may be justified internally, or a support cutoff may have a technical rationale, but consumers still need a clear explanation, a transition path, and enough notice to feel respected.

The stronger the ecosystem, the more sensitive these decisions become. When a device is tied to a phone, app, health history, subscription, warranty, and daily habits, a product update can feel personal. Reddit conversation gives brands an early view of that emotional reaction, along with the specific language consumers use to describe it.

Complaint-led topics often receive the most attention, but the smartwatch analysis also found lower-volume themes with favorable sentiment. Design and aesthetics represented 0.8% of posts, yet it was the most positive theme on balance. Price and value accounted for 3.9% of posts and was net positive. Band and strap comfort accounted for 3.8% and was close to balanced, often appearing in advice-led conversations about fit, care, and daily wear.

These findings are useful because they point to marketing territory that may be underused. Design conversations often involve personalization, watch faces, style, and how the device fits into everyday life. Value conversations can help brands understand which benefits make the price feel fair. Comfort conversations show the practical details that affect long-term usage.

This is an important distinction for campaign planning, because the loudest complaint is not always the best messaging opportunity. A theme with lower volume and favorable sentiment can offer cleaner territory for differentiation, especially when competitors are responding to negative spikes.

The brand breakdown shows why marketers should avoid relying only on aggregate category sentiment. Apple accounted for 39.4% of posts in the sample and 32.2% of engagement share. With that level of conversation volume, Apple-related controversy can influence how the whole category appears in a dashboard.

Fitbit had the highest negative share among brands with meaningful volume at 24.9%, shaped heavily by the Google Health migration controversy. Garmin had the strongest profile among high-volume brands, with 19.1% positive sentiment and 12.9% negative sentiment, anchored in fitness-tracking and battery credibility. Samsung conversation centered heavily on battery life, while Whoop stood out because subscription ranked among its top associated characteristics.

Those differences have direct implications for marketers. A category-wide spike in negative sentiment may come from one dominant brand, not a broad change in purchase intent. A competitor controversy may create an opening for brands that can credibly emphasize stability, transparency, battery life, durability, or customer choice.

Brand timing also becomes easier to assess with social listening. If a leading brand is facing backlash over support cuts or app changes, another brand may have an opportunity to launch messaging around compatibility, long-term support, or user control. Without brand-level analysis, that opening is easy to miss.

The smartwatch conversation analysis highlights several practical actions for marketing teams.

Track post-purchase control as a distinct trust signal. 

Product satisfaction and ownership confidence are related, but they are not identical. A customer may like the hardware and still lose confidence if the brand changes the app, software, support model, or subscription terms without enough explanation.

Separate recurring friction from event-driven risk. 

Battery life and accuracy require sustained investment and careful expectation-setting. App migrations and support cutoffs require launch communications, customer education, and response planning.

Reddit shows how people describe problems to each other, which is often more useful than internal product terminology. Those phrases can improve campaign copy, FAQs, product pages, lifecycle emails, support scripts, and sales enablement.

Design, value, and comfort may generate fewer posts than battery or app frustration, but they offer more favorable territory for positioning. Positive whitespace can be especially valuable when competitors are absorbed by issue management.

Monitor competitors by theme, not only by share of voice. 

A competitor can dominate conversation for negative reasons, while a smaller brand may own a more favorable association. Meltwater can help marketers see which brand is associated with which theme and whether that association is improving or weakening over time.

A simple dashboard could show that smartwatch mentions rose in May, negative sentiment increased in June, and Apple led share of voice. That would be useful, but incomplete.

The deeper value comes from connecting those signals to consumer interpretation. The May and June spikes were tied to post-purchase control. Battery life and accuracy were persistent trust barriers. Design, value, and comfort offered favorable space for differentiation. Brand-level analysis showed that Apple’s large footprint can affect the overall category read, while Garmin, Fitbit, Samsung, Whoop, and others each carried distinct associations.

That is the difference between monitoring conversation and surfacing actionable consumer insight.

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