Sale prices and brand offerings have always been under the microscope during Black Friday Cyber Monday (BFCM), but in 2026 consumers are talking about much more than just discounts. 

To understand how consumer expectations and conversations are changing, we used our Social Listening capability to analyze 5.67 million posts from August 2025 to July 2026 about the shopping event across social platforms, forums, blogs, product review sites and comment sections.

That conversation (70.6% of which occurred in November 2025) revealed where brand reputation risks can emerge, as well as what inspires consumers to complain about or praise their shopping experiences. Here’s what the data tells marketers about preparing for BFCM 2026.

Download a one-page summary of our findings and explore the capabilities that powered this analysis.

Key Black Friday and Cyber Monday consumer insights

Black Friday and Cyber Monday 2026 guidelines for brands

Black Friday and Cyber Monday consumer insights FAQ

<table class="table-primary">
<thead>
<tr>
<th>Methodology</th>
<th>Details</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Capability</strong></td>
<td>Social Listening</td>
</tr>
<tr>
<td><strong>Analysis window</strong></td>
<td>1 August 2025 to 31 July 2026</td>
</tr>
<tr>
<td><strong>Data sources</strong></td>
<td>Social platforms, forums, blogs, product review sites, and comment sections</td>
</tr>
<tr>
<td><strong>Keywords tracked</strong></td>
<td>Black Friday, Cyber Monday, BFCM</td>
</tr>
<tr>
<td><strong>Total number of posts analyzed</strong></td>
<td>5.67 million</td>
</tr>
<tr>
<td><strong>Sample groups analyzed</strong></td>
<td>
20,000 random posts overall<br>
2,000 of the most engaging posts<br>
2,000 random negative posts<br>
2,000 of the most engaging negative posts<br>
2,000 random positive posts<br>
2,000 of the most engaging positive posts
</td>
</tr>
<tr>
<td><strong>Key term definitions</strong></td>
<td>
<strong>Engagement:</strong> The number of shares, comments, likes, retweets, replies, direct messages, etc.<br><br>
<strong>Post:</strong> A single piece of content that can include multiple mentions.<br><br>
<strong>Mentions:</strong> The number of instances in which keywords and phrases appeared within a post.
</td>
</tr>
<tr>
<td><strong>Note on theme percentages</strong></td>
<td>Theme categories are non-exclusive. A single post can carry more than one complaint or praise theme, so category percentages should not be read as adding up to 100%.</td>
</tr>
</tbody>
</table>

Our Social Listening analysis of 5.67 million BFCM posts reveals how consumers engage with the shopping “holiday” along with their expectations of, and frustrations with, brands.

Only about 23% of BFCM posts came from consumers. 

In our sample of 20,000 random posts, less than a quarter of BFCM posts came from consumers themselves. By comparison, about 65% came from advertisers, underscoring just how easily the voice of the consumer can get lost in analyses of the broader BFCM conversation.

The consumer-brand distribution varied significantly across platforms. On Instagram, Facebook, and TikTok, BFCM conversation is dominated by promotional content. But for brands looking to zero in on the voice of the consumer, one platform stands out: On Reddit, BFCM conversation is 85% consumer-driven compared to only 5% of posts coming from brands.

The focus of consumer posts also changed depending on the source. In our negative sample, price integrity was the top complaint on Reddit and X, while boycotting BFCM altogether was the leading topic on Bluesky. Finally, complaints about product quality, delivery, fulfillment, and customer service were well represented in product reviews.

Reddit is where the consumer voice comes through loudest, but each data source has insight to offer brands analyzing customer frustration. Ultimately, where you look determines what you see.

Over 30% of negative consumer posts were about price integrity. 

Consumers can’t believe BFCM prices, and not in a good way. Price integrity was mentioned in about 34% of negative consumer posts in our random sample and 30% in our most engaging post sample. Simply put, today’s shoppers don’t trust that BFCM prices signal meaningful savings for them.

One Reddit post that drew engagement illustrates the frustration, claiming that a seemingly discounted laptop had not changed price in two months. The post does not provide two months of pricing data, but it doesn’t have to. Its point is clear, echoed in top comments calling BFCM sale prices misleading, if not outright “scams.”

Lack of consumer trust runs through other highly mentioned categories of negative conversation, such as BFCM boycotts and retail working conditions. However, it’s pricing honesty and transparency that consumers post about the most. 

BFCM brand risk now extends beyond price integrity.

Consumer BFCM conversations have shifted since 2025 from largely focusing on price integrity to including other value signals. Shoppers have grown increasingly selective about what they spend money on. According to McKinsey’s State of the Consumer 2026 report, 82% of global consumers are delaying buying new stuff to replace their old stuff. 

We can see that selectiveness reflected in the widening of consumer complaints to include other aspects of the shopping experience beyond price integrity: customer service, delivery, fulfillment, and product quality. Negative posts about affordability and debt, some of the drivers behind consumers’ shopping behaviors, have also grown since BFCM 2025.

In a nutshell, here’s how the past year in BFCM conversation panned out:

Pre-BFCM 2025: During the August to October consideration phase consumers debated whether or not to take part in BFCM 2025 sales. Price integrity and product quality were the leading two topics of negative posts.

The lead up to the sale: As the consideration continued, complaints about price integrity peaked as social media users put advertised discounts on trial.

Peak week: From November 24, 2025, Black Friday, through the end of the month, price integrity continues to be the top talking point.

Post-BFCM 2025: In December, negative posts about customer service, delivery, and fulfillment balloon in volume.

Months later: Negative posts about product quality reached their highest levels from January to July 2026.

So while price integrity is still a key discussion point heading into BFCM 2026, consumers are also scrutinizing more dimensions of their shopping experiences with brands. Today’s disappointing experiences can become tomorrow’s, next week’s, or even next year’s brand reputation risks.

About 38% of positive BFCM consumer posts focused on advice shared by other shoppers. 

Our last point explored what consumers critique, but what do they praise? Hint: It’s mostly not brands. Only 5% of positive consumer posts in our random sample were about a company’s customer service. Compare that to the 38.2% that were about another social media user’s recommendations and advice.

For brands, this drives home that offering a great customer experience is more likely to prevent complaints than generate praise. Offering a product or service worth recommending and having a presence where shoppers advise each other (which, in this data, mostly means Reddit) are both key to getting customers to say good things about your brand unprompted. Authentic customer voices have the potential to influence other shoppers more powerfully than even a well-executed advertising campaign.

Based on the data, here’s what marketers and brands should explore in their BFCM 2026 planning:

Use different platforms to understand each facet of the consumer conversation.

Different social platforms cultivate different audiences and posts. Reddit stands out for having the highest volume of consumer posts, but it’s not the only one that brands can tap for insights. Reddit and product reviews reveal the shopping experience frustrations around BFCM, while X and Bluesky highlight how political and economic narratives shape the discourse. Tailoring your audience and market research strategies to how different sources operate will point you to more nuanced insights valuable to marketers and brands.

Prove that sale (or non-sale) prices offer meaningful value.

Brands need to get creative with how they make the value of sale prices immediately clear to shoppers. Side-by-side comparisons with regular prices aren’t as impactful as they used to be, but other proof points like product lifespan comparisons or UGC are among the alternatives. Not doing a BFCM sale? That’s also something to promote. Brands like REI, Vitsœ, Lucy and Yak, Patagonia, and others use the occasion to reinforce their brand values around sustainability, social responsibility, and honest pricing. Either way, brands need to communicate a compelling rationale around their pricing that can stand up to price sensitivity and scrutiny.

Plan how to engage with consumers in the weeks and months after BFCM 2026.

Instead of treating BFCM sales as the endpoint, understand them as the midpoint of your customer interactions. What happens after shoppers place their orders impacts how they talk about your brand online. If a purchase comes with late shipping, bad customer service, or a disappointing product, one purchase can lead to a social post that turns thousands of people off your brand. Get ahead of bad reviews with proactive communications like realistic delivery estimates, prompt delay notifications, easy-to-access order tracking, and clear return and exchange instructions. 

The Social Listening data suggests that BFCM 2026 will be an opportunity for brands to prove the value of the entire customer experience for price-crunched shoppers. Sharpening communications based on cross-platform conversations can help marketers leverage this high-profile, high-risk shopping event to build lasting trust. 

Don’t stop here: Turn these insights into action with Meltwater. Explore how with a free demo.

How is the Black Friday and Cyber Monday consumer conversation changing in 2026?

Black Friday and Cyber Monday value now extends beyond discounts. Meltwater’s social listening analysis shows that consumers are increasingly discussing customer service, delivery, fulfillment, product quality, affordability, and debt alongside price integrity.

Why does price integrity still matter during Black Friday and Cyber Monday?

Shoppers still scrutinize whether sale prices offer meaningful value. Meltwater data shows that price remains a core Black Friday and Cyber Monday 2026 discussion point, even as consumers evaluate more aspects of the shopping experience.

Which sources reveal the most useful Black Friday and Cyber Monday consumer insights?

Different sources reveal different parts of the conversation. Meltwater analysis found that Reddit and product reviews surfaced shopping experience frustrations, while X and Bluesky highlighted political and economic narratives shaping Black Friday and Cyber Monday discourse.

What do consumers complain about most about Black Friday and Cyber Monday?

Over 30% of negative consumer posts were about price integrity according to Meltwater’s Black Friday and Cyber Monday analysis. Complaints also increasingly focus on customer service, delivery, fulfillment, product quality, affordability, and debt, making the full shopping experience a growing source of brand risk.

What drives positive Black Friday and Cyber Monday conversations among consumers?

Shopper advice appears far more often than praise for brands. In Meltwater’s analysis, 38.2% of positive consumer posts focused on another user’s recommendations and advice, compared with 5% focused on a company’s customer service.

Why should brands plan for the post-purchase Black Friday and Cyber Monday experience?

Post-purchase experiences can create lasting brand risk. Meltwater data shows that delivery, fulfillment, customer service, and product quality now shape more Black Friday and Cyber Monday consumer conversations, extending scrutiny well beyond checkout.

How can social listening strengthen a Black Friday and Cyber Monday strategy?

Social listening turns consumer conversations into actionable planning signals. Meltwater helps brands compare sources, identify emerging concerns, understand changing definitions of value, and prepare for reputation risks throughout the Black Friday and Cyber Monday customer journey.

This analysis of 5.67 million posts reveals how Black Friday and Cyber Monday consumer expectations are shifting and what brands can do to protect their reputations and prove value.

Black Friday and Cyber Monday Consumer Trends

A June 2026 Criteo Shopper Survey found that 38% of shoppers already use AI assistants when shopping for beauty and personal care products, and 57% of those shoppers said the products recommended in AI responses influence what they ultimately buy. For K-beauty brands competing for U.S. shelf space, that raises the question: which brands are the LLMs actually recommending to American consumers, and why? Meltwater used GenAI Lens, its AI Visibility Tracking capability, to find out.

Overall AI Visibility Across K-Beauty Brands

Each AI Engine Tells a Different Brand Story

Citation Sources Across K-Beauty AI Responses

AI Visibility Shifts by Consumer Priority

From Insight to Intelligence: How GenAI Lens Helps

FAQ: AI Visibility in the K-Beauty Category

<table style="width:100%; border-collapse:collapse; font-family:Arial, sans-serif; font-size:14px;">
  <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/products/genai-lens">Meltwater GenAI Lens</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;">1 July – 14 July 2026 (2 weeks)</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Market</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">United States</td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Data sources</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">Claude, DeepSeek, ChatGPT, xAI Grok, Meta Llama, Google AI Mode, Google AI Overviews, Perplexity</td>
    </tr>
    <tr style="background-color:#FFFFFF;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Prompts</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">
        <span style="font-weight:bold;">General:</span> "What are the best Korean skincare brands and products available in the U.S.?"<br>
        <span style="font-weight:bold;">Accessibility:</span> "Can you recommend a Korean skincare routine using products that are easy to find in the U.S.?"<br>
        <span style="font-weight:bold;">Anti-aging:</span> "What are the best Korean skincare products for anti-aging available in the U.S.?"<br>
        <span style="font-weight:bold;">Sensitive skin:</span> "What are the best Korean skincare products for sensitive skin available in the U.S.?"
      </td>
    </tr>
    <tr style="background-color:#F0F4F4;">
      <td style="padding:12px; border:1px solid #E1EFF0; font-weight:bold;">Key term definitions</td>
      <td style="padding:12px; border:1px solid #E1EFF0;">
        <span style="font-weight:bold;">Visibility Score:</span> a combined 0–99 measure of an entity's overall AI presence, blending Prevalence, Frequency and Prominence.<br>
        <span style="font-weight:bold;">Prevalence:</span> the % of mentions that also reference related products, people, or entities.<br>
        <span style="font-weight:bold;">Prominence:</span> how central an entity is within the response it appears in.<br>
        <span style="font-weight:bold;">Frequency:</span> average citations per response.<br>
        <span style="font-weight:bold;">Mention Rate:</span> the share of responses in which the entity appears.<br>
        <span style="font-weight:bold;">Sentiment:</span> a 0–100 score, primarily entity-level.
      </td>
    </tr>
  </tbody>
</table>

COSRX leads most performance metrics tracked across the four core prompts. It ranked highest in AI visibility with a visibility score of 84, sitting 17 points above Laneige (67) and 31 points clear of Beauty of Joseon (53).

COSRX’s visibility was primarily driven by volume, recording the highest mention rate and frequency among all brands at 69 and 2.32, respectively. Its mention rate was 21 points higher than its closest competitor, Laneige (48), while its mention frequency exceeded Laneige’s by 1.05 points. This strong presence across AI-generated answers ultimately reinforced COSRX’s overall visibility.

Prominence provided a different way of boosting visibility from mentions and frequency. Sulwhasoo ranked seventh in mentions (26), but second on prominence (73), just behind COSRX, which boosted its visibility ranking. This strong prominence lifted its overall visibility ranking, showing that while Sulwhasoo appeared less frequently in AI answers, it tended to be positioned more prominently when mentioned.

Unlike mentions, frequency, and prominence, sentiment did not seem to be an AI visibility differentiator for the k-beauty brands, with every top-10 brand scoring within a tight 50–54 range. 

Why it matters: There is more than one route to AI visibility. Brands should identify which levers - volume, frequency, prominence or sentiment - actually create separation in their category.

Across the 8 generative AI engines surveyed, brand visibility swings from 0 to 91 for the same brand depending on which AI engine is asked. Consensus across the eight models holds only for COSRX, the category leader, while every other brand's ranking reshuffles significantly from model to model.

DeepSeek surfaced the broadest range of brands with consistently strong visibility. It scored all 10 brands 45 or higher, ranking as the top-scoring platform for eight of them. While other AI engines sharply differentiated between brands, DeepSeek gave both category leaders and smaller challengers meaningful visibility, suggesting a broader set of k-beauty brands can enter the consideration set on DeepSeek.

Google AI Mode and Google AI Overviews were the hardest platforms for brands to appear on.   Six of the ten brands scored low on both, including Dr.Jart+, Missha, Dear Klairs and Purito, which scored 0. Even category leader COSRX posted its lowest scores on  Google AI Mode (42) and AI Overviews (41), suggesting that visibility on Google cannot be assumed from strong performance on other AI platforms.

Claude consistently gave lower visibility scores to all k-beauty brands. COSRX was the only brand to score above 50, while every other brand remained below that threshold. Brands that performed strongly on other platforms therefore appeared much less visible on Claude.

Why it matters: A strong position on one engine does not translate to visibility on another, meaning brands cannot treat “AI visibility” as a single metric. Brands can lead on one platform and not be visible on another, making platform-by-platform visibility critical to understanding their true position in AI discovery.

Across the top 10 cited sources, social platforms account for the highest individual citation counts, with beauty publications and retailers also featuring prominently. 

Social and community platforms are among the most-cited sources. Instagram leads with 217 citations, followed by YouTube at 189, while Reddit also ranks highly at 149. Their prominence shows that the content and conversations happening across these platforms form a significant part of the information AI draws on for K-beauty. 

Beauty publications remain among the most frequently cited sources.  Glamour (168 citations), Allure (155), and Byrdie (90) all place in the top 10, highlighting the importance established editorial sources continue to play in generative AI engine answers.

Beauty retail also ranks alongside social and editorial sources. Ulta (148 citations) and Sephora (107 citations) both rank in the top 10, showing the role multi-brand beauty retailers play in the information AI platforms draw on. For K-beauty brands, this reinforces the value of strong retail partnerships—not only for distribution and consumer discovery, but as part of the wider information ecosystem shaping AI responses.

Why this matters: AI engines draw on the full beauty ecosystem - from social conversations and editorial coverage to retailer content. Building AI visibility requires brands to show up consistently across the places consumers discover, discuss, and shop for beauty. 

The brands leading AI visibility change depending on the consumer priority and prompt they ask the generative AI Engines. General K-beauty queries establish COSRX, Laneige and Beauty of Joseon as the leading brands, but that hierarchy changes as consumers introduce more specific priorities. 

Anti-aging produces a different category leader, with Sulwhasoo overtaking COSRX on both Visibility (73 vs. 70) and Prominence (81 vs. 68), despite not leading general K-beauty queries. This shows that broad category visibility does not necessarily translate into leadership around a specific skin concern. 

Sensitive-skin queries bring specialist brands into view. COSRX remains firmly in first place at 75 Visibility, but brands like Pyunkang Yul and Purito all enter the top 10 despite not appearing in the k-beauty rankings. More specific skincare needs can therefore surface brands that are less prominent in broader K-beauty discovery.

Accessibility more closely mirrors the general K-beauty hierarchy. COSRX (84) and Laneige (75) retain the top positions, suggesting their broader visibility carries through when ease of access becomes part of the consideration.

Why it matters: AI visibility changes with what consumers are looking for. Broad category strength matters, but specific priorities can elevate different brands and reshape the competitive set. 

Use Case 1: Cross-Model Visibility Auditing

Track how your brand and named competitors score across every major LLM, not just one, so you're never blind to a platform where your visibility has collapsed to zero.

Monitor Visibility Score, Prominence, and Mention Rate for your brand across Claude, DeepSeek, ChatGPT, Grok, Llama, Google AI Mode, Google AI Overviews, and Perplexity in one place

Flag any platform where your brand scores near zero before a competitor or analyst notices it first

Benchmark your brand against named category leaders and challengers on the exact same prompts

The result: Brands stop mistaking one AI engine's results for the full picture and start managing AI visibility as a multi-platform discipline.

Identify exactly which sources — social, retail, or earned media — are driving citations for your brand and your category, model by model.

Pinpoint which platforms (Instagram, Reddit, Ulta, Sephora, Glamour) are feeding which LLMs in your category

Prioritise content and partnership investment toward the specific source-model pairings driving the most citations

Track shifts in source mix over time as retail and social content continues to displace traditional press coverage

The result: Marketing and PR spend gets redirected toward the channels demonstrably feeding AI recommendations, not just the ones that feel familiar from search-era SEO.

Use Case 3: Prompt-Level and Category Tracking

See how visibility shifts across specific use-case prompts — general, accessibility, anti-aging, sensitive skin — rather than relying on a single, blended brand score.

Reveal which specific prompts your brand under-performs on, even while leading overall

Identify open categories where no brand has established dominance yet

Monitor product-level Visibility and Mention Rate to see whether any single SKU is carrying disproportionate weight

The result: Brands can target investment at the exact use-case queries where a challenger is winning, rather than defending an overall score that may be masking a real gap.

K-beauty’s AI visibility landscape is far from settled. COSRX has emerged as the strongest overall brand, but leadership shifts depending on both the AI platform and what consumers are looking for. Sulwhasoo overtakes COSRX for anti-aging, while individual models surface markedly different competitive sets.

Beyond the overall leader, there is significant room for movement. Specialist brands emerge around specific consumer priorities, visibility varies widely between AI platforms, and no single product or channel defines a brand’s presence. For K-beauty brands, building AI visibility means establishing relevance across the different platforms, consumer needs, and sources shaping AI discovery.

Which K-beauty brand has the highest AI visibility in the U.S.?

COSRX leads with a Visibility Score of 84, according to Meltwater's GenAI Lens analysis — 17 points ahead of Laneige (67) and 31 ahead of Beauty of Joseon (53), based on two weeks of data across eight LLMs in July 2026.

Does one AI model represent the whole category?

No. Meltwater's GenAI Lens analysis found the same brand's Visibility Score can swing from 0 to 91 depending on the model, with DeepSeek scoring most brands highest and Google AI Mode/Overviews scoring several at zero.

What drives AI citations for K-beauty brands?

Social platforms account for the highest individual citation counts among the top 10 sources tracked, according to Meltwater's GenAI Lens data, with Instagram (217) and YouTube (189) ranking at the top two sources. Beauty publications (Glamour, Allure, Byrdie) and multi-brand retailers (Ulta, Sephora) also feature prominently, but rank behind the top social sources. 

Is COSRX’s  lead the same across every type of skincare query?

No. Meltwater's GenAI Lens analysis shows COSRX loses the anti-aging prompt outright to Sulwhasoo (73 vs. 70 Visibility), showing that category leadership is prompt-specific rather than absolute.

Does sentiment explain why some K-beauty brands outperform others in AI? 

According to Meltwater's GenAI Lens analysis, every top-10 k-beauty brand scores within a tight 50–54 sentiment band regardless of visibility, meaning frequency and prominence of mention are the real differentiators, not favourability.

Do individual products drive K-beauty AI visibility, or is it brand-level?

Meltwater's GenAI Lens analysis found even the top-scoring product (COSRX’s Advanced Snail 96 Mucin Power Essence) reaches only a 20% Mention Rate, showing LLMs favour brand recognition over any single "viral" product.

An analysis of AI visibility, sentiment, and citation sources of Korean skincare brands across eight LLMs in the US market.

K-Beauty and AI Visibility: How Korean Skincare Brands Are Represented Across Generative Search Engines

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

<table style="width:100%; border-collapse:collapse; font-family:Arial, sans-serif; font-size:14px;">
  <tbody>
    <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>
    </tr>
    <tr>
      <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. 

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