This past Black Friday and Cyber Monday, I delved into the fascinating insights from our Black Friday Index, crafted from a vast pool of 400 million genuine conversations. It was enlightening to see which brands stood out as AI’s top recommendations, especially as so many of us relied on Answer Engines to hunt down the best deals.
As I explored the data, the impact of AI on shopping trends became crystal clear. The technology not only streamlined how we search for deals but also influenced brand visibility and consumer choices. The excitement of seeing how AI is reshaping shopping habits made this year’s Black Friday and Cyber Monday particularly intriguing for me.
The findings from the Black Friday Index are a testament to the growing importance of AI in retail, showing us how indispensable it has become for both consumers and brands. Being part of this evolution makes me look forward to what future shopping events will bring, especially as technology continues to advance.
Every year, Black Friday offers a unique glimpse into how consumers search, compare, and decide. This year, it added another layer: it became a real-world arena to see how AI models comprehend commerce amidst genuine demand.
I embarked on a journey to test major large language models (LLMs), analyzing 10,000 responses to understand how these systems perceive the retail landscape and the signals that shape their responses.
As I dissected the dataset, a pattern was unmistakable: Black Friday acts as a genuine stress test for AI-driven discovery.
The sheer number of queries and the diversity of categories reveal the sources, structures, and behaviors LLMs rely on for reasoning about products, retailers, and consumer intent.
The outcomes offer a sneak peek into how AI search is transforming—and how this will impact the broader commerce ecosystem.
TLDR; LLMs lean heavily on a limited range of external domains with YouTube, large retailers, and U.S. review media leading the charge.
Generalist retailers dominantly capture nearly half of all retail citations, serving as the recurring funnel LLMs use to address shopping queries.
Social and user-generated content see an 8.1% surge during Black Friday, as conventional retail and media sites experience a decline.
Off-page signals like Reddit, YouTube, Amazon, and Consumer Reports are vital, equally important as on-page content for shaping LLM comparisons and recommendations.
Structured comparison content wields significant influence, far surpassing branded assets.
The behavior of LLMs differs not only from Google but also from each other, with each platform like Gemini, OpenAI, and Perplexity offering unique formats, lengths, and reasoning patterns.
Unlike traditional search, where the process begins with a query leading to a list of ranked results, AI search reverses this. It starts with a model’s internal web of relationships, sources, and signals to construct a response.
In our review of the top 50 most-cited domains across 10,000 LLM responses—all centered around deals, reviews, and product recommendations—the distribution was notably skewed:
YouTube led with 1,509 citations, followed by Best Buy with 950, Walmart with 885, Target with 477, TechRadar with 355, RTings with 342, and Consumer Reports with 325.
This cluster shapes much of the commercial “knowledge” from which LLMs draw. It gravitates towards large retailers, global media outlets, and platforms specializing in comparisons and reviews.
In analyzing 10,000 responses, I compared the week leading up to Black Friday with the event itself. Pre-Black Friday, responses reins focused on planning behavior.
Retail and brand domains: 59.6%
Media: 23.4%
Social and user-generated content: 17%
When Black Friday commenced, the mix rapidly evolved. Social and UGC content jumped to 25.1%, gaining significant share, while retail and media slightly retreated.
This shift within the models mirrors consumer behavior but also highlights the models’ reliance on conversation-driven content for in-the-moment decision cues.
One of the most transparent insights is the weight third-party domains carry on AI reasoning. Today’s LLMs thrive by absorbing as much human interest in products as possible. Huge volumes of consumer insights, reviews, product demos, sentiment, and structured data guide how models reason and decide.
An analysis revealed key off-page signals LLMs depend on:
Reddit: 34%
YouTube: 19.5%
Amazon: 15.5%
Business Insider: 9.2%
Walmart: 8.9%
Each domain influences different aspects of the model’s decision-making. Across the board, LLMs lean on content that captures human interest, organizes consumer options, and mitigates uncertainty through verifiable data.
While third-party domains reign supreme, brand websites still hold measurable sway. They are vital for any consumer brand aiming to excel in AI discovery.
A site’s architecture plays a crucial role in how a model interprets a brand. Homepages account for 40% and serve as the primary identity layer—establishing tone, positioning, and offering quick semantic signals to models.
Blogs and product pages clarify brand definitions and long-tail context, providing the factual details models need.
Brands that rely too heavily on promotional copy, weak hierarchies, or thin product content risk sacrificing major visibility.
Across the entire dataset, certain retailer categories led the charge in model responses.
Generalist retailers hold 48% of the conversation. Walmart, Target, and Best Buy capture almost half of all retail citations. Their range, familiarity, and content depth make them central figures in LLM commerce reasoning.
Electronics specialists grasp 23% of the share. Best Buy leads, trailed by Newegg and Micro Center, with tech-focused queries often directing models toward these sources.
Other verticals lag behind. Despite strong category leaders, sectors like fashion, beauty, and home capture smaller portions due to the content volume disparity compared with generalist retailers.
Reviewing the platforms uncovered another pattern: major LLMs not only offer different answers but exhibit distinct thinking styles. Each platform has its own rhythm, structures, and styles for presenting commercial information.
Gemini provides the most detailed responses, with essays averaging 606 words, using lists and headings extensively.
OpenAI stands in the middle, averaging 401 words per response, with high list usage and balanced headings.
Perplexity shifts towards brevity with an average of 288 words, favoring short summaries akin to executive briefs.
These differences define unique retrieval and reasoning methods, shaping how each platform interprets brands, categories, and commercial intent.
The data presents a clear direction: AI search is forging its ecosystem, driven by familiar SEO inputs, source quality, content structure, and off-page signals, all interpreted to deliver precise answers.
If your content isn’t well-structured and present across the web, it risks becoming invisible to AI platforms delivering answers or product suggestions.
As this new environment evolves, it’s crucial for retailers and brands to rethink their communication strategies across the entire digital landscape.
On-page actions that matter:
Develop semantically coherent homepages that convey the brand, product categories, and relevance to core queries. LLMs prioritize clarity over cleverness.
Strengthen product pages with factual content, clear specifications, and Q&A sections aligned with user research intents.
Establish educational content clusters tied to core product themes, serving as reusable frameworks for AI models.
Off-page actions that matter:
Foster comprehensive review ecosystems and discussion forums to validate trust signals LLMs recognize with product quality.
Ensure visibility in media driven by comparisons and recommendations. Regularly appear in “best of” lists, product roundups, and influencer content.
Invest in rich media showcasing product value, particularly on YouTube and TikTok. Video content helps train LLMs on product use cases, reflecting sentiment, and experiential value.
Maintain accurate, indexable product data in marketplaces like Amazon, Walmart, and Etsy to enhance AI discovery pathways.
OpenAI’s Shopping Research announcement escalates the stakes. With ChatGPT, OpenAI tracks real-time consumer research behavior, turning preferences into a user-trained targeting engine for commerce.
This isn’t just AI learning about your product. It’s AI absorbing consumer shopping behavior, revolutionizing discovery through an active AI participation model.
Brands not infused into these AI systems risk invisibility during AI-driven consumer journeys.
What Black Friday revealed was more than top-selling products; it showed how LLMs operate under real demand, revealing their reasoning, referencing, and prioritizing patterns.
The advent of AI-native visibility requires structured, semantically rich content, adequately represented across the right off-page ecosystems to align with major AI models’ reasoning.
Black Friday might be the stress test, but the real transformation is only just beginning.
I found Black Friday 2025 to be a puzzling experience. Although advertising costs went up, impressions decreased. Yet, clicks and engagement didn’t falter, which feels like both a challenge and an opportunity.
Here’s what I’ve gathered from the data so far:
I’ve been analyzing data from over 5,000 e-commerce and 16,000 lead generation advertisers who were active this year and last. While we’re still waiting on final numbers for conversion value and ROAS, the early insights are telling.
The insights:
Visibility Costs Increased Significantly.
It’s clear to me that spending rose by about 17% for both e-commerce and lead generation, even as impressions saw a reduction. Essentially, advertisers like us are paying more to reach a similar audience.
Engagement Metrics Held Strong.
Despite the rise in costs, clicks and CTR improved across various sectors. Lead generation, in particular, enjoyed lower CPCs and an uptick in clicks, showing that people are still actively responding to ads.
Implications for the Future:
Expect High Costs Ahead. Given the heightened competition during Black Friday, I anticipate this trend of higher costs per reach to continue into Q1 2026.
Clicks Are Not the Final Goal. The real challenge now lies in what happens after the click. Returns, conversions, and overall efficiency will be crucial, rather than just focusing on traffic volume.
Enhance Post-Click Strategy. I believe that improving landing pages, offers, checkout processes, and lead follow-up mechanisms will be key in turning clicks into conversions.
Here’s Why This Matters: Black Friday 2025 indicates that while getting attention is still feasible, turning that attention into results demands enhanced strategies post-click. The cost of ignoring these shifts is high – you might spend more while yielding lower returns.
Bottom Line: Staying visible during Black Friday 2025 came with a higher price, but engagement remains robust. The task ahead is not just driving traffic, but converting that traffic efficiently into results.
I recently came across a fascinating study highlighting how seasonality adjustments can actually backfire for advertisers during Black Friday, driving up costs and reducing efficiency.
A thorough analysis over three years, involving up to 6,000 advertisers, indicates that using Google’s seasonality bid adjustments during Black Friday and Cyber Monday (BFCM) often undermines efficiency, despite the platforms recommending them.
The big picture. Smart Bidding models are crafted to foresee predictable retail surges. Optmyzr analyzed tens of billions of impressions between 2022 and 2024, finding that advertisers who avoided seasonality adjustments usually had better efficiency metrics.
Without adjustments, Smart Bidding:
Recognized the BFCM conversion lift independently
Increased bids rationally
Maintained stable or improved ROAS, particularly in 2024
With adjustments: CPCs surged faster than the actual conversion rates, eroding efficiency.
Reality check: Google doesn’t need your “heads up.” Seasonality adjustments prompt Google to expect a conversion rate rise and to bid accordingly. If your prediction is off—and it usually is—Smart Bidding overshoots.
For example:
You predict a +50% CVR lift
The actual lift is +40%
This results in an overbid of about 7.1%
During BFCM’s high sales volumes, even minor mistakes become costly quickly.
The data: 3 years of the same story
1. Smart Bidding already adjusts for the CVR spike
2022: +17.5%
2023: +11.9%
2024: +7.5%
No additional guidance needed.
2. CPC inflation doubles with adjustments
Across all observed years, CPCs increased approximately twice as much when a seasonal adjustment was used.
3. ROAS drops significantly
Advertisers relying on Smart Bidding saw stable or improved ROAS, whereas those who intervened suffered double-digit losses.
The one exception: “Volume at all costs.” If the aim is pure revenue growth, disregarding margins, seasonality adjustments can be beneficial.
Revenue lifts were notably higher with adjustments:
2022: +50.5% vs. +25.0%
2023: +52.8% vs. +30.3%
2024: +39.9% vs. +33.8%
Efficiency may decline, but volume certainly increases.
When seasonality adjustments make sense. They’re useful when Google doesn’t have prior signals, like one-off or niche events.
Good for:
One-time flash sales
Email-only offers
Surprise clearance sales
Niche seasonal spikes
Not recommended for:
Black Friday
Cyber Monday
Christmas
Valentine’s Day
Any event with a predictable historic pattern
Why we care. Google already recognizes the significance of Black Friday. Smart Bidding is trained with years of BFCM data and can detect conversion rate spikes independently. Overriding this can lead to excessive bidding, increased CPCs, and reduced ROAS, so many marketers might be wasting their budget during this crucial week.
By recognizing when Smart Bidding has an adequate signal, advertisers can avoid expensive errors, maintain efficiency, and reserve seasonality adjustments for when they add true value.
Bottom line. Smart Bidding effectively manages major retail holidays. Seasonality adjustments often bring more chaos than benefits during predictable retail peaks. Keep them for unique, brand-specific events that Google can’t predict.
Smart move: Trust the algorithm — use tools like anomaly alerts, pacing monitors, and bid caps for control without conflicting with Smart Bidding’s core models.
During Black Friday, I’ve noticed many retailers, including myself, wasting substantial advertising budgets on Google Shopping ads. The main issue arises when these ads are still running for products that have already sold out, clearly demonstrating a pressing need for real-time stock management.
As we all know, Black Friday marks the peak of the retail season. However, it’s disheartening to find that so many brands, myself included, end up losing money on Google Shopping ads for items no longer available in inventory.
The problem: The ads continue to run even after items are out of stock, incurring cost-per-click charges with no possibility of conversion. Through a comprehensive study by ShoppingIQ involving 500 global retailers, it was revealed that a staggering 97% kept paying for clicks on items no longer in stock, sometimes persisting for 24–48 hours.
Why I care. Out-of-stock ads are not just a financial drain; they also skew campaign performance and disrupt algorithmic learning. When conversion rates plummet for unavailable products, it damages rankings, reduces ROI, and hampers future bidding strategies.
Example: Take Argos, for instance; they reportedly advertised items that were out of stock during Black Friday, leading to frustrated customers and depleted ad budgets.
Stock update refresh rates:
~24 hours: 90% of retailers
6–23 hours: 5%
48 hours: 2%
Other: 3%
Retailers’ response: Some companies, such as Mamas & Papas, have started leveraging ShoppingIQ’s real-time stock technology. This helps them focus ads solely on products that are actually available. Samantha Dabek, Senior Digital Marketing Manager, shares that they have managed to cut unnecessary costs and ensure advertising is targeted toward in-stock products.
The bigger picture: Google Shopping commands around 75% of US retail search spending. However, the default settings let out-of-stock ads run unchecked. ShoppingIQ strongly advocates for retailers to seek more transparency and control from Google to prevent wasted spending.
Bottom line: For those of us running high-stakes campaigns during Black Friday and other peak times, real-time stock management is essential. Otherwise, each wasted click represents money lost.
