April 7, 202616 min read

AI Search and Black Friday: How Neural Networks Are Reshaping Seasonal Sales in 2026

How ChatGPT, Yandex Alice, and Perplexity influence Black Friday and seasonal sale purchases: the role of AI recommendations, how to optimize your brand for seasonal queries, and monitoring data.

Vladislav Puchkov

Founder of GEO Scout, GEO optimization expert

Every November, marketers launch the same playbooks: email campaigns, promo codes, banners screaming "70% off." But 2026 is different — the shopper arrives at the sale not through a search engine or an ad, but through a question to AI: "Which laptop is actually worth buying on Black Friday?" or "Where are honest discounts, not price inflation?"

And if ChatGPT, Alice, or Perplexity do not mention your brand in the answer — you simply do not exist for that shopper. No ad budget or beautiful landing page will help. The user already got a recommendation and moved on to a competitor.

How AI Search Is Changing Seasonal Shopping

The traditional seasonal sales funnel looked like this: user sees an ad → goes to Google → compares prices → buys. In 2026, the funnel looks different: user asks AI → gets 3-5 recommendations → clicks one → buys.

The "search" step is being replaced by the "AI recommendation" step. And while in classic SEO you could buy an ad and land in first place, in AI search you cannot buy a recommendation — you earn it through content, structure, and reputation.

What People Ask AI During Sale Seasons

According to GEO Scout monitoring data, typical seasonal AI queries fall into four categories:

1. Discount evaluation — "Is the 70% discount on iPhone 16 at [store] real?" or "Which Black Friday discounts are not a scam?"
2. Offer comparison — "Where is the cheapest PS5 on sale: Ozon, Wildberries, or DNS?"
3. Product recommendations — "What laptop under 80,000 rubles should I get on Black Friday?"
4. Store selection — "Which marketplace is more reliable for buying electronics on sale?"

In each case, AI generates a list of 3-7 brands or stores. Making it onto that list is a GEO optimization task. Missing out means losing the customer.

Seasonal Patterns in AI Queries

AI answers shift depending on the season. Internet content is seasonal: more articles about sales are published in November, more about gifts in December, more about spring collections in March. AI absorbs this context.

Black Friday Through the Eyes of AI: How Neural Networks Form Discount Recommendations

Black Friday is the perfect example of how AI recommendations influence commercial decisions. Let's examine how ChatGPT, Perplexity, and Alice answer "where are the best Black Friday deals."

What AI Considers When Recommending Stores

Neural networks form answers based on several factors:

1. Mentions in review articles — AI scans media, blogs, rankings, and reviews. A store mentioned more frequently in the context of "honest Black Friday deals" gets priority.

2. Reviews and reputation — AI factors in user experience: if a store consistently receives negative reviews for "inflating prices before the sale," AI takes this into account.

3. Content structure on the site — a "Black Friday" category page with a discount table, price comparisons, and expert conclusions is more likely to be cited by AI than a promo landing page with no structure.

4. External mentions — reviews on independent platforms (comparison sites, tech blogs, VK) serve as "trust signals" for AI.

5. Historical data — if a store ran honest sales in previous years, AI remembers. If an "80% discount" turned out to be fake — it remembers that too.

How Different AI Providers Differ

How AI Determines "Real" Discounts vs Marketing Gimmicks

One of AI's key tasks during sale season is separating real discounts from marketing manipulation. And AI is getting better at this every year.

Signs of "Fake" Discounts That AI Recognizes

• The "before discount" price was higher than the historical average — AI analyzes price history through aggregators and reviews.
• The product model is discontinued — a "60% discount" on an outdated product that is no longer sold at full price.
• Limited assortment — the discount applies to 5 items out of 10,000, but is advertised as a "storewide sale."
• No competitive comparison — AI checks whether the "discounted" price is actually lower than other sellers.

Signs of Honest Discounts That AI Values

• Transparent price history — the store publishes prices for the last 6 months, and the discount is real.
• Broad assortment — the discount covers a meaningful portion of the catalog.
• Market comparison — the store or a review article shows the price is genuinely below market average.
• User reviews — real buyers confirm the deal was worth it.

What This Means for Brands

If your brand is associated with honest pricing and transparent sales — AI will recommend you more often. If not, AI may directly call out the manipulation, which drives customers away.

Strategy: publish reviews with real price comparisons, "was/now" tables, and expert conclusions. This is not just marketing — it is a signal to AI that shapes recommendations.

Optimizing for Seasonal AI Queries

Preparing for sale season is not a one-week project. It is systematic work that begins months before the peak. Here is a step-by-step plan.

When to Start

What Pages to Prepare

1. Category Pages with Seasonal Analytics

Not just a catalog with discounts, but a structured page with:

• A "before/after" price comparison table
• Expert conclusions: "Top 10 products with real discounts"
• An FAQ block: "How to spot a real discount vs price inflation"

2. Review Articles

Articles in the format "Best Black Friday deals on [category]" with:

• Specific models and prices
• Competitor comparisons
• Selection recommendations
• Specification tables

3. Comparative Materials

"Ozon vs Wildberries: where is Black Friday actually cheaper" — AI cites this kind of content most readily because it is structured and contains expert conclusions.

4. FAQ Sections with JSON-LD Markup

Question-answer pairs that match real user queries to AI:

• "Are the Black Friday deals at [store] real?"
• "What phone should I buy on sale for under 50,000 rubles?"
• "Where are the most honest 11.11 deals?"

FAQPage markup helps AI extract question-answer pairs and cite your content.

Content Strategy for Seasonal GEO

Content for seasonal GEO differs from regular SEO content in three ways:

1. Expertise over keywords — AI evaluates content depth, not keyword density. An article titled "Top 10 smartphones for Black Friday 2026: real prices and comparison" is more valuable than "buy smartphone Black Friday discount cheap."

2. Structure — tables, lists, clear conclusions at the beginning of paragraphs. AI finds it easier to cite content where the answer is obvious and not buried in the middle of a paragraph.

3. Citability — specific numbers, facts, comparisons. "30% discount on Samsung Galaxy S24, was 89,990 rubles, now 62,990 rubles" is a fact AI can use. "Incredible smartphone discounts!" is empty marketing noise.

The Four Major Russian Sale Seasons: A Breakdown

The Russian seasonal sales market has its own specifics. Let's examine the four key seasons from an AI visibility perspective.

11.11 (Singles' Day)

A sale holiday imported from China, associated in Russia primarily with AliExpress and marketplaces. In 2025-2026, Ozon, Wildberries, Yandex Market, and Megamarket actively joined 11.11.

AI pattern: users ask "what to buy on 11.11" and "where are the 11.11 deals." AI recommends marketplaces with promos and specific product categories.

What brands should do:

• Publish "What's worth buying on 11.11 in [category]" reviews by September
• Create a comparative discount table across marketplaces
• Set up monitoring for "11.11 deals [category]" prompts in GEO Scout

Black Friday

The most "Western" of the seasons, but one that has reached significant scale in Russia. The core query: "real Black Friday discounts" — users no longer blindly trust discount announcements.

AI pattern: AI generates a list of stores with honest discounts and warns about manipulations. Stores with a "fake discount" reputation end up in negative context.

What brands should do:

• Publish content with price history by August-September
• Create an "Honest Black Friday" page with a "was/now" table
• Prepare expert articles for external platforms (tech blogs, comparison sites)
• Set up monitoring for "Black Friday deals [category]" and "real Black Friday discounts [store]" prompts

New Year

The biggest gift-giving season in Russia. Characteristic query pattern: "what to gift for New Year [recipient category]" — AI generates picks by budget and interests.

AI pattern: AI generates gift lists by budget and recommends specific stores. Queries like "gifts for colleagues under 3,000 rubles" and "what to gift your girlfriend for New Year 2027" are typical.

What brands should do:

• Publish gift picks by category by October
• Create pages like "New Year gifts by budget: under 1,000 / under 3,000 / under 5,000 / 5,000+"
• Place reviews on external platforms — "Top 20 New Year 2027 Gifts"
• Set up monitoring for "New Year gifts [category]" and "where to buy New Year gifts" prompts

March 8th

The spring peak for flower delivery services, cosmetics brands, jewelry stores, and restaurants. A short season — most traffic concentrates in the last days of February and the first week of March.

AI pattern: queries like "what to gift on March 8th" and "order flowers March 8th delivery." AI recommends specific services and brands.

What brands should do:

• Publish content by January-February
• Create a "March 8th Gifts: ideas by budget and interests" page
• Prepare FAQs like "How to choose flowers for March 8th," "Where to order a bouquet with delivery"
• Set up monitoring for "March 8th gifts" and "flower delivery March 8th" prompts

Case Study: How a Brand Grew During Sale Season Through AI Recommendations

Let's examine a realistic scenario based on GEO Scout monitoring data.

Starting Point

A mid-sized online electronics store. During Black Friday 2025:

• Mention Rate in AI answers: 4% (mentioned in 1 out of 25 queries)
• Avg Position: 6-7 (when mentioned, at the bottom of the list)
• Main competitors: large retailers dominating AI answers

What Was Done

July-August (3-4 months before Black Friday):

• Published 12 expert articles: "How to Choose a Laptop for Study 2026," "Top 10 Smartphones Under 30,000 Rubles: Comparison," "Honest Processor Review: What to Buy in 2026"
• Each article contained comparison tables, specific prices, and expert conclusions
• Added JSON-LD markup: FAQPage, Product, Article

September (2 months before):

• Created a "Black Friday 2026: Real Electronics Deals" page with a "was/now" table
• Published 5 reviews on external platforms (tech blogs, comparison sites, forums)
• Set up monitoring for 30+ seasonal prompts in GEO Scout

October (1 month before):

• Published comparative articles like "Ozon vs DNS vs [Store]: Where Black Friday Is Actually Better"
• Created an FAQ section with 15 questions about electronics discounts
• Daily monitoring showed: the brand started appearing in Perplexity and Yandex answers

November (Black Friday):

• Mention Rate grew to 28% (mentioned in 7 out of 25 queries)
• Avg Position improved to 3-4
• The brand was consistently mentioned in Yandex with Alice and Perplexity answers
• ChatGPT started mentioning the brand in 2 out of 10 queries (generative models are slower)

Results

Key takeaway: content investments made 3-4 months before the peak delivered results. Search-based AI (Perplexity, Yandex) reacted faster; generative AI (ChatGPT) was slower, but growth in ChatGPT is expected by the next season.

Seasonal Sales Preparation Checklist

4-6 Months Before Peak

• Identify the key season (Black Friday / New Year / March 8th / 11.11)
• Build a list of seasonal prompts users will ask AI
• Begin publishing expert reviews and comparisons
• Set up seasonal prompt monitoring in GEO Scout
• Analyze which brands AI currently recommends in your niche

2-3 Months Before Peak

• Create a season landing page (Black Friday / 11.11 / Gifts) with tables and FAQ
• Publish 5-10 reviews on external platforms
• Add JSON-LD markup (FAQPage, Product, Article)
• Set up daily monitoring through GEO Scout
• Check which sources AI cites in your niche — and place content there

1 Month Before Peak

• Publish comparative articles "[Brand] vs competitors"
• Update prices and tables on the season page
• Activate monitoring for all seasonal prompt clusters
• Check AI answers: is the brand mentioned? In what position?
• Prepare content for "hot" queries in the final week

During the Season

• Check AI answers daily through GEO Scout
• React to changes: if a competitor pulls ahead — publish content in response
• Update prices and product availability on the season page
• Collect data for post-season analysis

After the Season

• Analyze results: Mention Rate, positions, AI channel traffic
• Compare with the same period last year (year-over-year)
• Preserve effective pages for the next season
• Update content strategy based on monitoring data
• Begin preparation for the next peak

Takeaways

The 2026 sale season will be different from all previous ones. The shopper reaches a decision through AI recommendations, not through ads or search engines. Brands that are not optimized for AI answers lose customers at the preference-formation stage.

Three core principles of seasonal GEO:

1. Start 4-6 months early — AI absorbs content slowly, especially generative models. July for Black Friday, not October.

2. Structure beats budget — comparison tables, expert conclusions, and FAQ markup work better than promo landing pages with "99% off."

3. Monitoring is foundational — without daily tracking of AI recommendations, you will not know a competitor displaced you from answers until you have already lost traffic.

The GEO Scout platform lets you monitor seasonal changes in AI recommendations in real time: set up prompt clusters for each season, track Mention Rate, positions, and Share of Voice — and distinguish seasonal fluctuation from actual position loss.