While monitoring product images and stock availability remains essential for Black Friday, these traditional practices are now just the starting point. In 2025, the true differentiator lies in leveraging advances in AI and chatbot technology to transform the customer journey and optimize operational efficiency.
Black Friday 2024 underscored a paradigm shift: AI and chatbots generated more than $14 billion in global online sales, with retailers leveraging generative AI achieving conversion rates 9% higher than those who did not.
Simultaneously, bot-driven visits to retail websites surged by 1,800% year-over-year. The digital shelf has entered a new era, where AI shopping assistants now play a critical role in determining which products are surfaced and recommended, often before price even factors into decision-making.
If your product data isn’t structured for machine readability, your offerings remain invisible to this rapidly expanding segment of digital commerce.
The Real Battle: AI-Driven Discovery
LLM-driven shopping increased by 4,700% over the last year. While this still represents a small percentage of overall shopping traffic, these shoppers represent the future of e-commerce.
These are high-intent customers using AI to cut through noise and make informed decisions. Mobile's share of LLM-driven traffic rose from 18% in early 2025 to 26% by mid-year, with projections suggesting more than one-third of LLM-driven traffic will come from mobile devices during Black Friday.
Shopping behaviour has fundamentally changed. Customers are no longer typing keywords into search bars. They're asking questions:
- "What's the best 55-inch TV for gaming with a fast refresh rate under £800?"
- "Compare Dyson vs. Shark vacuums for pet allergies"
- "I need an air fryer that's easy to clean, fits in a small kitchen, and feeds a family of four"
Amazon's Rufus assistant has already handled tens of millions of customer questions providing product recommendations based on detailed, conversational queries that traditional keyword search simply cannot address.
The AI will only recommend products it can understand and justify. If your product data isn't structured for machines to comprehend, your best-selling product is essentially invisible to this growing channel.
From Keywords to Concepts: An Important Shift
Traditional SEO optimised for keywords. AI optimisation requires thinking in concepts, questions, and structured relationships between product attributes.
Answer the Question Behind the Query
Don't just list features. Address customer anxieties directly.
Traditional approach: "30-hour battery life"
AI-optimised approach: "Worried about your headphones dying on a long flight? With 30 hours of playback and quick charging, you can listen from London to Tokyo with power to spare."
The difference is important. The first is a specification. The second answers the question an AI shopping assistant is trying to solve for a customer planning a long journey.
Master Structured Data: Your AI Sales Sheet
36% of e-commerce sites don't use any form of structured data markup. This represents a competitive blind spot.
For products to appear in AI-driven recommendations, search engines and shopping assistants need explicit signals about product attributes. According to research from SixthCity Marketing, pages with schema markup received a 40% higher click-through rate than pages without.
Consider a shampoo product. Simply listing "for curly hair" and "contains argan oil" in your description isn't enough. Structured data markup explicitly tags these attributes:
This tagging is how AI understands who your product is for. When someone asks an AI shopping assistant for "products for dry, curly hair" or "shampoo with argan oil," your product can only be recommended if these attributes are machine-readable.
The same principle applies across categories:
Ovens: cleaning_type: pyrolytic, smart_feature: air_fry ensures you appear for "self-cleaning ovens with air frying capability"
Electronics: battery_life: 30_hours, quick_charge: yes, noise_cancellation: active enables recommendations for "long-battery noise-cancelling headphones with fast charging"
Air fryers: capacity: 4_person, ease_of_cleaning: dishwasher_safe, footprint: compact matches queries for "compact air fryers for families that are easy to clean"
A benchmark study by Data World found that LLMs grounded in knowledge graphs achieve 300% higher accuracy compared to those relying solely on unstructured data. This therefore has implications for how products get discovered and recommended.
Cultivate Keyword-Rich Reviews
Customer reviews are pure gold for AI optimisation when they contain natural language that mirrors how people actually search.
A review for a robot vacuum that says, "This thing is brilliant with my golden retriever's hair and hasn't eaten a charging cable yet" directly helps the AI answer questions about pet hair performance and object avoidance. These authentic customer experiences provide the concept-based language that AI assistants use to match products to queries.
Protecting Your Margins
Here's the strategic reframe most brands miss: AI optimisation lets you compete on value rather than price.
When your product can be intelligently recommended based on meeting specific customer needs and not just appearing in a price-filtered list, you protect your margins. You're no longer in a race to the bottom on discount depth. You're competing on being the most suitable, most trustworthy, most recommendable product for the customer's actual requirements.
Electronic and appliance discounts peaked at 27% during Black Friday 2024 Black Friday Strategies For 2025: Learning From Last Year's Winning Tactics, but the brands that won weren't necessarily those with the deepest discounts. They were the ones whose products appeared in the right AI-driven recommendations at the right moment.
Consider the competitive dynamics: If a customer asks, "What's the best air fryer for healthy weeknight meals for a family of four that's easy to clean?" and your product has the structured data to match that query whilst your competitor doesn't, you've won the sale before price comparison even happens.
Beyond Black Friday: Building AI Readiness
The real value of AI optimisation extends beyond a single shopping event. Industry leaders predict that AI assistants will handle up to 20% of e-commerce tasks in 2025, from product recommendations to customer service.
Success no longer hinges solely on keyword ranking, but on how "AI-ready" your entire product profile is. This represents a fundamental shift in competitive dynamics.
Brands investing in structured data, concept-based content, and machine-readable product attributes are building a sustainable advantage. They're positioning themselves not just for Black Friday 2025, but for the next evolution of digital commerce where AI intermediates the majority of product discovery.
Your Strategic 4-Week Action Plan
Week 1: Audit Your Structured Data Completeness
Review your product catalogue across all major retailers. Identify which products lack structured data markup and prioritise your bestsellers and new launches.
Use Google's Rich Results Test to validate your current implementation. For products sold through Amazon, test how Amazon Rufus describes and recommends your products in conversational queries.
Week 2: Implement Concept-Based Content
Rewrite product descriptions to answer common questions rather than just listing features. Think about the anxieties and challenges your product solves, not just what it does.
For each product, identify 5-10 common customer questions and ensure your content explicitly addresses them. Structure your bullets and descriptions around use cases rather than specifications alone.
Week 3: Enrich Your Product Attributes
Work with your technical team to implement comprehensive schema markup. Focus on attributes that matter for discovery: size, capacity, compatibility, ease of use, cleaning requirements, dietary considerations, age appropriateness, and skill level requirements.
For retailer platforms, ensure every optional field is completed with accurate, detailed information. The more data points you provide, the more opportunities for AI-driven recommendations.
Week 4: Review Velocity and Real-Time Monitoring
Launch targeted review campaigns focused on encouraging detailed, use-case-specific feedback. Prime these campaigns by asking specific questions: "How did this product perform for your specific needs?" rather than generic "How would you rate this product?"
Establish monitoring protocols for Black Friday itself. Assign team members to track how AI shopping assistants are describing and recommending your products in real-time, with authority to make rapid content adjustments if needed.
The Competitive Reality
Black Friday 2024 brought in $10.8 billion in online sales, with mobile devices accounting for 55% of that total. The brands that captured disproportionate share weren't necessarily those with the biggest advertising budgets or deepest discounts.
They were the brands whose products appeared in the right AI-driven recommendations, whose structured data enabled intelligent matching to customer needs, and whose content answered the questions that mattered.
Brands that embraced AI-optimised campaigns saw up to 110% uplift in revenue compared to 2023 and significant increases in return on ad spend. This wasn't about spending more, it was about positioning products for the new discovery mechanisms that AI shopping assistants enable.
Winning Black Friday 2025
The brands that will win Black Friday 2025 are those that understand the basics aren't enough anymore.
Yes, ensure your fundamentals are solid. But recognise them for what they are, table stakes that keep you in the game, not strategies that win it.
The real competitive advantage lies in AI readiness: structured data that machines can understand, concept-based content that answers real questions, and product attributes that enable intelligent recommendations.
Prepare your digital shelf not just for the customer browsing on their phone, but for the AI assistant helping them make decisions. That's how you compete on clarity, trust, and genuine value rather than racing to the bottom on price.
The era of AI-driven, curated commerce is here. The question isn't whether to adapt, but whether you'll do it before your competitors do.
Sources:
- Adobe Digital Insights (2025)
- Salesforce Commerce Cloud data (2024)
- Search Engine Journal Black Friday analysis (2025)
- Schema App Solutions benchmark studies (2025)
- Amazon Rufus product documentation (2024-2025)
- PYMNTS.com retail AI research (2024)
- ResultFirst schema markup analysis (2025)
- Digital Commerce Partners e-commerce research (2025)
This article is part of eStore's Ignite Best Practice series, helping brands master the evolving digital shelf landscape. You can watch Shazia's eWebinar on Black Friday here. For more insights on optimising your digital retail performance, explore our complete library of guides and training resources.
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