Amazon Project Zero, powered by the new generation of Artificial Intelligence, is establishing itself in 2025 as the most advanced tool to curb unauthorised sales and counterfeiting in e-commerce. According to data from the company itself, in 2024 Amazon seized more than 15 million counterfeit products —more than double the previous year— as part of a growing effort to intercept fraudulent items before they reach customers. This programme marks a new stage in brand protection, allowing companies themselves to take direct control over the detection and removal of fraudulent products within the world’s largest marketplace.

In this article we analyse the most recent advances of Project Zero, its effectiveness in the fight against counterfeits, and the challenges still faced by a tool that has significantly raised the standard of security for brands and consumers.

What is Amazon Project Zero?

Amazon Project Zero is an initiative designed to eliminate counterfeit products and protect the reputation of brands within the Amazon marketplace. As they state on their website, with Project Zero you can:

• Remove counterfeits immediately: Use the Project Zero self-service feature to search for counterfeit listings and remove them immediately from the Amazon store.
• Improve automated protections: Each time you remove a counterfeit, you strengthen Brand Registry’s automated protections. These proactively block counterfeits before they affect your brand and your customers.
• Proactively prevent counterfeits: By combining instant counterfeit removal with serialisation generated by Transparency. This programme uses unique, scannable codes to prevent counterfeit units from being published or sold to customers.

Its main innovation is the integration of Artificial Intelligence and machine learning to detect and block more than 99% of counterfeiting attempts before they reach the end user.​

As Kebharu Smith, director of Amazon’s Counterfeit Crimes Unit, explained:

“We are seizing more products before they reach our store due to our efforts to stop counterfeit products at the source”

How does Project Zero’s Artificial Intelligence Work?

Project Zero analyses more than 5 billion listings every day, identifying patterns and anomalies in real time. Thanks to its machine learning algorithms, the tool feeds on huge volumes of data to anticipate fraud attempts and automatically remove suspicious products. This eliminates the need for manual intervention by brands.

Source: Amazon Project Zero, 2025

Amazon uses large multi-modal models capable of interpreting images, text and pricing patterns in product listings, which allows it to detect even subtle manipulations designed to evade control systems. In addition, the system examines less visible data—such as shared IP addresses, bank information or account activity—in order to identify criminal networks.

Automation also plays a key role in seller verification. Advanced tools are used that validate identity, detect forged documents and flag suspicious behaviour during the registration process. Finally, Amazon offers brand owners a self-management feature that allows them to remove fraudulent offers with just a few clicks. In this way, their ability to respond to fraud is strengthened.

Benefits for brands and consumers

Protection against counterfeits and unauthorised sales has evolved substantially thanks to automation and the use of advanced Artificial Intelligence. Amazon Project Zero puts tools that proactively combat fraud throughout the entire commercial process in the hands of brands.

The main benefits include:

• Instant removal of counterfeit products thanks to self-service tools, which reduces reputational risks and minimises the impact on the customer experience.​
• Greater operational efficiency and time saving, by not depending on manual requests and reviews; brands can act directly and the automated protections learn and are strengthened with each new action.​
• Serialisation and automated checking for each unit, adding a physical layer of protection that integrates with the platform’s AI systems.​
• Increased consumer confidence, from knowing they are buying only authentic products; this results in better ratings, fewer claims and greater brand loyalty.​
• Global system extension: once the brand is enrolled, the protection is activated in all marketplaces operated by Amazon. This simplifies management and ensures consistency in the defence of intellectual property.​
• Decrease in legal claims and loss of sales resulting from incidents with counterfeits, strengthening competitive positioning in global digital retail.​

Amazon has marked a turning point, allowing the benefits of protection to reach both companies and end buyers.

Technological innovation and global reach in 2025

During 2025, Amazon has reinforced Project Zero with different technologies. Advanced serialisation, Brand Catalogue Lock and an improved violation reporting system give registered brands unprecedented control over their products. The incorporation of generative artificial intelligence, capable of analysing even seller communications and profiles, has significantly increased accuracy in detecting fraud and unauthorised sales.

This technological leap is transforming the global e-commerce ecosystem. According to the latest reports, more than 35,000 brands are already part of Project Zero and the number of blocked counterfeits has soared. The new AI-powered protection capabilities have allowed Amazon to consolidate its position as an international benchmark in the fight against unauthorised selling and digital counterfeiting.

Challenges and limits: Is it really the end of counterfeiting?

Despite its achievements, Project Zero and systems based on Artificial Intelligence face evolving threats. The use of AI by cybercriminals has also grown, enabling more sophisticated fraud operations, such as the generation of false identities and the deployment of ransomware “as-a-service”. Threats have diversified, and security companies, alongside Amazon, must constantly adapt to mitigate new forms of attack.​

The main concerns that remain are:

• An increase in incidents related to hacking and breaching of AI systems in e-commerce, according to reports from IBM and Stanford, shows that the pace of adoption sometimes outpaces the ability to protect these systems effectively.​
• The proliferation of international fraudulent operations, such as networks of fraudulent remote workers and sophisticated manipulations of rankings and reviews.​

On the other hand, the adoption of Project Zero is not yet universal; some companies are still using traditional mechanisms that are less effective and more reactive to incidents. However, the trend is expected to continue to grow, driven by the consolidation of automation, new regulatory alliances and the integration of generative AI, capable of anticipating and acting on new fraud models.​

What to expect in the near future? When AI becomes the guardian of e-commerce

The future of brand protection in e-commerce will be marked by:

• AI and machine learning systems will become increasingly complex, with the ability to anticipate and adapt to new fraud models.​
• Brands that have not yet adopted Project Zero will be increasingly exposed to reputational and financial risks.
• The success of Project Zero could set the standard for international e-commerce regulation, with closer partnerships between platforms, legislators and security forces.​

Amazon Project Zero and advanced artificial intelligence are redefining protection against unauthorised sales and counterfeits in e-commerce. Although the challenges and threats continue to evolve, technology has allowed thousands of brands to control their products and reputation more effectively. This ensures user confidence and promotes sustainable growth throughout 2025. Amazon’s intention is clear: to make the sale of counterfeit products an anomaly and not the norm.​

For brands operating on Amazon and any company immersed in the digitalisation and automation of retail, the future demands a commitment to advanced solutions like Project Zero and to the adoption of Artificial Intelligence, understanding both its strengths and its new vulnerabilities.​

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The European luxury fashion sector in 2025 stands at a crossroads between timeless heritage and data-driven precision. In this first article from Flipflow’s Luxury Benchmark series, we analyze pricing structures, assortment distribution, and how these two dimensions reveal each brand’s strategic DNA. 

Powered by Flipflow’s market intelligence, our Q2 2025 benchmark (April–June; France, Italy, and the UK) analyzes 5 European luxury houses: Armani, Dolce & Gabbana, Etro, Missoni, and Versace. Our findings show how pricing signals define positioning, how catalogue breadth drives visibility, and how the full strategy becomes clear when both elements are evaluated together.

The European Luxury Context

The European luxury fashion sector in 2025 continues to be a powerhouse backed by resilience and strategic transformation. This market is projected to reach $124.3 billion in 2025, growing at an annual rate of 2.57% from 2025 to 2030 according to Statista. Despite recent macroeconomic tensions, luxury retail sales in Europe increased by 4% in 2024, though this growth represented a normalization compared to the post-pandemic surges of 7% in 2023 and an extraordinary 23% in 2022. These figures illustrate a market that is stabilizing after pandemic-driven spikes but remains fundamentally strong.

Millennials and Gen Z have emerged as increasingly influential demographics in luxury consumption. While exact growth shares vary by study, these younger generations are widely recognized as critical to future market expansion. They emphasize sustainability, digital engagement, and authentic brand experiences, shaping how luxury maisons evolve their strategies. According to Bain’s 2025 analysis, Millennials and Gen Z together will represent the majority of luxury spending growth in the coming years, even if short-term shifts in behavior occur. This generational influence requires brands to balance timeless heritage with innovation in product, pricing, and channel approaches.

Brands are increasingly focused on optimizing assortment breadth, refining price ladders, and creating new entry points for younger, aspirational consumers. Flipflow’s Q2 2025 benchmark highlights two dominant strategic archetypes emerging in this environment: expansive “destination houses” like Dolce & Gabbana and Versace that compete on scale and category leadership, and “focused specialists” like Armani, Etro, and Missoni that excel by curating selective, high-value assortments rather than saturating the market.

This evolving context drives the urgent need for data-driven insights to inform assortment and pricing strategies, a need Flipflow meets with precision analytics. 

Price benchmarks: ladders, ceilings, and gateways

Price ladders do heavy lifting in luxury. They welcome new clients with brand-true entry items, guide them into core categories, and reserve the top rung for halo pieces that concentrate desirability. In Q2, the clearest signal comes from Dolce & Gabbana. Jewelry for women reaches a remarkable €395,000 at the top and averages €10,741.40, while Handbags for women peak at €24,500 with a €3,231.97 average. Those figures indicate a house that uses scarce, high-ticket items to anchor a premium narrative. The effect of such ceilings extends beyond the categories themselves: they lift perceived value across the catalog and create reference points for price integrity.

Versace leans into prestige within apparel. Dresses for women reach €12,000 and Coats & jackets approach €9,500. These are categories with high visual impact and strong editorial potential, which helps explain why the brand invests in serious peaks here. The strategy aligns with Versace’s strength in statement fashion and supports its accessories ecosystem by keeping attention on standout silhouettes.

Armani takes a different path, building a wide ladder that stretches from approachable on-ramps to elevated caps. The men’s side supplies accessible entry through Beachwear at €190 and T‑shirts and polos at €270, while the women’s offer pushes up to €59,000 in Jewelry and €25,000 in Dresses. This architecture supports long-term client progression: discover the brand in casual or seasonal items, trade up to core tailoring or prêt-à-porter, and eventually consider rare pieces.

Meanwhile, Etro and Missoni maintained more moderate price spans, aligning with accessible luxury — the space between aspirational design and timeless craftsmanship.

Share of assortment: who dominates the shelves

If price sets the tone, assortment determines how often a brand appears in front of shoppers. Across thousands of SKUs tracked by Flipflow’s SaaS, Dolce & Gabbana commands 36.4% of total assortment share, or 10,712 unique models. Versace follows at 22.3% (6,563 SKUs). Together, they control nearly 60% of the total assortment space monitored among the benchmarked brands.

Scale translates into visibility. With larger catalogs, D&G and Versace appear repeatedly in search and navigation. They can present full looks, build adjacent recommendations, and cross-sell across categories. That scale also supports stronger bargaining power with partners and more robust icon programs because replenishment data compounds faster across many placements.

Armani stands apart with a selective approach — strong presence in “New for Him” and “New for Her” collections, but fewer listings in categories like dresses (35 SKUs) or skirts (12 SKUs). This focus translates into scarcity-driven desirability and operational agility, reinforcing the brand’s image of quality through curation rather than volume.

Etro exhibits balance, maintaining comprehensive assortments across apparel for both men and women, supported by sustained innovation in new arrivals. Missoni shows the narrowest scope, with notable emphasis on women’s beachwear (1,000 SKUs) but visible gaps in categories such as jewelry or belts. This skew highlights Missoni’s precision strategy — doubling down on lifestyle and knitwear identity rather than spreading thin across the luxury matrix.

When price meets assortment: defining strategic models

Overlaying price architecture on assortment share reveals 4 archetypes: 

1. Dolce & Gabbana and Versace form the high-assortment, high-price duo. They aim for leadership by being everywhere the shopper looks and by maintaining strong ceilings that protect brand equity. Success here requires tight forecasting and disciplined editing once sell-through data arrives, because the risk of inventory complexity grows with range size.

2. Armani represents a selective model with consistent luxury pricing. The brand chooses its arenas and invests in storytelling and product quality rather than blanket coverage. With fewer SKUs, every drop must work hard: imagery, copy, and PDP detail need to pull their weight, and replenishment on proven winners should be fast to maintain momentum.

3. Etro occupies a moderate-assortment, moderate‑high price space. The offer feels steady, considered, and frequently refreshed. Its challenge is to avoid being squeezed by volume-led leaders on visibility and by ultra‑luxury on aspiration. The answer lies in fabric stories, menswear credibility, and accurate allocation so size and color depth sit where sell-through is strongest.

4. Missoni follows a focused model with moderate prices and deliberate category gaps. The brand wins when its distinctive patterns, textures, and fits play to loyal clients who collect updates season after season. The beachwear surge in Q2 shows how a specialist can use seasonal capsules to generate reach without rebuilding the entire assortment.

Revenue drivers: luxury’s economic backbone

From a commercial perspective, accessories remain the undisputed engines of profitability. Bags, shoulder bags, and jewelry consistently occupy the upper end of price brackets across all five houses, confirming their role as identity markers and entry gateways. Dolce & Gabbana’s jewelry, priced above any peer benchmark, positions these pieces as collectible assets and brand symbols.

Conversely, footwear and small leather goods serve as accessible entry points — vital for recruiting younger and aspirational clientele. Categories like sneakers, sandals, and pumps maintain luxury credentials within more contained price ranges, acting as stepping stones into the world of high fashion.

Apparel categories such as dresses, coats, and blazers preserve their prestige function, anchoring perception of status. They are less about utility and more about expression—often commanding high average prices that directly correlate to brand desirability. In this mix, assortment depth equates to competitive power. 

The brands with broader inventories gain visibility, shelf priority, and algorithmic advantages in both digital and retail touchpoints.

Strategic insights: what data reveals about positioning

The Flipflow dataset portrays a market divided between expansionists and specialists. For Dolce & Gabbana and Versace, breadth secures dominance but also necessitates continuous storytelling and newness to prevent dilution. Their extensive assortment structures enable cross-selling but require uncompromising price discipline to maintain exclusivity.

For Armani, the opposite is true: less becomes more. Focused launches and limited assortments favor sharper brand coherence and efficient capital allocation. Etro’s equilibrium signals consistency, while Missoni’s narrow focus underscores identity preservation through craftsmanship, color, and textile artistry.

Across all players, strategic finesse lies in mastering the link between price elasticity and assortment visibility. Too many products risk eroding perception; too few may limit reach. The balance between desirability and accessibility defines the luxury continuum today, and Flipflow’s data shows how precision can elevate positioning beyond aesthetics into measurable performance.

Conclusion: compete with clarity, pace with newness

Q2 2025 confirms that European luxury rewards coherent pricing and deliberate assortments. D&G and Versace show how scale and premium ceilings build dominance across the digital shelf. Armani proves that a precise edit with consistent luxury pricing can direct attention and safeguard equity. Etro demonstrates the strength of refined everyday luxury, while Missoni illustrates the power of a specialist stance anchored in signature design.

The path forward is straightforward to state and demanding to execute. Protect the price ladder from entry to halo. Invest depth only where category leadership is credible and sustained. Maintain a cadence of meaningful newness that aligns product, content, and distribution. Brands that operate with this level of clarity compete not just on individual products but on a value architecture that customers understand and trust.

For buyers, planners, and marketers, the full report—European Luxury Houses: A Competitive Benchmark, Q2 2025—provides category‑level detail, best sellers, and deeper gender and season cuts.

Download it to explore the data behind these findings and guide your next buy, pricing review, or drop calendar. In 2025, clarity — not quantity — defines the new luxury advantage.

The post Data, Desire and Differentiation: A Deep Dive into the Pricing and Assortment Strategies of Europe’s Luxury Icons appeared first on Flipflow.

Walmart and OpenAI have announced a strategic alliance that promises to redefine the online shopping experience. From now on, Walmart customers will be able to shop directly through ChatGPT, taking advantage of the new Instant Checkout feature, which allows them to place orders without leaving the chat. This integration simplifies the shopping process, redefines the Customer Journey and drives a new form of relationship between consumer and technology: conversational commerce powered by Artificial Intelligence.​

This move hasn’t come out of the blue. According to data from Similarweb, in September 2025, 15% of the referral traffic received by Walmart.com came from ChatGPT (although this accounts for less than 1% of all the traffic the retailer receives). Furthermore, almost 3 in 10 OpenAI app users have visited Walmart in recent months. 

This reflects a new consumer pattern: shoppers are starting their journeys within conversations, not just on traditional search engines.

An AI-Native Experience

According to Doug McMillon, President and CEO of Walmart:

“For many years now, eCommerce shopping experiences have consisted of a search bar and a long list of item responses. That is about to change. There is a native AI experience coming that is multi-media, personalized and contextual.”

Customers will be able to ask ChatGPT things like “plan a vegetarian dinner for four” or “recommend a 4K television for under 400 dollars.” The system will respond with suggestions from Walmart’s catalogue and allow them to complete the purchase instantly with Instant Checkout, without needing to browse pages or fill shopping baskets.​

From Search to Discovery: the Rise of Agentic Commerce

The agreement with OpenAI promotes the concept of agentic commerce, in which AI shifts from being reactive to proactive. This means it learns from interactions, recognises purchasing patterns and anticipates consumer needs before they are even expressed.​

Walmart has been integrating Artificial Intelligence into its value chain for some time. It currently uses it to:

• Reduce fashion production times by up to 18 weeks.
• Optimise customer service, ensuring a smoother shopping experience and cutting response times by up to 40%.
• Manage inventory and logistics predictively.​

This new phase brings that intelligence directly to the consumer, transforming the act of shopping into a fluid and personalised conversation.

Instant Checkout: Speed, Convenience and Personalisation

The central feature of this alliance is Instant Checkout, initially developed by OpenAI for Shopify, as we discussed in a recent article. Walmart is now incorporating it into its ecosystem to offer instant in-chat purchases, processed securely through integrations with payment systems like Stripe.​

Initially, Instant Checkout will apply to individual products—such as snacks, cleaning supplies or tech—although Walmart plans to soon expand the capability to multiple-item purchases and more complex deliveries.​

Sam Altman, CEO of OpenAI, highlighted that this collaboration aims to simplify the everyday shopping experience: 

“We’re excited to work with Walmart to make everyday purchases a little simpler. It’s just one way AI will help people every day.”

The Race to Dominate Agentic Commerce

While Walmart and OpenAI are making progress on integrating shopping within ChatGPT, in parallel, a quiet but intense technological race is being fought to control agentic commerce. 

According to the latest analysis from CB Insights, four companies are consolidating their position as the cornerstones of this transformation: OpenAI, Perplexity, Google and Shopify. These firms have established more than 30 agreements in the last two years, far surpassing giants like Amazon, Anthropic or Meta.​

Source: 4 companies race to control agentic commerce through partnerships -CB Insights, 2025

OpenAI is leading the ecosystem with its Agentic Commerce Protocol, developed jointly with Stripe, which allows purchases to be made directly in ChatGPT securely and autonomously. Its collaboration with Shopify, Etsy and Walmart reinforces the strategy of turning ChatGPT into an AI-native transaction channel.​

For its part, Shopify is positioning itself as the infrastructure provider for agentic commerce, integrating its catalogue, Shop Pay and the new Universal Cart into AI environments so that different agents can make purchases from multiple stores in a single conversational flow.​

Google, through its Gemini ecosystem and agreements with major brands like Home Depot, Alaska Airlines and Behr, is seeking to connect assisted search with direct purchasing, strengthening its presence in AI-enabled consumer experiences.​

Meanwhile, Perplexity has opted to be the smart aggregator for the new retail landscape. Its conversational search agent model allows users to discover, compare and purchase products directly within its interface, in alliance with platforms like Shopify and Amazon.​

In this race, alliances are key. No single company can control all the components of agentic commerce on its own—AI models, data, sales platforms and payment systems—so collaboration between tech players has become the foundation that will allow this new digital retail paradigm to scale.

A New Business Model for OpenAI

The conversational integration with Walmart also inaugurates a new revenue stream for OpenAI. The company plans to charge commission on each transaction made within ChatGPT, creating a monetisation model that goes beyond subscriptions and APIs.​

This expansion into commerce positions ChatGPT as a universal shopping interface, connecting consumers with brands without intermediaries. A natural evolution from chatbot to transaction platform.

Implications for the Future of E-commerce: The Dawn of the “Smart Retail” Era

The impact of this move goes beyond Walmart. According to analysts at the RetailTech Innovation Hub, this alliance “should serve as a wake-up call for the entire retail sector”, due to its ability to increase personalisation, reduce friction and expand contextual data on consumers.​

By moving the act of shopping to a conversational context, Walmart:

• Reduces friction points and basket abandonment.
• Strengthens the emotional and personalised relationship with the user.
• Brings sales closer to everyday digital environments, such as virtual assistants and messaging apps.

Daniel Danker, Executive Vice President of AI and Product Design at Walmart, explained that this development:

“Brings the brand to the consumer where they already are: in the conversational environment”.

This reflects a strategic shift towards an AI-based omnichannel experience.​

The Walmart–OpenAI alliance is more than a technological innovation: it symbolises the birth of smart retail based on dialogue, personalisation and proactivity. With this collaboration, Walmart is consolidating its role as a leader in AI adoption in retail and redefining the way consumers interact with brands.

Commerce is no longer a search; it is becoming a conversation. And that conversation, increasingly, will speak the language of Artificial Intelligence.

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The customer journey to a product no longer runs solely through a results page: increasingly, it happens via conversational responses. According to a recent study by Salesforce, 7% of all shoppers currently start their product searches using digital AI assistants (such as ChatGPT, Perplexity or Gemini). For Gen Z consumers, this figure rises to 10%. In the last year, 17% of consumers have used an AI assistant or LLM to search for a product. The same study points out the following:

“During the upcoming festive season, we predict that $263 billion in online sales and $1.6 trillion in in-store sales will be generated by shoppers discovering products through artificial intelligence recommendations.”

The New Way of Discovering Products

Imagine someone opening an AI assistant and typing: “I’m looking for running trainers for mild pronation, under €120, that are suitable for daily training. What do you recommend?”. That person no longer expects an endless list of links, but a clear, reasoned answer with specific models, suggested brands and, perhaps, direct links to purchase. This is the new shop window where product discovery begins.

Bing Copilot was one of the first to demonstrate this, integrating its chat experience over the Bing index and citing sources in real time. Google is following the same path with AI Overviews, which summarises information directly within the search. ChatGPT, for its part, is striking deals with big names in retail—such as Shopify or Walmart—to drive AI-powered shopping experiences, supported by Instant Checkout, its agent-based commerce protocol.

In this new scenario, visibility transcends traditional SEO or performance marketing campaigns. Being visible means being understandable, verifiable and competitive for the engines that feed language models. This is where Digital Shelf Analytics takes on a new dimension. For years, it has been used to optimise product listings, prices and reviews; today, it can also determine whether an LLM “mentions” or recommends you when someone makes a query that matches what you offer. Being well-positioned on the Digital Shelf, in a broad sense, means being on the radar for these conversational responses.

Demystifying the Key Concepts

To understand the connection between your online shop and an AI’s responses, we first need to clarify 3 fundamental concepts that we will be using constantly.

1. What is the “Digital Shelf”?

The Digital Shelf represents all the spaces on the Internet where your products are available for consumers to discover, evaluate and buy them. Think of it as the modern version of a supermarket aisle, but distributed across multiple channels: your own online shop, third-party platforms like Amazon, specialised applications, price comparison sites, and even social networks that allow direct purchases.

Each of these spaces acts as a small shop window where your product competes for the customer’s attention. Your position, the quality of your images, the completeness of your descriptions, the price you display, stock availability, and the ratings you have accumulated all make up your presence on that particular shelf. Unlike a physical shop, where space is limited and static, the digital shelf is infinite and constantly changing according to algorithms, user behaviour and your competitors’ actions.

2. What is Digital Shelf Analytics?

If the digital shelf is the space where you compete, Digital Shelf Analytics is the set of tools, metrics and processes that allow you to understand how you are doing in that competition. It involves systematically monitoring dozens of variables for each of your products on every channel where you have a presence.

These metrics include aspects like the percentage of complete content in your listings, the quality of your images, or your position in search results for key terms. They can also include real-time product availability, price comparisons with direct competitors, and the volume and quality of customer reviews. The platforms specialising in these analytics, such as flipflow, collect data from multiple sources, structure it, and present you with dashboards that reveal strengths, weaknesses and opportunities.

The power of these analytics lies in their ability to turn scattered data into actionable insights. It allows you to detect when a competitor has lowered their price just before a major campaign, identify which product attributes shoppers value most in each category, or discover that your listings on a specific channel are incomplete and this is harming your conversions.

3. How do LLMs like ChatGPT “learn”?

LLMs do not “think” or “understand” like humans. They are extremely complex mathematical models that have been trained on an unimaginable amount of text and data extracted from the internet. Their main function is to predict the next word in a sequence, which allows them to generate coherent and contextually relevant responses.

When an LLM answers a question like “recommend a product for me”, what it does is synthesise the information it has processed during its training. It looks for patterns, identifies products that are frequently mentioned in positive contexts, analyses thousands of reviews to extract key features, and values information sources it considers authoritative (expert blogs, news articles, buying guides).

This is where it all connects: a well-built and optimised Digital Shelf is a source of structured, rich and reliable data for these LLMs. Your product page is part of its study material.

Practical Guide: How to Use Digital Shelf Analytics to Get AI to Recommend You

Search Engine Optimisation (SEO) taught us to think in terms of keywords. AI Optimisation (AIO) forces us to think in terms of answers and authority. We propose a 4-step action plan to get AI to recommend your products:

Step 1: Audit your digital shelf

The first strategic move is to get an accurate picture of your current situation. You need to assess the quality and completeness of your product information on all channels where you have a presence. Use flipflow, your Digital Shelf Analytics tool, to generate a report showing what percentage of your product listings contain all the required fields.

Pay special attention to fields that are often neglected but are crucial for language models: attributes like dimensions, materials, compatibilities or certifications. This structured data helps Artificial Intelligence to understand exactly what your product is and in what contexts it is appropriate to recommend it.

Also, identify inconsistencies between channels. If your product appears with different prices on your website and on Amazon, or if the description varies substantially, you are creating confusing signals that reduce the trust an AI system can place in your information.

Step 2: Spy on your competition

Digital Shelf Analytics really shines when you use it to understand what your direct competitors are doing right. Examine how they structure their product titles, the length of their descriptions, how many images they include, which technical specifications they highlight, how they manage product variants… And, especially, what kind of language they use in their listings.

Some competitors will have discovered particularly effective ways of communicating benefits or responding to common objections that you could adapt for your own strategy.

Step 3: Optimise for questions (AIO – AI Optimisation)

The concept of AI Optimisation represents the natural evolution of search engine positioning. Before, you optimised for keywords that people typed into Google. Now you need to think about the conversational questions they might ask an AI assistant.

Instead of focusing exclusively on terms like “women’s running trainers”, consider full questions like “what trainers do you recommend for starting to run if I’m overweight?” or “I need trainers for road running that will protect my knees”. Incorporate natural answers to these types of queries into your descriptions, using language that flows organically.

Structure your product descriptions as if you were answering your customers’ most frequently asked questions. Include sections that directly address who should buy this product, for what situations it is ideal, what specific problems it solves, or why your product might be preferable in certain contexts. This structure not only improves the human user experience, it also provides the type of information a language model looks for when trying to make a contextualised recommendation.

Step 4: Actively encourage detailed reviews

Customer reviews have been important for years, but their relevance is multiplied in the context of AI. Language models analyse reviews to extract information about the product’s real-world performance, specific use cases, durability and customer service.

Implement proactive strategies to increase not only the quantity, but especially the quality of your reviews. Send follow-up emails after purchase at the optimal time, when the customer has had time to try the product but the experience is still fresh in their mind. Instead of simply asking for a rating, ask specific questions that encourage detailed answers: how they are using the product, what problem it helped them solve, what pleasantly surprised them.

Respond publicly to all reviews, both positive and negative, in a professional and helpful manner. These interactions add additional context that language models can process. 

Measurement: From Digital Share of Shelf to LLM Share of Voice

Measuring the impact in this new context requires combining traditional Digital Shelf metrics with indicators that approximate your Share of Voice in LLMs. There is still no universal dashboard that tells you how many times an assistant has mentioned you. But you can observe signals and create an internal LLM monitoring system.

The concept of Share of Voice in language models measures the percentage of times that your brand or specific products appear in the response when someone asks a question related to your product category. This metric is more complex to track than traditional ones. It requires simulating user questions and analysing the responses generated by different Artificial Intelligence systems.

Some specialised agencies and tools are starting to offer this type of analysis. You can also implement a basic internal system where your team regularly asks relevant queries for your category in ChatGPT and other assistants, documenting when you appear in the recommendations, in what position, and what specific information the system uses to back up its suggestion.

Pay attention to the reasons the AI gives when it recommends your product. If it mentions specific features, particular use cases, or customer ratings, you are seeing direct evidence of which aspects of your digital presence are being processed and considered valuable. This information allows you to continually refine your content strategy.

Risks, Limits and Best Practices

Like any innovation in digital marketing, optimisation for artificial intelligence systems comes with temptations that can be counterproductive in the medium term. It is crucial to navigate this new territory with clear ethical principles and a realistic understanding of the current limitations.

Firstly, resist the temptation to artificially manipulate signals that the AI might interpret as quality indicators. Generating fake reviews, inflating product specifications, or creating multiple versions of listings with slightly different information to saturate the information space are strategies that might work for a moment but expose you to severe penalties from platforms and, fundamentally, erode the trust that is the foundation of any sustainable business.

Current language models, while impressive, have significant limitations and present certain risks:

• Hallucinations: They can sometimes invent information or attribute incorrect characteristics to a product.
• Biases: They may have a tendency to recommend larger, more established brands, as these dominate the volume of training data.
• Obsolescence: The information they were trained on may not be the most recent, which could lead to them recommending discontinued products.

The best strategy to mitigate these risks is to focus on best practices. Transparency, honesty and quality are the best defence. Ensure that all the information on your digital shelf is accurate, up-to-date and backed by an exceptional customer experience. The goal is to be such a reliable source of truth that LLMs consider you an authority in your niche.

Outlook and Future: Towards Conversational E-commerce

We are entering a new stage of e-commerce: the conversational e-commerce. Cold, purely transactional interactions are giving way to personalised, fluid dialogues, where artificial intelligence acts as an expert shopping assistant.

Although this landscape is still in its early stages, AI capabilities are advancing rapidly, and the ways in which consumers relate to it are only just beginning to take shape.

Amid all these changes, one principle remains true: the brands that generate real value, communicate it clearly, and build relationships based on trust and excellence are the ones that thrive. Technology evolves, platforms change, but deeply understanding the customer and exceptionally meeting their needs remains the key.

In this future, the Digital Shelf will no longer be just a product gallery, but will become a dynamic source of knowledge, capable of driving intelligent conversations and guiding customers towards the best decisions.

That is why optimising your product listings, collecting detailed reviews and analysing your competitive position is more important today than ever before. Every improvement to your digital shelf is a step towards a clear goal: so that when the customer of the future asks, your product is the answer.

The post From PDP to Prompt: How Digital Shelf Analytics Puts You on the LLM Radar appeared first on Flipflow.

On 12 September 2025, the Data Act comes into force. This ambitious European regulation on data access, use and sharing is transforming the foundations of digital marketing and, in particular, the Retail Media ecosystem.

This new regulation marks a turning point in the way brands, retailers and advertisers activate their audiences and measure the impact of their campaigns, forcing a profound rethink of the strategy and technologies associated with data management.

Why is the Data Act Revolutionising Data Access?

Until now, large platforms and manufacturers of smart, connected devices have centralised data access. This limited the ability to segment and measure in Retail Media. The Data Act breaks this de facto monopoly by establishing that users and companies who own connected products and services have the right to decide on the access and transfer of the data generated by their activity. This implies a radical opening up of the data market and enables new collaborative models under the user’s explicit control and consent.

With this new regulation, the focus shifts from data ‘ownership’ to ‘access control’. Now, data generated by IoT devices, point-of-sale terminals or digital services can be shared with third parties (for example, Retail Media providers or analytics solutions) if requested by the user or owning company. This must always be done under fair, transparent and secure conditions.

The Challenge: Adapting your Data Activation Strategy

The entry into force of the Data Act requires all players in Retail Media —retailers, marketplaces and brands—to reconfigure both their technical systems and their data governance models. It is no longer enough to have consent; it is essential to guarantee the interoperability, security and traceability of data, both personal and non-personal.

Some key recommendations include:

• Inventory and classify data generated by devices or digital platforms (what data is collected, where it is stored and what it is used for).
• Implement technical measures to guarantee the quality, secure access and portability of data between different parties.
• Define clear protocols for responding to requests for access to or transfer of data to third parties.
• Review and standardise contracts to include clauses on data use, portability and FRAND (Fair, Reasonable & Non-Discriminatory) conditions.

Clean Rooms: the Star Technology of the New Era of Retail Media

In this context, data clean rooms are establishing themselves as the great enabler for compliance with the Data Act, while at the same time boosting the effectiveness of omnichannel activation and measurement.

These secure platforms allow for the joint analysis of data between different companies —for example, between a retailer and a brand— without the exchange or leakage of personally identifiable data. In this way, both the law and the new standards of privacy and consent are met.

Clean rooms make it possible to:

• Activate personalised audiences and incremental attribution models, while respecting user consent.
• Measure the impact of advertising campaigns throughout the entire purchase journey, linking digital browsing data with offline transactions, while avoiding the direct exposure of sensitive information.
• Generate valuable insights and segmentations by combining data from different sources, supporting more open and collaborative ecosystems.

A New Competitive and Contractual Balance

The Data Act also protects against unfair contractual clauses, promotes transparency in the aggregation and sharing of data with third parties, and provides end-users with guarantees regarding the secondary uses of their data.

Furthermore, it expressly excludes large platforms considered “gatekeepers” from new access privileges if requested by the user. This prevents excessive accumulations of power in the digital ecosystem.

In practice, the regulation requires companies to:

• Reinforce explicit agreements on the secondary uses of non-personal data.
• Establish clear mechanisms for compensation or remuneration for access to certain data sets.
• Align legal, technical and marketing teams for comprehensive and strategic data management.

Implications for Attribution in Retail Media and Immediate Challenges for Companies and Retailers

With interoperability and responsible sharing as cornerstones, companies in the sector can:

• Create richer omnichannel attribution loops, cross-referencing online and offline sales data with journeys digitales bajo estrictas garantías de privacidad.
• Build personalised and dynamic segmentation models, activated within the same secure clean room environment.
• Take advantage of the flexibility to incorporate new data sources, from marketplaces to distribution networks or logistics partners, thereby enriching both advertising activations and performance reports.

The transition will not be simple. The main challenges include:

• Reviewing data sources to clearly differentiate the data affected by the right of access (data that is generated or pre-processed, but not inferred or derived from proprietary models).
• Implementing technical clean room solutions as standard, avoiding the systematic denial of access based on weak arguments of trade secrecy.
• Carrying out periodic compliance audits, for both the GDPR and the new Data Act framework. This will help to avoid penalties that can reach up to 4% of global turnover or 20 million euros.

The Role of Anticipation and Governance

The most agile and forward-thinking companies will see the Data Act as a strategic opportunity rather than a regulatory obligation. Investing in technologies like clean rooms, creating hybrid teams (data/legal/business) and strengthening data governance will be essential to leading a Retail Media built on privacy, trust and cooperation.

As Cristina Cantero (VML The Cocktail) points out:

“With the arrival of the Data Act, by giving users back control over their data, a great opportunity opens up for brands to design experiences with real value and explicit consent.”

The Data Act marks the beginning of a new stage: that of responsible data activation in Retail Media. After the era of cookies and closed ecosystems, the sector is moving towards collaborative models based on clean rooms, consent and clear attribution. In this new scenario, leading will mean going beyond compliance: it will mean turning ethical data management into the engine that drives the next generation of Retail Media.

The post How the Data Act is unlocking the value of data in Europe appeared first on Flipflow.

A customer walks down the aisle of an electronics shop, holding her mobile in one hand while examining a coffee machine on the shelf. She quickly looks up product reviews, compares the price with other online stores, and watches a YouTube tutorial on how to use it. Five minutes later, she adds the coffee machine to her physical shopping basket and heads to the checkout. This situation, far from being exceptional, reflects the reality of today’s shopper.

Up to 87% of shoppers use their mobile phones inside a physical shop to compare prices or look for reviews. This figure reveals an undeniable reality: the line that separated physical and digital commerce has vanished. Consumers no longer distinguish between online and offline; for them, there is only one shopping experience.

The Broken Frontier Between Online and Offline

This hybrid behaviour poses a fundamental problem for brands and retailers. Many companies continue to manage their advertising campaigns in separate silos. One team handles social media and digital advertising, while another deals with in-store promotions and point-of-sale displays. This artificial division leads to the loss of valuable information about the complete customer journey, wasted resources, and disconnected experiences that confuse the consumer.

The true power of Retail Media is unleashed when brands unify the digital and physical worlds, creating an integrated experience where each channel feeds and enhances the other. It is about creating a “phygital” experience, a hybrid that leverages the best of both worlds to offer a seamless shopping journey.

What Does Integrating Offline and Online Channels in Retail Media Mean?

The integration of channels in Retail Media involves designing advertising strategies that work in a coordinated manner in both digital and physical environments. Instead of parallel campaigns that do not communicate with each other, we are talking about creating a unified advertising ecosystem where data, messages, and objectives flow freely between all customer contact points.

Within the online channel, we find spaces such as retailers’ websites, mobile applications, social media, programmatic advertising, and ads on e-commerce platforms. In the offline channel, we have physical shops, posters and displays at the point of sale, digital out-of-home advertising, and in-person events.

Integration means that a person who sees an ad on Instagram about a special offer receives a geolocated reminder when they pass near the physical shop, and they can redeem that promotion both online and in person. Or that someone browsing a product in the shop can scan a QR code and immediately access more information, reviews from other buyers, and tutorials, all without leaving their shopping experience.

This synchronisation requires appropriate technology, unified data and, above all, a change in mindset that places the customer at the centre, recognising that their shopping experience transcends the traditional barriers between channels.

The Omnichannel Customer: The True Protagonist

At the heart of this revolution is the omnichannel customer. This consumer uses multiple channels simultaneously and in an integrated way, seeking a shopping experience that is agile, personalised, and frictionless. They demand coherence and continuity at all points of contact with the brand. And they are loyal to those that provide a positive and unified experience.

To understand this new consumer, it is essential to analyse two models of behaviour that define the interaction between the online and offline worlds:

• ROPO (Research Online, Purchase Offline): This model describes customers who research products on the internet but complete their purchase in a physical shop. They look for technical specifications, read reviews, and compare prices and features from the comfort of their home. But they prefer to see, touch, or try the product before buying it. They also value the immediacy of taking the product home the same day or receiving personalised advice from a salesperson. Sectors such as electronics, furniture, and fashion have high rates of ROPO behaviour.
• Showrooming: At the opposite end of the spectrum, some consumers visit physical shops to examine products, try them out, and ask questions to salespeople, but they complete the purchase online. The reasons vary: they are looking for better prices, want to pay with specific methods, prefer the convenience of having the product delivered to their home, or simply want more time to decide. This phenomenon initially worried physical retailers, who saw their shops turning into “showrooms” for online platforms.

Both behaviours demonstrate the importance of being present at all points of contact in the customer journey. A brand that only focuses on one of the two worlds is missing a great opportunity to connect with its consumers. And, ultimately, to sell.

Benefits of Channel Integration

Unifying Retail Media strategies across online and offline channels generates tangible advantages for both brands and consumers:

Improved customer experience 

When all channels offer coherent messages, the customer perceives professionalism and trust. Imagine receiving a 20% offer on face cream via email, arriving at the shop, and discovering that the promotion does not exist there. Or that the product shown online is not available at the nearest establishment. These disconnections generate frustration and mistrust.

On the contrary, an integrated experience where promotions, stock, and product information are synchronised across channels creates a sense of transparency and service that strengthens the relationship with the brand.

Optimisation of advertising return on investment

By coordinating campaigns across channels, brands avoid advertising saturation and budget waste. Unified data allows for an understanding of which channel combinations generate the best results.

For example, a campaign may discover that social media ads work better when complemented by advertising on digital screens near the shops. Or that sending personalised digital coupons after a visit to the shop significantly increases repeat purchases. This complete view of the customer journey allows for smarter allocation of the advertising budget, investing more in the combinations that truly drive sales.

Strengthening loyalty

Personalised and relevant experiences at all points of contact build long-term loyalty. When a brand remembers a customer’s preferences both online and offline, when it recognises their purchase history regardless of the channel used, and when it offers useful recommendations based on their complete behaviour, that customer feels that the brand truly knows and values them.

This emotional connection transcends a simple commercial transaction and creates brand advocates who not only buy repeatedly but also actively recommend the company to others.

Practical Strategies to Unify your Campaigns

Channel integration is not an abstract concept, but a series of concrete actions that brands can implement. Let’s look at the most effective tactics organised according to the direction of customer flow.

Bridges from Online to Offline

• Geotargeting (geographic segmentation): This technique uses the location of the user’s mobile device to show them relevant ads when they are near a physical shop. For example, a coffee shop chain can send a notification with a special offer when a regular customer passes by the premises. The key is balance: the right frequency and relevance generate visits, but an excess of notifications becomes invasive.
• Local stock in digital ads: More and more advertising platforms allow for displaying the availability of products in nearby shops directly in online ads. Google Shopping, Facebook, and Instagram offer this functionality. This transparency reduces the frustration of visiting a shop only to find that the desired product is out of stock.
• Click & Collect (buy online, pick up in-store): This service combines the convenience of buying online with the immediacy of picking up the product in the shop, often on the same day and without delivery costs. For brands, it represents a dual opportunity: it facilitates online conversion and generates traffic to physical shops, where customers frequently make additional purchases. Advertising campaigns should highlight this service as a benefit, especially for products that the customer needs quickly or when important dates are approaching.

Bridges from Offline to Online

• QR codes: Although they have been around for years, QR codes have experienced a massive resurgence and now function as natural bridges between the physical and digital worlds. Placed strategically on product packaging, in-store posters, or on displays, they allow customers to instantly access additional information, reviews from other buyers, user tutorials, recipes, or even augmented reality that shows how a piece of furniture would look in their home. They can also be used to redeem exclusive promotions or subscribe to loyalty programmes.
• Digital screens (DOOH): Digital out-of-home advertising has evolved enormously. Screens in shops, shopping centres, and public spaces can now display dynamic content. This content changes according to the time of day, the weather, or even who is watching (using non-invasive recognition technology that identifies general demographic patterns without identifying individuals). These screens can promote products, display user-generated content, or invite passers-by to interact by scanning codes, visiting websites, or downloading apps. The measurement of their effectiveness has also improved, allowing for an understanding of how many people saw the ad and what actions they took afterwards.
• Beacons and in-store Wi-Fi: Beacons are small devices that emit Bluetooth signals detectable by customers’ mobiles when they enter a shop. With the user’s permission, they can send personalised notifications based on their exact location within the establishment. For example, a customer who bought coffee last month is walking down the coffee aisle. They might receive a special offer for their favourite brand. The shop’s free Wi-Fi adds value for the customer. In addition, it allows for the collection of anonymous data about browsing behaviour within the establishment. It also helps to identify the most visited points and to optimise the layout of products and campaigns.

The Great Challenge: Measurement and Attribution

One of the biggest challenges in channel integration is identifying which point of contact influenced the purchase decision. It could also be a combination of several points. For example, a customer sees an ad on social media. They then receive a promotional email. Later, they see a digital billboard near the shop. Finally, they purchase the product in the physical establishment. In these cases, it is complex to determine which action deserves credit for the conversion.

Traditional attribution models fall short in this omnichannel environment. Therefore, the current trend is to adopt platforms that integrate and unify data collected both online and offline, allowing for a complete view of the customer journey: from the first advertising impact to the final purchase, whether on the web or in the physical shop. In this context, the standardisation of metrics and the use of clean rooms —secure spaces that allow data to be analysed without compromising privacy— are emerging as key elements for the future of measurement in Retail Media.

Although achieving perfect measurement is probably impossible, brands that invest in improving their tracking and analysis capabilities gain significant competitive advantages, as they can continuously optimise their strategies based on real consumer behaviour data. In this process, collaboration between brands and distributors is essential, as both possess complementary pieces of the complex customer data puzzle.

Conclusion: Beyond Channels — Towards a Unified Experience

Integrating the offline and online worlds has become the key for Retail Media to generate true value. Today’s consumer does not think in terms of channels: they seek consistent, useful, and personalised experiences, regardless of where they interact with the brand. Those companies that understand this dynamic are achieving better conversion levels, building stronger relationships, and gaining a more complete view of purchasing behaviour.

Understanding the role that each point of contact plays—and translating data into strategies that bring coherence and profitability—is now the real challenge, rather than merely being present at multiple touchpoints. Meanwhile, the evolution of Retail Media demands a shift from simple technological integration to a genuine cultural transformation: teams, platforms, and formats must work without borders to offer a fluid and meaningful journey to the customer.

The future will belong to the brands and distributors capable of breaking down silos and creating seamless phygital experiences, where the physical and the digital merge into a single narrative. Technology and data are the instruments; strategic vision and the ability to adapt are the true engines of change. Ultimately, leading tomorrow’s commerce will depend on who manages to understand that the customer experience is, today, at the centre of every successful strategy.

The post Retail Media 360: How to integrate offline and online channels to build effective campaigns appeared first on Flipflow.

The recent partnership between Shopify and OpenAI is revolutionising the e-commerce landscape, integrating the best of conversational artificial intelligence with the traditional online shopping experience.

This agreement, announced in September 2025, will allow millions of users to interact with ChatGPT and buy products from merchants with a store on Shopify directly, seamlessly and in a way that is fully integrated into the conversation.

In this article, we will explore the scope of the announcement, its benefits, its impact on the market and how it will redefine the strategy of e-commerce brands.

Shopify and OpenAI: Conversational Commerce Takes Shape

The official integration of Shopify into ChatGPT marks the beginning of a new era in e-commerce. Users can search for and buy products without leaving the chat, doing away with fragmented processes based on links or external windows.

Shopify merchants will be able to sell directly in ChatGPT.

We’ve been working with @OpenAI for quite some time so people can search and buy products in chat, and it’s something we’ve had a hard time keeping quiet.

Rollout is coming very very soon. pic.twitter.com/11GWCy5DOo

— tobi lutke (@tobi) September 29, 2025

Soon, businesses using Shopify will be able to display their catalogue and sell products directly through conversations in ChatGPT, leveraging the natural and immediate nature of the AI interaction.

Vanessa Lee, Vice President of Product at Shopify, sums it up as follows:

“People are discovering products in conversations with AI, not just by searching or viewing ads… Now our merchants will appear organically in those moments and shoppers will have a direct path to purchase without interrupting their flow. It’s a really exciting change for commerce.”

With over 700 million weekly active users on ChatGPT, the opportunity for Shopify merchants is enormous: to be present at the moment of inspiration, when a user is looking for recommendations or comparing products.

Instant Checkout and the Agentic Commerce Protocol

At the heart of this partnership is the new “Instant Checkout” functionality, powered by the Agentic Commerce Protocol, a technology developed with Stripe and shared as open-source. This protocol enables secure and efficient transactions between users, businesses and AI agents, avoiding unnecessary steps and protecting the buyer’s privacy.

Source: Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol – OpenAI, September 2025

Some specific advantages of the shopping experience on ChatGPT include:

• Relevant and organic results: ChatGPT offers recommendations based on the user’s query, without commercial influence or payment for positioning.
• Simplicity and speed: The user can buy a product without leaving the chat, which reduces the abandonment rate and simplifies the conversion funnel.
• Secure payments: The integration allows payments with existing cards and digital wallets such as Apple Pay and Google Pay, managed by Stripe.
• Direct relationship: The merchant continues to manage payment, shipping and post-sale communication, preserving their brand identity and control over the customer relationship.

According to OpenAI, Instant Checkout only shares the minimum information necessary to complete the purchase. This way, security and user control are prioritised in every transaction.

Market Impact and Reactions

The announcement of the functionality immediately sent tech stocks soaring: Etsy rose by 16% and Shopify by more than 6%, reflecting investor enthusiasm and the project’s transformative potential. Analysts see it as a direct response to the dominance of giants like Amazon and Google, which had until now monopolised the online search and shopping ecosystem.

Conversational commerce, powered by AI, transforms chat into the new channel for discovery, inspiration and transaction, consolidating a space where users and brands can interact in a more relevant and personalised way. In the words of Reuters: 

“The partnership makes direct in-chat purchasing available to millions of people, opening a new channel that could radically change online consumption habits.”

What are the Advantages for Sellers and Consumers?

The big winner from this integration will be the digital merchant, whose potential customer base will be radically expanded. This will allow brands to directly reach potential customers who are looking for recommendations, inspiration or specific products in the chat.

For businesses using Shopify as their e-commerce solution, the partnership means:

• Immediate access to millions of active users on ChatGPT.
• A higher probability of direct recommendation, guided by AI based on the user’s tastes and interests.
• Fewer barriers to purchase: fewer steps, less cart abandonment, a higher conversion rate.

Source: Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol – OpenAI, September 2025

For the consumer, the process is simplified. They can search, compare and buy without leaving a single platform, which increases the likelihood of conversion and reduces friction. From now on, they will enjoy:

• More efficient, faster and more relevant shopping experiences.
• The convenience of searching for and comparing products within the same conversational flow.
• Security and transparency: payments are managed securely, the information shared is minimal and is under the buyer’s control.
• A wide range of products: Shopify’s global catalogue is accessible from any conversation, including brands like Glossier, Spanx and Steve Madden.

Ultimately, the integration brings e-commerce to the most natural and relevant space for the modern user: digital conversation.

How Does it Work? Next Steps and Future Outlook

Currently, the functionality is available in the US for ChatGPT Free, Plus and Pro users, allowing direct purchases from sellers on Etsy. The addition of over a million Shopify merchants is scheduled for the coming weeks. The feature currently supports single-item purchases, although OpenAI plans to add support for a multi-item cart and expand to new geographical regions.

Source: Buy it in ChatGPT: Instant Checkout and the Agentic Commerce Protocol – OpenAI, September 2025

Technical adoption for Shopify merchants is automatic: with no need for complex development or additional configurations. Merchants simply need to be registered on Shopify to take advantage of the conversational channel. Furthermore, Shopify and OpenAI have shared the Agentic Commerce Protocol technology as open-source, inviting the entire e-commerce sector to develop secure solutions for agentic transactions across various AI agents.

This development places OpenAI and Shopify in direct competition with giants like Amazon and Google, by transforming conversation into the new arena for commercial discovery and transaction.

Conclusion: The Beginning of the Agentic Commerce Era?

The partnership between Shopify and OpenAI signals the beginning of a new era where conversation and AI become the epicentre of the shopping experience. Agentic Commerce eliminates barriers, facilitates secure transactions and prioritises relevance for the user, putting a powerful, low-friction tool for selling and relationship-building in the hands of brands.

The companies that adapt will be the ones to lead the way in an increasingly conversational, mobile and personalised economy. The consolidation of this technology demands competitiveness, adaptation and a strategic vision oriented towards what’s next: a market where chat and AI are the new global shop window.

The post Shopify and OpenAI Transform E-commerce with Shopping on ChatGPT appeared first on Flipflow.

One of the most dangerous cracks in a customer’s trust opens up in the gap between your online store and your physical store. It’s a critical moment that materialises like this: a person finds a fantastic offer for your product on a marketplace like Amazon. Convinced, they go to your store to buy it, but there the price is different, the description doesn’t match, and availability is a mystery. At that very moment, their good experience is ruined. You have just damaged their trust and, almost certainly, lost a customer.

It happens every day, and it’s a disconnect that costs brands dearly. In fact, 71% of customers expect complete consistency when they interact with a company, regardless of whether it’s online or in person. Today, customers don’t distinguish between your online store, your marketplace profile, and your physical store. For them, it’s all one single brand: yours.

The key concept here is the “Digital Shelf”. Think of it as the online version of your physical shelves. It includes everything a customer sees about your product on the internet: photos, videos, the title, descriptions, price, ratings, and availability. When this digital shelf doesn’t match the physical shelf, the customer experience is broken.

This article is a practical guide to avoiding this. We’ll show you the strategies and tools needed to unify these two worlds and build a seamless shopping experience that fosters loyalty and boosts your sales.

Why is Synchronisation a Strategic Necessity, Not a Luxury?

Maintaining consistency across your sales channels goes beyond a simple aesthetic detail. It is fundamental to the growth of your business in today’s environment.

According to a survey conducted by EMARKETER in collaboration with Amazon Ads, a fifth (20%) of shoppers already use a balanced combination of in-store and online shopping.

Let’s look at the main reasons why synchronisation becomes a strategic necessity:

Strengthen trust through brand consistency

A consistent brand isn’t down to luck; it’s down to a system. The title, images, key benefits, ingredient lists or technical specifications must tell the same story wherever the consumer interacts. Consistency builds recognition, reduces friction and strengthens reputation. It also limits misinterpretations arising from outdated product details, truncated content or inaccurate translations. Consistency also protects against distributors who improvise text or use old images. A master catalogue, updated and shared with marketplaces and stores, ensures that the visual and verbal identity remains intact.

Presenting yourself as a consistent brand builds trust, and trust is the foundation of customer loyalty. Every inconsistency, no matter how small, sows a seed of doubt that can lead the customer to look to your competition.

Optimise conversion throughout the customer journey

The modern Customer Journey is complex. A customer might discover your product on a marketplace, research its features on your website, and decide to buy it in your physical store. If they encounter contradictory information at each of these steps, friction is created. A different price, a technical specification that doesn’t match, or a photo that doesn’t correspond to the real product are all obstacles that hinder the purchase decision.

Conversion rates increase when the right information appears at the right time. High-quality photos, how-to videos or size comparisons influence both the online basket and the decision at the shelf. Unifying information eliminates friction and makes it easier for the customer to move forward without hesitation to the final conversion.

Increase the operational efficiency of your teams

Duplicating tasks is a drain on time and budget. E-commerce, trade marketing and retail teams often maintain parallel versions of the same data. Someone creates content for marketplaces. Then, they adapt it for physical stores. And they create something different for their own e-commerce site. This fragmentation leads to errors. For example, the price is updated on one channel, but forgotten on the others.

By centralising and synchronising information, the manual workload is reduced, update cycles are shortened, and the risk of errors is cut. Launches reach all channels at the same time, regulatory changes are reflected without delay, and promotions are scheduled with uniform rules. Operational efficiency not only frees up resources, but it also improves the quality of execution and the speed of response to the market.

Defend and differentiate yourself from the competition

In competitive categories, the first mistake can be costly. An unanswered review, a blurry photo, or a poorly explained price difference are the perfect invitation for a competitor to capture the customer’s purchase intent.

Synchronisation allows you to detect deviations, act quickly, and maintain an advantage on the “shelf” that consumers consult daily: the search results page, the product page, and the physical shelf. Those who control the experience on both fronts can compete more on value and less on price.

The 6 Pillars of Omnichannel Synchronisation

To build a unified customer experience, you need to work on 6 fundamental areas. These are the pillars that will support your synchronisation strategy.

Pillar 1: Product content

Content is the heart of the Digital Shelf. Descriptions, technical specifications, images, videos, and user guides must be identical and of the highest quality across all your channels. A customer shouldn’t find a detailed description on your website and a poor, summarised version on the marketplace.

The problem is that this information is often scattered across different spreadsheets, folders, and systems. The solution is to centralise it. Specialised tools exist for this purpose, such as a PIM (Product Information Management) system. A PIM acts as a central brain for all your product information. From there, you can manage, enrich, and distribute all content to your different channels automatically and without errors. It’s the foundation for ensuring everyone is speaking the same language.

Furthermore, customer FAQs and observations from store staff enrich the content and reduce returns. A workflow with clearly defined responsibilities prevents gaps and ensures a consistent update schedule.

Pillar 2: Price and promotions

Price discrepancies are one of the biggest sources of frustration for customers. Do you have to have the exact same price on all channels? Not necessarily. You can have an exclusive online promotion or a special discount in the physical store. The key is that this difference is a deliberate strategic decision, not an accident due to a lack of control.

You must clearly communicate why these differences exist. Price communicates positioning, and promotions shape the perception of opportunity. To manage this effectively, you need a centralised pricing system. In physical stores, technologies like Electronic Shelf Labels (ESLs) allow you to update the prices of hundreds of products in minutes from a central system, ensuring that promotions are applied instantly and consistently with the online channel, if you choose to do so.

Pillar 3: Stock and availability

Few things disappoint a customer more than seeing a product as “available” online, buying it for in-store collection, and discovering upon arrival that it’s out of stock. This bad experience seriously damages your brand’s image.

Availability must be visible and accurate, both online and in-store. For the digital channel, synchronising inventory, safety stock rules, and replenishment times reduces stockouts and overselling. In physical retail, sharing forecasts with the point of sale and enabling replenishment alerts improves actual on-shelf presence. A click-and-collect option strengthens the bridge between channels. Including store-specific availability information on the online product page guides the customer’s journey and prevents wasted trips.

Pillar 4: Ratings and reviews

Reviews are modern-day word-of-mouth. Collecting, responding to, and systematically redistributing them activates social proof and provides quality insights . Social proof, meaning the opinions of other customers, is a powerful decision-making factor. Traditionally, reviews live in the online world. However, you can bring their power into your physical store to help undecided shoppers.

Imagine a customer in your store, hesitating between two models of headphones. Now, imagine that next to each product there is a small digital screen showing its average 5-star rating and the most helpful comments from other online shoppers. This adds a valuable layer of information at the point of sale. In turn, you can encourage physical store shoppers to leave their opinion online, for example, via a QR code on the receipt, thus enriching the review ecosystem.

Reviews also provide valuable product information. They identify recurring problems and reveal unexpected uses. They also help to improve both the product and its presentation.

Pillar 5: Data governance and compliance

Without data governance, synchronisation degrades. Defining owners for each attribute, review cycles, change traceability, and naming conventions prevents inconsistencies. Regulatory compliance demands rigour: ingredients, warnings, sizes, safety data sheets, allergens or certifications must be validated before publishing. For markets with local requirements, it is advisable to maintain country-certified variants.

Privacy and security also matter: data exchange with marketplaces and retailers under clear contracts, internal access controls, and scheduled backups. A data governance committee, with representation from marketing, legal, operations and sales, ensures continuity.

Pillar 6: Digital Shelf Analytics and enabling technologies

What isn’t measured fades away. Monitoring search result rankings, content coverage, availability, prices, share of search, conversion rate, and review sentiment allows you to prioritise efforts. Proactive alerts detect drops in visibility, unauthorised price changes, or imminent stockouts.

In terms of the technology to achieve this, a platform like flipflow, with our Digital Shelf 360 feature, will help you monitor and optimise your brand’s position and image, making an impact across all sales channels.

Want to know more about this feature? Contact us and speak to an expert about your Digital Shelf.

Practical Guide: How to Start Synchronising your Channels

Understanding the theory is important, but putting it into practice is what generates results. Here are 3 steps to start building your omnichannel ecosystem.

Step 1: Carry out an internal audit

Before investing in any technology, you need to understand where you are. Gather your teams and analyse the current situation. Ask yourself key questions: Do the prices on our website and on Amazon match? Are the product photos the same across all channels? Does our online stock reflect the reality in our stores? How long does it take us to update a product description on all sites?

Identify the biggest points of friction and the most obvious gaps. This diagnosis will allow you to prioritise your efforts.

Step 2: Centralise information with the right technology

As we have seen, technology is a great enabler. A PIM system is essential for centralising product content. Similarly, a DAM (Digital Asset Management) system acts as the guardian of all your digital assets: photos, videos, logos, and documents. And a platform that helps you to monitor your Digital Shelf performance in a unified way, like flipflow, will be the cornerstone for optimising and increasing your brand’s visibility across all channels.

These systems connect to your sales channels via APIs (Application Programming Interfaces), which act as bridges that allow different programs to communicate with each other automatically. Investing in a solid technological foundation will save you problems in the future.

Step 3: Break down organisational silos

Synchronisation is not just a technological challenge; it’s also a cultural one. The best technology will fail if the e-commerce, marketing, and store operations teams work in isolation, each with their own objectives. It is essential to foster collaboration.

Teams must share information and work towards common goals. For example, the success of a “click and collect” strategy depends as much on the online team that promotes it as it does on the store team that prepares the order. A collaborative culture is the engine that drives a true omnichannel strategy.

Conclusion: Building an Ecosystem, Not Just Sales Channels

The customer moves between screens and aisles without thinking about organisational charts. They expect clarity, availability, and a promise that is kept. A Digital Shelf that is synchronised with physical retail organises the narrative, gives coherence to the proposition, and allows for precise competition. The effect is noticeable in conversion metrics, in the workload of the teams, and in the brand’s reputation.

This path requires discipline: auditing rigorously, centralising with technology that serves the business, and coordinating the people who bring the experience to life. The investment pays off for years because it reduces errors, accelerates launches, and strengthens ties with customers and distributors. A well-assembled ecosystem learns from every interaction, responds faster to changes, and turns every touchpoint into a reinforcement of the same story.

The shelf and the algorithm don’t compete. When they are synchronised, they complement each other. The physical shelf benefits from dynamic content, and the digital presence gains credibility thanks to flawless in-store execution. That is the advantage that separates those who survive from those who lead: a brand that is recognised, convinces, and delivers, wherever the consumer decides to buy.

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Juice continues to be a staple on the Mexican table: it accompanies breakfasts, snacks and ‘on-the-go’ moments. Demand is concentrated in three main product families that share the same aisle: 100% juice (made from fruit), juice drinks (with a lower fruit concentration) and functional options like green juice. 

After several years of inflationary pressure, shoppers are comparing more, valuing the price per litre and reacting to clear signs of savings. At the same time, there is growing interest in lower-sugar alternatives, practical packaging for home use and formats that make portion control easier. 

Against this backdrop, three levers are defining the competition on physical and digital shelves: promotional activity, the choice of formats and investment in Retail Media. Based on our recent report on the Mexican juice market, we are focusing on these three levers to understand what works, where and why.

The Promotional Landscape: Restrained Strategies with Selective Impact

Promotional presence

Promotional mechanics in the Mexican juice market reveal a scenario where moderation prevails over commercial aggressiveness. With just 13.6% of SKUs on promotion during the period analysed, the industry is demonstrating a cautious approach to discounts.

The 100% Juice category leads promotional activity with 21.9% of products on offer, followed by Juice Drinks with 18%. In contrast, Green Juice maintains a virtually unchangeable position with only 0.9% promotional penetration. This reinforces its positioning as a premium category where the price reflects the perceived value.

The takeaway is straightforward: where competition for volume is most intense—100% juice—offers are used more frequently. At the other end of the spectrum, green juices support the purchasing decision based on attributes (perceived benefits, formulations, consumption occasions) rather than on discounts.

Promotional mechanics

The way products are promoted also varies by category. In 100% juice, the ‘2-for-X’ mechanic dominates, accounting for 44.12% of recorded activations. Direct discounts represent 26.47% and ‘small quantity’ promotions 17.65%, while combos and ‘mix and match’ offers make up the rest with a marginal presence. 

In juice drinks, the order is reversed: direct discounts lead with 32.14%, followed closely by ‘small quantity’ (28.57%) and ‘2-for-X’ (28.57%) offers. In green juice, the pattern is even clearer: direct discounts lead with 35.90% and ‘small quantity’ promotions account for 33.33%; ‘2-for-X’ offers drop to 23.08%.

The operational conclusion is simple: the category responds best to concise messages that are immediately noticeable on the receipt. When the objective is to move volume in family-sized formats (1 litre), the ‘2-for-X’ offer works well. When the value lies in proposing specific benefits or encouraging trial, direct discounts and limited quantity promotions are more effective.

Differences by retail chain

The differences between retail chains confirm that it is not a level playing field. H‑E‑B records the deepest discounts, with price drops reaching up to 36.44% in the cases analysed. Chedraui follows with an observed maximum of 27.80% and Soriana with 23.39%. Bodega Aurrera and Walmart operate in more moderate ranges (20.69% and 16.15%, respectively), consistent with everyday competitive pricing strategies and tactically selected peaks. La Comer is the retailer with the most restrained approach (7.05%). 

This complex picture calls for planning by location and by chain: the same SKUs perform differently if the depth of discount, the campaign calendar and the in-store exposure are not adjusted to the context.

Format Analysis: Tetra Pak Dominates and Defines Value Perception

Packaging and size dictate choice. In 100% juice, the format with the greatest presence is the 960 ml Tetra Pak, followed by the 475 ml Tetra Pak; the 200 ml and 125 ml mini-cartons maintain their place for children’s occasions and school lunchboxes. 

In juice drinks, the 1-litre Tetra Pak is the anchor format, with PET or glass bottles between 500 ml and 1 litre as a secondary alternative; among the smaller sizes, the 125–330 ml Tetra Pak appears with limited exposure. 

In green juice, the 960 ml Tetra Pak leads again, and less traditional variants are emerging: powdered concentrates in 300–500 g formats and frozen options for smoothies (600 g–1 kg), designed for consumers looking to personalise their drinks or who prioritise freshness.

This pattern favours brands that build their portfolio around the one-litre format. The 960 ml–1 L size strikes a balance between price per litre, convenience at home and the ease of creating simple promotional mechanics. Half-litre and individual formats serve tactical functions: immediate consumption, portion control and entry-level pricing. Cans and pouches or flexible packaging continue to have low traction in the traditional juice aisle, which suggests being selective with their distribution and reserving them for channels or occasions where they offer a real advantage.

When the promotional strategy is based on formats, coordination makes the difference

In 100% juice and juice drinks, the 1-litre format is a natural candidate for ‘2-for-X’ or ‘small quantity’ promotions because the saving is easily understood and the impact on volume is significant. For individual formats, a direct discount with a rounded final price is usually clearer for the shopper and better protects the margin. In green juices, the objective is to lower the barrier to entry: a controlled discount on the 960 ml Tetra Pak or a trial price for concentrates and frozen products, accompanied by messaging about servings per pack, can accelerate adoption.

It also helps to manage the breadth of the portfolio in each store. Too many SKUs in slow-moving sizes dilute visibility and make execution more expensive. The report underlines that management by SKU, retailer and city is a key control point: optimising the mix at each point of sale increases the effectiveness of promotions and of any media investment within the retail environment.

Retail Media: Defending Generic Terms and Focusing on Key Retailers

Investment in Retail Media accompanies and amplifies pricing and format decisions. When analysing the word “juice” on the main retailer websites, the investment ranking places Jumex first, followed by Del Valle, Gerber, Clamato and Herdez. These players prioritise high-reach environments: Walmart and Bodega Aurrera account for a large part of the budget for Jumex, Del Valle and Gerber, with additional spend in Sam’s Club for family-sized formats. Clamato diversifies into H‑E‑B and Soriana as well as Walmart, and Herdez stands out for its focus on Bodega Aurrera.

The logic is clear: defending generic terms captures shoppers who enter the category without a specific brand in mind, avoids ceding share to competitors and protects the space from own-brand labels. Coordinating paid visibility with price activations improves demand capture. When a ‘2-for-X’ offer is active on 1-litre formats, for example, sponsored positions in search and in the category listings increase the incremental reach of the message and reduce the effective cost per click. It is also advisable to adjust bids by location according to available stock and the promotional aggressiveness of each chain. An investment model that ignores stockouts or limited inventory wastes traffic and damages the shopping experience.

Keywords can also map to usage occasions. Terms like “1 litre juice”, “juice for kids”, “green juice” or “no added sugar” connect the format and value proposition with search intent. When building campaigns, it is useful to think in three layers of visibility: search defence (generic and brand terms), presence on the virtual category shelf—especially for anchor formats—and display ads during promotional weeks to capture additional traffic.

Operational Keys by Category: From Plan to Shelf

The data reveals a market that is becoming more sophisticated, where traditional volume-driven strategies coexist with more refined approaches to value creation. 

For 100% juice, the basic plan is built around the 960 ml Tetra Pak. The most effective combination is everyday competitive pricing with well-scheduled ‘2-for-X’ windows, supported by Retail Media on generic terms. Mini-cartons, grouped in multipacks, can be supported by ‘small quantity’ promotions to facilitate top-up purchases and keep the brand present in the lunchbox.

For juice drinks, the 1-litre format is the decisive price point for in-home consumption. A moderate direct discount, alternated with weeks of high exposure on the retailer’s e-commerce category page, helps to drive trial without damaging the margin. Individual bottles work as an extension for on-the-go occasions, especially when the flavour and variety messaging is well-executed.

For green juice, the priority is to accelerate adoption and repeat purchases. A clear direct discount on the 960 ml Tetra Pak, supported by ‘small quantity’ promotions during high-traffic periods, reduces friction. For concentrate and frozen variants, it is advisable to highlight the yield per portion and functional benefits, relying on specific keywords rather than generic terms where competition is intense.

Conclusion: Coordinating Three Levers to Gain Market Share Profitably

In short, the juice market in Mexico is defined by well-coordinated tactical decisions. Promotions carry weight, although their scope is limited: only 13.6% of SKUs are on offer, with more activity in 100% juice and less in green juice. The 1-litre Tetra Pak acts as an anchor due to its value-per-litre proposition and its suitability for volume-driving mechanics. Investment in Retail Media ultimately sways the shopper’s choice when it defends generic searches and coincides with promotional windows at the highest-reach retailers.

The path to growth lies in coordinating price, packaging and visibility by chain and by location. Prioritising anchor formats, using simple mechanics and supporting them with in-retail media drives turnover and market share without damaging margins. With a disciplined test-and-learn approach, brands can scale what works and quickly correct course. The sector is moving towards data-driven decisions, more personalised offers and building perceived value, which favours those who develop omnichannel capabilities and a nuanced understanding of the digital consumer.

Want to know more? Download the full report by clicking on this link.

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