Just a few years ago, generative AI was more novelty than necessity. It was a fascinating experiment that made us stop and say, “Wait, a computer wrote that?” 

I remember testing out ChatGPT when it first launched and being amazed that it could string together paragraphs that not only made sense but actually sounded human.

Fast forward to today, and GenAI has moved well beyond a fun curiosity. It’s woven into everyday decision-making, helping people plan vacations, decide what’s for dinner, and, increasingly, discover and choose the products they want to buy.

According to a recent Prosper Insights & Analytics survey, nearly one-third of U.S. adults already use AI tools to assist with everyday decisions, and Adobe also reported a 1,200% spike in AI-driven referral traffic in just eight months, showing just how quickly AI is shifting digital behaviors.

And it’s not just consumers. B2B buyers are adopting GenAI-powered search at an incredible pace. G2 recently announced that 8 in 10 business decision-makers believe AI search has already changed how they conduct research, and almost one-third say they now start their search on platforms like ChatGPT more often than Google.

The way people find information, products, and brands is fundamentally changing, and businesses need to adapt and grow in order to keep up.

The Impact of GenAI Search on SEO

For decades, SEO (Search Engine Optimization) has been the backbone of digital marketing and product search, with ranking high on Google synonymous with being discoverable. But GenAI is rewriting the rules of the game. 

Instead of scanning a list of blue links, people now receive AI-generated answers that summarize content from across the web. Google’s AI Overviews, ChatGPT responses, and other GenAI-driven platforms often present concise, synthesized answers, which means users don’t always click through to websites.

We’re already starting to see the impact of these AI-powered search experiences. Some industries are reporting organic declines of 15 to 64 percent as a direct result of Google’s AI Overviews. Meanwhile, nearly 80 percent of search users admit they rely on AI-generated summaries, often turning to them for quick answers rather than digging deeper into websites. Even on traditional search engines, almost 60 percent of queries now end without a single click because the answer is provided upfront without needing to click a link. 

Taken together, these shifts show that while SEO still matters, it’s no longer enough on its own. To stay visible in this new landscape, businesses need to expand their strategy to include what’s called GenAI Engine Optimization (GEO).

What is GenAI Engine Optimization (GEO)?

If SEO is about optimizing your content to rank higher on search engines like Google, GEO is about making your brand and products discoverable within generative AI platforms.

Large language models (LLMs) like ChatGPT, Claude, and Gemini aren’t just crawling keywords; they’re synthesizing knowledge from a wide range of sources. They’re looking for clear, structured, authoritative information that they can confidently surface in their generated answers.

Put simply: GEO is the practice of shaping your product information, content, and digital presence so that GenAI engines can easily find, understand, and include your brand in their outputs.

How to Optimize for GEO

Optimizing for genAI isn’t about throwing out everything you know from SEO, but about evolving your approach to meet the way people (and machines) now search. Traditional search engines rewarded keyword alignment and link-building strategies, but large language models like ChatGPT, Gemini, and Perplexity operate differently. They prioritize structured, reliable, and authoritative data they can confidently summarize and recommend to users. So let’s take a look at a few ways businesses can start optimizing their data for genAI engines.

1. Understand where and how you already appear in LLMs

To begin optimizing your presence in generative AI platforms, you first need to understand your starting point – how AI actually “sees” your brand and products. A simple way to do this is by running test queries in tools like ChatGPT, Gemini, or Perplexity: Are your products showing up? Are they described accurately? 

This baseline check often reveals gaps, such as incomplete data, outdated details, or even total invisibility. This is where a solution like Akeneo’s AI Discovery Optimization can become incredibly helpful as it allows you evaluate precisely how your products appear in AI-driven search experiences.

With these insights, businesses can move beyond guesswork and better understand not only whether your products surface but also why, pinpointing missing attributes, inconsistent formatting, or content that doesn’t match how customers search in natural language. Armed with clear, actionable recommendations, you can enrich product information strategically, improve visibility across platforms like ChatGPT and Perplexity, and ensure your products are represented accurately and persuasively in the fast-growing world of generative AI search.

2. Focus on creating structured, organized, and well-labeled product content

AI thrives on structure, and the clearer and more consistent your product data is, the more likely it is to be understood and referenced by large language models. Standardizing attributes such as product titles, descriptions, categories, and metadata helps create a consistent framework that AI can easily interpret. 

Equally important is labeling data clearly through schema markup and structured product information, which gives AI stronger signals about meaning and context. Eliminating ambiguity is also key, as vague or incomplete descriptions can easily be misinterpreted by generative AI systems, and lead to poor visibility or inaccurate representations of your products.

This is where Product Information Management (PIM) systems like Akeneo can play a vital role. By centralizing product data in one place, ensuring accuracy, and maintaining consistent formatting across channels, PIM makes your content both customer-friendly and AI-ready. A well-structured data foundation not only improves the way your products are presented to human audiences but also enhances their chances of being accurately referenced and recommended by AI-powered search and discovery tools.

3. Create a system for data consistency and clarity

Inconsistent or unclear data doesn’t just confuse customers, it also undermines how generative AI tools understand and present your products. If the same item looks different on your website, in a marketplace, and on a partner channel, AI may struggle to connect the dots, which can weaken your presence in search results and reduce discoverability.

That’s why consistency and clarity matter. Product attributes should remain uniform across all platforms, using plain and descriptive language that AI can easily parse. For companies operating internationally, ensuring multilingual consistency is equally important to prevent misinterpretation across regions. At the end of the day, AI tools are only as strong as the data they’re trained on, so the clearer and more reliable your product information, the stronger your visibility will be in GenAI-driven discovery.

4. Publish authoritative, original content

GenAI engines are designed to pull information from trusted, authoritative sources, which means that if you want your brand to appear in AI-generated answers, you need to establish yourself as one of those sources. Building authority starts with publishing original, high-quality content that demonstrates your expertise. Thought leadership pieces such as articles, whitepapers, and research can position your brand as a go-to resource within your industry, while product-focused content like guides, FAQs, and explainers ensures that customers—and AI tools—have clear, accurate information to draw from.

Credibility also plays a critical role. Incorporating citations, references, and expert commentary signals to both human readers and AI systems that your content can be trusted. The stronger and more authoritative your content, the more likely it is to be surfaced by generative AI engines when users are searching for answers.

The Future of Search and Discovery

Generative AI has already reshaped the digital landscape, turning search into a conversation and discovery into an AI-driven experience. For businesses, this shift presents both a challenge and an opportunity: the challenge of declining organic traffic through traditional channels, and the opportunity to stand out in the new environments where buyers are making decisions.

GEO provides the bridge between yesterday’s SEO playbook and today’s AI-powered reality. By investing in structured product data, ensuring consistency and clarity across channels, and publishing authoritative, trustworthy content, businesses can position themselves not just to survive this shift, but to thrive in it. 

Search has always been about connecting people with the right information at the right time. What’s different now is the medium. The organizations that embrace GEO will not only remain discoverable in this new era, they’ll build stronger, more trusted connections with customers who are increasingly guided by AI.

In a relationship, any therapist will tell you that clear communication is the foundation for success. Both people need to understand each other fully, or misinterpretations and frustration quickly follow. 

The same holds true for a relationship between a customer and a business; if the customer doesn’t receive clear, consistent, and accurate information, trust begins to erode and the relationship starts to break down.

The unfortunate reality is, this miscommunication and misalignment happens all too often. In the past year alone, two-thirds of consumers abandoned a significant purchase because product information was missing or inaccurate. That’s a direct hit to revenue, trust, and retention. Customers can’t make confident decisions when the details they need aren’t there, and in today’s competitive market, they won’t hesitate to move on to a competitor who delivers the clarity they expect.

But how exactly does poor product data damage sales, and what exactly does ‘poor product data’ even look like in the market? Let’s break down the biggest ways poor content holds brands back and how you can avoid these costly pitfalls!

What Does Poor Product Data Look Like?

Before we can fix bad data, we need to know how to spot it. Poor product information isn’t always obvious—it doesn’t just mean missing details. It shows up in many ways across the customer journey, each with the power to lessen a customer’s commitment to a product or even worse, a brand.

It might look like incomplete specs that leave shoppers guessing about size, fit, or compatibility. Or inconsistent pricing between your website and marketplace listings that makes buyers suspicious. It can even be vague or generic descriptions that make your product sound like everyone else’s, or worse, conflicting details across channels that create confusion instead of clarity!

How Poor Product Data Impacts Your Business 

As we all know, today’s shoppers expect clarity, relevance, and transparency, and when businesses fall short, the consequences are immediate and costly. From abandoned carts and missed conversions to rising return rates and eroded loyalty, poor product data doesn’t just create frustration; it directly translates into lost sales and weakened customer trust.

1. Rising Dissatisfaction Hurts Conversions

Customer patience with poor product information is wearing thin. In 2023, only 13% of consumers said they were dissatisfied with the comprehensiveness of product data, but by 2025, that figure jumped to 30%, more than doubling in just two years. As frustration rises, shoppers won’t hesitate to abandon their carts, turning poor product data directly into lost sales.

2. When Details Are Missing, Purchases Disappear

As we mentioned earlier, a lack of product information led two-thirds of shoppers to give up on a major purchase within the past year. And the damage doesn’t just stop there. Globally, we found that 77% say they’d even consider switching to a lower-cost, lower-quality alternative if the right details weren’t available. Essentially, your poor product data can result in your cheapest competitor making a sale!

3. Returns Reveal the High Price of Inaccurate Data

Two-fifths of consumers globally returned a product in the past year because the details didn’t match the reality. Returns like these drain your bottom line and your credibility, leaving customers cautious instead of confident.

4. Generic Content Pushes Shoppers Away

One-size-fits-all descriptions don’t persuade modern shoppers. Customers expect details that speak to their needs, values, and preferences. Without relevance, they disengage and abandon businesses. It’s no surprise that over half of consumers say they would become more loyal to brands that provide personalized shopping experiences. In today’s world, generic content fails to keep shoppers’ attention, costing long-term relationships.

5. Lack of Transparency Leaves Money on the Table

Today’s consumers want brands to reflect their principles. Yet brand values, sustainability claims, and supply chain transparency are consistently rated among the least comprehensive areas of product information. The cost of this omission is high: 42% of consumers say they would pay more if a brand clearly shared its values, with those willing to do so prepared to spend an average of 25% extra. Without a doubt, customers will spend less and look elsewhere for a brand they can believe in.

What Does Good Product Data Look Like

So, we know what bad product data looks like, and how it can impact your business. But now, we need to take a look at what good product data looks like – and how you can provide it to customers.

1. Accurate and Consistent

Make sure every detail, from pricing to specs to compatibility to availability, matches across all channels. Customers see the same story whether they’re on your site, browsing a marketplace, or shopping in-store. Utilizing a syndication tool such as Akeneo Activation can allow you to automatically send enriched, accurate, and reliable product information to every channel and marketplace, ensuring consistency and a unified product experience wherever your shoppers engage!

2. Completeness That Builds Confidence

Good product data answers every key question before it’s asked. Size, dimensions, technical features, and compatibility details are all covered, leaving no room for hesitation!

3. Enrichment With Context

Beyond the basics, strong product content includes sustainability credentials, allergen or nutritional details, brand values, and more. These enrichments should meet the rising demand for context and authenticity.

4. Personalization and Relevance

Great product data adapts to the customer! Tailored recommendations, personalized messaging, and content that reflects prior behaviors all turn information into a driver of loyalty. But in order to provide such a tailored experience, you need to understand what your customers want, and how they actually speak about and use your products. This is where a tool like Akeneo’s PX Insights can come in handy. By pulling in real-time insights from real customer feedback directly into your product information management solution, you’ll be able to personalize your product data to match customer language and expectations.

5. Visual and Multimedia Support

High-quality images, videos, and other types of media help customers understand products better than words ever could. And the future of product storytelling goes even further. Virtual reality (VR) and augmented reality (AR) tools allow shoppers to interact with products in immersive ways, seeing how furniture looks in their living room or visualizing clothing fit in 3D. Rich visuals close the gap between physical and digital experiences.

How PIM Can Help

Now that we have a proper understanding of what sets good product content apart from bad product content, the question becomes, how do you provide high-quality product data to customers? 

The truth is, many businesses struggle to provide consistent, reliable product data to consumers because their product information is scattered across spreadsheets, legacy systems, or siloed teams, which makes errors inevitable. Without a centralized way to manage content, those gaps only widen, causing even more damage to your business.

This is where a Product Information Management (PIM) solution changes the game. By creating a single source of truth, a PIM ensures every detail is accurate, complete, and consistent, no matter where customers encounter it. Beyond solving errors, PIM scales your content across channels and integrates with enrichment technologies like AI to tailor and enhance experiences for individual shoppers. Pair that with personalization and the result is clear: higher conversions, fewer returns, and stronger customer loyalty in an increasingly competitive market!

Turning Product Data Into Growth

Poor data creates friction, and in today’s competitive market, where customers are less forgiving, they won’t wait around for you to fix it. They’ll turn to a competitor who delivers the clarity they need.

The good news is, businesses don’t have to settle for disjointed, error-prone content. With the right strategy and the right tools, you can transform scattered, inconsistent data into a powerful driver of conversion, satisfaction, and long-term growth. Put simply: clear communication builds trust in personal relationships, and it does the very same in commerce. If you want to win customers—and keep them—start with better product data.

 Want to dive deeper into how consumer expectations are evolving? Download our latest Consumer Survey Report to discover the full findings and uncover what today’s shoppers really need from your product information.

In today’s world, information is everywhere, but consistency is rare. As organizations expand across channels, regions, and customer touchpoints, the cracks in disconnected or poorly managed data systems become harder to ignore.

That’s why many businesses are turning to MDM and, more importantly, a clear MDM strategy to bring order to the chaos. Without a strategic approach, even the most advanced tech stack can result in conflicting product information or missed opportunities for personalization and efficiency.

So, what does a successful MDM strategy actually look like? Let’s dive in so you can get a better idea of what it is, why it matters, and how you can implement it within your own business!

What Is Master Data Management?

More than just a technology solution, Master Data Management (MDM) is a holistic approach that creates and manages a single, consistent, and accurate source of master data across an organization, ensuring accuracy and uniformity. It’s the framework that ensures your organization’s core data, including customer, product, supplier, and other information, is accurate and accessible to all who need it.

By centralizing and standardizing master data, MDM helps create a unified view that supports everything from day-to-day business processes to long-term strategy. The goal is to eliminate data silos and provide a single source of truth that teams across departments, like marketing, sales, and operations, can rely on to work with the same set of accurate data.

What Is a Master Data Management Strategy?

Essentially, an MDM strategy is the blueprint for how your organization implements and governs MDM over time. It’s a structured, long-term plan that defines how you’ll collect, maintain, and scale your master data across systems and stakeholders, ensuring alignment between people, processes, and technology. 

A strong MDM strategy defines everything from the role of data stewards and data governance policies to how MDM integrates with your broader business processes, tech stack, and operational goals.

Key Aspects of a Master Data Management Strategy

Whether you’re just getting started or rolling out enterprise-wide MDM programs, your strategy is the foundation that keeps your data initiatives focused, scalable, and future-ready.

Here are the key ingredients that turn MDM from simply a word into a competitive advantage:

1. Data Governance

At the heart of any MDM strategy is a strong data governance framework. This includes the rules, policies, and standards that define how data is created, maintained, accessed, and retired across the organization. Governance provides clarity around who owns which data domains and ensures all departments are aligned on how to manage that data.

It’s not just about structure, it’s about your sanity. With the right policies in place, it reduces the risk of data silos and duplication, supports regulatory compliance, and ensures your master data remains a trusted foundation for operations and decision-making. Without it, even the best MDM solutions are at risk of becoming disorganized and underutilized.

2. Data Stewardship

Data stewards play a crucial role in translating your MDM strategy into day-to-day results. They’re responsible for upholding data quality standards, resolving discrepancies, and applying the governance rules to ensure every piece of master data meets your organization’s standards. Think of them as the quality control team for your information infrastructure!

Stewards also serve as the bridge between business and IT teams, translating techy rules into real-world workflows. They make sure the data used in analytics and operations is not only accurate but also relevant and accessible, and their involvement helps drive accountability and fosters a culture of data ownership, critical for the long-term success of any brand.

3. Data Quality

No strategy can succeed without a strong focus on data quality. Complete and consistent data ensures that your operations run smoothly, your analytics are trustworthy, and your customers have accurate and up-to-date information at every touchpoint. Without it, you’re essentially flying blind—or worse, making decisions based on flawed assumptions.

An MDM strategy should include tools and processes that actively monitor and improve data quality over time. This can include validation rules and duplicate detection. When done right, you get accurate analytics, smoother operations, and fewer customer support emails that start with “this isn’t what I ordered.” Everyone wins!

4. Data Integration

Your MDM implementation doesn’t get to live on an island. It needs to shake hands with your ERP, CRM, PIM, and any other acronym-heavy platforms you rely on to keep the business running! If your systems don’t talk to each other, your data won’t either. This ensures that accurate data flows consistently across the organization. Without strong integration, data fragmentation persists, and your MDM efforts risk becoming disconnected from the tools your teams rely on regularly.

A fully integrated MDM approach helps maintain a unified view of your business, where product, customer, and supplier data stay consistent, no matter where they’re being used. 

5. Data Security

As data volumes grow, so do the risks. A robust MDM strategy must include clearly defined security policies to protect sensitive data, particularly customer information, from breaches, misuse, or non-compliance. This includes setting access controls, encryption protocols, and role-based permissions that limit exposure and ensure data integrity.

In addition to protecting data from external threats, your security measures should also support compliance with data privacy regulations like GDPR or CCPA. A secure MDM strategy gives your teams the freedom to work confidently and your customers peace of mind that their data isn’t being passed around.

Challenges of Implementing an MDM Strategy

Even the best MDM strategy can run into roadblocks on the way to becoming a reality. Here are some of the most common (and frustrating) challenges organizations face when trying to get their master data management efforts off the ground:

1. Internal Silos and Misaligned Teams

It’s hard to manage data as “one version of the truth” when every department is working off its own. Without alignment across teams, MDM can be challenging if priorities, data definitions, or ownership responsibilities are unclear or inconsistent.

2. Legacy Systems and Integration Challenges

Many organizations rely on outdated or rigid legacy systems that weren’t built with modern data integration in mind. Connecting these systems to a new MDM solution can require significant customization, which increases cost and complexity.

3. Pre-Existing Data Quality Issues

Introducing an MDM strategy doesn’t instantly resolve poor data quality. Many organizations begin with master data that is already inconsistent or duplicated, issues that must be addressed early on to avoid carrying old problems into a new system.

4. Ongoing Governance and Maintenance Requirements

MDM is not a one-time project, it’s an ongoing commitment. Effective data governance and data stewardship must be continuously maintained to adapt to changing business processes and new data sources.

MDM and PIM

While Master Data Management provides a centralized approach to managing an organization’s core data, Product Information Management (PIM) focuses specifically on product data: descriptions, attributes, images, translations, and channel-specific content. PIM systems are purpose-built to enrich and distribute product information across platforms such as eCommerce.

Together, MDM and PIM form a powerful combination. MDM creates consistency and control over foundational data across systems, while PIM delivers the flexibility and depth needed to manage complex, customer-facing product content. When integrated, they help ensure high-quality, accurate product experiences while aligning with your broader data management strategy!

Laying the Foundation for Long-Term Data Success

A successful Master Data Management strategy is more than a technical initiative, it’s a business-critical investment in accuracy and agility. From improving data quality and supporting governance to enabling better decision-making and scalable growth, MDM plays a foundational role in how organizations manage their most valuable data assets.

While the journey can involve challenges, establishing the right strategy, supported by the right tools and people, sets your business up for long-term success! And when paired with a focused solution like PIM, MDM becomes even more powerful, turning consistent data into a true competitive advantage.

Here’s a question that is crucial to the success of any business, but can be incredibly difficult to answer:

What matters most to today’s shoppers?

It may sound like a simple question, but in reality, the path to purchase has become more complex than ever.

Shoppers move fluidly between digital and physical channels, engaging with an average of six touchpoints before making a purchase. In fact, 73% of consumers use multiple channels throughout their shopping journey. This modern shopper is not only more informed but also more values-driven, weighing factors such as brand ethics, sustainability, and authenticity alongside price and product specs.

And of course, adding to this complexity is the growing, and sometimes contradictory, role of AI in shaping the customer experience. Many consumers express hesitation and even skepticism about how companies deploy AI, worrying about misuse, privacy, or impersonal automation. Yet, at the same time, they increasingly expect deeply personalized, intuitive, and seamless experiences; demands that are, in large part, powered by AI. 

With all of that in play and more, it’s no wonder that a simple six-word question becomes convoluted and nuanced. That’s why we set out to find real answers by surveying 1,800 consumers across eight countries (the United States, United Kingdom, Germany, France, Netherlands, Sweden, Australia, and Italy) to get a clearer view of what today’s shoppers value, what turns them off, and how businesses can better meet their expectations. 

You can check out the full report of our findings here, but let’s take a quick look at some of the key insights into the modern shopper that we uncovered.

7 Key Insights Into the Modern Shopping Journey

1. Inaccurate Product Information Hurts Sales

Shoppers expect up-to-date, accurate product information, and they won’t hesitate to walk away when it’s missing. In our survey, we found that two-thirds of consumers worldwide were reported to have done this in the last year, showing that weak product information directly fuels lost trust and brand switching.

The good news is that it’s not all doom and gloom; we also found that almost half of all consumers say they would pay more if retailers offered complete, high-quality product information—on average, about 25% more per product. For brands, the answer is simple: investing in richer, more reliable product data, fueled by Product Information Management (PIM), leads to both increased revenue and stronger customer relationships.

2. Personalized Shopping = Stronger Customer Bonds

A tailored shopping experience streamlines the process and encourages higher spending. Two-fifths of consumers say they would pay more for personalization, on average, about a quarter more when interactions feel relevant. For brands, building these touchpoints is a proven way to increase value with every purchase.

The benefits don’t stop at revenue. Over half of consumers say they would become more loyal to a brand or retailer that offers a personalized experience, proving that relevance builds stronger relationships as well as bigger baskets. In a crowded marketplace, meaningful experiences set brands apart, turning one-time visitors into repeat customers and long-term advocates.

3. AI Tools Are Guiding Shoppers to the Right Choice 

AI-powered shopping tools, whether virtual assistants, virtual reality, or chatbots, are becoming trusted guides in the buying journey. By quickly surfacing relevant specs and features, they remove friction and give customers the confidence to move forward with a purchase.

In markets like France, where 40–45% of shoppers are interested in AI-driven tools such as AI agents or voice assistants that can list features and answer questions, the potential is clear. By simplifying complex choices, these tools empower shoppers to make decisions quickly and confidently.

Read about Akeneo’s most recent AI capabilities in our latest summer release. 

4. Free Returns Are the New Expectation

Incorrect or incomplete product information does more than just cost a sale. The lack of the two can bring a product right back to your warehouse. In the past year, two-fifths of consumers have sent their items back because the pre-purchase information didn’t match reality. That’s a clear sign that terrible content has terrible consequences long after checkout.

And shoppers aren’t forgiving when it comes to return policies. Two-thirds feel negatively if a retailer charges them for returns, while only a small fraction are understanding. The takeaway here? Getting product content right the first time is critical to avoid operational headaches as well as protect your margins and preserve your customers’ confidence.

5. Sustainable Values, Sustainable Revenue

Today’s shoppers want their purchases to reflect their values, and they look for transparency as reassurance. Yet brand values like sustainability or regulations compliance, as well as nutritional information, supply chain practices, and even influencer testimonials, are still rated as some of the least comprehensive parts of product content.

That gap presents a real opportunity. 42% of consumers say they would pay more if brands clearly shared their values as part of product information, and those who would are prepared to spend an average of 24% more. In fact, over a third would even pay more than 10% extra because transparent commerce is enough to justify a higher price. More than an ethical stance, brand values are a requirement for expanding your customer base.

6. How Influential Voices Steer Shoppers’ Choices

User reviews stand out as one of the most powerful forces guiding purchase decisions. Globally, two-thirds of consumers have bought a product based on comments or feedback from other shoppers, making reviews even more influential than expert or influencer endorsements. Authentic content builds confidence in a way polished product pages alone can’t.

We also found that influencers still play an important role, with more than half of consumers saying they’ve made a purchase based on their recommendations, especially in categories like beauty, skincare, supplements, and sports equipment. But when it comes to credibility across categories, reviews often carry more weight. In France, for example, 67% of shoppers say user comments have swayed their purchases.

This influence extends beyond impulse buys. Nearly half of global consumers say they would be more likely to purchase decorative items, cultural products, sports gear, or luxury goods if they saw candid reviews or demonstrations from similar shoppers. The message is clear: social proof, especially those like user reviews, remains one of the strongest drivers of buying behavior worldwide.

7. In-Store and Online Work Hand in Hand for Today’s Buyer

Today’s shoppers don’t rely on a single channel when making purchases. In fact, general and specialty retail stores (30%) and online marketplaces (27%) rank as the most common shopping destinations, while for product discovery, consumers lean heavily on traditional search engines (26%) and marketplaces (22%). Bouncing between these channels highlights how important it is for brands to deliver accurate, consistent product information across every touchpoint, whether digital or physical.

But availability alone isn’t enough—experience matters just as much. Shoppers expect free delivery (38%), free returns (33%), and an easy return process (28%) as part of the standard retail package. Inconsistent product information and poor service drive customers away, while consistency and flexibility keep them coming back.

Turning Insights Into Action

Today’s B2C shoppers demand accuracy, personalization, and seamless experiences across every channel. They expect brands to deliver reliable product information, reflect authentic values, and connect with them through relevant, meaningful content. Miss these expectations, and you risk weakening one of the strongest foundations for growth—long-term trust.

The findings from our B2C survey show that accurate product information is key to growth. Brands that treat it as a strategic asset and maintain consistency across touchpoints won’t just win the sale, they’ll win loyalty, advocacy, and a competitive edge in a crowded market.

For a deeper dive into the trends and statistics shaping the global B2C landscape, download our latest 2025 B2C Consumer Survey Report and learn how to place your brand ahead at the moments that define customer decisions.

If there were one word that perfectly captures the essence of product detail pages (PDP), it would be transparency. These pages lay it all out, offering a clear and comprehensive view of what a brand is really offering. Just by scrolling through one, you’re immediately presented with an intricate mix of information, the kind you want to know as a curious shopper and the kind you need to know to make a confident purchase.

However, there’s far more happening behind the scenes than its simple appearance suggests. For a page to look seamless and engaging on a customer’s screen, it takes careful planning, thoughtful design, and ongoing maintenance. Let’s see what makes an effective PDP!

What is a Product Detail Page (PDP)?

At its core, a product detail page is designed to inform. It provides essential facts like product descriptions, pricing, specifications, images, and availability, and should include customer reviews, size and color options, shipping costs, and an easy‑to‑spot add to cart button. Basically, all the essentials that customers need to make informed purchase decisions! 

In practice, a PDP does so much more. It answers questions. It tells stories. It builds trust. And ultimately, it gives customers the information they need to hit “Add to Cart” with confidence.

A well‑designed product detail page is where interest turns into action. This is often the page where shoppers decide whether to proceed with a purchase, making it a critical driver of sales and customer confidence. By consolidating everything from product specifications, availability information, reviews, and other supporting content, a PDP equips customers with reassurance and confidence.

Beyond influencing individual buying decisions, PDPs also play a strategic role in overall site performance. They enhance trust in a brand, reduce friction in the shopping process, and support long‑term customer loyalty by delivering an experience that feels both reliable and engaging. In short, a strong PDP doesn’t just sell a product, but instead reinforces your brand promise and improves the entire customer experience.

What Makes Up a Great Product Detail Page?

You’ve probably gathered by now that a product detail page actively shapes the customer journey, not only introducing shoppers to a product but also building the confidence they need to make a purchase. 

And a cohesive appearance surely helps to make all of that happen! However, while a PDP may look polished on the surface, the impact of it comes from different elements of the page working together with a purpose:

• Menu & search bar: Easy access to navigation and search tools helps shoppers explore your site beyond a single product.
• Breadcrumbs navigation: No, I don’t mean actual food. Breadcrumbs are a navigational aid located at the top of a page. It shows users (and search engines) the path to the current page, helping customers understand where they are on the site and easily navigate back to previous pages.”
• Product title: A concise, descriptive title that immediately tells shoppers exactly what they’re viewing!
• Detailed product descriptions: Go beyond the basics to highlight features and benefits in a way that resonates with your audience.
• High‑quality images: Include images of the product with multiple angles, zoom options, and even lifestyle imagery or videos that bring the specific product to life. However, some platforms, such as Amazon, require images to look a certain way. Make sure your photos follow the requirements of the platform it’s being sold on!
• Customer reviews & ratings: Social proof helps build trust, addresses hesitations, and improves conversion rates. It’s also a way of advertising how global your audience is.
  Pricing, shipping & return details: Provide transparent shipping costs, shipping options, return policies, and availability information to reduce surprises at checkout.
• Variation and customization: Options for size, color, and other attributes should be easy to find and select.
• Compelling calls‑to‑action: A clear, prominent buy/add to cart button keeps the next step obvious and accessible. It guides customers seamlessly from learning to buying. Make sure your CTAs stand out and grab attention instantly. 
• Policies: Make your shipping, return, and warranty policies visible and easy to understand to build customer trust and minimize pain points.

When these components come together, a PDP transforms from a static page into a powerful tool that both informs and converts!

Walmart Leading By Example

A great example of a well‑designed product detail page can be seen with one of our own partners, Walmart. As you can see on the PDP for a children’s backpack, it features several key elements of an effective product detail page, including a clear title, pricing, an add‑to‑cart button, a detailed product description, and more!

The Right Technology to Power Enhanced Product Detail Pages 

A well‑crafted PDP is only as good as the data behind it. And that’s where Akeneo Product Cloud comes in. By centralizing and enriching product information, Akeneo PIM (Product Information Management) ensures every PDP displays consistent and accurate content across all eCommerce sites and channels. From detailed product descriptions and standout features to images, videos, specs, and care instructions, PIM makes it easy for brands and retailers to deliver rich, engaging product experiences. This level of detail not only enhances customer trust and satisfaction but also plays a key role in reducing returns and boosting conversion rates.

But creating enriched product information is only half the battle—you also need to get that content where it matters most, which is where a solution like Akeneo Activation comes in.

Akeneo Activation helps brands seamlessly syndicate their product information to top retail and marketplace channels like Amazon, Walmart, Target, Zalando, and more. Whether you’re selling across your own D2C site or third-party marketplaces, Akeneo Activation ensures your product content is tailored to meet each channel’s unique format, requirements, and audience expectations.

No more manually reformatting product data or worrying about inconsistent messaging across your channels. With Akeneo Activation, enriched PDP content flows smoothly from your PIM into the hands of your shoppers.

The Product Detail Page Drives the Product Experience

A product detail page is ultimately a tool that guides shoppers, answers their questions, and helps them feel confident about buying. When elements like detailed product descriptions, customer reviews, clear features and benefits, transparent shipping costs, easy‑to‑find return policies, and a standout add‑to‑cart button all come together, a PDP transforms into a trust‑builder that drives sales and boosts conversion rates.

And with the right tools, like Akeneo PIM, creating and maintaining these pages doesn’t have to be overwhelming! By centralizing and enriching product data, you can ensure every PDP on your eCommerce site is consistent, accurate, and engaging — whether viewed on desktop or mobile devices. In doing so, you don’t just enhance individual specific product pages; you elevate the entire online shopping experience.

Have you ever tried cooking with a poorly written recipe? You know the kind – vague measurements, missing steps, ingredients listed out of order. You spend more time second-guessing than actually cooking, and the end result rarely turns out the way it should.

Working with artificial intelligence (AI) on top of messy product data feels a lot like that.

AI has incredible potential to transform how businesses manage and scale product information. It can generate product descriptions, power intelligent search, personalize recommendations, and help you go to market faster across every channel. But just like a recipe, it needs clear, complete, and reliable instructions—your product data.

When your product data is inconsistent, incomplete, or scattered across systems, AI can’t perform at its best. In fact, it may even cause more problems than it solves. That’s why the first step in any AI journey should be getting your product data in order.

Why Focus On Product Data First?

Artificial intelligence isn’t magic. It’s pattern recognition at scale. Whether you’re tapping into generative AI to write compelling product descriptions or using predictive models to suggest upsells, all AI systems depend on one critical ingredient: data. And not just any data – clean, structured, and consistent product data.

AI thrives on well-organized inputs. It needs reliable patterns and clear relationships between data points to draw insights or make predictions. When you feed it high-quality product information, it can identify trends, fill gaps, and even anticipate customer needs. But when that data is messy or incomplete? Things fall apart.

Let’s say your AI is tasked with generating SEO-friendly product titles. If it pulls from inconsistent naming conventions where one item is called a “crewneck pullover” and another a “long-sleeve fleece” for nearly identical products, it won’t know which terminology to standardize or prioritize. Or imagine a recommendation engine working off missing sizing information; it may suggest irrelevant or ill-fitting products to shoppers, causing frustration and returns.

That’s why product data should come first, before you automate, optimize, or personalize.

Think of it like building a smart home. You wouldn’t start wiring your house for voice-activated lighting or automated blinds before making sure the floors are level and the plumbing works. Without a solid foundation, all that smart functionality is compromised. 

It’s the same with AI: get the basics of product data right, and automation, personalization, efficiency becomes far more effective and reliable.

Good AI doesn’t replace good data hygiene – it builds on it.

Risks of Implementing AI with Messy Data

Companies excited to deploy AI without cleaning up their data often encounter unexpected consequences, including:

• Inaccurate or misleading product listings: AI-generated descriptions pulled from poor source data can lead to incorrect claims, like saying a jacket is waterproof when it’s not. That’s a fast track to unhappy customers, negative reviews, and costly returns.

• Faulty recommendations and inaccurate personalization: Product recommendation engines depend on well-structured attributes (size, color, use case, materials). If those fields are missing or incorrect, AI might suggest winter coats to shoppers browsing bikinis.

• Poor search functionality and discoverability: AI-powered search and filtering tools rely on good taxonomy and attribute tagging. If similar products use different terminology (e.g., “blush pink” vs. “light rose”), they may not appear in the same search results.

• Amplification of errors at scale: AI accelerates everything, including mistakes. If your product feed contains incorrect dimensions or pricing, and AI uses that feed to populate 10,000 listings across channels, the error now lives in 10,000 places.
• Regulatory and legal compliance risks: Bad data can lead to non-compliance with product labeling laws, ingredient disclosures, or safety regulations, especially in industries like food, cosmetics, and electronics. AI doesn’t inherently know what’s legal or ethical; it follows your lead. 

What AI-Ready Product Data Looks Like

So what exactly does clean, AI-ready product data look like? There are 6 key traits of AI-ready product data:

1. Structured

Your data needs to follow a clearly defined format with proper categorization and hierarchy. Think of it like a family tree for your products. Parent products should be connected to their variants (colors, sizes, styles), and attributes should be broken down into specific fields, like material, size, dimensions, or use case. Without structure, AI can’t navigate your data or draw reliable conclusions.

2. Complete

AI can’t work with what it can’t see. Incomplete data like missing product titles, specs, or images leads to poor outputs. Ensure that every product listing contains all the necessary fields and content across every category. Completeness is a prerequisite for AI performance.

3. Consistent

Standardization is crucial. If one product lists its color as “navy” and another as “midnight blue,” AI might treat them as unrelated even if they’re the same item in different channels. Consistency allows AI to recognize patterns across your product catalog and make smart associations.

4. Enriched

Basic specs alone won’t cut it. AI needs context to do its best work, whether that’s generating creative copy, powering search results, or optimizing listings for SEO. That means providing rich text descriptions, high-quality images, videos, customer reviews, usage guidelines, sustainability certifications, and more. The more context AI has, the better it can craft engaging, accurate, and tailored content.

5. Centralized

Your product data shouldn’t live in 15 spreadsheets and a handful of legacy systems. To be usable by AI, data must be centralized in a single, trustworthy location, ideally a Product Information Management (PIM) system. Centralization eliminates version control issues, reduces duplication, and makes it easier to audit, update, and govern your data. It also ensures every AI application is drawing from the same source of truth.

6. Channel-Ready

Your product data must be flexible and adaptable. AI can help tailor content to each platform, whether that’s your eCommerce site, marketplaces like Amazon, print catalogs, or social media. But only if your data is already segmented and prepped for multichannel use. That means having different title lengths, formats, and tones for different channels, and language or region-specific versions when needed.

6 Questions to Determine if Your Product Data is Ready for AI

Before you dive into AI, it’s important to pause and assess the foundation you’re building on. Not all data is created equal, and not all data is ready for AI. These six questions will help you evaluate your current state and identify any red flags that could trip up your AI ambitions.

1. Do you have an established product data hierarchy?

Clear parent-child relationships (e.g., product families, variants) are essential for AI to understand your catalog structure. Without a clear hierarchy, AI might treat similar products as unrelated or miss opportunities to apply shared attributes. That leads to duplication, messy data, and irrelevant recommendations. A clean, logical structure is essential for training AI models to recognize patterns and apply rules efficiently.

2. Do you have at least 100 pieces of product data?

AI needs a critical mass of data to perform well. If you feed it too little, it won’t be able to detect patterns, test hypotheses, or generate meaningful results. Generally speaking, the more high-quality data you have, the smarter your AI becomes. This typically means 100+ well-documented, attribute-rich product records. Each should include structured fields like dimensions, materials, color, brand, and use cases. With a rich dataset, AI can make accurate inferences, generate tailored content, and even predict customer preferences.

3. Does all of your product data live in a single, centralized source?

Scattered data is one of the most common, and most frustrating, roadblocks to effective AI. If your product information is siloed across spreadsheets, outdated databases, DAMs (Digital Asset Management systems), or inside someone’s email inbox, AI won’t have access to the full picture. Worse, it may pull from inconsistent or conflicting versions of the truth. By storing your product data in a centralized system like a PIM platform, you create a single source of truth. That makes it easier to maintain, govern, and feed into AI applications. Centralization also reduces duplication, accelerates updates, and ensures that everyone (and every system) is using the same, up-to-date data.

4. Is your product data consistent and coherent?

If your data looks different from one product to the next, AI won’t know how to apply logic across your catalog. If one product is listed as “Large” while another says “L,” and another is “LG”, your AI model won’t recognize them as equivalent. That’s a recipe for bad recommendations, broken filters, and mismatched descriptions. Coherent data follows consistent naming conventions, formatting rules, and attribute structures. This uniformity allows AI to detect patterns, group similar products, and apply transformations or enrichments more effectively.

5. Does your product data have rich text fields?

Structured fields like specs and dimensions are important, but they’re only half the story. Rich text fields add depth and context. They include product descriptions, usage instructions, brand stories, SEO keywords, or care guidance. These fields provide the raw material for generative AI tools to create engaging content. Without rich text inputs, your AI won’t have enough “language” to work with. You might end up with generic, uninspired product copy or worse, AI-generated content that lacks accuracy or relevance. Rich text not only enhances the shopper experience, but it also helps AI create natural, persuasive, and informative product content at scale.

6. Is your product data localized for different markets?

If you sell internationally, localization is non-negotiable. Your product data needs to reflect the languages, cultural preferences, currencies, and regulatory standards of each region you operate in. AI tools can help automate translation, unit conversion, and channel-specific adaptations, but only if your data is set up to accommodate those needs. Localized data ensures that AI can produce relevant, compliant, and personalized content across geographies.

6 Tips for Creating a Strong Foundation of Product Information

If your answers to the above questions raised some red flags, don’t worry. Here’s how to get your data in shape:

1. Audit your existing data and technology: Start by evaluating the current state of your product data. What’s missing? What’s inconsistent? Which systems are involved? This gives you a roadmap for where to focus.
2. Identify key internal and external stakeholders: Involve product managers, marketing, IT, customer support, and compliance early. Everyone touches product data at some point, and their input will be vital to success.
3. Establish a clear and consistent taxonomy: Create a standardized naming and classification system for your products. This helps both humans and AI understand how products relate to each other.
4. Create a single source of truth for product data: Invest in a PIM or other centralized system where all product information lives. This ensures consistency across teams, regions, and sales channels.
5. Set up processes for translation and localization: Make sure your data can flex to serve multiple languages, units of measurement, and cultural nuances. AI tools can help, but they need clean inputs to get started.
6. Establish on-going data governance policies: Treat product data like a living asset. Set rules for how it’s created, reviewed, and maintained, and assign owners to ensure accountability over time.

Smarter AI Starts With Stronger Data

AI can be a powerful ally in managing, enriching, and scaling your product information. But it’s not a magic wand, it’s a multiplier. If your data is strong, AI will help you move faster, reach further, and deliver better product experiences. If your data is weak, AI will only amplify the gaps.

That’s why investing in clean, complete, and consistent product data is a strategic move. It lays the groundwork for automation, personalization, and innovation that actually work.

So before you dive into the latest AI tools, take a moment to look at the foundation you’re building on. Ask the right questions. Fill in the gaps. Organize what you have. With the right data in place, you’ll be ready to unlock the full potential of AI and turn it into a true competitive advantage.

We’re pleased to share that Akeneo has been positioned in the Leaders Category of the IDC MarketScape: Worldwide Product Experience Management and Product Information Management Plus Applications for Digital Commerce 2025 Vendor Assessment! 

We believe this recognition reflects Akeneo’s continued commitment to helping organizations deliver accurate, enriched, and consistent product experiences across every channel. As the digital commerce landscape evolves, Akeneo remains focused on empowering businesses with flexible, modular, and AI-enhanced tools to meet both operational and customer-facing needs at scale.

What is the IDC MarketScape: Worldwide PXM and PIM+ Applications for Digital Commerce 2025 Vendor Assessment?

The latest IDC MarketScape assessment explores how technology vendors support the evolving needs of digital commerce through Product Experience Management (PXM) and Product Information Management Plus (PIM+) platforms. This evaluation, conducted by IDC’s Heather Hershey, highlights the philosophical and functional distinctions between PXM and PIM+, and evaluates 11 vendors based on their capabilities and strategies.

These systems are designed for marketing, merchandising, and digital experience teams to personalize and orchestrate product content across multiple channels. They typically include dynamic enrichment tools, asset personalization, and modular API-based architecture with embedded artificial intelligence for syndication and content generation.

PIM+ platforms, by contrast, emphasize operational mastery. These are data-first systems that support ingestion, normalization, and syndication of structured product data at scale. They often integrate with ERP, CRM, and commerce platforms, providing regulatory compliance, workflow automation, and multilingual data governance.

How the Vendor Evaluation Works

IDC MarketScape evaluated vendors based on factors such as active customer base, support for digital commerce channels, SaaS/cloud readiness, and whether platforms support both B2C and B2B use cases. A minimum of 20 active customers with annual revenues of $50 million or more and the ability to syndicate across five or more major channels were part of the inclusion criteria.

Akeneo’s Position in the 2025 IDC MarketScape

Akeneo is positioned in the Leaders Category of the IDC MarketScape for 2025. Founded in 2013 and headquartered in Nantes, France, Akeneo supports businesses in over 165 countries with a PXM solution that can also function as a PIM+ platform. Akeneo Product Cloud centralizes product data and supports use cases across in-store displays, POS systems, ecommerce, sales enablement, and customer support.

Akeneo provides enrichment through generative AI, automated translations, and collaboration workflows. Its syndication module, Akeneo Activation, connects to over 400 retailer channels including Amazon, Walmart, and Google Shopping. The Akeneo Supplier Data Manager (SDM) is available both as part of the suite and as a standalone solution to streamline data exchange with suppliers.

Key technical characteristics include:

• 100% RESTful API exposure and partial GraphQL API availability
• Cloud-native, multitenant SaaS on Google Cloud Platform
• Modular architecture built using microservices
• Workflow engine and support for universal product attributes
• Support for digital product passports and design/performance declarations

Strengths and Considerations

The report notes several strengths of Akeneo:

• Omni-channel consistency: Akeneo Product Cloud ensures consistent product information across all sales channels, both online and offline, which is essential for maintaining brand consistency and providing a cohesive customer experience.
• Advanced data enrichment and automation: The solution utilizes AI and machine learning to automate data enrichment, tagging, and categorization, thus reducing manual effort, minimizing errors, and ensuring product information is always current and comprehensive.
• Customization and scalability: Akeneo Product Cloud offers customizable workflows, flexible data models, and scalable infrastructure, making it suitable for businesses of various sizes and enabling them to grow alongside the business.

Challenges include:

• Industry-specific limitations: Akeneo does not actively target the food and beverage sector due to the maturity of the sector and the specific product requirements that Akeneo does not natively support (but it can support these requirements through its partner ecosystem if needed).
• GDSN capabilities: While Akeneo supports customers with GDSN requirements, Akeneo lacks native GDSN capabilities, which limits its ability to scale and recruit customers needing these features.
• Target market focus: Akeneo’s business model and core product offerings are better suited for midmarket and enterprise businesses rather than micro businesses and small businesses. While Akeneo has micro business and SMB clients, its advanced functionalities and go-to-market model are aimed at larger enterprises.

Final Thoughts

As commerce environments become increasingly fragmented and dynamic, flexible and AI-augmented platforms are key to managing complexity while meeting customer expectations.

We believe Akeneo’s placement in the Leaders Category of the 2025 IDC MarketScape underscores its role in supporting businesses through the complexities of modern digital commerce. With capabilities that span both product information mastery and product experience delivery, Akeneo provides a flexible foundation for organizations seeking to scale operations, enrich product content, and adapt to changing customer expectations. As enterprises navigate the convergence of data governance and experience optimization, Akeneo’s approach offers a pathway to meet these demands with clarity, agility, and confidence.

Download the excerpt 2025 IDC MarketScape Worldwide PXM and PIM+ Applications for Digital Commerce Vendor Assessment now to learn more.

Product search and  discovery were two very different experiences in the past. You see, online search was simple. After typing something simple like “black shoes” into Google, you crossed your fingers and hoped the algorithm would find information that didn’t look like it belonged in a 2002 fashion catalog. 

Discovery, on the other hand, was mostly accidental—you’d stumble across something interesting while navigating a labyrinth of categories, filters, and miscategorized products and click ‘add to cart’. But in today’s digital world, where attention spans are short and expectations are sky-high, that kind of experience just doesn’t cut it anymore.

This is where Artificial Intelligence (AI) comes in, the not-so-silent partner behind eerily accurate recommendations, voice assistants that finish your sentences, and platforms that seem to know what you want before you do. From semantic search to generative answers, AI is fundamentally changing how we find, explore, and even think about product information.

Traditional Search and Matching Keywords 

As you might have guessed, search refers to the act of looking for a product, while discovery encompasses the results (and sometimes the unexpected opportunities) that come from that search. From the early days of digital search and eCommerce until now, most systems have relied on traditional, keyword-based search engines. These engines operate on a simple principle: they match the exact words in a user’s query with terms found in a brand’s product catalog or content database. If a customer entered the precise name of a product or category, they were usually presented with relevant results.

This approach worked reasonably well when product catalogs were small and customer expectations were even smaller. But as online inventories expanded and customer expectations evolved, the limitations of keyword search have become increasingly apparent. If a customer uses a synonym, introduces a typo, or describes the product in a way that didn’t match the catalog’s wording (such as saying “sofa” instead of “couch” or misspelling “headphones”), the search engine often fails to return useful results.

In short, traditional search and discovery helps users hunt, but it doesn’t help them explore.

What is AI Search and Why is it Important?

As customer expectations grow more sophisticated, so does the technology designed to meet them. AI-powered search engines have emerged as a powerful evolution of traditional search, one that is smarter and far better at figuring out what customers actually mean. Instead of treating every query as a literal checklist, AI search is an intelligent approach that uses technologies like machine learning and natural language processing (NLP) to understand user intent, context, and behavior.

Rather than relying solely on rules and product tags, AI search can learn from user behavior, adapt to evolving trends, and return results that align with the user’s intent, even when their query doesn’t match exact keywords. It draws insights from what people click on, what they skip, and how they refine their searches.

For example, let’s say a customer is searching for “best trail running shoes for wet conditions”. A traditional search engine would return any results that are tagged “running shoes”, “trail running”, or “shoes for wet conditions”, leaving our customer to weave through trail running backpacks, mesh running shoes, or even rubber rain boots.

But if we run the same search query with an AI-powered search engine, it will analyze thousands of customer reviews to find the trail running shoe with the most mentions of being waterproof, check the most frequently purchased sneaker in areas where muddy trail running is popular, and cross-reference return rates after a particularly rainy season to populate the most relevant products possible. Instead of simply matching specific keywords, AI offers the ability to better understand the intent behind the search, surfacing the most relevant results rather than generic products that contain a single word from the original search.

AI in Action: Retail Search and Discovery Examples

To better understand how AI is reshaping search and discovery in retail, it helps to look at real-world applications. From interpreting vague queries to delivering hyper-personalized recommendations, AI is driving improved shopping experiences. Below are several examples that highlight how leading retailers are using artificial intelligence to transform the way customers find and engage with products.

1. Conversational AI: Amazon’s Rufus

In early 2024, Amazon launched Rufus. Trained on Amazon’s vast product catalog and supplemented with information from across the web, Rufus acts as an intelligent shopping assistant that provides real-time answers, product comparisons, and tailored recommendations. Instead of typing a product name, users can ask Rufus things like: “What should I consider when buying running shoes for flat feet?” or “What are some fun birthday gifts for a 5-year-old?”. Rufus then responds with context-aware, human-like answers and curates relevant product suggestions based on millions of product listings, reviews, and customer behavior. This turns product search into a discovery journey, particularly helpful for undecided or first-time buyers! 

2. Personalized Product Discovery: Sephora

AI can analyze user behavior across sessions to anticipate what individual customers want, sometimes before they even know it themselves. It can factor in previous purchases, time spent on certain product pages, items viewed together, and more to suggest relevant items. Take Sephora, for example. Their AI-powered “Virtual Artist” app lets users virtually try on makeup, but beyond that, their recommendation engine uses a customer’s skin tone, past purchases, and browsing habits to suggest personalized products, such as foundation in the right shade or moisturizers perfectly suited to their complexion and concerns, even if they hadn’t explicitly searched for it.

3. Visual and Voice Search for Seamless Shopping: IKEA

AI-powered visual search allows users to upload a photo, like a jacket they saw on Instagram, and find similar styles available for purchase. This eliminates the guesswork of trying to describe a visual item with keywords, transforming casual inspiration into direct shopping opportunities. An example would be IKEA’s app that features visual search so a customer can snap a picture of a table they like and get matching or complementary furniture suggestions.

Voice search, on the other hand, enables hands-free browsing. Instead of typing, customers can simply speak their queries, making shopping more intuitive and accessible. Walmart incorporates voice shopping via smart speakers, letting users add items to their cart by simply saying what they need. This technology understands context and intent, providing a seamless, conversational user experience, whether you’re multitasking at home or on the go!

The Challenges of AI Content Discoverability 

While AI significantly enhances search and discovery, it’s not without its challenges. Here are some key ones:

• Data quality and quantity: AI models are only as good as the data they’re trained on. Poor, inconsistent, or insufficient product data leads to inaccurate recommendations and search results.
• Limited data: For new products or new customers, AI has limited data to learn from, making it harder to provide relevant recommendations or search results initially.
• High initial investment: Implementing advanced AI search and discovery systems often requires significant upfront investment in technology, infrastructure, and specialized talent.
• Lack of transparency: It can be difficult to understand why an AI made a particular recommendation or search ranking, which can hinder troubleshooting and trust.
• Scalability: Handling the immense volume of real-time data for search and recommendations, especially during peak shopping periods, requires a robust and scalable AI infrastructure.

AI and PIM

AI and Product Information Management (PIM) work together to transform the search experience by ensuring that product data is not only accurate but also optimized for how users search, speak, and browse. A PIM system provides the structured foundation needed for any search engine—centralizing attributes, categories, and rich product descriptions. Artificial intelligence, especially technologies like natural language processing and machine learning, enhances this foundation by analyzing how people express their needs, interpreting search queries, and enriching product data to align with users’ intentions.

By feeding enriched and structured product information into search systems, AI in search can better understand context, anticipate intent, and support the future of search where discovery is intuitive and personalized. The result is a more dynamic and satisfying user experience, one where customers can quickly find information and uncover the right products, even when their queries are vague, conversational, or unstructured.

The Search Experience Is Evolving

AI is redefining search and discovery by moving beyond rigid keyword matching to deliver experiences that are more aligned with user intent. Whether through natural language understanding, visual search, or behavior-driven recommendations, AI is turning search into a powerful engine for exploration and engagement.

As this technology continues to evolve, the focus will shift even more toward delivering relevant, context-aware results that help users find what they need quickly and meaningfully. To support this shift, businesses will need to ensure that the underlying product information is clear, consistent, and structured enough for AI to do its job effectively. The future of search is already here and it’s driven by intelligence, not just input.

Data quality can be a wonderful thing when it goes right! But when it takes a wrong turn? Oh, it can be a real headache, or worse. Poor data quality often feels like a confusing jigsaw puzzle – and if you’re missing half the pieces, or they just don’t fit, you’re left with a problem that’s as frustrating as it is messy. 

Good news: there’s a solution. If anything truly conquers data quality problems, it’s AI. And when it comes to putting AI to work, Akeneo leads the way. As a pioneer in AI-powered PIM, Akeneo helps brands turn messy, inconsistent product data into polished, high-quality assets that drive great customer experiences. Whether it’s cleaning up errors or accelerating your time-to-market, Akeneo’s AI transforms complexity into clarity. Curious to see how?

What is Artificial Intelligence?

First, let’s define the simple, but powerful, two-letter acronym. Artificial Intelligence (AI) refers to the ability of computer systems to perform tasks that typically require human intelligence, like learning, reasoning, and problem-solving. Essentially, it’s about machines thinking and acting smarter! 

The 5 Most Common Data Quality Problems

Now that we know what AI is, the real question is, what can it actually do for your product data? As it turns out—a lot. From spotting errors to enriching product listings automatically, AI doesn’t just tidy up your catalog, it streamlines your workflow and frees up your team for higher-impact work.

Now let’s break down five data quality headaches that AI is surprisingly good at solving, and how Akeneo Product Cloud makes it all click into place:

1. Inconsistent Product Data

Product data inconsistencies, like conflicting specs or mismatched attributes, often emerge when information travels across systems, regions, or teams. Distributors might receive differing inputs from suppliers, creating confusion and delays. Retailers then deal with inaccurate listings that lead to stock errors or missed sales. For consumers, it all results in frustration, mistrust, and higher return rates.

The Solution

Akeneo Supplier Data Manager (SDM), solves this at the source. It facilitates real-time collaboration between distributors and suppliers to ensure product information is aligned, complete, and up to standard from day one. Data requirements (whether from your PIM, ERP, or other systems) are surfaced through an intuitive UI, making it easy to guide suppliers and enforce consistency through AI-powered automation and rule engines.

Akeneo’s AI strengthens this process by classifying products intelligently, spotting inconsistencies, and auto-enriching records with the correct values and linked assets. The result? Less cleanup, more confidence, and product data that’s consistent across every channel.

2. Incomplete Product Data

When product data sheets are missing key information like specifications, usage instructions, or safety details, it’s more than just a formatting issue. Incomplete data makes products harder to find in search, harder to trust, and much easier to skip. For suppliers, it means delays in processing and potential compliance risks. For distributors and retailers, it leads to poor product discoverability and missed sales. And for consumers, it ultimately results in cart abandonment.

The Solution

Akeneo PIM simplifies the challenge of filling in the blanks. Acting as your single source of truth, it allows teams to manage, enrich, and sync data across every channel with accuracy. AI accelerates this by detecting gaps, recommending attribute values, and efficiently organizing products into categories, so you can focus on the big picture.

With Akeneo’s latest enrichment features, the process goes even further. Our AI can automatically extract critical details (such as color, material, or category) from assets like images and PDFs, and use them to populate relevant attributes in your PIM. These insights feed into the correct fields automatically, helping teams work faster, eliminate manual entry, and bring your complete, high-impact product content to market without delay.

3. Duplication Product Data

Data duplication isn’t always obvious, but it can wreak havoc on catalogs and reporting. It often creeps in during imports, transfers, or catalog updates, and snowballs into skewed metrics, messy reports, and a cluttered customer experience. Suppliers may submit the same product more than once, and retailers may see sales data split across duplicates. Consumers? They’re left wondering why the same product appears twice with different info or prices.

The Solution

With AI-driven data cleansing and deduplication in Akeneo PIM, you can automatically identify and eliminate duplicate entries as product data is imported, saving your team hours of manual effort and ensuring your catalog stays clean and accurate from the start.

But what about complex product relationships like configurable items, bundles, or component-based products? That’s where Composable Products comes in. This new, powerful feature lets you model real-world product relationships without creating redundant records or workarounds. From pairing electronics with accessories to managing modular furniture sets, Composable Products simplifies linking items while keeping your data clean and duplication-free. That means fewer errors and a system that evolves with your merchandising needs.

4. Poor Quality Supplier Data

Sourcing product information from external parties leaves you at the risk of receiving outdated or incorrect data. Suppliers may struggle to validate key information, leading to expensive returns, contractual disputes, and strained relationships. For distributors and retailers, poor-quality data slows processing times, delays time-to-market, and erodes brand trust. And as you can imagine, consumers get the wrong impression or the wrong product altogether.

The Solution

Akeneo tackles this problem head-on with smart workflows and AI-backed validation tools. Instead of relying on manual review, you can define what “good data” looks like from the start. Suppliers work within shared templates, with real-time guidance and automated checks via SDM, so issues are caught early, not when a product goes live.

Meanwhile, Akeneo PIM continuously tracks data quality metrics like completeness and relevance. AI flags anomalies and automates routine tasks like formatting or attribute mapping. Add in robust permissions and workflow controls, and your teams can trust that the data entering your system is usable and aligned across departments and regions.

5. Outdated Product Data

Product data isn’t a “set-it-and-forget-it” situation. When prices, features, or availability shift and updates lag behind, the fallout hits everyone. Suppliers may spread outdated info, hurting margins. Business teams are bogged down by manual corrections. Customers lose interest when they constantly see out-of-stock items still on display and gain an experience that feels anything but modern.

The Solution

Akeneo’s PIM helps brands stay ahead by enabling quick, controlled updates across every touchpoint. You can respond to changing trends or business needs at speed, whether you’re launching new products or tweaking existing ones for different markets. AI speeds this up by automating tasks like attribute updates, asset swaps, and content changes across your catalog.

To ensure accuracy and control, Akeneo’s new channel-level access control feature allows different teams (regional or brand-specific, or channel-based) to manage only the data they’re responsible for. That means fewer mistakes, faster updates, and full visibility over who’s changing what. Combined with AI-powered enrichment, your catalog stays fresh and accurate, no matter how fast your business moves.

Say Goodbye to Dirty Data

Like we said before, messy product data is like a puzzle. With the wrong pieces jammed in, it can be frustrating to work with it and nearly impossible to solve. From inconsistent specs to outdated listings, every mismatch sends your operations, partners, and customers slightly off track.

But with Akeneo’s AI-powered Product Cloud, the pieces finally fit! From smarter supplier workflows to automated enrichment and real-time updates, Akeneo helps you turn scattered data into a complete, high-quality product story, ready for every channel, every time.

Ready to let AI take your product data from messy to market-ready? Let’s talk.