They say you can’t polish a diamond that hasn’t been cut. The same goes for your products. In today’s hyper-competitive digital landscape, even the most brilliant product can get buried if it isn’t refined and presented with care. But the challenge is customers don’t just want to shop; they want to discover, compare, and enjoy a seamless user experience before they click “buy.”

That’s where the real work begins. Behind every smooth user journey and five-star review is a deliberate strategy that ensures each product performs. For product teams and every product manager, it’s about tackling pain points, aligning with customer needs in real time, and building a path toward long-term customer satisfaction. 

So, what does it take to achieve that?

What is Product Optimization?

Product optimization is the deliberate process of improving a product so it consistently meets customer expectations and business goals. At its core, it’s about turning raw product information into commerce-ready experiences through structured data and enriched content, as well as insights from analytics tools. It’s like giving your product strategy a GPS: data points the way, and continuous improvement keeps you from taking wrong turns! It makes sure every detail works in harmony to boost product discovery, create a seamless user flow, and ensure each product delivers consistently across every channel.

Why is Product Optimization Important?

Product optimization is a growth driver. For businesses, it means reducing costly errors and returns. It also accelerates time-to-market and unlocks better ROI from every business channel. For customers, it delivers clarity and the confidence to make purchasing decisions without hesitation. When product teams use an insight-led approach, they not only smooth the user flow but also uncover opportunities to increase conversions and stay competitive in a crowded marketplace. 

In short, a strong product optimization strategy turns product data into a business advantage that directly fuels revenue and long-term customer satisfaction.

Steps to an Efficient Product Optimization Process

Getting started with product optimization doesn’t have to feel overwhelming with the right structure in place; product teams can turn scattered data and content into a powerful engine for growth and customer satisfaction. Here’s a six-step framework that puts your optimization efforts into action:

Step 1: Audit and Identify Gaps in Your Product Data

Every great product optimization strategy starts with a clear-eyed audit. This means examining product catalogs to uncover missing attributes, inconsistent descriptions, duplicate entries, and outdated information. These gaps may seem small, but they quickly create pain points for customers who can’t find the details they need to feel confident in their purchase. An inaccurate size guide or a vague product description can easily derail the user flow and lead to higher return rates.

An audit shouldn’t just focus on errors. It also needs to explore how each product performs in product discovery across various channels. Are product titles SEO-friendly? Do your specifications meet marketplace requirements? Using analytics tools (such as Akeneo’s Business Analytics) and even session recordings allows product teams to understand where shoppers drop off and which gaps hurt visibility.

Documenting these findings gives product managers a practical roadmap. Ranking issues by importance helps teams score quick wins now while laying the groundwork for bigger improvements later.

Step 2: Standardize Your Product Information

Once the weaknesses are clear, consistency is the next priority. Standardization means creating clear rules for naming conventions, attribute formats, units of measurement, and taxonomies across the entire product catalog. Without this structure, product information becomes fragmented, leading to confusion both internally and externally. Standardization is the glue that holds everything together, enabling product teams to work more efficiently. 

For customers, standardization builds trust. Shoppers expect product information to be accurate no matter where they encounter it, such as a brand website, a marketplace, or a social feed. When details are inconsistent, it disrupts the user experience and undermines customer satisfaction. Standardization reduces these risks and ensures product data is always reliable and ready for multi-channel syndication.

Step 3: Enrich Product Content for Better Experiences

If standardization lays the groundwork, enrichment brings products to life! High-quality images, detailed descriptions, localized translations, and video content transform raw product data into experiences that resonate with shoppers. Rich, compelling content makes it easier for customers to imagine how a product fits into their lives, directly improving the customer journey and supporting higher conversion rates. 

Enrichment also helps with differentiation. Adding sustainability claims, compliance data, or certifications positions products more effectively in crowded marketplaces. Done right, enrichment is optimization in action, showing customers not just what a product is, but why it’s the right choice for them!

For product managers, enrichment is about balance. Too much fluff can overwhelm, but too little detail leaves gaps in the decision-making journey. A strong enrichment strategy ensures each product performs by blending factual accuracy with engaging storytelling. To make this process efficient and consistent, both enrichment and standardization are best achieved through a Product Information Management (PIM) system, which centralizes product data and facilitates the delivery of the right content across every channel.

Step 4: Optimize Product Data for Every Channel

Not all platforms are created equal. A product listing that works on your eCommerce site may underperform on Amazon, and what looks great in a print catalog may not translate well to social media. Channel-specific optimization ensures your products are adapted for every environment while still staying true to your brand. Titles, keywords, and descriptions can be fine-tuned to meet platform requirements and boost visibility in search algorithms.

Channel optimization also plays a critical role in product discovery. Marketplaces reward well-structured product information, and search engines favor content that’s clear and consistent. By tailoring listings to each channel, product teams enhance discoverability and create a seamless user experience across touchpoints!

Step 5: Analyze Product Performance and User Behavior

Optimization doesn’t stop when your product data looks clean — that’s just the foundation. The real test comes once products are live and competing for attention. At this stage, it’s not about missing fields or outdated specs (that’s the audit’s job!), but about how each product actually performs in the market. With analytics tools, product teams can track KPIs like conversion rates, cart abandonment, bounce rates, and time spent on product pages, which are all signals of how effectively products are driving engagement and sales.

Performance analysis shifts the lens from data quality to customer behavior. With session recordings, heatmaps, and click tracking, you can uncover where shoppers hesitate, which details cause drop-offs, and how they move through the purchase journey. This goes beyond checking for errors, as it identifies friction points that quietly drain revenue and customer satisfaction.

The insights gained here complete the feedback loop. By linking behavioral data with optimization strategy, product managers can restructure content layouts or test new imagery. This kind of performance analysis can be achieved with tools like Akeneo PX Insights, which helps to deliver smarter product experiences everywhere your customers are.

Step 6: Leverage AI and Technology for Smarter Optimization

The final step — and the one that keeps businesses future-ready — is leveraging Artificial Intelligence (AI) and emerging technologies. Manual updates alone can’t keep pace with customer demands or the growing number of sales channels. AI-powered analytics tools and automation platforms make it possible to flag missing content, correct errors, and even generate enriched product descriptions in real time.

Beyond efficiency, AI unlocks predictive insights. By analyzing user behavior across platforms, machine learning models can anticipate customer needs and recommend content adjustments at scale. This takes optimization efforts beyond reactive fixes into a proactive strategy, where technology empowers product managers to stay ahead of the competition.

When combined with a clear optimization plan, AI and technology transform product optimization into a scalable discipline. Because in the end, it’s about creating a smarter and more resilient approach to ensuring every product performs.

Product Optimization in Action: Trotec GmbH

Understanding the strategies for driving product optimization is only half the story. Let’s take a look at a real life example of how to create efficient product optimization.

Trotec GmbH, a leader in precision environmental control technologies, faced a challenge familiar to many growing manufacturers: how to scale its product experience with the same rigor and excellence that defined its products. With product data fragmented across CRMs and more, teams worked in silos, slowing down enrichment, delaying time-to-market, and limiting the ability to deliver consistent product information across regions and brands.

By adopting Akeneo Product Cloud with Akeneo PIM at its core, Trotec unified its product data into a single, composable, AI-powered hub that centralizes, enriches, and activates product information everywhere it’s needed. The results have been transformative: enrichment time for complex technical data cut by 75%, Shared Catalogs fueling faster collaboration across subsidiaries, and digital product coverage scaling from 2,500 to 25,000 SKUs. With AI-driven enrichment and structured, search-ready attributes, Trotec is building the foundation for smarter discovery and an estimated 35% increase in online sales by 2027!

Driving Growth Through Smarter Product Optimization

Product optimization is the backbone of scalable growth in today’s commerce landscape. From auditing product data and enriching content to analyzing performance and leveraging AI, each step creates the foundation for stronger customer satisfaction and products that truly perform. Real-world leaders like Trotec show how a clear product optimization strategy, powered by tools such as Akeneo Product Cloud with Akeneo PIM, can turn complex challenges into opportunities.

The takeaway couldn’t be clearer: optimization is not a one-time project but an ongoing commitment to continuous improvement. By investing in the right processes and technologies, product teams and managers can meet customers where they are and unlock the kind of growth that lasts — everywhere commerce happens.

Halloween is just around the corner, bringing with it ghouls, ghosts, and all kinds of supernatural fun. But the one spooky power that stands out the most? Mind-reading. Imagine being able to hear people’s thoughts, to instantly know what they like, dislike, and what they’re searching for! It’s a trick anyone would love to master, especially businesses.

Because in reality, knowing what customers want often feels more like a dream than something you can actually achieve. A Deloitte study uncovered a striking perception gap: while 80% of business leaders believe shoppers are impressed with the online shopping experiences they provide, fewer than half of consumers agree. And when it comes to in-store shopping, the disconnect is even wider—by 12 percentage points. There’s a gap between what brands think they deliver and what customers actually experience. And closing that gap has never been more critical. 

The real question is: what do shoppers truly want to know before they buy? And how can businesses use those insights to close the gap between perception and reality?

What Shoppers Want to Know Before Buying Products

Customers want clear information on a range of topics, but as a business, it can be tricky to know what customers are actually looking for and what they consider to just be marketing fluff. Luckily, the team here at Akeneo ran a survey of real consumers to gain a better understanding of what they’re looking for.

Here are the key things shoppers really want to know before they buy:

1. Shoppers Want Complete Information

Before buying, shoppers want to feel absolutely certain about what they’re getting. The basics like price, size, and technical specs are usually present, but too many important details still slip through the cracks. Sustainability commitments, allergen information, and supply chain transparency are often missing, leaving shoppers with more questions than answers. When information is incomplete, hesitation creeps in, and trust in the brand begins to erode.

When these types of information are missing, the impact is huge. Two-thirds of consumers said they abandoned a significant purchase in the past year because product information like those mentioned was missing or inaccurate. That’s customer frustration as well as lost revenue. 

This is where a solution like Akeneo Product Information Management (PIM) can make a big difference. By centralizing product data into a single source of truth, teams can catch gaps, automate repetitive tasks, and enrich product details for consistency across every channel. The result is accurate content that reduces returns and helps customers make informed choices faster.

As consumer expectations climb, the stakes only get higher. Brands that view product content as central to the customer experience will be the ones to win loyalty. Investing in accuracy and detail is a driver of long-term growth.

2. Shoppers Want Consistency

Shoppers today navigate a non-linear path to purchase, jumping from one channel to another before deciding to buy. They might begin with a quick Google search, compare details on an online marketplace, check reviews on social media, and finally walk into a store to see the product in person. Our survey demonstrates just how fragmented the journey is: 30% of consumers shop in general and specialty retail stores, 27% turn to online marketplaces, 26% rely on traditional search engines, and 22% use marketplaces specifically for product discovery. With so many different entry points, consistency in product information becomes critical. When details change between touchpoints, it raises doubts and undermines trust.

But managing product data isn’t enough on its own. It also needs to evolve with shopper behavior. With PX Insights, part of Akeneo Product Cloud, real signals like search performance and AI-driven rankings are turned into actionable insights directly inside the PIM. This allows businesses to adapt content in real time and respond to customer feedback quickly. By closing that feedback loop, companies deliver smarter product experiences that feel connected and relevant across every channel, strengthening shopper trust.

When customers trust what they see, they’re more likely to complete purchases, less likely to return items, and more inclined to engage with the brand again. In a world where consumers bounce between multiple touchpoints before making a decision, consistency isn’t optional, it’s the foundation of long-term loyalty and brand credibility.

3. Shoppers Want Accuracy

Returns are a customer inconvenience, and they represent a major operational and financial drain for retailers. According to the survey, nearly 40% of shoppers sent an item back last year because the product didn’t match its description. Whether it was a misleading photo, an inaccurate size chart, or vague technical details, the result was the same: disappointment and frustration.

Each return cuts into margins through restocking costs, lost shipping expenses, and wasted inventory. More importantly, it damages brand credibility. A shopper who feels misled is less likely to purchase again. The solution is straightforward, though not always glamorous: brands need to invest in consistent and accurate product data from the very beginning. When shoppers know exactly what to expect, they’re far more likely to keep their purchase, saving costs for the retailer and reinforcing trust with the customer.

4. Shoppers want Convenience and Simple Service

While product details drive initial interest, convenience and service often determine whether customers will buy—and whether they’ll come back. Free delivery, simple return policies, and responsive customer support have moved from “nice extras” to baseline expectations. 

The data from our consumer survey makes this clear: 38% of shoppers now expect free delivery, 33% expect free returns, and 28% expect an easy return process as part of the standard shopping experience. 

This demand for convenience reshapes how brands must operate. A flexible, customer-first approach reduces churn, lowers operational friction, and signals respect for the shopper’s time and money. Even small service improvements, like clearer return instructions or proactive shipping updates, can turn a potentially frustrating experience into one that reinforces loyalty. Shoppers who feel a brand has made their lives easier are much more likely to stick around.

5. Shoppers Want Value Beyond Price

Price will always influence purchase decisions, but today’s consumers look for more than just a good deal. We found that values such as sustainability, ethical sourcing, and transparency often carry more weight than cost alone. Two-fifths of shoppers said they would willingly pay extra for products when a company communicates its values clearly, showing that shoppers are no longer simply comparing numbers on a price tag, but they are also evaluating the ethics and commitments behind the brand.

The problem most brands run into is that this type of information is hard to manage and hard to communicate, which is where a solution like Akeneo Supplier Data Manager (SDM) can come into play. Businesses can scale supplier data onboarding and ensure accurate information flows from the very start, meaning that they can confidently share sustainability commitments, compliance standards, and sourcing practices without friction. By collaborating better with suppliers and distributors, SDM helps companies keep their promises visible and consistent.

By weaving values into product content and storytelling, brands can resonate with consumers who are making decisions not just with their wallets, but also with their conscience. Clear communication of values helps companies differentiate in crowded markets and build emotional connections that outlast price wars.

6. Shoppers Want Personalization

Consumers want information as well as experiences tailored to them. Nearly half of those surveyed said they would pay extra for personalization, whether it comes through smarter product recommendations, targeted messaging, or customized offers. When customers feel that a brand understands their needs and preferences, the transaction feels more like a relationship.

Personalization also drives repeat engagement. Shoppers who receive relevant recommendations are more likely to explore additional products, increasing basket size and lifetime value. But personalization must be done thoughtfully. Generic or overly aggressive tactics can backfire, making customers feel like just another data point. True personalization means blending product content with customer insights to deliver experiences that feel genuinely helpful and human. Brands that strike this balance gain a significant competitive advantage in today’s crowded commerce landscape.

Closing the Gap

At the end of the day, all consumers truly want is confidence in their choices. Brands that consistently provide accurate product information and highlight brand values stand out in a crowded market. Done well, product content becomes the foundation for stronger, more connected customer experiences!

Success comes when businesses stop thinking in transactions and start thinking in trust. By investing in enriched product information, listening to customer feedback, and aligning with values that matter, businesses can deliver meaningful customer experiences that create repeat customers. Build on it, and you’ll avoid the tricks while reaping the sweetest treat of all—long-term loyalty.

Walk down any supermarket aisle or scroll through Amazon, and you’ll see the sheer scale of the CPG industry. From snacks and beverages to beauty products and cleaning supplies, these everyday essentials seem far removed from cutting-edge technologies like Artificial Intelligence (AI). After all, what does AI have to do with laundry detergent or breakfast cereal?

The answer: more than you might think. AI has steadily moved from the background into the spotlight, helping brands strengthen the supply chain and personalize the shopping experience. In a market expected to expand by $1.5 trillion by 2029, it’s clear that AI is becoming the engine that powers growth and ushers in a new era of commerce.

What once seemed like an odd pairing is now reshaping the entire sector. But how exactly is AI transforming the CPG industry? And how does PIM play a role?

What is CPG?

Before we dive into how AI is reshaping the industry, let’s quickly clarify what we mean by CPG. Consumer Packaged Goods (CPGs) are the non-durable products that households and individuals purchase regularly—think personal care products, toiletries, or cleaning supplies. These are items designed for frequent use, often consumed immediately or within a short lifespan (typically less than three years).

How AI Impacts CPG 

If you think the CPG industry is lagging behind in terms of embracing technological innovation, think again; a McKinsey survey found that 71% of CPG leaders have already adopted AI in at least one business function, and more than half are already using generative AI regularly to accelerate innovation and improve customer experience.

Let’s take a look at a few key ways in which AI is already being utilized and implemented throughout the CPG industry.

1. Smarter Supply Chain Management

A resilient supply chain is critical in the CPG industry, and AI tools are giving companies the power to predict, adapt, and optimize like never before. By analyzing sales and data in real time, AI uncovers demand patterns that help reduce waste and ensure products are available where consumers expect them.

A good example would be the CPG giant, Unilever, who leveraged AI to make their supply chain more resilient and sustainable. Instead of just reacting to disruptions, the company is leaning on data-driven insights to rethink how products are made, whether that’s finding alternative ingredients or streamlining formulations without sacrificing quality. By running virtual simulations and automating parts of the design and manufacturing process, Unilever is cutting complexity and freeing up its experts to do what they do best: cook up the next big innovation.

Unilever’s move to harness AI-powered insights is just one chapter in a broader story. Across the CPG industry, companies embracing autonomous AI-driven supply chain planning are seeing real results, like 10% lower costs, 20% less inventory, and 4% revenue growth. AI is about dialing up efficiency just as much as it’s about building agility. It gives companies the ability to rebalance inventory and keep their operations running smoothly, even when markets shift under their feet.

2. Accelerated Product Innovation

Innovation in CPG has traditionally been slow, but AI-powered insights are changing that. By scanning social media, reviews, and trend reports, AI tools can uncover consumer insights that guide everything from new flavors to shorter time-to-market.

Take Nestlé as an example. The company uses AI-powered tools to analyze consumer trends, from online chatter to ingredient preferences, and cluster these insights into new product ideas. This approach has led to innovations such as Nescafé Dalgona coffee mixes and Nesvita plant probiotic supplements in China. Nestlé has also dramatically accelerated their development cycle, cutting it from 33 months to just 12 on average, thanks to AI-enhanced R&D processes.

Building on these advances, generative AI takes product innovation even further. Beyond analyzing consumer insights, it can simulate product concepts or even test packaging designs before launch. For CPG brands, this means not only the ability to experiment faster, but cut risks and stay ahead of shifting trends with greater confidence!

3. Personalized Marketing & Digital Commerce

Today’s consumers expect brands to know them better than they know themselves, and AI is happy to oblige. CPG leaders are turning to AI-powered marketing platforms that serve up spot-on product recommendations and timely promotions and campaigns built on real consumer insights.

Coca-Cola, for instance, has leveraged AI-powered vending machines to capture real-time customer insights and tailor offerings at the local level. These smarter machines did more than just create a more engaging experience; they drove a 15% jump in transactions and reduced restocking visits by 18%, proving the business value of data-driven personalization. 

This level of tailoring is no longer optional for businesses. In a competitive landscape, CPG brands that embed AI into their digital commerce strategies are the ones building loyalty and capturing growth.

4. Operational Efficiency & Cost Savings

AI is steadily boosting productivity across the CPG industry. Whether it’s spotting defects on production lines with machine vision or optimizing prices through data-driven models, AI tools enable companies to cut costs without compromising on quality.

Dynamic pricing, one of the most impactful Gen AI use cases, allows CPG brands to adjust prices in real time based on demand and inventory levels. The result is sharper competitiveness in the marketplace while still protecting healthy margins.

For CPG leaders, these AI-powered efficiencies free up resources, enabling teams to focus more on innovation and customer value rather than getting buried in routine operations!

5. Enhancing Customer Experience

Perhaps the most noticeable transformation is happening in customer service and the overall customer experience. With AI-powered chatbots, virtual assistants, and recommendation engines, CPG companies can now meet consumers wherever they are—be it on websites, mobile apps, or social media.

By blending consumer insights with real-time data, CPG brands can anticipate what shoppers need and proactively suggest replenishments or complementary products. This type of engagement goes beyond convenience, as it builds trust and strengthens long-term loyalty.

As consumers expect seamless interactions, CPG leaders who weave AI-powered solutions into every stage of the journey will be the ones redefining what great customer experience looks like in the modern marketplace.

How CPG Businesses Can Adopt AI

Believe it or not, adopting AI isn’t just about adopting new tech. It’s about building the right foundation and scaling thoughtfully. Here’s how CPG companies can start making AI their reason of growth:

Start With Data-Driven Foundations

Every AI initiative depends on high-quality data. For CPG brands, this means consolidating product information, customer feedback, and supply chain metrics into unified systems. This is where a Product Information Management (PIM) solution proves essential. By centralizing and standardizing product data, a PIM creates the clean foundation AI needs for reliable insights. With accurate, consistent data across teams and channels, CPG companies can unlock more impactful Gen AI use cases.

Identify High-Impact Use Cases

Not every process needs AI on day one. CPG leaders should prioritize areas with the biggest payoffs, like demand forecasting, supply chain management, or personalized customer service. By starting small in focused areas, teams can prove ROI and build momentum before expanding to other parts of the business.

Equip Marketing Teams and Experts

AI works best when paired with human expertise! Marketing teams can use AI-powered platforms to personalize campaigns and analyze consumer insights, while R&D teams can apply generative AI to accelerate product development. Upskilling employees ensures AI becomes a tool for empowerment, not replacement.

Keep Your Consumer at the Center

At the end of the day, consumers expect better products and a seamless customer experience. Any AI adoption plan should be designed with those expectations in mind. Whether it’s smarter recommendations, more sustainable product design, or agile logistics, the goal is to use AI to strengthen trust and loyalty!

How AI and Akeneo Product Cloud help the CPG Industry

Within the Akeneo Product Cloud, Akeneo PIM gives CPG companies a single source of truth for product information, ensuring scalability and consistency across every channel and market. Whether it’s clothes, cosmetics, or cleaning products, Akeneo PIM helps brands activate their story everywhere customers shop while maintaining data completeness, legal compliance, and a seamless brand experience. With PIM as the central hub, teams can manage product data and processes more efficiently and accelerate growth.

Remember those insights from real customer reviews we were talking about earlier? Akeneo PX Insights can help with that as well, by bringing customer behavior signals like product reviews and AI-powered search rankings directly into the PIM! This makes it easier for CPG brands to understand how products are discovered, refine content based on real consumer feedback, and fix issues that hurt visibility or ad performance. By closing the feedback loop, teams can take faster actions and create smarter product experiences across all channels.

And finally, Akeneo Shared Catalogs, the solution that simplifies collaboration by giving sales teams, distributors, and retailers instant access to the latest product information. Instead of chasing updates or relying on manual processes, stakeholders can pull accurate catalogs from a private portal that syncs automatically with the PIM. This reduces delays and ensures everyone has what they need to get products to market faster.

Building Tomorrow with AI Today

The CPG industry is moving fast, and AI is becoming the engine that drives innovation and better customer experiences. From supply chain management to customer service, leading CPG brands are showing how AI-powered tools and Gen AI use cases can unlock real growth and agility.

But AI is only as good as the data behind it. That’s why Akeneo Product Cloud is essential, providing a single source of truth that empowers product developers and supply chain experts to act with confidence. Together, AI and PIM give CPG businesses the foundation to create faster, adapt smarter, and deliver the seamless experiences today’s consumers expect.

As we all know, digital commerce is moving at lightning speed. Shoppers expect more, new technologies like AI are reshaping how we sell, and channels keep multiplying. For businesses, that means one thing: adapt quickly, or risk falling behind.

Global eCommerce is projected to reach $8.5 trillion by 2026, marking a substantial 56% increase since 2018. What does this mean? Businesses of all sizes are prioritizing digital innovation and reinforces why choosing the right commerce technologies now is critical to staying ahead of the curve.

Looking ahead, it’s clear to see that commerce is only getting smarter, more personalized, and more flexible. Businesses across all industries are seeking tools that can scale with them and unify product experiences across channels. So, why shouldn’t you too? 

Whether you’re a B2C brand, a B2B seller, or somewhere in between, staying ahead means knowing which solutions work best for your situation, which is why we’re here today to break down some of the most popular digital commerce solutions, and what to look for when thinking about adopting a new solution.

What Is A Digital Commerce Solution?

Before deciding what you want, you should probably know how to identify it. A digital commerce solution is a system or platform that enables businesses to sell products and services online while managing the full scope of digital operations, from product data to customer engagement and fulfillment. It’s a core part of any modern eCommerce platform, powering both the buying experience and the backend processes that keep commerce running smoothly.

What Makes A Good Digital Commerce Solution?

A good digital commerce solution empowers growth and adaptability, unifying customer data and product content into one intelligent system while also enabling real-time updates. No matter your position in the eCommerce world, a great solution aligns your digital marketing, product management, and fulfillment processes to streamline your supply chain and drive efficiency across channels.

Equally important is the ability to enhance customer experience at every touchpoint. The best digital commerce solutions make it easy to connect with consumers across all channels, adapt content for different markets, and personalize experiences automatically. They also play well with the rest of your stack, integrating smoothly with tools like PIM, ERP, and analytics tools. By reducing friction at checkout and making your commerce platform more responsive, these solutions help you stay in sync with what your customers actually want. 

Best Digital Commerce Solutions In 2026

So, when it comes to it, which one do you choose? Well, below are ten leading commerce solutions to watch in 2026, each offering distinct capabilities that support both different commerce models:

1. Shopify

Perhaps the most known and dominant player in the eCommerce space, Shopify continues to stand out for its ease of use, fast deployment, and extensive app ecosystem. Ideal for small to mid-sized businesses, Shopify offers powerful tools for storefront customization and multi-channel selling.

Learn more about Akeneo and Shopify

2. Adobe Commerce

B2C and B2B commerce in 2026 might want to adopt Adobe Commerce, a platform previously known as Magento. It delivers enterprise-grade flexibility with robust customer data management and advanced personalization while offering seamless integrations and real-time insights!

Learn more about Akeneo and Adobe Commerce

3. BigCommerce

If you want to scale in 2026, turn your head towards BigCommerce! A scalable platform designed for growing brands, BigCommerce supports headless commerce and composable architectures. With robust APIs, it’s ideal for brands focused on data-driven strategies, fast time to market, and omnichannel expansion.

Learn more about Akeneo and BigCommerce

4. Salesforce Commerce Cloud

A cloud-based solution designed for enterprises, Salesforce Commerce Cloud leverages AI and real-time personalization to drive engagement. It integrates deeply with the Salesforce ecosystem, offering powerful customer data capabilities and channel synchronization.

5. Volusion

Volusion is a cloud-based eCommerce platform tailored to small businesses, offering built-in marketing tools and mobile-responsive design. It’s a user-friendly solution that helps merchants get to market quickly.

6. OpenCart

As an open-source platform, OpenCart offers flexibility and control for businesses with development resources. It supports multiple stores, payment methods, and extensions, making it a strong choice for merchants who want a customizable commerce platform.

7. WooCommerce

For content-heavy brands and publishers, WooCommerce might be your best asset in 2026. It combines content and commerce seamlessly, and is great for those looking to launch an integrated store with strong user experience and digital marketing capabilities.

8. commercetools

Known as a pioneer of composable commerce, commercetools is built on MACH principles (Microservices, API-first, Cloud-native SaaS, Headless). It’s ideal for large enterprises looking to innovate at scale and adapt quickly across a complex supply chain.

9. Wix

Wix has evolved from a website builder into a capable eCommerce platform for small businesses and creators. With drag-and-drop simplicity and SEO tools, it empowers brands to launch quickly without deep technical expertise.

10. Sana Commerce Cloud (SCC)

Sana Commerce Cloud is tailored for manufacturers and distributors. It connects directly to ERP systems like Microsoft Dynamics and SAP, enabling real-time inventory, pricing, and product updates—making it a powerful option for B2B commerce!

Digital Commerce And Product Cloud

As digital commerce grows more complex and omnichannel, delivering accurate, compelling product information is essential. The Akeneo Product Cloud helps businesses centralize and enrich product data, making it easier to manage content across every commerce platform and sales channel.

By streamlining the flow of product data across teams and technologies, Akeneo reduces manual effort and accelerates time to market. Its integration with key systems, including PIM, DAM, ERPs, and digital marketing platforms, ensures your teams work with a single source of truth. This not only improves internal alignment but also strengthens the entire supply chain, helping you respond faster to market demands and deliver high-quality experiences that enhance customer experience at every step.

For both B2C and B2B commerce, Akeneo Product Cloud commerce enables growth by bridging the gap between backend data and front-end experiences. With real-time collaboration and scalable localization, it empowers businesses to build trust through product content that’s always contextual and channel-ready, turning customer data into a competitive advantage!

Choosing the Right Solutions Shapes the Future

The digital commerce landscape in 2026 demands data-driven and customer-centric commerce solutions that scale with your business. From composable architectures to AI-powered tools and integrated product cloud platforms, the technologies you choose today will shape how well you compete tomorrow.

Whether you’re looking to enhance your user experience, optimize your supply chain, or improve time to market, selecting the right digital commerce stack is key. In a real-time, omnichannel world, success depends on your ability to adapt and deliver value across every touchpoint.

For years, organic traffic has been the golden KPI, the metric marketers obsessively tracked, the one that reassured us our carefully crafted SEO strategies were working. But the digital landscape is shifting, and the culprit isn’t your keyword strategy, your content team, or even your competitors. It’s artificial intelligence.

As AI-powered search tools start to become more popular, they’re quietly reshaping the way people discover and consume information online. SEMrush even predicts that AI-driven search will fully replace traditional search engines by 2028. That may sound like science fiction, but the early signals are already here, and they’re showing up in your analytics dashboards.

What does this mean for marketers? The playbook we’ve been running for over a decade is being rewritten in real time. Traffic dips are no longer just seasonal fluctuations or the result of algorithm updates, they’re the ripple effects of a fundamental shift in how people find and consume information. And that brings us to the big question: if organic traffic is declining everywhere, what’s really happening under the hood?

The Impact of AI on Organic Traffic

At the heart of this shift is Google’s effort to transform from a search engine into an “answer engine.” AI Overviews, launched in 2024 as part of Google’s Search Generative Experience (SGE), now appear on roughly 13% of queries—more than double from January to March 2025. These summaries leverage machine learning to compile concise, context-aware answers drawn from across the web, often satisfying user intent without the need to click any link. 

Other features like featured snippets, knowledge panels, People Also Ask boxes, and local packs also contribute to the zero‑click dynamic, with nearly 60% of all search queries now ending without a single click.

Some industries have reported organic traffic declines as steep as 15 to 64 percent since AI Overviews were rolled out. 

For marketers who’ve spent years optimizing for long-tail keywords, chasing backlinks, and crafting blog posts to lure visitors, this shift can feel discouraging. But it’s important to remember that it’s not that your efforts are going unnoticed, it’s simply that the playing field has changed.  

Less Traffic, Higher Intent

At first glance, this sounds like a doomsday scenario for SEO. Fewer clicks mean fewer visitors, which means fewer opportunities to engage, nurture, and convert. But here’s the twist: while overall traffic is down, the visitors who do arrive on your site are more intentional than ever.

Casual browsers who might have clicked your link just to skim the answer are being filtered out by AI-generated summaries. The people still coming through are the ones who want more than a quick definition. They’re seeking deeper insights, detailed resources, or product-specific information. And when they land on your site, they’re more primed to take action.

In fact, one study found that visitors driven by large language models (LLMs) are about 4.4 times more likely to convert compared to traditional search visitors. Adobe’s analysis of retail site data during the 2024 holiday season also revealed that visitors coming via AI-driven search stayed 8% longer, visited 12% more pages, and bounced 23% less than those from traditional searches.

So yes, your volume of traffic may be lower, but the quality of leads has the potential to skyrocket.

It’s a tradeoff worth paying attention to. Chasing vanity metrics like sheer visitor numbers might no longer make sense. Instead, success will be measured by how well you capture and serve this smaller but far more valuable audience.

How to Adapt to the Future of AI-Powered Commerce

The landscape may be shifting, but marketers are nothing if not adaptable. This isn’t the end of SEO, it’s just the next evolution of it, so you don’t need to completely rewrite the playbook. Instead, here’s how you can start to rethink your approach and adapt it to the new future of commerce.

1. Start by understanding how these LLMs interpret and talk about your product

The first step in adapting is to understand how LLMs actually process and interpret data. These systems don’t “read” content like a human would; instead, they analyze enormous datasets (product descriptions, technical specifications, schema markup, contextual signals from across the web, etc.) to decide how to surface information. That means accuracy, structure, and consistency matter more than ever. If your product data is incomplete, inconsistent, or overly generic, it’s less likely to be picked up and reflected in AI-generated answers. Optimizing for LLMs is about ensuring your content is machine-readable, semantically clear, and rich enough to stand out in a generative response.

This is where tools purpose-built for the new AI-driven search landscape can make a difference, like Akeneo’s AI Discovery Optimization feature, which helps businesses enrich and structure their product information in ways that align with how LLMs interpret data. By bridging the gap between human-friendly product storytelling and machine-friendly precision, our tool increases the likelihood that your products will be correctly understood, represented, and recommended in AI-powered search experiences. In short, it equips you not just to adapt to the shift but to thrive in it.

2. Incorporate measuring referral traffic from LLMs into your KPIs

The next step is to rethink the way you measure success. Large language models often act as intermediaries, surfacing and contextualizing your content within their own responses before a user ever clicks through. That means valuable touchpoints with your brand are happening outside the walls of your website, and if you’re only looking at conventional web analytics, you’re missing a big part of the picture. 

Start by broadening your reporting to include traffic from AI-powered discovery channels: conversational search tools, generative platforms, and embedded assistants inside apps. These sources may not look like classic referral traffic, but they’re increasingly where high-intent buyers begin their journey.

Tracking this kind of engagement is about recognizing the quality and behavior of the visitors arriving through these new pathways. Segment your analytics to compare how AI-driven referrals perform against traditional organic search: Are they spending more time on site? Are they converting at higher rates? Over time, this will help you identify the true economic value of AI referrals and adjust your strategy accordingly. By aligning KPIs with this new reality, you’ll be better positioned to understand where your most valuable customers are coming from and how to serve them effectively, even as the search landscape continues to evolve.

3. Focus on delivering original, thought leadership content over keyword-driven overviews

The era of casting a wide net with dozens of keyword-targeted posts is fading fast. AI-powered search doesn’t reward sheer volume, it rewards clarity, authority, and depth. Large language models are trained to synthesize content, and when faced with a sprawling collection of surface-level articles, they’ll often bypass them in favor of sources that go deep into a subject. That means your content strategy should shift from trying to capture every possible keyword variation to building comprehensive, authoritative resources that showcase your expertise. A well-researched guide or in-depth explainer will not only perform better with AI-driven search but also resonate more with the high-intent visitors who do land on your site.

This shift also changes the way we think about content planning. Instead of aiming for dozens of quick-turn blog posts, focus on cornerstone content pieces that can serve as definitive resources on key topics relevant to your audience. These pieces can then be supported by complementary assets like case studies, product guides, and customer stories that reinforce the same themes and strengthen your authority in the eyes of both human readers and AI systems. In short, less is more, provided “less” means strategically curated, deeply valuable, and optimized for how modern discovery tools evaluate relevance.

4. Treat customer feedback as the valuable ranking signal it is

LLMs don’t just scan your website—they also draw on a wide range of external inputs, from reviews and testimonials to social media discussions and industry forums. In fact, a recent study shows that Reddit, Quora, and LinkedIn were amongst the most cited websites for Google AI Overviews. 

Every authentic mention of your brand helps reinforce its authority, making it more likely that AI-generated answers will reference you as a trusted source. This makes it critical to foster real, verifiable proof points: customer success stories, ratings on third-party platforms, and a steady cadence of mentions in industry conversations.

This also means that social engagement is no longer just a brand-building exercise, but a direct lever for search visibility. Rather than shying away from platforms where conversations may feel less controllable, marketers should lean into them strategically. Encourage customers to share their experiences, amplify positive feedback, and actively participate in discussions where your expertise adds value. When AI models repeatedly encounter your brand in credible, context-rich settings, they “learn” to trust it.

How to Win in the Age of AI

The decline in organic traffic can feel unsettling, especially for teams that have long relied on SEO as their primary growth driver (that is to say, pretty much every team). But the reality is that search is simply changing, not disappearing. AI may be taking over the top of the funnel, but it’s also filtering out casual visitors and leaving you with the kinds of prospects you’ve always wanted: serious, high-intent buyers.

By shifting your mindset and adapting your strategy, you can turn this challenge into an opportunity. Learn how AI interprets your brand, measure new referral paths, focus on content depth, and listen closely to the voice of your customers. The future of search belongs to those who embrace change, not resist it.

So the next time you open your analytics dashboard and see fewer visitors, don’t panic. Remember: fewer doesn’t mean worse. In fact, in the age of AI-powered search, fewer might just mean better.

When you think of fashion, Artificial Intelligence (AI) probably isn’t the first thing that comes to mind. Nothing about systems that mimic human tasks makes you think of clothing racks. Fashion is all about fabrics and runway models, while AI is more about digits and data models. At first glance, they seem like a very odd couple.

Yet, since the early 2000s, AI has been quietly making its way into the fashion world, starting with tools for data analytics and inventory optimization. In recent years, its presence has become increasingly visible, driving innovations such as virtual try-ons and personalized shopping experiences.

What began behind the scenes is now reshaping the entire industry. Let’s take a closer look at how AI is transforming fashion, one breakthrough at a time!

How AI is Transforming the Fashion Industry

As AI continues to evolve, its role in fashion is becoming harder to ignore. With growing adoption across the industry, AI is actively shaping the way fashion is produced and experienced:

AI in Fashion Design

Nowadays, trends within the fashion industry seem to change every other week, and generative AI is enabling designers to explore more ideas more quickly. Tools like Fashable or Raspberry can turn sketches or mood boards into dozens of generated designs in seconds, helping to accelerate the design process and spark unexpected inspiration. For fashion brands, this means fewer physical prototypes and a better shot at keeping up with ever-evolving fashion trends!

But creativity isn’t the only thing AI brings to the table. With the help of AI algorithms and machine learning, brands can analyze sales history, trend cycles, and customer behavior to guide smarter design decisions, which can lead to collections that better reflect what customers actually want, boosting customer satisfaction and reducing supply chain waste. Whether it’s in-store or online shopping, AI is making the shopping experience more relevant and responsive than ever.

Moncler leads as a good example, as they used generative AI to create the Verone AI Jacket. AI tools helped create quilted textures and concepts for extreme-weather gear, laying the groundwork for both the design and marketing of the collection. It struck the right balance between creativity and precision.

AI in Visual Content Creation

In the visually driven fashion world, content is everything. And AI is transforming how that content gets created. From automated image editing to virtual model generation, AI-driven tools are helping fashion brands produce high-quality visuals faster and at scale. For instance, platforms like FASHN enable rapid fashion content creation through virtual try-on technology, allowing designers and retailers to showcase garments on different models—no traditional photoshoots required. 

These AI tools deliver efficiency as much as they enhance the shopping experience, especially online. By generating polished content at scale, brands can elevate visual consistency and drive higher customer satisfaction. With AI algorithms powering automated editing and virtual try-ons, fashion brands can deliver high-impact visuals while optimizing resources across their content pipelines.

AI in Consumer Experience

One of the most immediate and noticeable impacts AI has on the consumer experience is its ability to personalize both the in-store and online shopping journeys. From smart size suggestions to style matching and helpful chatbots that can answer simple queries, AI helps brands deliver more seamless customer experiences that make shopping easier and more enjoyable.

AI is also reshaping how consumers discover and interact with products. Intelligent systems can analyze browsing behavior and user preferences to suggest items that feel tailored to each individual. For example, Sephora’s Virtual Artist app lets users try on makeup virtually, but the real power lies in its personalized product suggestions. By analyzing skin tone and browsing habits, Sephora’s AI recommends products (like the right shade of foundation or targeted skincare) that shoppers may not have explicitly searched for. It’s as if the algorithm reads their mind before they even know what they want!

AI in Marketing, Data & Forecasting

Marketing in the fashion world has evolved far beyond seasonal campaigns and gut-feel decisions. Today, AI systems and machine learning give brands a data-powered edge, helping them spot emerging fashion trends and optimize campaigns across every channel.

By analyzing real-time data from search behavior and purchasing trends, AI enables more accurate planning and smarter decision-making. Through predictive modeling, brands can identify upcoming trends and even determine the best time to launch new products or campaigns. It helps cut down on overproduction, keeps inventory in check, and ensures marketing hits the right note at the right time, giving brands a much-needed edge in a competitive market!

AI-Powered Search and the Rise of LLMs

As AI continues to evolve, so does the way people search for products. More consumers are turning to large language models (LLMs) like ChatGPT, Google Gemini, and others to ask natural-language questions. This shift signals a growing preference for conversational, AI-powered search experiences that go beyond traditional keyword-based queries.

For fashion brands, this means product data needs to be more than just complete. It needs to be structured, accurate, and easily understood by AI systems. If your product content isn’t accessible or readable by LLMs, you risk being left out of the recommendation loop entirely. By ensuring your product information is enriched and available across the right channels, you position your brand to capture this emerging search traffic and remain visible in a rapidly evolving digital landscape.

AI-Powered Shopping Assistants

AI-powered shopping assistants are redefining how people ask questions and make decisions while shopping online. These assistants, often in the form of intelligent chatbots or voice-activated tools, guide users through the buying journey by answering questions and recommending products in real time.

What makes them so effective is their ability to learn and adapt. These tools use customer data and conversational AI to respond naturally, offering personalized suggestions and mimicking in-store assistance. Take Rufus, Amazon’s AI shopping assistant, for example. It helps users discover products by answering natural-language questions like “What do I need for a beginner ski trip?” or “What’s a good gift for a new parent?”. Rufus provides contextual responses that make product discovery intuitive, enhancing the overall shopping experience.

AI and Sustainability

AI has a reputation for damaging the environment, and for good reason, but when utilized correctly, AI can help to make the fashion industry faster, smarter, and more sustainable in the long run. With the help of intelligent forecasting and supply chain optimization, AI can help brands avoid overstock and reduce waste.

Some companies are even using AI to assess the environmental impact of materials or track the lifecycle of a product. Some AI systems can also help ensure that each product includes the right documentation, like Digital Product Passports (DPPs), and complies with evolving industry standards. By improving transparency and traceability, AI empowers fashion brands to make more responsible choices that align with both their values and consumer expectations.

Integrating AI with PIM

In the fast-moving fashion industry, where product ranges shift rapidly and trends evolve overnight, maintaining high-quality product data is a constant challenge. Akeneo AI-powered PIM helps fashion brands streamline and scale their product information by automatically enriching listings with attributes like color and style directly from product images or descriptions. This automation ensures consistency and helps teams launch collections faster across multiple channels.

A great example of this in action is Courir, a leading fashion retailer that turned to Akeneo Product Cloud to move away from a fragmented, manual process toward a centralized, AI-supported system. By consolidating their product information and automating key workflows, Courir reduced product description and translation time from 10 days to just 24 hours. Manual data entry was cut by 97%, freeing teams to focus on more strategic merchandising. With 96% of their products going live faster and more accurately, Courir not only improved operational efficiency but also elevated the quality of product experiences across every channel.

Where Fashion Meets Intelligence

AI is no longer a future concept, it’s a present-day advantage for fashion brands ready to adapt and innovate. From design and visual content to personalized shopping, AI is reshaping how fashion operates and grows. 

As consumer expectations evolve, integrating AI thoughtfully across the value chain is essential. The brands that embrace this shift will successfully keep up with change as well as lead it.

The eCommerce landscape has expanded rapidly, and it’s showing no signs of slowing down. In 2025, global eCommerce sales are projected to reach $6.83 trillion, and by 2027, online sales are expected to account for an impressive 41% of all retail sales worldwide. Clearly, the future is wide open for brands that embrace eCommerce!

But simply having an online presence is no longer enough. High return rates and inaccurate product content can take away everything you’ve built, damaging both your revenue and your customer relationships. 

Selling online brings scale, but it also brings complexity. That’s why it’s essential to understand not just what’s happening across your digital channels, but why. You need to be able to look back and identify what’s working, what’s not, and what needs to change, before small issues turn into costly setbacks, and that’s where eCommerce performance analytics become critical.

What are eCommerce Performance Analytics?

eCommerce performance analytics refer to the data that’s collected, measured, and interpreted in order to understand how your eCommerce store is really performing. It goes beyond tracking sales or traffic by helping you monitor key performance metrics that influence every stage of the customer journey, from product discovery and the product page experience to checkout and post-purchase interactions.

With the right eCommerce analytics tool, you can identify what’s driving growth and what’s holding you back. Whether it’s a high cart abandonment rate or inconsistent results across marketing channels, tracking the right analytics gives you the visibility needed to take action. By interpreting key data points and listening to customer feedback, you can adjust your eCommerce performance and create a more seamless, engaging customer experience across all your online stores!

eCommerce Performance Analytics That Brands Need to Track

Not all metrics are created equal. While there’s no shortage of data in today’s eCommerce platforms, focusing on the right performance metrics is what separates high-growth brands from overwhelmed ones. 

Here are the key analytics that every eCommerce business should track to gain meaningful insights and improve its eCommerce performance:

1. Conversion Rate

This is the north star for most online stores. Conversion rate tells you what percentage of visitors actually become customers. It reflects how effective your product pages, checkout flow, and overall customer experience really are.
To calculate a conversion rate, use this formula: 

(Total Number of Conversions / Total Number of Interactions) x 100

Small improvements to your conversion rate can really have a big impact on your revenue!

2. Customer Acquisition Cost (CAC)

Knowing how much it costs to acquire each customer helps you assess the efficiency of your marketing channels. Pair it with customer lifetime value (CLV) for a complete picture of whether your acquisition strategy is sustainable—or just expensive.

To calculate a CAC, use this formula:

(Total Cost of Sales and Marketing) / (Number of New Customers Acquired)

3. Customer Lifetime Value (CLV)

This metric estimates the total revenue you’ll generate from a customer over the course of their relationship with your brand. 

To calculate a CLV, use this formula:

(Customer Value) x Average Customer Lifespan

A healthy CLV means strong retention, quality engagement, and a product experience that keeps people coming back. Basically, all the great stuff needed for your business.

4. Average Order Value (AOV)

AOV tells you how much customers typically spend per transaction. Use it to evaluate upselling efforts and how persuasive your product content and pricing strategies really are.
To calculate a CLV, use this formula:

(Total Revenue / Total Number of Orders Placed)

5. Shopping Cart Abandonment Rate

Cart abandonment rate measures the percentage of shoppers who add items to their cart but leave the site before completing the purchase. A high rate signals problems in the final steps of the customer journey—whether due to surprise fees, slow load times, or even a lack of payment options. Fixing this can unlock revenue that’s already sitting in your cart.

To calculate a cart abandonment rate, use this formula:

 (Number of Completed Purchases / Number of Shopping Carts Created) x 100

6. Bounce Rate

If visitors leave your site after viewing just one page, it’s time to rethink your landing experience! High bounce rates often point to disconnects between your ads and product pages. It can also be a mismatch between what you offer and what your customers actually expect.

To calculate a bounce rate, use this formula:

(Total of Single-page visits / Total visits) x 100

7. Click-Through Rate (CTR)

CTR shows how effective your links, ads, or email campaigns are at driving interest. Whether you’re testing subject lines or optimizing calls to action, this metric keeps your marketing performance honest.

To calculate a CTR, use this formula:

(Total Clicks / Total Impressions) x 100

8. Traffic Sources

Understanding where your visitors are coming from, be it social media, search, or direct, is key to optimizing marketing channels and allocating your budget where it matters most.

Some of the most common traffic sources:

• Organic search – Visitors who find your site via unpaid search engine results (e.g., Google).
• Paid search – Traffic from paid ads on search engines (e.g., Google Ads).
• Social media – Clicks from platforms like Instagram, Facebook, LinkedIn, or TikTok.
• Direct – Users who type your URL directly or click a saved bookmark.
• Referral – Visitors who arrive via links from other websites or blogs.
• Email – Traffic driven by email campaigns or newsletters.
• GenAI – This is a newer traffic source, but delineates when traffic comes from LLMs like ChatGPT or Perplexity

9. Rate of Return & Refunds

These metrics offer a window into product satisfaction and fulfillment quality. High return rates often point to misleading content or post-purchase friction, problems that affect both profit and brand trust.

To calculate a return rate, use this formula:

(Final Value – Initial Value) / Initial Value x 100.

10. Churn Rate

Churn shows how many customers stop buying from you over a given period. When paired with retention efforts and sentiment tracking, it helps brands build a more loyal base and learn how they can improve their services.

To calculate a churn rate, use this formula:

(Total of customers lost / Total of customers at the start of the period) x 100

eCommerce Performance Analytics Tools

Tracking eCommerce performance requires more than a spreadsheet and hope. To truly understand what’s working (and what isn’t) across your eCommerce store, you need the right tools, ones that turn raw data into clear takeaways:

1. Google Analytics (GA4)

A staple for nearly every online store! Google Analytics offers deep insight into user behavior, traffic sources, bounce rate, conversion rate, and more. GA4 also brings in enhanced event tracking, making it easier to monitor key actions like cart adds and product views.

2. Shopify Analytics

For brands using Shopify, the built-in analytics dashboard provides a wealth of performance metrics. This includes AOV, cart abandonment rate, top products, and customer segmentation. It’s especially useful for tracking store sessions by device and marketing channels.

3. Hotjar

Hotjar adds a layer of behavioral data through heatmaps, session recordings, and its feedback and surveys. It helps you visualize how customers interact with your product pages and where pain points may be affecting the customer experience—especially when unfinished transactions are high.

4. Adobe Analytics

A more advanced enterprise-level tool, Adobe Analytics allows for deep segmentation, attribution modeling, and predictive analysis. It’s powerful for businesses with complex data needs and large-scale eCommerce platforms looking to scale with precision.

5. Mixpanel

Mixpanel focuses on user behavior over time, ideal for tracking CLV and product usage. It’s especially helpful for businesses offering subscriptions or multi-step customer journeys where engagement is key.

6. Glew.io

Tailored specifically for eCommerce businesses, Glew.io brings together sales, product, customer, and marketing data into a unified dashboard. It’s great for identifying high-performing SKUs and analyzing acquisition costs by channel.

How Akeneo Business Analytics Helps

While clean product data is essential, understanding how that data impacts your eCommerce performance is where real value is unlocked. Akeneo Business Analytics, part of the Akeneo Product Cloud, gives brands visibility into how product content quality drives results across their eCommerce platforms.

With a centralized dashboard, teams can monitor key performance metrics like total page views, conversion rate, revenue (and more) all across up to 10 digital and physical sales channels. This unified view replaces fragmented data silos and helps brands understand how product content is performing in both their online and physical retail channels. Whether your strength lies in eCommerce or in-store selling, you’ll have the insights needed to optimize the customer journey and build a more data-driven growth strategy!

Analyse Harder, Perform Better

As eCommerce performance becomes a defining factor in retail success, the ability to measure and act on data is a competitive necessity. Whether it’s optimizing a product page or lowering your cart abandonment rate, the right analytics turn insights into impact.

But performance doesn’t start with analytics—it starts with high-quality data. With solutions like Akeneo Product Information Management (PIM), brands can ensure their product information is not only accurate and consistent but also ready to drive smarter decisions across every stage of the customer journey. Because when your data works harder, your eCommerce business performs better.

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

The first step to optimizing your product data for AI-powered search experiences is to understand how and why these systems rank your brand. A solution like Akeneo’s PX Insights can be incredibly helpful in this area by providing visibility into how AI models interpret and present your products and highlighting missing, incomplete, or confusing data that might limit your visibility across both AI-generated responses and traditional search results. By uncovering these gaps, PX Insights empowers your teams to enrich product data in ways that resonate with both human shoppers and the algorithms shaping the next generation of discovery.

PX Insights also unifies data from AI search, Google Shopping, and customer ratings and reviews to give you a complete view of your product performance across every key channel. By helping you to take action directly within Akeneo and turning insights into enrichment workflows, recommendations, and activation opportunities, you’ll be able to create a feedback loop where every product improvement enhances your visibility across AI and search ecosystems simultaneously.

The result? Smarter, more conversational product data that’s optimized once but performs everywhere, ensuring your products stay discoverable, credible, and competitive as the landscape of digital search continues to evolve.

Learn more about Akeneo’s PX Insights

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.