Global retail ecommerce sales are projected to surpass $8 trillion by 2027, making managing digital storefronts increasingly complex. Merchandisers must now juggle massive product catalogs, provide personalized customer expectations, and drive KPIs – often with limited resources and time.
This guide explores the intricacies and challenges of modern ecommerce merchandising and provides actionable strategies and best practices to help you create exceptional shopping experiences while meeting critical business goals.
What is Modern Ecommerce Merchandising?
Ecommerce merchandising has evolved far beyond recreating physical store displays online. Today's digital merchandising combines data-driven insights with merchandising expertise to create shopping experiences that feel both personalized and intuitive.
Modern merchandising encompasses:
- Strategic product presentation. Rather than simply displaying products, modern merchandisers carefully orchestrate how products appear across all digital touchpoints — from search results and category pages to personalized recommendations and promotional spaces. For example, a leading beauty retailer might ensure that their private label products appear prominently alongside premium brands during key shopping events, but only for customer segments who have shown interest in value-focused options.
- Intelligent customer journeys. Today's merchandising is about creating paths to purchase that adapt to how customers actually shop. When a customer searches for "summer dress," they're not just shown a grid of dresses. They see carefully curated results that consider factors like current trends, their past preferences, and even local weather patterns. This level of sophistication requires combining merchandiser expertise with advanced technology.
- Data-informed decision making. Modern merchandising teams leverage real-time data to make strategic decisions. For instance, rather than waiting for end-of-month reports to adjust product positioning, merchandisers can now see immediately how customers interact with their assortment and make rapid adjustments to maximize performance.
Top Challenges Facing Enterprise Ecommerce Merchandising Teams
The sheer number of moving parts to the modern ecommerce merchandising experience provide many challenges for digital teams. The most common ones are outlined below:
Resource constraints vs. growing complexity
“Merchandisers spend a lot of time in suboptimal or aging online merchandising tools, putting out fires,” said Amanda Brooks, former product manager and ecommerce lead at Best Buy Canada. “And without enough hours in the day, teams end up cutting other activities like strategic planning or digging into insights to really drive KPIs.”
This challenge manifests in several critical areas:
- Catalog management at scale. Managing thousands of SKUs across multiple categories has become increasingly complex. A home goods retailer, for example, might need to coordinate seasonal collections, maintain consistent product attributes, and ensure proper categorization across tens of thousands of items – all while keeping pace with new product launches and retirements.
- Cross-channel consistency. Modern merchandisers must maintain consistent experiences across desktop, mobile, apps, and even in-store digital displays. This isn’t just about showing the same products, yet also requires understanding how shopping behavior differs by channel and adjusting product recommendations accordingly.
- Market responsiveness. The speed of ecommerce requires merchandisers to be increasingly agile. When a product goes viral on social media or a competitor launches a major promotion, merchandising teams need to respond quickly while ensuring their actions align with broader business strategies.
Data overload and analysis paralysis
Just because ecommerce merchandising teams these days have access to more data than ever before doesn’t mean this abundance of information doesn’t create its own challenges. Here’s what they’re grappling with:
- Disconnected data sources. Most enterprise retailers collect data across multiple touchpoints - website analytics, inventory management systems, customer service platforms, and in-store POS systems, to name a few. Each system provides valuable insights, but connecting these data points to create a coherent picture of customer behavior and product performance can be overwhelming. For example, a fashion retailer might see high engagement with a product online but struggle to connect this with in-store try-on rates or return patterns.
- Real-time decision-making. The sheer volume of data being generated means that traditional monthly or weekly analysis cycles are no longer sufficient. Modern merchandisers need to spot trends and make decisions in real-time, but analyzing massive datasets quickly enough to be actionable is increasingly challenging. For example, if a furniture retailer notices a surge in search queries for a particular furniture style, they need to quickly determine whether this represents a genuine trend or just a temporary spike.
- Balancing metrics. With dozens of possible KPIs to track (think: conversion rates, AOV, RPV, time on site, return rates), merchandisers often struggle to determine which metrics matter most for their specific goals. A beauty retailer might see strong CTR on a new product collection but need to understand how this translates to long-term customer value and brand perception.
Rising customer expectations
Likewise, the bar for what constitutes a great online shopping experience continues to rise, driven by technology and changing consumer behaviors:
- Demand for personalization. Customers now expect experiences tailored to their preferences, past behavior, and current context. For instance, when a beauty retailer’s customers shop online, they expect to see not just relevant product recommendations, but also content and advice that matches their beauty profile, purchase history, and loyalty tier. This level of personalization needs to feel natural and helpful rather than intrusive.
- Omnichannel consistency. Modern shoppers move fluidly between channels (i.e., mobile to desktop to in-store) and expect consistent experiences across all these touchpoints. As a matter of fact, customers who engage with products across multiple channels have a 30% higher LTV than those purchasing from a single channel. Maintaining consistency across channels — and more recently, across technology, data, and organizational structures in a tactic called unified commerce — requires significant coordination.
- Immediate gratification. The Amazon effect has raised expectations for product findability and availability. When customers search for products, they expect relevant results instantly — even for complex or ambiguous queries.
Essential Strategies for Ecommerce Merchandising Success
Modern ecommerce merchandising requires a balanced approach that combines human expertise with technological capabilities. Here's how leading retailers are doing this:
Intelligent product discovery
Gone are the days when merchandisers needed to manually manage every aspect of site search. Strategic teams are using intelligent product discovery to handle the brunt of their work via:
- Strategic search optimization. Leading retailers have stopped thinking of Search as a free-standing silo. Instead, they implement the tool as part of a product discovery suite that automatically learns from your buyers and optimizes product results accordingly, reducing manual work for teams. Through strategic integration, this is how brands like home24 automatically handle common issues like typos and synonyms, freeing their merchandising team to focus on high-impact decisions related to marketplace sites, marketing, and more.
- Dynamic category management. Ecommerce teams can now enlist the help of AI handle 1:1 personalization of category pages at scale, while preserving merchandiser control for strategic rules that align with business KPIs.
Via this product discovery platform, merchandisers partner with AI to ensure the best Browse experience for customers. AI surfaces key insights for merchandisers to then step in to make high-impact changes that drive revenue.
Data-driven decision making
Making decisions based on a hunch is no longer acceptable for ecommerce companies where ROI isn’t optional.
- Actionable analytics. Successful merchandising teams are moving beyond basic metrics to focus on actionable insights. They use real-time analytics to understand not just what products are selling (and why) so they can make informed decisions about product placement, promotional strategies, and inventory management.
- Testing and optimization. Rather than making assumptions about what will work, leading retailers establish systematic approaches and run controlled experiments to validate hypotheses. For example, one major apparel retailer discovered through A/B testing that showing product badges increased browse clicks by 1.6%.
Balanced automation
Ecommerce merchandisers need to work in tandem with AI to get the best business and customer results.
- Strategic rule creation. Modern merchandising platforms allow teams to create sophisticated rules that automatically adapt to changing conditions. For instance, a rule might automatically adjust product visibility based on inventory levels, margin targets, and seasonal relevance — but behind the scenes, merchandisers can still override them for special events.
- Collaborative personalization at scale. Successful ecommerce teams drive high-level merchandising strategies, allowing AI to compute personalization automatically in real time. This ensures the best possible recommendations in a current session and over time while maintaining the overall brand experience.
See how Sephora's search engine automatically personalizes the shopper's online experience based on subtle brand affinity cues.
Cross-channel and -device integration
Ecommerce teams need to consider the quality of their product data and how it appears across channels and devices for merchandising success.
- Consolidated product data. Ecommerce teams can outsource manual product data pruning to AI-native attribute enrichment tools. These tools fill in the gaps from disparate sources of product data to create a single source of truth for product information, reducing merchandising team workload while significantly improving product findability and customer satisfaction.
For this apparel brand, AI generated attributes (indicated by lightning bolts) for color and fabric facets, automatically adding them to these products’ data without merchandiser intervention.
- Channel-specific optimization. While maintaining consistent product information, successful merchandisers optimize the presentation for each channel. For example, a desktop-friendly product description might need to be condensed for mobile, while in-store digital displays might emphasize different product features entirely.
- Device-specific optimization. Likewise, ecommerce merchandisers also approach optimization based on device-specific shopping patterns. Mobile users tend to browse in shorter sessions, making it important to prioritize quick-access categories and simplified filters. Tablet users could engage more with visual content, warranting enhanced image galleries and 360-degree views. And desktop users could do more comparison shopping across several windows, benefiting from detailed specification displays and side-by-side comparisons.
Visual ecommerce merchandising excellence
Effective ecommerce merchandising heavily relies on clear, high-resolution visuals to create stories throughout the entire on-site experience.
- Product photography standards. Features like 360-degree product views, zoomable photos, and videos help replicate the tactile experience of physical shopping online, boosting customer confidence in their purchase, reducing returns, and driving conversions.
- Visual storytelling. Top retailers use on-brand visual hierarchies that adapt automatically across screen sizes to guide customer attention. This includes a strategic mix of hero images, educational content, and product displays.
Sephora uses valuable real estate on one of their category pages to promote free shipping via their loyalty program and highlight new makeup products.
Advanced cross-merchandising
Modern cross-merchandising expands beyond product recommendations based on purchase history. It can touch context, geolocation, and more.
- Intelligent bundling. Bundling involves more than "frequently bought together" suggestions. For a furniture retailer, dynamic bundling could also consider room context and style preferences. Their ecommerce merchandising team could then set strategic rules — like ensuring bundles maintain target margins — while AI handles real-time optimization.
- Contextual recommendations. Successful retailers are moving beyond simple product relationships to understand purchase context. For example, when a customer views patio furniture, the same furniture retailer’s personalization system could automatically suggest complementary items based on local weather patterns and typical outdoor living setups in that region.
Serena & Lily recommends furniture items of the same collection for shoppers searching for a specific piece to start or complete their patio set.
The Role of Technology in Modern Ecommerce Merchandising
Technology shouldn't replace merchandiser expertise — it should enhance it. Here are some guardrails to keep in mind to strike the right balance:
1. AI and machine learning are enhancement tools.
Predictive analytics
Modern AI systems don't just analyze past data. They help predict future trends.
With predictive analytics, merchandising teams can anticipate category trends weeks in advance. Rather than replacing intuition, the AI system provides data-backed insights that help validate or challenge their assumptions about upcoming trends.
Automated routine tasks
AI-native searchandising, the strategic placement of ecommerce items with the goal of optimizing for a business metric, can be helpful for automating routine tasks.
Rather than merchants needing to manually adjust search results to boost products that offer a higher profit margin or bury products that are deemed less attractive, a product discovery platform can optimize results and set rules automatically based on a number of inputs, such as clickstream data, product attribute, or inventory management.
Those rules can apply site-wide — from search results to browsing pages, recommendation pods, and more — providing a consistent shopping experience.
AI created rules automatically and algorithmically based on behavioral data paired with group attractiveness.
The result? Ecommerce teams have the time to proactively create new strategies to drive KPIs and improve customer experience.
Deeper personalization
Applying insights to customer behavioral data, retailers are now able to personalize and optimize shopping experiences like never before.
Every click on your ecommerce site is a vote for a product’s attractiveness, and every search query is an opportunity to learn how users actually behave on your site and what they’re looking for.
This is why the best product search engines built on AI and machine learning help you collect and leverage first-party customer data anonymously to ensure the right shopping experience, no matter the channel or device.
2. The human element remains critical
Strategic decision making
While AI can process vast amounts of data and identify patterns, successful retailers recognize that human judgment is essential for strategic decisions.
Use AI-generated insights to inform decisions, but rely on your team’s deep domain expertise to develop frameworks that combine human expertise with technology to maintain brand positioning and customer relationships.
Creative problem solving
Likewise, ecommerce merchandisers bring contextual understanding that AI can't replicate. For example, during the pandemic, merchandising teams quickly pivoted to push home office furniture and workout equipment — adaptations that required human understanding of rapidly changing customer needs.
3. Leverage integrated solutions strategically
Unified platforms upon a flexible technology foundation
Move away from disconnected point solutions toward integrated platforms that provide a complete view of their ecommerce merchandising operations. When built upon MACH-first architecture, these flexible technologies can adapt to changing customer behaviors and new channels.
Target Australia saw a 9% lift in search revenue after implementing a unified approach that connected their Search, Browse, and Recommendation systems.
Real-time optimization and continuous testing
Modern platforms provide immediate feedback on merchandising decisions. Rather than waiting for weekly reports, you can see the impact of your strategies in real-time and make adjustments accordingly.
Keeping tabs on new approaches, but not sure which ones to test? Lean on your technology partner, who should be able to help you regularly experiment with new approaches and technologies so you can maintain a strong foundation in merchandising fundamentals.
Making the Transition: Practical Next Steps
Moving toward modern ecommerce merchandising doesn't happen overnight. Here's how successful retailers are making the transition:
1. Assess your current state
Before making changes, evaluate your current merchandising operations. Which tasks consume most of your team's time? Where are the bottlenecks? Next, look for opportunities where small changes can create immediate impact.
For one furniture retailer, AI-native searchandising capabilities — that powered Autosuggest, analytics, synonyms, and facet configurations — allowed their ecommerce merchandising team to regain hours back in their day.
2. Prioritize investments
Start by building a strong foundation, which typically means:
- Cleaning and organizing product data
- Establishing clear merchandising processes
- Implementing basic automation for routine tasks
- Setting up measurement frameworks
Then, rather than attempting a complete overhaul, change little by little. This means you could start with search optimization and then gradually expand to category pages and recommendations. Building incrementally also allows your team to adapt and learn at each stage.
3. Empower your team
In addition to new merchandising technologies, provide training on data analysis, strategic thinking, and cross-channel optimization.
Need someone to spearhead the initiatives? Appoint one (or several) change agents. They’ll be in charge of communicating the need for change to key stakeholders, actively involving employees throughout the transition, and overseeing overall progress.
4. Measure success
Go beyond traditional metrics to measure your progress.
This includes tracking KPIs, such as customer-centric metrics — like product findability rates, time to purchase, customer satisfaction scores, and return rates by category — and business impact metrics, like RPV, conversion rates by channel, margin contribution, and inventory turn rates.
5. Monitor long-term value
Improving your product search and discovery experience isn’t just increasing sales. It’s also about long-term customer loyalty, brand perception, and operational efficiency via reducing manual effort over time. For an enterprise fashion retailer, this could mean monitoring their team's ability to handle larger catalogs and more complex campaigns without proportional increases in headcount.
The Key to Ecommerce Merchandising Success Is Human Creativity Paired with Technology
Long-term success is the product of many present-day actions in the right direction.
In order to drive business growth, merchandisers need to embrace technology as an enabler, not a replacement for hands-on expertise; build scalable processes; maintain focus on customer experience; and invest in tools and team capabilities.
The retailers who thrive will be those who view ecommerce merchandising as a strategic capability that combines the best of human creativity with the power of modern technology.
