Creating rules to better align rankings with KPIs, preparing for product launches, analyzing customer behavior data, collaborating with engineers to fix search. The job of an online merchandiser is never done. And you're doing all this while trying to stay ahead of demanding consumer expectations and new technology.
The challenge? Resources are tight, and manual tasks eat up valuable time that could be spent on strategic initiatives. It’s no surprise that merchandising teams worldwide spend more time maintaining systems than innovating customer experiences.
Whether you're planning for 2025 or optimizing your current approach, this guide will arm you with a proactive framework for building an effective online merchandising strategy that drives revenue while reducing manual workload.
An online merchandising strategy is more than just deciding which products to display and promote. It's a comprehensive framework that defines how you'll create intuitive, personalized shopping experiences that drive business results.
At its core, your merchandising strategy should answer three key questions:
The most successful strategies blend human expertise with AI-powered automation. While technology can handle data processing and routine tasks, your team's understanding of customer needs and business goals remains crucial for strategic decisions.
Traditional merchandising focused heavily on manual curation and rules-based systems. Merchandisers spent countless hours:
Today's approach is dramatically different. Modern merchandising platforms use AI to automate routine tasks while giving merchandisers powerful tools for strategic intervention.
This shift means teams can focus on high-impact activities like planning campaigns and analyzing performance data to make business-aligned algorithm tweaks — all with the end goal of creating unique shopping experiences that convert.
The underlying technology that powers enterprise search and product discovery has evolved throughout the years. Present-day solutions are able to automatically return extremely attractive and precise results out of the box, decreasing the need for merchandiser intervention.
Online merchandising strategies that drive results balance two crucial elements: meeting concrete business targets and creating intuitive shopping experiences. Modern merchandising has evolved far beyond simply arranging products on digital shelves — it now encompasses the entire customer journey, from search to purchase.
In the past, leadership teams provided specific KPIs focused on revenue, AOV, add-to-carts, conversions, and RPV to measure efforts against. Now, many are expanding these traditional metrics to include broader indicators like brand loyalty strength and return rate reduction, recognizing that sustainable growth requires both immediate sales and long-term customer satisfaction.
When you own a specific category or section of the site, understanding how your piece contributes to the whole becomes critical. For instance, if you manage the electronics category of a marketplace, your visual merchandising and product discovery strategies need to work in concert with other departments. A promotion in the accessories category might create opportunities for cross-category merchandising that boosts overall site performance.
Leading and lagging indicators help you monitor progress toward these goals, too. While conversion rates tell you what happened, metrics like search refinement patterns and browse-to-detail page progression reveal how customers interact with your merchandising decisions.
In this merchandising dashboard, you can see almost 300 shoppers searched for the term “jetsetter.” Out of those, just a few users immediately changed their search to “jetsetter stretch wool suit jacket” and then clicked or added an item to their cart.
These insights let you adjust your strategy proactively rather than reactively.
Via a strategically placed tool tip, the platform provides advice on how merchandisers can optimize the query for better results.
The key lies in aligning your site experience with business objectives, made possible by AI. For example, if increasing AOV is your priority, an AI-native platform can automatically adjust site-wide rankings — in product recommendations, category page layouts, etc. — while letting you maintain oversight of the customer experience.
Before digging into all this, it’s necessary to hone down the fundamentals:
Deeply understanding your customer base goes beyond historical purchase data and basic demographics. It requires a more nuanced view of shopping behavior and preferences across all digital touch points.
By combining real-time behavioral data with traditional analytics, you create a more comprehensive view of:
Your core customers’ expectations shifted over the past year. Key areas to consider include:
See how Sephora's search engine automatically personalizes the shopper's online experience based on subtle brand affinity cues.
The merchandising landscape continues to evolve, driven by shifts in consumer behavior and technological advancements. Understanding where your market is heading helps you get ahead of emerging opportunities rather than playing catch-up.
Today's shoppers discover products through multiple channels, with social media playing an increasingly crucial role.
For instance, when customers arrive at your site after seeing products on TikTok or Instagram, they expect a seamless transition from inspiration to purchase. Your merchandising strategy needs to account for these social-first discovery patterns.
Consider how competitors' brands approach social integration. Are they creating dedicated landing pages — sometimes with just the click of a few buttons — for trending items? How do they maintain consistent product storytelling across channels?
These insights can inform your own cross-channel merchandising decisions.
Leveraging automated attribute enrichment is one way to keep up with social trends. The tool adjusts product data and attributes daily, based on search and shopping trends. This helps merchandisers turn social demand into sales without needing to intervene manually.
New technologies reshape how customers expect to interact with your site. As shoppers use ChatGPT and other forms of Generative AI (GenAI) in their personal lives, they’re increasingly receptive to using them to discover products online, too.
More than half (52%) say that as they seek out the best products for them, they’d be “very” or “somewhat” comfortable using GenAI tools on an ecommerce site to help, which is a 10% jump over last year.
Different sectors face unique challenges and opportunities:
The key is identifying which trends matter most for your specific market and customer base. Not every trend warrants immediate action, but understanding their potential impact helps you prioritize your merchandising initiatives.
Defining goals for your ecommerce merchandising strategy also involves creating connected experiences that enhance the customer journey regardless of entry point or conversion channel.
For retailers with physical locations, your online merchandising strategy should complement in-store experiences. This means considering factors like:
When it comes to health and beauty retailers like Sephora, timely access to personal care items, like skincare products, via BOPIS can make or break a consumer’s decision to purchase.
As of Q3 2024, approximately 77% of retail site traffic globally comes from smartphones, yet many merchandising strategies still prioritize desktop experiences. Essential mobile considerations include:
If you sell through marketplaces or third-party channels, your merchandising strategy should:
The goal is creating a unified strategy that flexes appropriately for each channel rather than managing multiple disconnected approaches.
In order to define better goals, you need to know where you stand today. A thorough audit reveals both quick wins and areas requiring longer-term transformation.
Start by evaluating how effectively customers find products. Examine metrics like:
Your current category structure and collection strategy deserves close examination, too:
Evaluate your existing merchandising tools and processes:
The insights from this audit should inform your strategy development, helping you prioritize improvements where they'll have the most impact.
A successful merchandising strategy requires selecting and implementing the right combination of tactics for your specific business context.
Success starts with getting fundamental merchandising elements right, such as:
Different retail sectors require specialized merchandising approaches:
Your chosen tactics should directly support your priority KPIs:
Modern merchandising requires balancing personalization with operational efficiency:
Continuous improvement requires systematic testing:
Thinking of the full-funnel journey, which other teams do you need to engage and work with to be successful? This next section is about breaking down silos to create more aligned merchandising strategies.
Your merchandising strategy must work in concert with marketing initiatives:
Close collaboration with buying teams that control product assortment strengthens your merchandising effectiveness:
Building effective product discovery and site experiences requires tight integration with Product and UX teams:
For larger organizations, working with dedicated optimization teams maximizes impact:
Modern merchandising increasingly relies on data science capabilities:
A comprehensive framework to measure success goes beyond basic metrics tracking. It also helps you understand not just what's happening, but why, enabling data-driven refinements to your merchandising strategy.
Your measurement strategy starts with the KPIs defined in your goals.
Revenue metrics tell part of the story: conversion rates, AOV, RPV. But look deeper at how merchandising decisions influence these numbers. For instance, when you manually adjust rules to adjust product rankings for an upcoming Browse campaign, how does this impact category-level performance?
Does your product discovery platform break down key metrics by category page? If not, you could be losing out on revenue.
Customer engagement metrics provide context: time on site, pages per session, return visit rates. These indicators help validate whether your merchandising strategy creates compelling shopping experiences.
While inventory performance metrics complete the picture: sell-through rates, margin performance, return rates. Your merchandising strategy should balance customer experience with commercial objectives.
Detailed analyses of how customers find products reveals opportunities. For instance, go beyond the search performance basics like zero-results rates. Examine query patterns, refinements, and the path from search to purchase.
The search journey interface of this product discovery dashboard allows merchandisers to dive into individual browsing sessions to observe a user’s engagement with search.
Or take things a step further with Browse behavior metrics to show how effectively your category organization serves customer needs. Track navigation paths, filter usage, and category bounce rates.
Or, product discovery patterns help optimize placement and promotion strategies. It’s equally important to understand how customers find their way to products to inform your merchandising decisions.
Regular testing validates merchandising decisions.
A/B test results should consider both immediate impact and longer-term effects. Some changes might show modest immediate gains but build significant value over time.
On the other hand, multivariate testing helps understand interaction effects between different merchandising elements. Your strategy should account for how changes in one area impact others.
And then there’s customer segment analysis, which reveals how different groups respond to merchandising changes. What works for one segment might not work for another.
Calculate the full value of your merchandising initiatives via resource efficiency metrics, which show how automation impacts team productivity. Track time saved through AI-assisted merchandising and reallocation to strategic work.
Another could be ROI on the technology investment, considering both direct revenue impact and operational benefits. (Think: was your product discovery platform a profit center, or a cost center?)
And then there’s long-term value creation that extends beyond immediate sales lift. Consider factors like customer lifetime value and brand perception.
Core supporting ecommerce tech might include solutions for search & product discovery, ecommerce platforms, product information management (PIM), digital asset management (DAM), customer data, content management, testing, analytics, augmented reality (AR), and visualization, among others.
To choose solutions that scale with your business and adapt to changing market needs, consider the following:
Monolithic platforms offer integrated functionality but can limit flexibility. Consider whether the convenience of an all-in-one solution outweighs potential limitations.
On the other hand, composable architecture enables best-of-breed selection of interchangeable components, or microservices. When combined into one architecture, they create a tailored experience that aligns perfectly with your brand's unique needs.
Integrating Constructor's Search & Product Discovery platform with your PIM, CMS, ecommerce platform, and front-end enables you to harness the full potential of your product, clickstream, search results, and behavioral data.
Keep in mind that implementation timelines vary significantly between approaches. A modern, API-first platform will enable you to deploy rapidly for the least downtime and maximum benefit.
Enterprise ecommerce demands a precise balance between automation and human expertise, especially in search and product discovery, where hundreds of thousands of visitors query millions of times each month.
AI works hand-in-hand with merchants, blending automation with manual controls. While AI efficiently manages the long-tail tasks — like sorting low-priority queries or identifying patterns across vast data sets — merchants can focus on high-impact moments in the customer journey and dedicate their attention to critical initiatives, such as crafting targeted promotions, curating brand-centric experiences, and refining strategies for key seasonal campaigns.
AI also enhances product content and catalog quality by enriching attributes and even generating detailed descriptions. With seamless search and filtering, AI makes it easier for customers to find exactly what they need, improving overall discoverability to boost customer satisfaction.
And furthermore, tools like an AI Shopping Assistant (ASA) represent the next frontier. These GenAI technologies offer personalized guidance for complex purchasing decisions, creating a more engaging and efficient shopping journey. They have many use cases, including style assistants, recipe generators, and more.
ASA can act as a style assistant, suggesting and showing items aligned to a shopper’s needs, tastes, and intent.
Through strategic AI use, delivering highly personalized customer experiences is more manageable. It becomes less about keeping up with repetitive tasks and more about leveraging AI-driven insights and tools to deliver exceptional results. This allows merchants to retain control while achieving better efficiency, scalability, and personalization.
But alas, we live in the real world, where carefully written contracts and other constraints prohibit organizations from implementing ideal solutions immediately.
So, here are some quick tips to make the most of your current situation:
Success in ecommerce merchandising for 2025 and beyond starts with a clear assessment of your current state and customer needs. By methodically implementing improvements and continuously refining based on results, you can craft strategies that seamlessly balance technology and human insight. The key lies in maintaining this balance while staying sharply focused on core business objectives, ensuring every effort aligns with both immediate goals and long-term growth.
For additional data you can use to analyze consumer trends and prepare yourself for next year, check out the latest survey report on the State of Ecommerce Search and Discovery 2024.
An effective online merchandising strategy benefits both short-term performance and long-term business growth in many ways. First, it directly increases revenue by optimizing product discovery and relevance. When customers can easily find products that meet their needs, they are more likely to make the right purchases, leading to higher conversion rates and greater AOV while reducing return rates.
It also enhances the customer experience by creating seamless, intuitive shopping journeys. Personalization, tailored recommendations, and well-organized navigation make shopping more enjoyable, reducing friction and building brand trust.
Moreover, thanks to strategic AI use and automation, effective online merchandising improves operational efficiency by significantly reducing time spent on manual, repetitive tasks. This enables merchandisers to focus on high-value activities like strategic planning, trend forecasting, and performance optimization.
Last but not least, a strong merchandising strategy unifies cross-channel efforts, ensuring consistency across ecommerce sites, social media, and physical stores. This omnichannel integration enhances the overall shopping experience and fosters greater customer retention.
How do I decide between traditional KPI metrics (like conversion rates) and broader indicators (like brand loyalty) for measuring my strategy’s success?
It depends on your business goals. If your primary focus is immediate revenue growth, traditional metrics like conversion rates, AOV, and RPV are essential. However, if your strategy emphasizes long-term customer retention and sustainable growth, indicators like brand loyalty, CLV, and return rate reduction should also play a key role. Ideally, your approach should balance both, using traditional metrics to measure short-term outcomes while tracking broader indicators to ensure a healthy long-term trajectory.
How can I integrate emerging technologies like GenAI or AI Shopping Assistants if my platform isn’t ready for a full upgrade?
If a full platform upgrade isn’t feasible, consider integrating standalone AI tools that enhance existing functionalities. For example, you can use GenAI-powered chatbots or shopping assistants as overlays to your current system, providing guided selling experiences without disrupting your backend. Start with tools that have API-first designs, enabling easy integration with minimal changes to your core architecture.
What practical steps can be taken to implement omnichannel merchandising strategies beyond inventory visibility and BOPIS?
To go beyond inventory visibility and BOPIS, focus on creating seamless transitions between online and offline channels. For example, tailor online recommendations to highlight products available at a customer’s nearest store or incentivize in-store visits with exclusive offers. Use location-based merchandising to adapt content and promotions dynamically, ensuring relevance to local audiences. Additionally, emphasize connecting customer data across channels to provide personalized interactions regardless of where they engage.
How can ecommerce businesses avoid conflicts or inconsistencies when coordinating merchandising strategies across marketplaces, social media, and third-party channels?
To maintain consistency across channels, centralize your merchandising strategy with unified guidelines for product presentation, promotions, and branding. Use data from each channel to inform your overall approach, ensuring that insights are shared across platforms. Collaboration between teams managing these channels is critical to align messaging and avoid conflicting strategies. Leveraging tools that integrate omnichannel data and provide a holistic view of customer interactions can also help synchronize efforts effectively.
How can a retailer effectively identify and prioritize market trends to ensure relevance without overextending resources?
Retailers can monitor market trends by analyzing consumer data, industry reports, and competitor strategies. Prioritize trends that align with your core customer base and business goals. Tools like predictive analytics can help identify patterns in customer behavior, guiding you toward actionable trends. Regularly review performance data to ensure resources are allocated to initiatives with the highest potential impact.
What specific strategies can be used to balance personalization with consumer privacy in regions with strict data protection laws?
To balance personalization with privacy, rely on anonymized, first-party data rather than third-party data. Use privacy-compliant technologies, such as consent-based tracking systems and data aggregation methods that ensure individual customer anonymity. Communicate transparently with customers about how their data is used, and allow them to opt-in for enhanced personalization. AI systems designed for edge computing can process data locally, reducing the need for extensive data storage while delivering personalized experiences.
What are best practices for phasing improvements when transitioning from manual to AI-powered merchandising approaches?
Begin by auditing your current processes to identify areas with the highest manual workload or inefficiencies. Introduce AI tools to automate these tasks first, such as rule-based personalization or search optimization. Gradually expand the use of AI into strategic areas like dynamic pricing based on location, permissions, etc. Throughout the transition, maintain a feedback loop between your team and the AI system to fine-tune its performance and ensure alignment with business goals.