Merchandisers, the strategic visionaries of retail, are experts in creating compelling product presentations that convert browsers into buyers. As consumers continue to embrace digital channels, the role of merchandisers has evolved far beyond physical store displays. Merchandising remains a sophisticated practice that demands creativity and experience, but it’s also increasingly enabled by data, automation, and AI.  

AI merchandising amplifies the ecommerce experience with tools that enhance human expertise and expand retailers’ capabilities. By automating repetitive tasks and scaling digital merchandising approaches like personalization, AI frees merchandisers to focus on strategic initiatives that drive growth. Merchandisers who incorporate AI proactively into their digital merchandising strategies, are discovering new opportunities to motivate shoppers, increase sales, and create much more connected buying journeys.

What is AI Merchandising?

AI merchandising combines artificial intelligence and machine learning with merchandiser expertise to optimize how products are presented, promoted, and discovered online. This digital merchandising approach uses customer behavior and purchase patterns to put the right products in front of shoppers. AI merchandising tools can be aligned with ecommerce KPIs, helping merchandisers achieve sales goals and generate higher revenue by selling more products. 

Key components of AI merchandising include:

• Real-time data: Tracking customer behavior data in real-time—through interactions, searches, and purchases—helps retailers understand shopping patterns and allows platforms like Monetate to provide more personalized buying experiences.
• Automated product organization: AI merchandising tools use machine learning technology to group and display products based on customer preferences and browsing behavior.
• Dynamic pricing: Some solutions enable “smart merchandising,” which uses AI to adjust prices automatically based on factors like demand, competition, and inventory levels.
• Predictive analytics: Forecasting trends and customer needs (using lots of data) helps merchandisers optimize inventory and product positioning.
• Personalization engines: Personalization platforms like Monetate use data, AI, and ML to tailor the entire buying journey to an individual shopper, creating tailored experiences like custom home page content, personalized product recommendations, and smart search.

How has the Role of an Ecommerce Merchandiser Evolved?

Twenty years ago, online shopping was still in its infancy. Merchandisers focused mainly on physical store displays and inventory management. But websites like Amazon changed that, making online shopping easier and more intuitive for shoppers. 

Today, digital merchandising requires a broader skill set that combines retail expertise with strategic thinking and data analysis. The modern retail merchandiser wears a few different hats (all of them fabulous). Here’s a breakdown of different roles these key team members play in creating the best digital shopping experiences possible: 

1. Product Discovery Expert

Modern ecommerce merchandisers optimize search functions and category pages to help customers find products quickly. They use AI tools to analyze search patterns and adjust product placement based on customer behavior.

2. Data Strategist

Data creates the foundation for successful online experiences. Merchandisers use customer data and shopping trends to make informed decisions about product promotion and placement, create customized experiences, and align merchandising strategies with business strategies (to name a few data-focused tasks). 

3. Experience Designer

Creating engaging digital shopping experiences means merchandisers must understand how to use AI tools to personalize content, product recommendations, and promotions. This experience extends beyond the retailer’s website. It must encompass every offline and online touchpoint so they’re connected (e.g., unified) and the buying journey remains consistent. 

4. Optimization Specialist

Today’s merchandisers run tests to validate their strategies and optimize their merchandising approach across different audience segments. This is another data-intensive skill, since data is used to refine product displays, pricing, promotional tactics, and inventory management approaches. Much of this process is automated with platforms like Monetate, which features A/B/n, multi-variate, and dynamic testing capabilities built into its platform.

Why Should Merchandisers Adopt AI Rather Than Fear It?

For ecommerce merchandisers, AI may seem a bit daunting, with its ability to tirelessly complete and streamline various tasks. There’s a real fear that this technology might diminish a merchandiser’s strategic role in retail decision-making. But AI can’t replace your merchandisers’ hard-won experience. It is a tool programmed to perform time-consuming tasks while providing valuable insights. These are things that enhance (not replace) merchandiser expertise:

• Analyzing huge stores of customer data which it uses to identify trends and opportunities that might otherwise go unnoticed.
• Supporting merchandisers with tools that automate product bundling, optimize search results, and connect multichannel buying experiences
• Measuring the impact of merchandising approaches on revenue 
• Testing in real-time to optimize experiences as they’re happening

Merchandisers can incorporate AI into their toolkit to help them make better decisions about product placement, pricing, and promotions. Time is finite, after all. For merchandisers who embrace AI technology, the tech means less time spent doing repetitive tasks, less errors, and a renewed focus on broadly improving the holistic customer buying journey.  

Be Proactive With These AI Merchandising Tips

Lock in, we’re about to get specific. Here are 5 tips to help you tie AI merchandising to ecommerce KPIs.

Tip 1: Lead with data, not assumptions

AI plus data means you don’t have to guess what your customers want. AI-powered tools like Monetate’s customer journey analytics let you view and examine customer behavior across the entire buying journey, so merchandisers can see which products shoppers are viewing, purchasing, and abandoning. 

For instance, seeing which products are viewed, but rarely purchased, could signal a roadblock or issue. Look at pricing, product placement, product descriptions, or other elements and test different page layouts to adjust your approach accordingly. This data-first approach helps you spot problems quickly and act based on real customer behavior rather than hunches.

Tip 2: Automate the boring stuff

AI doesn’t get bored or tired or distracted. This makes it the perfect tool for repetitive work like sorting through performance metrics or managing basic inventory updates. AI merchandising tools enable human merchandisers to focus on solving customer problems and keeping shelves stocked with items that customers love. 

Monetate automatically adjusts product recommendations based on inventory levels and shopping patterns, for example. The platform also adjusts ecommerce content dynamically, targeting content, products, and offers to different audiences. It’s a merchandising strategy that increased outdoor gear Helly Hansen’s revenue per session (RPS) on product detail pages by 100%.

Tip 3: Use testing to validate your decisions

Testing is an important part of the optimization process. With AI, you can test more iterations of a given experience and get results more quickly. Dynamic testing uses AI to automate experiences in real time, helping you run smarter tests that target specific customer segments and buying behaviors. Different product layouts, varied promotional messaging, wildly exciting pricing strategies—all of these things can be tested simultaneously. Focus on experiments that directly impact your KPIs, including metrics like conversion rate, average order value, and revenue per session.

Tip 4: Personalize everything

Your customers expect personalized experiences, but manually creating them for thousands of shoppers—across multiple touchpoints—is impossible. AI helps you deliver tailored experiences consistently and in a way that scales. Use AI to automatically customize product displays, adjust search results, modify recommendations, and test connected merchandising experiences across the buying journey.

Tip 5: Turn customer behavior into action 

Watch how your customers shop on your site—what they search for, click on, and buy. AI tools like intelligent search, powered by AI and machine learning, can identify patterns in this behavior and automatically adjust search results and product recommendations to match your merchandising strategy. One example of this is how customers often search for products using different terms than what’s in your catalog. 

AI search, also called “intelligent search”, helps connect these customers to the right products  even when the terms in the product description don’t exactly match the search query. Smart search uses natural language processing and machine learning algorithms to understand user context. Here’s another example of using behavior to improve CX. When a shopper abandons certain product pages more than others, AI can spot the trend. Merchandisers can then use these insights to fine-tune the buying experience by testing different product page designs.