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Applying merchandising rules to product recommendations — Mastercard Dynamic Yield
2026-02-28 19:38 Diff
https://www.dynamicyield.com/lesson/merchandising-rules/
Summarize this articleHere’s what you need to know:
Merchandising rules let you fine-tune product recommendations to achieve specific goals, overriding the default machine learning engine’s suggestions.
There are three main rule types: include (show only specific products), exclude (hide specific products), and pin (manually place products in specific slots).
Include and exclude rules can be combined to create powerful product selections, but conflicting rules should be avoided.
Pinning allows you to showcase specific products regardless of their relevance or popularity, giving you more control over the initial visual impression.
Remember, merchandising rules are most effective when used strategically to complement your overall recommendation engine, not replace it entirely.
Today, marketers and merchants need the flexibility to adapt algorithms to their objectives, helping them put their best foot forward with consumers when crafting unique and captivating experiences. Marketers can use a number of strategies to power recommendations, any of which can be customized using filtering rules. These rules will allow you to exclude or include products, guaranteeing users are shown the most suitable items based on their preferences and behaviors.
What are merchandising rules?

When using an automated product recommender system, marketers can program manual conditions. These conditions, known as merchandising rules, allow users to override the machine learning engine to better specify targeting criteria.
Using these rules, marketers, and merchandisers can customize the algorithms at work, dictating which items appear in each widget on a granular level. For example, if a retail brand is eager to sell a larger quantity of its best-selling coat, its eCommerce team can set up rules that pin this item to a prime spot in a recommendation widget.
Types of filtering rules
The first type of merchandising rule is an “include” rule. This rule ensures only a subset of products is presented in selected slots of a recommendation widget. These products are defined by conditions based on each product’s attributes, ingested by the engine using a product feed. The products are then listed in the widget according to the recommendation strategy in place.
Example: A retailer wants to ensure men arriving on the site are only shown popular products for men in a homepage recommendation widget. The products presented in the widget will be ordered by each product’s popularity score (calculated by the number of interactions, add-to-carts, purchases, etc.).
An example of a homepage product recommendation widget targeted at the “Men’s” audience, displaying only new products for men.
The second type of merchandising rule is an “exclude” rule. This rule, opposite of the include rule, ensures a subset of products is not presented in selected slots of a recommendation widget. These blacklisted products are defined by conditions based on each product’s attributes.
Example: A retailer looking to increase cart sizes wants to ensure a recommendation widget on triggered emails using the “Similarity” recommendation strategy does not present products priced over $20 for all users. Only eligible products will be presented in the widget, each of which will be ordered according to their similarity score to items already in a user’s cart.
An example of an email product recommendation widget targeted at cart abandoners, displaying similar items under $20.
A third merchandising rule type is the “Pin” rule. This rule can completely override any recommendation strategy, allowing marketers to manually choose products to attach to specific slots in a recommendation widget.
Example: If a retailer has an excessive amount of a specific pair of shoes in inventory, they can choose to pin this item to the first slot of a recommendation widget on the homepage to expose it to more users.
An example of a homepage product recommendation widget targeted at new site visitors, displaying a “pinned” product in the largest slot of a recommendation widget.
Using merchandising rules

When setting up new rules, marketers can drill down on criteria associated with each rule, designating time frames, values, audience segments, and more. A few of these examples include:
Display popular items in “women’s dresses” to a visitor that has viewed at least two dresses in the 14 days
Exclude showing items on sale to high-value customers
Recommend highly-profitable items to consumers who have purchased or added items to their carts valued at $150+ in the last week
An example of a recommendation widget using a “Bought Together” strategy targeted at users who have added an item to their cart with an “exclude products over $10” rule in place.
Multiple filter rules
Marketers are not limited to a single rule when crafting merchandising rules for its recommendation widgets. They can create more than one include / exclude rule, all of which will be applied, so long as every condition is met by the visitors being served the recommendations. It’s crucial to never create conflicting rules, as they can yield unexpected or no results.
In addition, marketers using the right engine should be able to manage priorities when dealing with multiple rules. For example, when pinning items, setting up multiple pins is possible. The marketer or merchandiser will simply need to manage the rule priorities, identifying which rule supersedes others.
Getting started with merchandising rules

In order to maximize revenue generated from machine learning-powered product recommendations, marketers need to squeeze every ounce of potential from every campaign they create. When it comes down to it, merchandisers know their products (and each product’s attributes) better than anyone else. Using this knowledge to inform strategies, craft rules, and deploy product recommendations won’t just improve the discovery process – it will help users narrow down purchase decisions, generating revenue and returns.
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                                }</style><div class="lesson-content-sidebar-list"><ol><li class=""> <a href="https://www.dynamicyield.com/lesson/introduction-to-product-recommender-systems/">An introduction to product recommender systems</a> <span class="mark-as-read" data-id="118631"></span> <span class="article-read-mark">Already read</span></li><li class="current"> <a href="https://www.dynamicyield.com/lesson/merchandising-rules/">Applying merchandising rules to product recommendations</a> <span class="mark-as-read" data-id="121426"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/product-recommendation-strategies/">The essentials for choosing the right product recommendation strategy</a> <span class="mark-as-read" data-id="118684"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/affinity-based-recommendations/">An Introduction to Affinity-Based Recommendations</a> <span class="mark-as-read" data-id="10075"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/deep-learning-recommendations/">The rise of deep learning-based recommendations</a> <span class="mark-as-read" data-id="130571"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/product-recommendations-guide/">Impactful techniques to help you ace your product recommendations</a> <span class="mark-as-read" data-id="16834"></span> <span class="article-read-mark">Already read</span></li><li class=""> <a href="https://www.dynamicyield.com/lesson/apmex/">The gold standard for recommendations: An APMEX case study</a> <span class="mark-as-read" data-id="126002"></span> <span class="article-read-mark">Already read</span></li></ol><div class="view-all-courses-wrap"> <a href="https://www.dynamicyield.com/learning-paths/" class="view-all-courses">Discover more courses<svg xmlns="http://www.w3.org/2000/svg" width="10.928" height="9.501" viewBox="0 0 10.928 9.501" style="&#10;"><path d="M10.929,4.753a.859.859,0,0,0-.19-.491L6.938.224A.835.835,0,0,0,5.892.16.736.736,0,0,0,5.9,1.2L8.577,4.041H.746a.733.733,0,0,0-.734.586.711.711,0,0,0,.7.84H8.577L5.9,8.3a.784.784,0,0,0-.008,1.038.8.8,0,0,0,1.037-.063l3.8-4.039a.652.652,0,0,0,.2-.483Z" transform="translate(-0.001 -0.003)"/></svg></a></div></div></div></div><div class="article-content"><div class="lesson-content-inner"><div class="article-breadcrumbs"> <a href="https://www.dynamicyield.com/learning-paths/" title="Learning Paths">Learning Paths</a> <a href="https://www.dynamicyield.com/course/product-recommendations/">Product Recommendations</a></div><h1 class="article-title">Applying merchandising rules to product recommendations</h1><h2 class="article-excerpt">Using merchandising rules, marketers can adapt product recommendations strategies to their desired objectives, tapping their expertise to improve consumer experiences.</h2><div class="article-authors lc-flex"><div class="article-author-box lc-col lc-flex"><div class="article-author-image lc-col"><div class="entry_author_image"><img src="https://www.dynamicyield.com/wp-content/authors/yaniv-127.jpg"  alt="Yaniv Navot" width="76" height="76" /></div></div><div class="article-author-title lc-col"><div class="article-author-name"> <a href="https://www.dynamicyield.com/blog/author/yaniv/" title="Posts by Yaniv Navot" rel="author">Yaniv Navot</a></div><div class="article-author-title-text"> CMO, Dynamic Yield</div></div></div></div><div class="entry-content article-entry-content"><p></p><div id="WYNTK-Sec" class="what-you-need-to-know-sec"><p><img decoding="async" src="/wp-content/uploads/2020/09/star-icon.svg" style="width: 20px; margin-right: 5px; vertical-align: -2px;"><strong class="first">Summarize this article</strong><strong class="hidden">Here&#8217;s what you need to know:</strong></p><ul style="margin-left: 4px; font-size: 16px;"><li style="margin: 10px 0;">Merchandising rules let you fine-tune product recommendations to achieve specific goals, overriding the default machine learning engine&#8217;s suggestions.</li><li style="margin: 10px 0;">There are three main rule types: include (show only specific products), exclude (hide specific products), and pin (manually place products in specific slots).</li><li style="margin: 10px 0;">Include and exclude rules can be combined to create powerful product selections, but conflicting rules should be avoided.</li><li style="margin: 10px 0;">Pinning allows you to showcase specific products regardless of their relevance or popularity, giving you more control over the initial visual impression.</li><li style="margin: 10px 0;">Remember, merchandising rules are most effective when used strategically to complement your overall recommendation engine, not replace it entirely.</li></ul></div><p></p><p>Today, marketers and merchants need the flexibility to adapt algorithms to their objectives, helping them put their best foot forward with consumers when crafting unique and captivating experiences. Marketers can use a number of <a href="https://www.dynamicyield.com/blog/product-recommendation-strategies/">strategies to power recommendations</a>, any of which can be customized using filtering rules. These rules will allow you to exclude or include products, guaranteeing users are shown the most suitable items based on their preferences and behaviors.</p><h2>What are merchandising rules?</h2><p>When using an automated product recommender system, marketers can program manual conditions. These conditions, known as merchandising rules, allow users to override the machine learning engine to better specify targeting criteria.</p><p>Using these rules, marketers, and merchandisers can customize the algorithms at work, dictating which items appear in each widget on a granular level. For example, if a retail brand is eager to sell a larger quantity of its best-selling coat, its eCommerce team can set up rules that pin this item to a prime spot in a recommendation widget.</p><p><strong>Types of filtering rules</strong><br /> The first type of merchandising rule is an “include” rule. This rule ensures only a subset of products <em>is presented</em> in selected slots of a recommendation widget. These products are defined by conditions based on each product’s attributes, ingested by the engine using a product feed. The products are then listed in the widget according to the recommendation strategy in place.</p><ul><li><em>Example</em>: A retailer wants to ensure men arriving on the site are only shown popular products for men in a homepage recommendation widget. The products presented in the widget will be ordered by each product’s popularity score (calculated by the number of interactions, add-to-carts, purchases, etc.).</li></ul><p><img fetchpriority="high" decoding="async" class="size-large wp-image-117663 aligncenter" style="box-shadow: 0 0 10px #9e9e9e;" src="https://www.dynamicyield.com/wp-content/uploads/2020/05/image3-1024x654.png" alt="homepage product recs" width="1024" height="654" /></p><div style="margin-bottom: 30px; margin-top: -5px; font-size: 13px; color: #999; text-align: center;">An example of a homepage product recommendation widget targeted at the “Men’s” audience, displaying only new products for men.</div><p>The second type of merchandising rule is an “exclude” rule. This rule, opposite of the include rule, ensures a subset of products <em>is not presented</em> in selected slots of a recommendation widget. These blacklisted products are defined by conditions based on each product’s attributes.</p><ul><li><em>Example</em>: A retailer looking to increase cart sizes wants to ensure a recommendation widget on triggered emails using the “Similarity” recommendation strategy does <em>not</em> present products priced over $20 for all users. Only eligible products will be presented in the widget, each of which will be ordered according to their similarity score to items already in a user’s cart.</li></ul><p><img decoding="async" class="size-large wp-image-117663 aligncenter" style="box-shadow: 0 0 10px #9e9e9e;" src="https://www.dynamicyield.com/wp-content/uploads/2020/05/email-rec-1024x683.png" alt="email recs" width="1024" height="683" /></p><div style="margin-bottom: 30px; margin-top: -5px; font-size: 13px; color: #999; text-align: center;">An example of an email product recommendation widget targeted at cart abandoners, displaying similar items under $20.</div><p>A third merchandising rule type is the “Pin” rule. This rule can completely override any recommendation strategy, allowing marketers to manually choose products to attach to specific slots in a recommendation widget.</p><ul><li><em>Example: If a retailer has an excessive amount of a specific pair of shoes in inventory, they can choose to pin this item to the first slot of a recommendation widget on the homepage to expose it to more users. </em></li></ul><p><img decoding="async" class="size-large wp-image-117663 aligncenter" style="box-shadow: 0 0 10px #9e9e9e;" src="https://www.dynamicyield.com/wp-content/uploads/2020/05/precious-metals-1024x489.png" alt="homepage recs" width="1024" height="489" /></p><div style="margin-bottom: 30px; margin-top: -5px; font-size: 13px; color: #999; text-align: center;">An example of a homepage product recommendation widget targeted at new site visitors, displaying a “pinned” product in the largest slot of a recommendation widget.</div><h2>Using merchandising rules</h2><p>When setting up new rules, marketers can drill down on criteria associated with each rule, designating time frames, values, audience segments, and more. A few of these examples include:</p><ul><li>Display popular items in “women’s dresses” to a visitor that has <em>viewed at least two dresses in the 14 days</em></li><li>Exclude showing items on sale <em>to high-value customers</em></li><li>Recommend highly-profitable items to consumers who have purchased or added items to their carts <em>valued at $150+ in the last week</em></li></ul><p><img loading="lazy" decoding="async" class="size-large wp-image-117663 aligncenter" style="box-shadow: 0 0 10px #9e9e9e;" src="https://www.dynamicyield.com/wp-content/uploads/2020/05/cart-product-rec-1024x489.png" alt="cart product recs" width="1024" height="489" /></p><div style="margin-bottom: 30px; margin-top: -5px; font-size: 13px; color: #999; text-align: center;">An example of a recommendation widget using a “Bought Together” strategy targeted at users who have added an item to their cart with an “exclude products over $10” rule in place.</div><p><strong>Multiple filter rules</strong><br /> Marketers are not limited to a single rule when crafting merchandising rules for its recommendation widgets. They can create more than one include / exclude rule, all of which will be applied, so long as <em>every </em>condition is met by the visitors being served the recommendations. It’s crucial to never create conflicting rules, as they can yield unexpected or no results.</p><p>In addition, marketers using the right engine should be able to manage priorities when dealing with multiple rules. For example, when pinning items, setting up multiple pins is possible. The marketer or merchandiser will simply need to manage the rule priorities, identifying which rule supersedes others.</p><h2>Getting started with merchandising rules</h2><p>In order to maximize revenue generated from machine learning-powered <a href="https://www.dynamicyield.com/lesson/product-recommendations-guide/">product recommendations</a>, marketers need to squeeze every ounce of potential from every campaign they create. When it comes down to it, merchandisers know their products (and each product’s attributes) better than anyone else. Using this knowledge to inform strategies, craft rules, and deploy product recommendations won’t just improve the discovery process – it will help users narrow down purchase decisions, generating revenue and returns.</p><div class="article-separator"><svg width="54" height="26" viewBox="0 0 54 26" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M53.0088 25.6367C52.7884 21.9564 51.8025 18.3625 50.1141 15.0849C48.4256 11.8073 46.0718 8.9181 43.2031 6.60203L43.2031 25.6367L53.0088 25.6367Z" fill="black"/> <path d="M14.4395 3.59038C10.3237 5.69174 6.82743 8.83082 4.29638 12.6973C1.76533 16.5638 0.28711 21.0239 0.0078125 25.6367L14.4395 25.6367L14.4395 3.59038Z" fill="black"/> <path d="M17.6563 2.20809L17.6562 25.6367L39.9829 25.6367L39.9829 4.36701C39.9829 4.36701 30.7303 -2.31342 17.6563 2.20809Z" fill="black"/> </svg></div><div class="article-tags-block"> <strong>Categories:</strong> <a href="https://www.dynamicyield.com/course/product-recommendations/">Product Recommendations</a></div><div class="article-tags-block"> <strong>Tags:</strong> <a href="https://www.dynamicyield.com/tags/marketing-strategies/">Marketing Strategies</a><a href="https://www.dynamicyield.com/tags/product-recommendations/">Product Recommendations</a></div><div class="article-pagination"><h5>Continue reading</h5><div class="article-pagination-row lc-flex"> <a rel="next" style="background-color: #f08b86" class="article-next-link pagination-link" href="https://www.dynamicyield.com/lesson/product-recommendation-strategies/" title="The essentials for choosing the right product recommendation strategy">The essentials for choosing the right product recommendation strategy</a><a rel="prev" style="background-color: #f08b86" class="article-previous-link pagination-link" href="https://www.dynamicyield.com/lesson/introduction-to-product-recommender-systems/" title="An introduction to product recommender systems">An introduction to product recommender systems</a></div></div></div></div></div></article></div><section class="lc-subscription-section"><div class="lc-subscription-form-block lc-subscription-inner"><h4 class="lc-subscription-title">Sign up for the XP&sup2; newsletter</h4><div class="lc-subscription-text">Join thousands of readers from Target, Citi, Spotify, Hulu, Google, Sephora, and other innovative brands who read our bi-weekly XP² newsletter, delivering educational content, research, and insights straight to your inbox</div><form class="mktoForm" data-formId="3876" data-formInstance="sub-inline"></form><div class="lc-privacy-wrapper"><div class="lc-privacy"> You may unsubscribe at any time. 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