An overview of Dynamic Yield’s Essential Recommendation Capabilities
After spending nearly a decade developing the most powerful recommendation engine in the market, take a look at an overview of the current recommendation capabilities available to Dynamic Yield customers.
In today’s hyper-competitive retail environment, consumers expect all of their digital experiences to be personalized and relevant, including the recommendations they receive from eCommerce companies. Machine learning has become the crux of personalizing the recommendation experience for visitors, assisting in making generalizations from observed data to better find the best products or offers to serve each specific shopper.
We have spent nearly a decade developing the most powerful recommendation engine in the market, built based on direct feedback from thousands of merchandisers and retailers across hundreds of global brands and verticals.
With Dynamic Yield, you can exercise full merchandising control over your content or product recommendations; adapt the recommendation layout according to context, fuse multiple strategies into a single recommendation widget, test and optimize any element – including the layout, design, recommendation strategy, location, widget headers, and more.
You can also predict customers’ next purchase through a self-training, deep learning recommendation strategy to automatically predict the next product a user may be interested in by exploiting the co-occurrence of products in a user’s browsing history.
This in-depth overview includes a comprehensive look at all of our existing recommendations capabilities, broken down across 13 categories.
Widget Creation
(UX & Design)
1. Flexible Design & Customization
Developers can build and design templates that are flexible for marketers to re-use, alter and scale across the site.
2. Recommendation Widget Preview
When the widget is rendered - this provides the ability to see the recommended results on the page without publishing the experience.
3. Out-of-the-box Widget Templates
Enables fast implementation of ready made designs.
4. Templating Engine
Allows saving recommendation widget design as templates - to be reused across multiple widgets.
5. Template History
View the history of changes made to the code of the templates. This can be used to trace the source of a problem (troubleshooting), or retrieve a previous version of your code.
Targeting
Conditions
6. Time-Based Targeting
Schedule when experiences should be live and removed from site.
7. Affinity-Based Targeting
Target users who expressed high interest - measured by their interactions with products (views, add to cart and purchase events) - in a specific product property (e.g. blue color, show category).
8. New User (First Session) Targeting
Target users who are currently in their first session ever (since Dynamic Yield script was implemented to the site).
9. Session Behavior Targeting
Enables targeting the recommendation widget experience by session behavioral properties like number of page views, current URL, previous URL, elapsed time, etc.
10. Shared Audience-Based Targeting
Target users who belong to a Dynamic Yield audience from a different Dynamic Yield site.
11. Traffic Source-Based Targeting
Enables targeting for users arriving via a specific traffic source.
12. Audience-Based Targeting
Target users who belong to a Dynamic Yield audience from this Dynamic Yield site.
13. Technology-Based Targeting
Enables targeting the recommendation widget experience by device, browser, operation system, operating brand, screen resolution, and user agent.
14. Location-Based Targeting
Enables targeting the recommendation widget experience by the user’s current location.
15. Weather-Based Targeting
Enables targeting the recommendation widget experience by the current or forecasted weather in the user’s current location.
16. Product Engagement / Social Proof
Enables targeting the recommendation widget experience to product pages displaying products that have received a given amount of views and purchases.
17. Page Property Targeting
Enables targeting the recommendation widget experience to product pages displaying products that have received a given amount of views and purchases.
18. Custom Evaluators
Enables targeting the recommendation widget experience by any property calculated adhoc using custom JS code.
Recommendation
Strategies
19. Automatic Recommendation Strategy
This strategy selects the best algorithm based on best practices for the page type chosen.
20. Affinity-Based Personalized Recommendations
Selects the most relevant products by all product engagements tracked for the user. Considers engagements from 2 years back up to engagements performed in the current session (real-time).
21. Purchased Together Recommendations
Selects products frequently purchased with the product being viewed or the set of products in the user’s cart. Can consider purchases made online or offline.
22. Viewed Together Recommendations
Selects products frequently viewed together with the product being viewed in the same session.
23. Recently Viewed-based Recommendations
Recommends the last items viewed by the current user (most recent appears first).
24. Recently Purchased-based Recommendations
Recommends the last items purchased by the current user (most recent appears first).
25. Most Popular Recommendations
Scores items based on the weighted sum of all interactions – such as purchase, add to cart and product view – favoring recent interactions over historical ones.
26. Most Popular in Category
Returns items by popularity, but only includes items in the category specified in the page context of the category page.
27. Similar Items
Recommends items that are similar to the item currently displayed, factoring in item popularity.
28. Personalized Recommendations (Collaborative Filtering)
Selects the most relevant products by comparing similar behaviors across all users using sophisticated machine learning models.
29. Purchased Together Offline Recommendations
Recommends products that have been purchased offline together with the item currently displayed or the set of products in the user’s cart.
30. Purchased Together Offline & Online Recommendations
Recommends products that have been purchased together offline or online with the item currently displayed or the set of products in the user’s cart.
31. Purchased with Recent Purchased Recommendations
Items which are usually purchased together with the last items purchased by the current user.
32. Last Purchased Recommendations
Presents the cart content of the most recent purchase by the current user.
33. Purchased with Last Purchased Recommendations
Items which are usually purchased together with items in the most recent purchase by the current user.
34. Fallback Strategies
In case the strategy returns fewer items than number of slots, the system will run a respective fallback algorithm for each strategy.
35. Deep Learning Recommendations
A self-training deep learning recommendation strategy to automatically predict the next product a user may be interested in based on the sequence of both historical and real-time interactions.
Dynamic
Strategy Filters
36. Product Properties
Setting allows the option for recommendations to show or exclude products that have all or a selected amount of product properties as the product being viewed. This capability is limited to the product page only.
37. Exclude recently viewed products
Removes recently viewed products from being displayed.
38. Exclude recently purchased products
Removes recently purchase products from being displayed.
Recommendation
Strategy Management
39. Shuffle Recommendation Results
This strategy selects the best algorithm based on best practices for the page type chosen.
40. Mixed Recommendation Strategies (Algorithmic Fusion)
Enables setting different recommendation algorithms per widget slot.
Merchandizing
Rules & Control
41. Real-Time Filters with Recommendation API
Add real-time filter rules to filter the results based on data obtained within the session (e.g. show products priced higher than the currently viewed product, present products based on the visitors explicit selection, etc.).
42. Custom Filtering Rule Creation
Allows setting rules that restrict the recommendations results.
43. Pin Products
Allows the user to force displaying specific products.
44. Whitelist Products (Include)
Ensure that only a subset of products will be presented in a specific widget or a specific slot.
45. Blacklist Products (Exclude)
Allows the user to restrict what products should not be recommended in a specific widget or a specific slot.
46. Dynamic List Creation
Allows selecting products to pin / whitelist / blacklist using a set of product attribute conditions (e.g. price is higher than $100, category is ‘Dresses’, etc.).
47. Merchandizing Rule Prioritization
Allows resolving conflicts between 2 contradicting rules.
48. Merchandizing Rule Targeting
Allows setting the conditions in which a rule should be applied (to what users, in what pages, etc.).
49. Merchandizing Rule Scheduling
Allows setting the date and time in which a rule should be applied.
Testing &
Optimization
50. Recommendation Algorithms Testing
A/B test different recommendation algorithms.
51. Recommendation Widget Layouts Testing
A/B test different recommendation widget layout and design (vertical vs horizontal, number of slots, showing/hiding the price, etc.)
52. Merchandizing Merchandizing Rule Testing
A/B test different recommendation filtering rules.
Channels
53. Web & Mobile Web
Renders recommendations anywhere on the web and mobile web site.
54. Mobile App
Renders recommendations anywhere on the mobile app.
55. Email
Renders recommendations on emails.
Reporting
56. Primary Metric Reporting
Renders recommendations anywhere on the web and mobile web site.
57. Enhanced Experience Reports – Secondary Metrics
Displays how variations are affected by metrics that are not the primary metric of the test. The available secondary metrics are CTR (in campaigns types that inject HTML), page views, Purchases, Revenue, AOV, and any custom event.
58. Probability To Be Best
In A/B testing - indicates each test variation’s probability to be the test’s winning variation (in terms of a specific primary metric. e.g. purchase revenue) based on DY’s Bayesian statistics model.
59. Enhanced Experience Reports – Audience Breakdowns
Allows comparing the performance of a recommendation widget between different audience groups.
60. Custom Detailed Recommendations Reports
Displays fine granular reports for recommendation widgets. (i.e show the direct & assisted impact of product recommendations on site’s performance with the ability to have flexible attribution windows).
61. Outlier Filtering
Prevents individual events from skewing results and incorrectly predicting average future user behavior.
62. Predictive Targeting
Automatically suggests what recommendation strategy should be applied per audience based on predicted performance.
Dynamic
Recommendations
63. Open-Time Rendering (Email Recommendations)
Recommendations are rendered at open-time to ensure algorithm calculations and product properties are up-to-date.
64. Triggered Emails
Sends an email with featured product(s) to users who have left site (or app) without completing a purchase. The email includes content relevant to the abandoned browsing session.
65. Push Notifications
Sends a push notification comprised of dynamically selected product(s) and (optional) action buttons at a schedule time or if an event is triggered.
66. Ads
Replaces static Display Recommendation Ads with recommendations driven by machine learning algorithms that choose the best variation in real time.
67. Landing Pages
Delivers personalized recommendation variations for each user instead of a single variation (one-size-fits-all).
Integrations
68. Product Feed Support
Contains the entire product catalog (products, with product metadata) and power product recommendations. Can fully ingest and handle large feeds with millions of product SKUs to power recommendations.
69. Delta Feed API
Sends real-time changes of items to the product feed.
70. API Control
Allows receiving recommendation results in raw format - to leverage recommendations in any external system and to render using custom code, using both server- or client-side deployments.
71. Contextual Widget Placement
Allows configuring where a recommendation widget should be placed without any coding.
72. Multi-Feeds Support
Automatically combine multiple files or sources into one feed.
73. Multi-Language Campaign Support
Allows multiple values for the same item in a data feed that represent different languages such as multiple product names and descriptions
Testing
Types
74. Manual Traffic Allocation (Standard A/B Tests)
Allows to test recommendation variations based on the defined percentage of traffic allocation.
75. Automated Traffic Allocation (Multi-Armed Bandit)
Automatically decides what percentage of site users will receive each variation. It leverages a Multi-Arm Bandit strategy, which means that variations that performed better in the past are allocated more traffic.
Recommendation
Types
76. Product/Item Recommendations
Displays the most relevant products/items for each user based on the context and goal of the recommendation widget.
77. Content Recommendations
Displays the most relevant content for each user based on the context and goal of the recommendation widget.
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href="https://www.dynamicyield.com/experience-os/"><span class="menu-banner-img-wrap"><img class="menu-banner-img-desktop" src="/wp-content/uploads/2024/10/expos-nav-updated.png" width="218" height="131" /><img class="menu-banner-img-mobile" src="/wp-content/uploads/2024/10/expos-nav-updated-mobile.png" width="114" height="98" /></span><span class="menu-cta-text-wrap"><span class="menu-cta-subtitle">Get an overview of Dynamic Yield’s product and see why we’re known as the Experience OS.</span> <span class="menu-cta-button">Learn more →</span></span></a></li></ul></div></li><li class="left"><div class="key_caps-wrapper"><h2>Core Capabilities</h2><ul class="links-list"><li class="key-caps-segment menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/segmentation/"><svg version="1.1" id="audience" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 30 30" style="enable-background:new 0 0 30 30;" xml:space="preserve"><path id="audience-green" fill="#4FB294" d="M9.291,30H30V9.291C18.562737,9.291,9.291,18.562737,9.291,30z"/><path id="audience-black" d="M12.527,15.947C9.240353,12.385597,4.806085,10.093244,0,9.471V30h7.639 C7.632218,24.897682,9.35532,19.943764,12.527,15.947z"/><circle id="audience-circle" fill="#4FB294" cx="15" cy="4.967" r="4.967"/></svg>Segmentation<span>Identify, build, and analyze audiences</span></a></li><li class="key-caps-omnichannel menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/targeting/"><svg version="1.1" id="personalization" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 20.987 30" style="enable-background:new 0 0 20.987 30;" xml:space="preserve"><path id="personalization-black-2" d="M20.987,19.507c0,5.795401-4.698099,10.4935-10.4935,10.4935S0,25.302401,0,19.507H20.987z"/><path id="personalization-pink" fill="#EE3968" d="M18.143,10.245c0,4.224978-3.425022,7.649999-7.65,7.649999S2.842999,14.469978,2.842999,10.245 H18.143z"/><circle id="personalization-black-1" cx="10.494" cy="4.307" r="4.307"/></svg>Targeting<span>Serve each customer with unique offers</span></a></li><li class="key-caps-recs menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/recommendations/"><svg version="1.1" id="recs" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 30 30" style="enable-background:new 0 0 30 30;" xml:space="preserve"><path id="recs_4" d="M15.953,30H30V15.953H15.953V30z"/> <path id="recs_3" d="M0,15.953V30h14.047V15.953H0z"/><path id="recs_2" fill="#FFA354" d="M30,14.047V0H15.953v14.047H30z"/><path id="recs_1" d="M14.047,0H0v14.047h14.047V0z"/></svg>Recommendations<span>Algorithmically predict customers’ interests</span></a></li><li class="key-caps-email menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/journey-orchestration/"><svg version="1.1" id="trigger" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 26.017 30" style="enable-background:new 0 0 26.017 30;" xml:space="preserve"><path id="trigger-black" d="M14.017,12h12l-12,18V12z"/><path id="trigger-purple" fill="#5D39B9" d="M12,18H0L12,0V18z"/></svg>Journey Orchestration<span>Reach customers at critical moments</span></a></li><li class="key-caps-abtesting menu-item menu-item-type-custom menu-item-object-custom"><a href="/optimization/"><svg version="1.1" id="testing" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 28.713001 30" style="enable-background:new 0 0 28.713001 30;" xml:space="preserve"><path id="testing-blue" fill="#6BDADA" d="M15.831,0c7.114532,0,12.882001,5.767468,12.882001,12.882S22.945532,25.764,15.831,25.764l0,0V0z"/><path id="testing-black" d="M12.882,30C5.767468,30,0,24.232533,0,17.118S5.767468,4.236,12.882,4.236V30z"/></svg>Optimization<span>Experiment on any digital property</span></a></li><li class="key-caps-search menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/search/"><svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_4082_5186)" id="cap-search-svg-bg"> <path d="M11.6757 9.81054C8.73958 9.81054 6.35089 7.42177 6.35089 4.48562C6.35089 2.62899 7.30664 0.992133 8.75134 0.0388074C8.50309 0.0155936 8.25193 0.00238037 7.99762 0.00238037C3.58066 0.00238037 0 3.58304 0 8C0 12.417 3.58066 15.9976 7.99762 15.9976C12.4146 15.9976 15.9952 12.417 15.9952 8C15.9952 7.86848 15.9916 7.73782 15.9853 7.60785C15.0166 8.94122 13.4462 9.81054 11.6757 9.81054Z" fill="black"/> <path id="cap-search-svg-circle" d="M11.6757 0.161346C11.2226 0.161346 10.7857 0.231773 10.3749 0.361649C8.62429 0.915114 7.35156 2.55436 7.35156 4.48561C7.35156 6.87003 9.29141 8.80988 11.6757 8.80988C13.5373 8.80988 15.1277 7.62753 15.7358 5.97437C15.9066 5.51 16 5.00851 16 4.48561C16 2.10119 14.0602 0.161346 11.6757 0.161346Z" fill="#FFC61E"/> </g> <defs> <clipPath id="clip0_4082_5186"> <rect width="16" height="16" fill="white"/> </clipPath> </defs> </svg>Search<span>Help customers find what they want</span></a></li></ul></div></li><li class="right"><div class="scale-wrapper"><h2>Made for Scale</h2><ul class="links-list"><li class="key_caps-element menu-item menu-item-type-custom menu-item-object-custom"><a href="/element/">Element<span>A groundbreaking new dimension of hyper-personalization</span></a></li><li class="key_caps-adaptml menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/ai/">AI & Automation<span>Increase automation, lower operational costs, and drive more revenue with a holistic AI personalization system</span></a></li><li class="key_caps-apis menu-item menu-item-type-custom menu-item-object-custom"><a href="https://www.dynamicyield.com/experience-apis/">Experience APIs<span>Give developers the tools they need to build the best customer experiences</span></a></li><li class="key_caps-enterprise menu-item menu-item-type-custom menu-item-object-custom"><a href="https://www.dynamicyield.com/enterprise-grade-personalization/">Enterprise solutions<span>Scale with security, privacy, and compliance, made for the enterprise</span></a></li></ul></div></li></ul><a href="/shopping-muse/" target="" class="banner"><div class="menu-banner"><span class="menu-cta-text"><strong>Meet Shopping Muse: </strong> Conversational commerce meets cutting-edge personalization.</span> <span class="menu-cta-button">Learn more →</span></div></a></div></li><li id="solutions" class="dropdown links"><a href="" class="label">Solutions</a><div class="content"><ul><li class="left"><div class="industries-wrapper"><h2>Industries</h2><ul class="links-list"><li class="industry-ecomm menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/ecommerce/">eCommerce</a></li><li class="industry-finserv menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/financial-services/">Financial Services</a></li><li class="industry-qsr menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/restaurants/">Restaurants</a></li><li class="industry-grocery menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/grocery/">Grocery</a></li><li class="industry-b2b menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/b2b-ecommerce/">B2B eCommerce</a></li><li class="industry-travel menu-item menu-item-type-post_type menu-item-object-page"><a href="https://www.dynamicyield.com/travel/">Travel</a></li><li class="industry-gaming menu-item menu-item-type-post_type 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Capabilities</div><div class="lc-header-right lc-flex"><ul class="lc-header-social lc-flex"><li class="et-social-icon"> <a href="#" title="Share on Facebook" class="icon" onclick="window.open('https://www.facebook.com/sharer/sharer.php?u=' + encodeURIComponent(document.URL) + '&t=' + encodeURIComponent(document.URL)); return false;"> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 460.1990662 458.5138245"><path 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class="button-inner"> XP² Newsletter </span> </a></div></div></div><div class="lc-progress-wrap"><div class="lc-scroll-progress"></div></div></header><article id="post-152578" class="et_pb_post post-152578 post type-post status-publish format-standard has-post-thumbnail hentry category-product tag-recommendations"><div class="article-content"><div class="article-breadcrumbs"> <a href="https://www.dynamicyield.com/blog/" title="Blog">Blog</a><ul class="post-categories"><li><a href="https://www.dynamicyield.com/blog/category/product/" rel="category tag">Product</a></li></ul></div><h1 class="article-title">An overview of Dynamic Yield’s Essential Recommendation Capabilities</h1><h2 class="article-excerpt">After spending nearly a decade developing the most powerful recommendation engine in the market, take a look at an overview of the current recommendation capabilities available to Dynamic Yield customers.</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>In today’s hyper-competitive retail environment, consumers expect all of their digital experiences to be personalized and relevant, including the recommendations they receive from eCommerce companies. Machine learning has become the crux of personalizing the recommendation experience for visitors, assisting in making generalizations from observed data to better find the best products or offers to serve each specific shopper.</p><p>We have spent nearly a decade developing the most powerful recommendation engine in the market, built based on direct feedback from thousands of merchandisers and retailers across hundreds of global brands and verticals.</p><p>With Dynamic Yield, you can exercise full merchandising control over your content or <a href="https://www.dynamicyield.com/lesson/product-recommendations-guide/">product recommendations</a>; adapt the recommendation layout according to context, fuse multiple strategies into a single recommendation widget, test and optimize any element – including the layout, design, <a href="https://www.dynamicyield.com/lesson/product-recommendation-strategies/">recommendation strategy</a>, location, widget headers, and more.</p><p>You can also predict customers’ next purchase through a self-training, <a href="https://www.dynamicyield.com/lesson/deep-learning-recommendations/">deep learning recommendation strategy</a> to automatically predict the next product a user may be interested in by exploiting the co-occurrence of products in a user’s browsing history.</p><p>This in-depth overview includes a comprehensive look at all of our existing recommendations capabilities, broken down across 13 categories.</p></div></article><div style="clear: both;"></div></div></div><div class="post-capabilities"><style>.post-capabilities-section-1:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-01.svg");
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.post-capabilities-section-1:after { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-bottom-01.svg");
}</style><div class="post-capabilities-container"><h3>Widget Creation <br>(UX & Design)</h3></div><div class="post-capabilities-section post-capabilities-section-1" style="background-color: #8bc8b8"><div class="post-capabilities-container"><div class="post-capabilities-list"><div class="post-capabilities-item" style="border-left-color: #8bc8b8"><h4>1. Flexible Design & Customization</h4><p>Developers can build and design templates that are flexible for marketers to re-use, alter and scale across the site.</p></div><div class="post-capabilities-item" style="border-left-color: #8bc8b8"><h4>2. Recommendation Widget Preview</h4><p>When the widget is rendered - this provides the ability to see the recommended results on the page without publishing the experience.</p></div><div class="post-capabilities-item" style="border-left-color: #8bc8b8"><h4>3. Out-of-the-box Widget Templates</h4><p>Enables fast implementation of ready made designs.</p></div><div class="post-capabilities-item" style="border-left-color: #8bc8b8"><h4>4. Templating Engine</h4><p>Allows saving recommendation widget design as templates - to be reused across multiple widgets.</p></div><div class="post-capabilities-item" style="border-left-color: #8bc8b8"><h4>5. Template History</h4><p>View the history of changes made to the code of the templates. This can be used to trace the source of a problem (troubleshooting), or retrieve a previous version of your code.</p></div></div></div></div><style>.post-capabilities-section-2:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-02.svg");
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.post-capabilities-section-2:after { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-bottom-02.svg");
}</style><div class="post-capabilities-container"><h3>Targeting <br>Conditions</h3></div><div class="post-capabilities-section post-capabilities-section-2" style="background-color: #f2d377"><div class="post-capabilities-container"><div class="post-capabilities-list"><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>6. Time-Based Targeting</h4><p>Schedule when experiences should be live and removed from site.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>7. Affinity-Based Targeting</h4><p>Target users who expressed high interest - measured by their interactions with products (views, add to cart and purchase events) - in a specific product property (e.g. blue color, show category).</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>8. New User (First Session) Targeting</h4><p>Target users who are currently in their first session ever (since Dynamic Yield script was implemented to the site).</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>9. Session Behavior Targeting</h4><p>Enables targeting the recommendation widget experience by session behavioral properties like number of page views, current URL, previous URL, elapsed time, etc.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>10. Shared Audience-Based Targeting</h4><p>Target users who belong to a Dynamic Yield audience from a different Dynamic Yield site.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>11. Traffic Source-Based Targeting</h4><p>Enables targeting for users arriving via a specific traffic source.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>12. Audience-Based Targeting</h4><p>Target users who belong to a Dynamic Yield audience from this Dynamic Yield site.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>13. Technology-Based Targeting</h4><p>Enables targeting the recommendation widget experience by device, browser, operation system, operating brand, screen resolution, and user agent.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>14. Location-Based Targeting</h4><p>Enables targeting the recommendation widget experience by the user’s current location.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>15. Weather-Based Targeting</h4><p>Enables targeting the recommendation widget experience by the current or forecasted weather in the user’s current location.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>16. Product Engagement / Social Proof</h4><p>Enables targeting the recommendation widget experience to product pages displaying products that have received a given amount of views and purchases.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>17. Page Property Targeting</h4><p>Enables targeting the recommendation widget experience to product pages displaying products that have received a given amount of views and purchases.</p></div><div class="post-capabilities-item" style="border-left-color: #f2d377"><h4>18. Custom Evaluators</h4><p>Enables targeting the recommendation widget experience by any property calculated adhoc using custom JS code.</p></div></div></div></div><style>.post-capabilities-section-3:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-03.svg");
}
.post-capabilities-section-3:after { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-bottom-03.svg");
}</style><div class="post-capabilities-container"><h3>Recommendation <br>Strategies</h3></div><div class="post-capabilities-section post-capabilities-section-3" style="background-color: #f6b3b9"><div class="post-capabilities-container"><div class="post-capabilities-list"><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>19. Automatic Recommendation Strategy</h4><p>This strategy selects the best algorithm based on best practices for the page type chosen.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>20. Affinity-Based Personalized Recommendations</h4><p>Selects the most relevant products by all product engagements tracked for the user. Considers engagements from 2 years back up to engagements performed in the current session (real-time).</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>21. Purchased Together Recommendations</h4><p>Selects products frequently purchased with the product being viewed or the set of products in the user’s cart. Can consider purchases made online or offline.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>22. Viewed Together Recommendations</h4><p>Selects products frequently viewed together with the product being viewed in the same session.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>23. Recently Viewed-based Recommendations</h4><p>Recommends the last items viewed by the current user (most recent appears first).</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>24. Recently Purchased-based Recommendations</h4><p>Recommends the last items purchased by the current user (most recent appears first).</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>25. Most Popular Recommendations</h4><p>Scores items based on the weighted sum of all interactions – such as purchase, add to cart and product view – favoring recent interactions over historical ones.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>26. Most Popular in Category</h4><p>Returns items by popularity, but only includes items in the category specified in the page context of the category page.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>27. Similar Items</h4><p>Recommends items that are similar to the item currently displayed, factoring in item popularity.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>28. Personalized Recommendations (Collaborative Filtering)</h4><p>Selects the most relevant products by comparing similar behaviors across all users using sophisticated machine learning models.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>29. Purchased Together Offline Recommendations</h4><p>Recommends products that have been purchased offline together with the item currently displayed or the set of products in the user’s cart.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>30. Purchased Together Offline & Online Recommendations</h4><p>Recommends products that have been purchased together offline or online with the item currently displayed or the set of products in the user’s cart.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>31. Purchased with Recent Purchased Recommendations</h4><p>Items which are usually purchased together with the last items purchased by the current user.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>32. Last Purchased Recommendations</h4><p>Presents the cart content of the most recent purchase by the current user.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>33. Purchased with Last Purchased Recommendations</h4><p>Items which are usually purchased together with items in the most recent purchase by the current user.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>34. Fallback Strategies</h4><p>In case the strategy returns fewer items than number of slots, the system will run a respective fallback algorithm for each strategy.</p></div><div class="post-capabilities-item" style="border-left-color: #f6b3b9"><h4>35. Deep Learning Recommendations</h4><p>A self-training deep learning recommendation strategy to automatically predict the next product a user may be interested in based on the sequence of both historical and real-time interactions.</p></div></div></div></div><style>.post-capabilities-section-4:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-04.svg");
}
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}</style><div class="post-capabilities-container"><h3>Dynamic <br>Strategy Filters</h3></div><div class="post-capabilities-section post-capabilities-section-4" style="background-color: #dec9f2"><div class="post-capabilities-container"><div class="post-capabilities-list"><div class="post-capabilities-item" style="border-left-color: #dec9f2"><h4>36. Product Properties</h4><p>Setting allows the option for recommendations to show or exclude products that have all or a selected amount of product properties as the product being viewed. This capability is limited to the product page only.</p></div><div class="post-capabilities-item" style="border-left-color: #dec9f2"><h4>37. Exclude recently viewed products</h4><p>Removes recently viewed products from being displayed.</p></div><div class="post-capabilities-item" style="border-left-color: #dec9f2"><h4>38. Exclude recently purchased products</h4><p>Removes recently purchase products from being displayed.</p></div></div></div></div><style>.post-capabilities-section-5:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-05.svg");
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}</style><div class="post-capabilities-container"><h3>Recommendation <br>Strategy Management</h3></div><div class="post-capabilities-section post-capabilities-section-5" style="background-color: #edecc9"><div class="post-capabilities-container"><div class="post-capabilities-list"><div class="post-capabilities-item" style="border-left-color: #edecc9"><h4>39. Shuffle Recommendation Results</h4><p>This strategy selects the best algorithm based on best practices for the page type chosen.</p></div><div class="post-capabilities-item" style="border-left-color: #edecc9"><h4>40. Mixed Recommendation Strategies (Algorithmic Fusion)</h4><p>Enables setting different recommendation algorithms per widget slot.</p></div></div></div></div><style>.post-capabilities-section-6:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-06.svg");
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}</style><div class="post-capabilities-container"><h3>Merchandizing <br>Rules & Control</h3></div><div class="post-capabilities-section post-capabilities-section-6" style="background-color: #f2937e"><div class="post-capabilities-container"><div class="post-capabilities-list"><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>41. Real-Time Filters with Recommendation API</h4><p>Add real-time filter rules to filter the results based on data obtained within the session (e.g. show products priced higher than the currently viewed product, present products based on the visitors explicit selection, etc.).</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>42. Custom Filtering Rule Creation</h4><p>Allows setting rules that restrict the recommendations results.</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>43. Pin Products</h4><p>Allows the user to force displaying specific products.</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>44. Whitelist Products (Include)</h4><p>Ensure that only a subset of products will be presented in a specific widget or a specific slot.</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>45. Blacklist Products (Exclude)</h4><p>Allows the user to restrict what products should not be recommended in a specific widget or a specific slot.</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>46. Dynamic List Creation</h4><p>Allows selecting products to pin / whitelist / blacklist using a set of product attribute conditions (e.g. price is higher than $100, category is ‘Dresses’, etc.).</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>47. Merchandizing Rule Prioritization</h4><p>Allows resolving conflicts between 2 contradicting rules.</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>48. Merchandizing Rule Targeting</h4><p>Allows setting the conditions in which a rule should be applied (to what users, in what pages, etc.).</p></div><div class="post-capabilities-item" style="border-left-color: #f2937e"><h4>49. Merchandizing Rule Scheduling</h4><p>Allows setting the date and time in which a rule should be applied.</p></div></div></div></div><style>.post-capabilities-section-7:before { background-image: url("https://www.dynamicyield.com/wp-content/uploads/2021/05/bg-top-07.svg");
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id="article-script-js"></script> <script type="text/javascript" id="dc_conversion_api-js-extra">var ajax_obj = {"nonce":"3cd30fb921","ajax_url":"https:\/\/www.dynamicyield.com\/wp-admin\/admin-ajax.php","ajax_action":"conversion_api_call"};</script> <script type="text/javascript" src="https://www.dynamicyield.com/wp-content/cache/autoptimize/js/autoptimize_single_34a6a3100a2ecbc7706af2d4c798f856.js?ver=935c7f8ce03960b02800bc8ae3231df8" id="dc_conversion_api-js"></script> <script type="text/javascript" data-noptimize>var dy_search_params = new URLSearchParams(window.location.search);
if(dy_search_params.has('utm_source')) {
localStorage.setItem('utm_source', dy_search_params.get('utm_source'));
}
if(dy_search_params.has('utm_medium')) {
localStorage.setItem('utm_medium', dy_search_params.get('utm_medium'));
}
if(dy_search_params.has('utm_content')) {
localStorage.setItem('utm_content', dy_search_params.get('utm_content'));
}
if(dy_search_params.has('utm_campaign')) {
localStorage.setItem('utm_campaign', dy_search_params.get('utm_campaign'));
}
if(dy_search_params.has('utm_term')) {
localStorage.setItem('utm_term', dy_search_params.get('utm_term'));
}
if(dy_search_params.has('mkt_tok')) {
localStorage.setItem('mkt_tok', dy_search_params.get('mkt_tok'));
}
// Also, fill in the data inside the Marketo form once its available
if(typeof window.MktoForms2 === 'object') {
MktoForms2.whenReady(function(_form) {
var form = _form.getFormElem();
console.log('MKTO FORM READY');
// Append the mkt_tok param if it exists in the URL
var ls_mkto_tok = localStorage.getItem('mkt_tok');
if(ls_mkto_tok) {
_form.addHiddenFields({
mkt_tok: ls_mkto_tok
});
_form.vals({ mkt_tok: ls_mkto_tok });
}
// Fill in the UTM data from localStorage
var dy_utms = {
'utm_source': 'txt_source__c',
'utm_medium': 'txt_medium__c',
'utm_content': 'txt_content__c',
'utm_campaign': 'txt_campaign_name__c',
'utm_term': 'txt_term__c',
'gclid': ['GCLID__c', 'GCLID_c__c']
};
for(var i = 0; i < Object.keys(dy_utms).length; i++) {
var ls_key = Object.keys(dy_utms)[i],
ls_value = localStorage.getItem(ls_key),
form_key = dy_utms[ls_key],
form_field;
if(ls_value) {
if(ls_key === 'gclid') {
ls_value = JSON.parse(ls_value).value;
}
if(typeof(form_key) === 'string') {
form_field = form[0].querySelector('input[type="hidden"][name="' + form_key + '"]');
if(form_field) {
form_field.value = ls_value;
}
} else {
for(var j = 0; j < form_key.length; j++) {
form_field = form[0].querySelector('input[type="hidden"][name="' + form_key[j] + '"]');
if(form_field) {
form_field.value = ls_value;
}
}
}
}
}
// IP Address
fetch('https://www.cloudflare.com/cdn-cgi/trace').then(function(result) {
result.text().then(function(values) {
values.split("\n").map(function(item) {
var pair = item.split('=');
if(['ip'].includes(pair[0])) {
form[0].querySelector('input[type="hidden"][name="IP__c"]').value = pair[1];
}
});
});
});
// GA ID and Referrer
var dy_utms_cookie = {};
document.cookie.split(';').forEach(function(el) {
var splitCookie = el.split('=');
var key = splitCookie[0].trim();
var value = splitCookie[1];
dy_utms_cookie[key] = value;
});
if(dy_utms_cookie["_ga"]) {
form[0].querySelector('input[type="hidden"][name="Visitor_ID__c"]').value = dy_utms_cookie["_ga"].substring(6);
}
if(dy_utms_cookie["personalize_referrer_string"]) {
form[0].querySelector('input[type="hidden"][name="Referral__c"]').value = dy_utms_cookie["personalize_referrer_string"].substring(6);
}
// ZoomInfo
if(localStorage.getItem('ZoomInfoData')) {
var zoominfo_data = JSON.parse(localStorage.getItem('ZoomInfoData'));
if(zoominfo_data) {
form[0].querySelector('input[type="hidden"][name="Alexa_Rank__c"]').value = zoominfo_data.alexaglobalrank;
form[0].querySelector('input[type="hidden"][name="Employees__c"]').value = zoominfo_data.employeesrange;
form[0].querySelector('input[type="hidden"][name="Company_Revenue_Range__c"]').value = zoominfo_data.estimatedannualrevenue;
}
}
// Also, dispatch a dataLayer event on success
_form.onSuccess(function(values, follow_up_url) {
window.dataLayer = window.dataLayer || [];
if([3739, 4802, 5094, 5110, 4946, 5076, 5116, 5171, 5172, 5173].includes(_form.getId())) {
window.dataLayer.push({
'event': 'demo_request_complete',
'eventCallback': function() {
if(follow_up_url) {
window.location.href = follow_up_url;
}
}
});
return false;
} else if([3876].includes(_form.getId())) {
window.dataLayer.push({
'event': 'newsletter_signup_complete',
'eventCallback': function() {
if(follow_up_url) {
window.location.href = follow_up_url;
}
}
});
return false;
}
});
});
}</script> <script>if(typeof(jQuery) !== 'undefined') {
$ = jQuery.noConflict();
}
function getUrlParameter(sParam) {
var sPageURL = decodeURIComponent(window.location.search.substring(1)),
sURLVariables = sPageURL.split('&'),
sParameterName,
i;
for (i = 0; i < sURLVariables.length; i++) {
sParameterName = sURLVariables[i].split('=');
if (sParameterName[0] === sParam) {
return sParameterName[1] === undefined ? true : sParameterName[1];
}
}
}
$(window).load(function(){
var videoID = getUrlParameter('video');
if(videoID !== undefined){
if($('#wistia-' + videoID + '-1').length){
var videoName = $('#wistia-' + videoID + '-1').parents('.tab-pane');
videoName = videoName[0].id
}
if($('a[href="#' + videoName +'"]').length){
$('a[href="#' + videoName +'"]').each(function(){
$(this).click();
});
}
addScript('https://fast.wistia.com/embed/medias/'+ videoID +'.jsonp');
addScript('https://fast.wistia.com/assets/external/E-v1.js');
$('body').append('<span class="wistia_embed wistia_async_'+videoID+' popover=true popoverContent=link dy-wistia-popup" style="display:inline"></span>');
window._wq = window._wq || [];
_wq.push({ id: videoID, onReady: function(video) {
var video = Wistia.api(videoID);
video.popover.show(); // open the popover
video.play(); // play the video
setTimeout(function(){video.unmute();}, 500);
}});
}
});</script> <style>#adBanner {
background-color: transparent;
height: 0;
width: 1px;
}</style><div id="wrapfabtest"><div id="adBanner"></div></div></body></html>
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