A centralized AI system to automate decision making and intensify personalization across Experience OS.
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Similar to an in-store salesperson suggesting suitable products for a shopper, the AdaptML™ system mimics human decisioning to present and recommend the most relevant products and offers to each person.
The system breaks down silos between applications and consolidates data to identify in-the-moment buying intent signals, ensuring learnings are shared and applied across channels.
Going head-to-head with decades of human data science experience, AdaptML™ alleviates the need to allocate heavy development resources or build costly in-house algorithms.
Recommend the next best product
series with a self-training
multifaceted predictive model.
Upload a product feed with millions of SKUs to power your deep learning-based recommendations.
Self-learning quickly, frequently, and off a huge amount of data, recommendation results are continuously optimized.
Speed up time to value with an algorithm pre configured based on site trends, user behavior, customer journey, popularity by geo-location, and more.
“We no longer have to manually choose a strategy for our Homepage recommendations, helping us deliver exceptional digital experiences while also saving us time.”
Nadav Yekutiel Head of Data, GlassesUSA.com
Understand your customers like never before with a neural network algorithm that predicts affinity and future intent with unrivaled accuracy.
Affinity profiles and recommendation results update in real-time to account for one-time purchases and subsequent complementary products.
AffinityML analyzes historical site-wide patterns to intuitively gauge purchase intervals, tailoring user affinities to each individual's unique rhythm.
Our affinity neural network algorithm is trained on both the behavior of each individual user and the site activity of all users, enabling it to understand user behavior on both micro and macro levels to deliver precise, predictive, and relevant content.
Surface attributes that can't be described with keywords to open new possibilities for your catalog and recommendations.
Help your customers find exactly what they’re looking for, faster, by recommending visually similar items to the product they’re currently viewing.
Surface similar, relevant items to the current product regardless of popularity score or metadata tags, broadening discovery across your entire catalog.
Take your email campaigns to the next level with recommendations predicted to drive click-through, tailored at time of open.
Increase retention, engagement, and session length by recommending products that are anticipated to drive action.
Beyond past behavior, take into consideration the shopper’s current activity as well as the ever-changing trends seen across the site to refine your recommendations.
In addition to client-side support, launch your deep learning recommendations and product listing page personalization entirely through the server code for increased flexibility, control, and privacy.
Our deep learning recommendation algorithm works with any type of product feed and isn’t dependent or sensitive to the richness of the metadata in your feed.
Go from serving additional products that may be of interest with global, contextual, or even affinity-based strategies to predicting items a user is most likely to engage with.
Compare the deep learning algorithm against any other recommendation strategy to validate your results.
From the homepage to within emails and the mobile app, our deep learning algorithm matches consumers at various stages of the funnel with the products they are looking for, faster.
Increase time to market with by selecting from dozens of recommendation templates which are ready-to-be modified and set live using the deep learning strategy.
Use out-of-the-box testing capabilities to automatically calculate the incremental revenue uplift from deep learning recommendations.
Break free of relying on visual attributes and product metadata to serve similar or complementary items, using real historical and in-session activity to deliver 1:1 recommendations.
Select either an out-of-the-box KPI or create your own custom metric to optimize towards when experimenting with the different experiences.
Get further insight into deep learning-based experiences by understanding how additional secondary metrics performed.
Determine how results are calculated to align with your business goals with both session- and user-level attribution options.
Personalize every step of the customer’s journey with your brand across digital channels
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The rise of deep learning recommender systemsAs brands across industries continue to adopt deep learning, learn how it is being adapted for the delivery of product recommendations that enhance the customer experience and generate meaningful revenue.
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