Human-like recommendations

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A self-training deep learning system that adapts the digital experience individually to each user by extrapolating buying intent from customer data and predicting products they may be interested in.

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Personalize using predictive and deep learning models on web, in emails, and via mobile apps

Replicate the personal touch of in-store shopping digitally

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.

Eliminate blind spots in insight activation and learnings

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.

Lower the barrier to AI adoption and accelerate data-driven decisions

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.

Maximize revenue with a deep
learning recommendation system

Use state-of-the-art, multi-faceted,
self-training recommendation models to
predict the next best series of products

Algorithm modeled off the entire product catalog

Upload a product feed with millions of SKUs to power your deep learning-based recommendations.

Rapidly trained & adaptive to changes in user behavior

Self-learning quickly, frequently, and off a huge amount of data, recommendation results are continuously optimized.

Optimal strategy set up automatically determined

Speed up time to value with an algorithm pre configured based on site trends, user behavior, and customer journey location.

“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
Nadav Yekutiel Head of Data, GlassesUSA.com
88% increase in ARPU
68% increase in purchases
Read the full case study →

Make the most out of every listing page
with personalized ranking

Upload any list of products and give your most critical pages a shot of personalization for increased conversions and purchases

Results tailored on a 1:1 level

Mine both historical and in-session activity to automatically showcase the right items from any list of products you wish to personalize.

Cross-listing page personalization

Target all or a specific set of listing pages and measure the impact of personalization using a control group.

Based on a single product interaction

Results are generated from as little as one engagement and then continuously updated as new product data and user events come in.

“The Ranking engine ensures products displayed on our category pages are interesting and relevant to the individual, allowing for not only simpler but deeper exploration.”
Ekta Chopra
Ekta Chopra Chief Digital Officer, e.l.f. Cosmetics
3.2X ROI
30% uplift in ARPU
Read the full case study →

Additional capabilities baked into AdaptML TM

Rank listing page results based on customer behavioral data

While site visitors shop, product listing pages are updated in real-time, adjusting to users’ entire purchase behavior, browsing activity, and more.

Enhance emails with deep learning recommendations

Take your email campaigns to the next level with recommendations predicted to drive click-through, tailored at time of open.

Serve deep learning recommendations in the app

Increase retention, engagement, and session length by recommending products that are anticipated to drive action.

Instantly identify customer intent, even in-session

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.

Deploy API-based deep learning campaigns

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.

Supports product feeds of all shapes and sizes

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.

Deliver on the expectation of personalization

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.

Prove deep learning’s efficacy with robust A/B testing

Compare the deep learning algorithm against any other recommendation strategy as well as the native default sorting order on product listing pages to determine the validate your results.

Accelerate the product discovery process and increase sales

From the homepage to product listing pages, even within emails and the mobile app, our deep learning algorithm matches consumers with the products they are looking for, faster.

Out-of-the-box recommendation templates

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.

Understand the business impact of deep learning

Use out-of-the-box testing capabilities to automatically calculate the incremental revenue uplift from deep learning recommendations and product listing page personalization.

Drive decisions based on actual user behavior

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.

Flexible KPI selection

Select either an out-of-the-box KPI or create your own custom metric to optimize towards when experimenting with the different experiences.

Advanced reporting & analytics

Get further insight into deep learning-based experiences by understanding how additional secondary metrics performed.

Custom attribution settings

Determine how results are calculated to align with your business goals with both session- and user-level attribution options.