Customers implementing the self-training algorithm have witnessed double-digit uplifts in purchases and incremental revenue, compared to other personalized recommendation strategies
New York, April 28, 2021– Dynamic Yield, the Experience Optimization platform, today announced the gradual release of its state-of-the-art, self-training Deep Learning Recommendations Algorithm, enabling brands to predict the next series of products a consumer is most likely to buy.
Today, product recommendations are an essential requirement for any ecommerce business looking to increase engagement, purchases, and loyalty. However, a consistent challenge for marketers and merchandisers has been determining which products among a massive catalog of items to serve customers with various preferences and levels of intent.
Dynamic Yield’s Deep Learning-Based Recommendations instantly identify intent, even from the first session, to automatically match customers with the products they are most interested in or likely to buy, adapting as new data is ingested. The model employs the item2vec method, derived directly from its Natural Language Processing (NLP) counterpart, word2vec, to learn the products in a user’s browsing history, in-session activity, and trends seen across the site to recommend products each individual is predicted to engage with as they shop.
“Consumers have come to expect a high level of personalization in online retail interactions,” said Liad Agmon, CEO of Dynamic Yield. “Our Deep Learning model exploits cutting-edge neural network technologies of natural language processing that are found to be extremely effective within the recommendations domain, providing a superior approach for predicting customer wants and needs.”
Leading brands such as URBN Brands, OFFICE, GlassesUSA.com, and more are currently using Dynamic Yield’s deep learning algorithm to maximize the performance of their product recommendations. The advanced machine learning-powered strategy has already generated substantial double-digit uplifts in purchases and incremental revenue for Dynamic Yield clients.
Key benefits of the Deep Learning Recommendation model include but are not limited to:
- Results optimized per user – The deep learning algorithm automatically determines the right subset of parameters for each user based on their behavior, their position along the customer journey, as well as any trends identified across the site, eliminating the need to manually apply custom filter rules.
- Rapidly trained and adaptive – The algorithm is constantly and rapidly self-learning based on a huge amount of behavioral and product data to power recommendation results that instantly identify customer intent, even from the first session, and are continuously refined as new information comes in.
- Available within key digital channels – Apply the same advanced deep learning technology for delivering product recommendations predicted to drive engagement on the site within the mobile app as well as via email campaigns, which are tailored at the time of email open.
“With Dynamic Yield, we no longer have to manually choose a strategy for our homepage recommendations widget,” said Nadav Yekutiel, Head of Data, GlassesUSA.com. “Its deep learning algorithm automatically determines the right subset of parameters for each user based on their behavior, where they are in the customer journey, as well as trends seen across the site, making it superior to any other strategy available – not only in terms of output but also time saved.”
Dynamic Yield’s Deep Learning Recommendation model is part of AdaptML
, the company’s self-training deep learning AI 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.
For more information, visit: dynamicyield.com/adaptml/
About Dynamic Yield
Dynamic Yield helps enterprise brands quickly deliver and test personalized, optimized, and synchronized digital customer interactions. Marketing, Product, Development, and eCommerce teams from more than 350 global brands are using Dynamic Yield’s Experience Optimization platform as the technology layer on top of existing CMS or Commerce solutions to iterate faster and algorithmically match content, products, and offers to each individual for the acceleration of long-term business value.
Press Contact
Terri Shapiro
terri@headline.media
+1 347 344 5316
GlassesUSA.com Deploys a Deep Learning Algorithm to Adapt its Recommendations to Each Shopper
Leading eyewear retailer experiments with self-training recommendation model and achieves an 87% increase in revenue.
Introduction
Twelve years ago, the founders of GlassesUSA.com set out to provide high-quality prescription eyewear at a more reasonable price point than others in the market. A decade later, the company cut out the middleman within the manufacturing process and is now the world’s largest online eyewear retailer, offering a variety of prescription glasses, sunglasses, contact lenses, and more to consumers in over 92 countries. Today, with a customer-centric philosophy at the heart of how it operates, GlassesUSA.com personalizes to perfection, using data to inform how the brand serves a diverse base of customers with different tastes, preferences, and optical needs. But after years of optimizing its digital experiences, the eCommerce team was ready to move beyond recommending additional products of interest to those predicted to drive engagement. And after running a test against its traditional machine learning-based recommendations, discovered Dynamic Yield’s sophisticated deep learning algorithm was able to yield a 68% uplift in purchases and an 87% increase in revenue, all from a single widget.
Results
87% increase in revenue and 68% increase in purchases
After deploying a deep learning strategy recommendation widget against a traditional collaborative filtering strategy
45% increase in add-to-cart rate
From using the deep learning algorithm to drive shoppers further down the funnel, which immediately matches them with the products they are looking for
Testimonial
“With Dynamic Yield, we no longer have to manually choose a recommendation strategy for our Homepage recommendations. Its deep learning algorithm automatically determines the right subset of parameters for each user based on their behavior, where they are in the customer journey, as well as trends seen across the site, making it superior to any other strategy available – not only in terms of output, but also time saved.” – Nadav Yekutiel, Head of Data, GlassesUSA.com
The Challenge
Home to private label brands as well as over 60 designer names, GlassesUSA.com understands the difficulty of finding the perfect pair of eyewear among thousands of styles available in its catalog. Prioritizing ease of discovery, recommendations are a major component of its eCommerce site, running across various pages to better facilitate the buying process. Looking to maximize the performance of its product recommendations there, the team required a solution that could:
- Self-train quickly to recommend the most accurate items based on its extensive product catalog as well as trends seen across the site
- Take into consideration not just historical behavior, but also activity within the session to showcase items shoppers are most likely to engage with or buy
- Continue to learn with each bit of new data ingested into the model to ensure recommendation results are continuously optimized over time
That’s when the team began running deep learning recommendations with Dynamic Yield.
Execution
Dynamically recommended products predicted to drive action per individual with an advanced deep learning algorithm
The eCommerce team hypothesized that if it could provide recommendations more heavily tailored to the individual upon entry to the site, it could not only improve add-to-cart rates but increase purchases and revenue overall. After all, a classic collaborative filtering strategy that showcases items of interest based on what other similar users have interacted with can be highly effective, but the recommendations are not truly personalized.
After hearing about how Dynamic Yield’s deep learning recommendations could not only mine the past behavior of users but also their in-session activity, GlassesUSA.com set up an experiment to test the strategy against collaborative filtering.
Automatically configured per site, product feed, and individual, the team made but a few minor tweaks to the strategy before quickly seeing impressive results, most notably a 45% increase in add-to-cart rate, a 68% increase in purchases, and an 87% uplift in revenue attributed to the deep learning-based recommendations. And after running a similar test on mobile, the advanced algorithm proved yet again to be the strongest performer when compared to the control, with the team at GlassesUSA.com making deep learning the sole strategy for its popular homepage widget on this channel.
Key Takeaway
On its mission to match customers with the best possible eyewear at affordable prices, GlassesUSA.com recognized it had to move beyond serving similar or complementary items to those that are truly personalized to the user. The company’s willingness to push the boundaries of customer experience delivery led them to experiment with Dynamic Yield’s deep learning recommendation technology to better anticipate customer needs and automatically predict the products each individual is most likely to engage with, even at the very top of the funnel. The results of its initial homepage tests, both on desktop and mobile, have already proven a significant impact on the team’s ability to drive meaningful action, with the advanced algorithm generating a 68% uplift in purchases and an 87% increase in revenue.
About GlassesUSA.com
GlassesUSA.com is the fastest growing, leading online eyewear retailer in the US. Offering a variety of high-quality designer brands and established house brands in a wide-range of customizations and styles – single vision glasses, progressives, sunglasses, sports glasses, kids glasses, contact lenses, digital protection and more – GlassesUSA.com was built on the belief that purchasing eyewear shouldn’t break the bank and is on a mission to change the way consumers purchase eyewear. As a disruptor in the eyewear category, GlassesUSA.com continues to innovate the industry with tools that further the brand’s mission, including their proprietary Prescription Scanner App, which extracts current prescription from any pair of glasses and their AR Virtual Mirror, which enables consumers to see how the frames will look on them from the comfort of their home. The online retailer has been recognized by Internet Retailer and Inc. 5000 as a category leader. For more information, please visit www.GlassesUSA.com, https://ift.tt/2nVTpu3, and https://ift.tt/2nqiTvF.
About Dynamic Yield
Dynamic Yield is an AI-powered Personalization Anywhere platform that delivers individualized experiences at every customer touchpoint: web, apps, email, kiosks, IoT, and call centers. The platform’s data management capabilities provide for a unified view of the customer,
allowing the rapid and scalable creation of highly targeted digital interactions. Marketers, product managers, and engineers use Dynamic Yield daily for launching new personalization campaigns, running server-side and client-side A/B tests, leveraging machine learning for product and content recommendations, and employing algorithms for smartly triggered email and push notifications.
The post Dynamic Yield’s Deep Learning Product Recommendations Generate Exponential Revenue Returns appeared first on Multichannel Merchant.
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