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GlassesUSA.com deploys a deep learning algorithm to adapt its recommendations to each shoppers

This case study is related to the Dynamic Yield product.

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Reaching next-level personalization with Dynamic Yield to deepen customer relationships and increase sales

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 is now the world’s largest online eyewear retailer, offering a variety of sunglasses, contact lenses, and more. With the largest selection of styles and brands offered online, with offerings from Ray Ban, Oakley and more, and the ability to try everything online using the virtual mirror and enjoy free shipping and 100% money back guaranteed, GlassesUSA.com is your one stop shop for all your vision 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 on the homepage, GlassesUSA.com discovered Dynamic Yield’s sophisticated deep learning algorithm was able to yield a 68% uplift in purchases and an 88% increase in revenue, all from a single widget.

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Examine and compare basket composition, size and frequency based on customizable bundles and value offers.

Identify assortments which benefit the business the most to support product optimization

Find pairings of items that appear most commonly in baskets to facilitate strategies for new promotions and in-store displays

Explore subsequent purchase behavior and loyalty when certain trigger items are bought

Uncover top sellers and attachments and frequent item combinations in an automated report

"With Dynamic Yield recommendations, 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
glasses on a percentage statistic

A 68% uplift in purchases and an 88% increase in revenue, all from a single widget

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, including the homepage, which represents the initial point of entry for most online shoppers. 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.

Representing the very top of the funnel in the customer journey, GlassesUSA.com decided to revisit an area just below the fold where it had historically displayed a recommendation widget showcasing up to six different products. Hoping to extract as much value out of this front-and-center placement, the eCommerce team hypothesized that if it could provide recommendations more heavily tailored to the individual upon entry to this page, 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.

 

  1. Self-train quickly to recommend the most accurate items based on its extensive product catalog as well as trends seen across the site
    

  2. 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

  3. Continue to learn with each bit of new data ingested into the model to ensure recommendation results are continuously optimized over time

A homepage display of products heavily tailored to the individual entices add to cart

six pairs of priced glasses frames

Image courtesy of glassesusa.com

The 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 88% increase in revenue.

Contributors: Einat Haftel, Chief Product Officer; Ori Bauer, CEO, Dynamic Yield; Susan Grossman, EVP, Marketing Services

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