Go from static to fully personalized results on listing pages
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Go from static to fully personalized results on listing pages

Find out how brands are capitalizing on the massive revenue opportunity presented by product listing page personalization.

For years, marketers have strived to perfect the homepage experience. The initial point of entry to a site for many, everything from the brand’s homepage hero banner, content modules, messaging, and recommendation widgets are optimized to orient visitors at the beginning of their shopping journey.

However, as acquisition costs have continued to rise, many brands have come to realize that it’s often more strategic to direct visitors to category or product listing pages (PLPs) further down the funnel, meaning some may never even be exposed to the homepage. In fact, in our work with leading global retailers over the last decade, Dynamic Yield has found that almost 80% of users go through PLPs before making their final purchase.

Yet, despite how vital they have become to the buying process, most listing pages remain unpersonalized, with merchandisers often lacking the bandwidth to curate experiences beyond a set number of slots or pages. But in a highly saturated eCommerce market, tailoring these high-trafficked areas of the site according to each visitor’s interest and in the moment needs is growing increasingly important.

Accelerating discovery with personalized ranking on listing pages

Amazon, Netflix, and Spotify have already moved to customer-centric recommendation strategies, adapting their results based on data collected from each user to better match them with the appropriate products, movies, songs, and more.

Industry leaders are creating fully contextual and personalized adaptations of their inventory

Amazon, Netflix, and Spotify are creating fully contextual and personalized adaptations of their inventory

Similarly, eCommerce brands can eliminate the needless time visitors spend browsing hundreds upon thousands of options on a listing page, and without the extensive in-house development resources as the giants referenced above.

Dynamic Yield’s deep-learned based Ranking engine

Watch a quick overview of how Dynamic Yield’s personalized product listing page solution works

Designed to allow brands to seamlessly go from static to fully personalized results on listing pages, marketers and merchandisers can now employ Dynamic Yield’s Ranking engine to instantly tailor the default sorting order of any list of items, showcasing the most relevant ones first.

Using a state-of-the-art self-training deep learning model, our Ranking engine predicts which products an individual is most likely to engage with or purchase based on their past behavior, in-session activity, as well as trends seen across the site at any given moment. Further, the results are updated continuously to adjust to new data as it comes in, ensuring products reflect the ever-changing needs of the user.

Simply target all or a specific set of pages, upload your preferred list of products, set it live and measure the impact of personalization using a control group.

Creating Personalized Rankings for Listing Page Campaigns in Dynamic Yield

Creating a Personalized Product Listing Page Campaign in Dynamic Yield

How e.l.f. Cosmetics drove a 29.7% increase in RPU by tailoring its PLP sorting

With 48% of e.l.f. shoppers visiting its product listing pages (PLPs), the popular beauty retailer hypothesized it could increase key business metrics across the site by tailoring the sorting order of its category pages.

The team ran an experiment targeting nine of its most popular category pages for eyeshadow, lipstick, lip gloss, lip balms & treatments, face primer, and more with a 50/50 traffic split against its native default sorting order. After running the test from the end of October to late November, the product listing page personalization, which leverages a deep learning model to surface items the user is most likely to interact with next, led to a 3.3% increase in revenue per user (RPU) across all categories.

Primed to capitalize on the busy Black Friday / Cyber sales events, e.l.f. then scaled its dynamic PLP ranking to the majority of the brand’s traffic. This change led to 29.7% more revenue per user being generated over the short-lived Cyber week. And today, the team continues to leverage Dynamic Yield’s deep learning-based Ranking engine to automatically optimize the sorting order of products on its listing pages, helping visitors find what they are looking for faster, and without hassle.

e.l.f. Cosmetics implemented personalized product listing pages, resulting in a 29.7% increase in revenue per user above the native sorting order

Read more about how e.l.f. Cosmetics tailors the beauty regimen to every shopper.

Massive personalization opportunities exist beyond the homepage, on listing pages

The initial stages of product discovery are no longer reserved solely for the homepage anymore, with brands introducing their products further along the funnel across various channels within the customer journey. Given this significant change, teams must now place the same emphasis on personalizing the homepage as they would their listing pages, which are crucial to determining what items a user sees and ultimately buys.

With Dynamic Yield’s Ranking engine, delivering 1:1 personalization on these pages is now within reach, allowing brands to deliver experiences on par with some of the most innovative players in the space. The impact of which has already proven to increase product clicks, add-to-cart rates, and most importantly, average revenue per user (ARPU).

The solution outlined in this post is part of AdaptMLTM, a system made up of Dynamic Yield’s self-training deep learning algorithms, which adapt the digital experience to each individual user by extrapolating buying intent from customer data and predicting products they may be interested in.

If you are a Dynamic Yield customer interested in personalizing your product listing pages, please contact your Customer Success Manager. And click here to learn more about the implementation process.

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