SABON achieves 22% uplift in ARPU
Enhanced web experience using Dialogue AI
A leading lifestyle & beauty brand with global operations with over 180 branches world-wide, increases engagement and sales using automated content driven personalization.
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22%
ARPU Uplift
18%
Conversion Rate Uplift
56%
Session Time Uplift
INTRODUCTION
From Tel Aviv to Around the World
In 1997, two childhood friends opened a small soap shop with a 70-year old native Australian recipe they discovered during their travels.
From the Hebrew word for “soap”, SABON was born. 20 years Later - SABON is a multinational brand with 180 branches worldwide and a strong online presence. SABON is a global brand with a unique personality and language.
They offer their customers a variety of unique and exclusive fragrances that have become associated with the brand over the years. Each customer has their own product preferences, based on fragrance, product type, design, and more. The need to identify the user's exact preferences in real time and make accurate product recommendations is critical. This allows the seller to offer the most relevant products tailored to the customers’ preferences.
“An impressive combination of terrific product and great support. Dialogue’s technology has proven that it can operate automatically and show results”
Shani Ben Shushan
E-commerce Director
CHALLENGES
Customization & Automation
The company searched for a solution that could help them enhance their online shopping experience and maximize online sales.
SABON maintains an exclusive membership club that promotes special offers to members. Analyzing each unique user’s journey using existing data and advanced technology was imperative for them.
They asked for our help to identify whether a user is a member of the club or not, and personalize the user’s shopping experience accordingly. The ability to adjust and personalize offers, content, and recommendations to both club members and new users was their primary goal en route to improving their overall website performance.
The team had to find an effective solution that could operate automatically without requiring their constant attention. Such a solution would need to produce fast, proven, and quantifiable results
EXECUTION
Proven results with full automation
After deep analysis of SABON’s needs and limitations, Dialogue was able to offer a solution tailored to their website from both a technical and visual perspective.
Fully customized widgets (see more about each widget’s functionalities and qualities below)
- Tailored machine learning personalization algorithm - that is able to operate in a low-data environment and use multiple data points to produce superb results.
- “Low-touch” fully automated solution - that does not require manual maintenance, automizes content creation, and optimizes operations independently using machine learning technology.
- Performance transparency via Dialogue’s dashboard + Google Analytics - ability to track and quantify results in real-time.
#STORYTELLER
ENGAGING CONTENT. AUTOMATICALLY GENERATED
Personalized engaging content units presented inside the catalog pages. The content is generated automatically and is personalized to each user based on their online journey.
A returning customer who’s order history indicates that Jasmine is his favorite scent will see a promotion for the Jasmine scent exfoliating soap product, maximizing purchase potential.
A new customer who browsed the new collection will be exposed to the exciting new Mango-Kiwi scent.
#Booster™
CART RECOMMENDATIONS TO UPLIFT ARPU
Personalized recommendations, based on the user’s engagement and sales patterns that drive up sales before check-out.
The recommendations in the cart will function as products placed near the POS, just before the end of the shopping spree.
Our algorithm considers each customer's current shopping journey, taking into account preferences related to their favorite product types, scents, texture, price range, and more.
Therefore, complementary products and attractive offers will appear, utilizing all the data collected during the user’s current and past shopping journeys.
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