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Success Story

EF Shop

Appier’s deep learning algorithms achieved a sales performance 3X better than conventional models

"Appier has been a trusted, long-term partner for EF Shop. Its AIQUA solution has transformed how we engage with our customers and the level of personalization we are able to provide. From automation to advanced hybrid algorithm recommendation, Appier's solution empowered us to offer more precise product recommendations for onsite visitors and boost revenue growth. We look forward to continuing to work with Appier to continue to provide an outstanding experience for our customers."

EF Shop Marketing Director

Grace Yang

EF Shop_Single


Phase 1: Lacking effective multichannel engagement approach

The client, like most e-commerce stores, had multiple channels to reach potential customers but lacked an easy way to manage engagement on all channels, including the website, social media, and emails. This inability to juggle all marketing channels made it difficult for the company to capture sales opportunities from all touchpoints.

Phase 2: Need for a recommendation engine

As the client grew larger and stocked its platform with more items, it needed a way to better promote its products to shoppers. The recommendation section has always been a great place for shoppers to find relevant products that they may not have thought to look for. At this stage in their business, the client needed a recommendation engine for shoppers to better explore their site.

Phase 3: Time for more refined, personalized recommendations

In about a year’s time, the client saw tremendous growth in its business from the basic recommendation feature it adopted in Phase 2. To continue to cater to its shoppers and their evolving need for a diverse range of products and personalization in online shopping, the client needed more advanced recommendations for both products and content.


Phase 1: Multichannel marketing automation for easier engagement

The client used AIQUA to automatically push out messages to customers on different channels such as its website, the LINE app, and email inboxes. In Phase 1, the client improved its web engagement that resulted in 4X the subscription rate and 3.6X the active subscribers count of the industry benchmark. With AIQUA, the client sent out 1 to 2 million marketing emails per month and effectively re-engaged dormant users to shop with the platform.

Phase 2: Fundamental recommendation engine

The client onboarded one of AIQUA’s recommendation engines that displays items that people who “viewed also viewed.” With this one recommendation scenario alone, which shows what people who looked at the item a shopper is currently looking at also browsed, Appier was able to increase and accelerate conversion. This section was strategically featured on product and shopping cart pages, where shoppers are likely to be thinking about what else to buy.

Phase 3: Advanced recommendation engines with AI

The client onboarded five other recommendation scenarios, and using Appier’s advanced, hybrid algorithms, was able to serve shoppers with personalized recommendations. The engines used deep learning to analyze text and images from each viewed product to predict what other similar and relevant products a user may be interested in. The engines also used machine learning to analyze and predict user behavior to recommend products that will likely be viewed or purchased, achieving a sales performance 3X better than conventional models. The AI-powered engine continues to learn as a shopper continues to interact with content and products, allowing the experience to become more and more personalized as time goes on. Going beyond engagement and recommendation, Appier is working with the client now on new AI applications for the best customer experience.

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