XEIBO

Challenge: The Personalization Bottleneck of the Legacy System 

Before adopting Appier, XEBIO used a well-known recommendation solution in the market. However, as consumer demands diversified, the system's multiple limitations began to hinder XEBIO from achieving its business goals. Key challenges included:

• Limited Model Variety: The system relied too heavily on user-based models, creating severe bias against low-traffic, low-exposure items and preventing consumers from discovering new products.
• Deployment Rigidity: Once deployed, the marketing team lacked the flexibility to swap models in real-time or optimize strategies on the fly.
• Poor Analytics Visibility: Without integrated performance and conversion tracking reports, the team struggled to measure immediate ROI.
• Lack of Post-Sales Support: After system deployment, there was no dedicated technical or customer success team to assist with troubleshooting and operational optimization.

For a retailer with a high SKU count like XEBIO, over-reliance on a single user model created severe "Recommendation Silos." This led to three major business obstacles: Discovery Bottlenecks for new products, Conversion Stagnation caused by repeatedly recommending the same categories, and Associative Failure due to weak cross-product logic.

Solution: Appier AIQUA's Multi-Model Architecture and Local Support 

To overcome these challenges and find the best answer, XEBIO decided to conduct a rigorous 28-day A/B test (January 21, 2025 – February 17, 2025), directly comparing the performance of Appier AIQUA recommendation models against competitors on the homepage and product pages.

Strategy 1: Agile Model Orchestration

Appier provided 6 core architectures and 28 scenario-based recommendation models (covering User-Based, Product-Based, Popularity, Advanced, Custom, and Autopilot). This gave XEBIO immense agility and the ability to orchestrate models on demand, freeing the team from past deployment rigidities.

• User-Based Models: Continuously process individual customer signals—such as products viewed , items added to cart, and historical purchases — to push personalized product selections tailored to specific interests like running, golf, or swimming.
• Product-Based Models: Focus on item-to-item associations by analyzing what other customers typically buy after viewing a specific item, ensuring recommendations remain relevant to the
  customer's current browsing focus

Strategy 2: Multi-model Architecture to Break "Recommendation Silos"

To address the chronic pain point of low-exposure products failing to reach consumers, Appier helped XEBIO simultaneously deploy different strategic models on the website (especially product pages):

• Product Discovery: Utilizing user-based models such as "Recommend for you" to push relevant content based on dynamically cross-referencing a visitor's active browsing with past behaviors to surface tailored sports gear, turning site navigation into a personalized product discovery engine. 
• Boost Cross-selling: Utilizing product-based models such as "Related High-Converting Products," operating on the logic of "what other customers bought after viewing this item," to precisely stimulate cross-category sales.

By combining User-Based and Product-Based architectures, Xebio overcomes traditional discovery bottlenecks. It ensures that customers see both highly relevant personal recommendations and high-converting, popular products, creating a richer, more engaging shopping journey that boosts conversion and add-to-cart rates. 

Strategy 3: Real-time Analytics and Enterprise-grade Local Support

Addressing the pain points of the legacy system, Appier introduced transparent, real-time performance tracking. More importantly, Appier provided a robust enterprise Customer Success Management (CSM) team locally in Japan. With strict SLA standards (first response within 1 hour, updates within 2 hours, solution provided within 8 hours), they became XEBIO's strongest operational backbone.

Result: Outstanding Conversion and Future Outlook

Following the rigorous 28-day A/B test (each model tested for 14 days in the top position), XEBIO witnessed the significant business impact delivered by the Appier solution:

• Outstanding Add-to-Cart Conversion: Appier achieved an Add-to-cart Rate of 20% on product pages, outperforming the competitor model's 16.7% by a significant margin of 3.3 percentage points.
• Building a Dynamic Discovery Engine: XEBIO successfully replaced its rigid legacy recommendation mechanism. Leveraging Appier's diverse architecture, which combines behavioral traits and popular trends, they enriched the personalized shopping experience.
• Enhancing Customer Experience and Driving Growth: The innovative recommendation mechanism infused the shopping journey with the joy of discovery, directly driving higher conversions and revenue performance.

 By implementing the Appier AIQUA Recommendation Solution, XEBIO not only broke through technical recommendation bottlenecks but also fully realized its brand vision as a "Sports Navigator," delivering a tailor-made sports lifestyle to every digital customer.

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