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success case
Internationally Renowned Animation Software E-Commerce Platform
Replaced its original recommendation system which could not apply business logic and tended to repeat its recommendation result. With Recommend HQ, the platform is able to recommend based on user behaviors, which significantly optimizes conversion rate and customer experience.
Recommend related products based on users’ browsing in the online store.
Overview
The customer is a global top-tiered platform for 3D character creativity and animation across Media Entertainment, Metaverse, Digital Twins, ArchViz, and AI Simulation based in Silicon Valley, USA.
The customer recently upgraded its recommendation engine to KKLab’s solution, Recommend HQ, and yielded impressive results. Compared to the previous recommendation system, Recommend HQ not only increased the conversion rate but also customized algorithms based on Reallusion’s business objectives to enable agility.
Customer Testimonial
KKLab‘s Recommend HQ provided Reallusion with a fully managed consulting service. In the process of helping us implement Amazon Personalize, they not only assisted with the initial data cleaning, but also continuously adjusted the algorithm and business logic based on our needs. This includes optimizing our product recommendations using metadata, addressing cold start issues, and adjusting recommendation logic based on our explicit business indicators. For example, increasing average order value (AOV). KKLab has a very professional team for recommendation algorithms and business logic. We are grateful for AWS’s assistance in referring us.
- Reallusion eCommerce Marketing Manager Alan - -
The Customer's Recommendation Chanllenges
Prior to implementing Recommend HQ, the scenarios where creators purchase 3D materials were not fulfilled by traditional recommendation engines. For instance, if a creator makes a query on "desert scene", traditional recommendation systems usually use keywords to match suggestions. Recommend HQ goes beyond keyword match and makes recommendations based on both keywords and user intent, providing cross-category recommendations such as cactus, frontier buildings, cowboys, etc.
How to customize a recommendation engine to fulfill the needs and strategies of a business?
Traditional recommendation systems rely heavily on consumer purchasing behaviors. As a result, best sellers are repeatedly recommended while the remaining products are not exposed. In this customer's case, the best sellers have high exposure. With the implementation of Recommend HQ, Reallusion is now capable of exposing a longer tail and diversifying product exposure. This helps the customer increase conversion, improve long-term revenue and strengthen user loyalty.
How to curate precise recommendations to meet the intent of 3D creators?
What's Recommend HQ Solutions
Recommendation based on AI semantic analysis
Applying NLU (Natural Language Understanding) algorithm, Recommend HQ analyzes label semantics and cross-language data with user behaviors to generate better recommendation results.
Optimization through hybrid algorithms
Recommend HQ is capable of integrating multiple algorithms to generate recommended results to meet the needs of various strategies.
With the customer, we integrated the analysis of product content and excluded previously purchased items to optimize results.
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