Recommendation | Skelter Labs
AIQ.AWARE Recommendation
Granular product recommendation
Based on analysis of real-time behavioral data, the solution first understands the customers and then recommends the products they need. You can increase the repurchase rate and average order value (AOV) through the selective application of the recommendation engine.
AIQ.AWARE Recommendation
Granular product recommendation
Based on analysis of real-time behavioral data, the solution first understands the customers and then recommends the products they need. You can increase the repurchase rate and average order value (AOV) through the selective application of the recommendation engine.
Increase customer engagement
By inducing customers to view product pages and consume content, the recommendation solution enhances customer engagement and increases customer lifetime value (LTV).
Cross-selling and upselling
Additional purchases are induced by recommending related products based on the combination of customer profile, purchase history and metadata.
First-time visitors converted into customers
Recommendations that drive purchases are offered based on the latest trends and similarities to other users’ behavioral data.
Various AI recommendation models
Different recommendation models are required depending on the customer decision journey. Selectively apply a variety of recommendation models depending on the location of exposure, from the landing page to the product detail page, or according to seasonal hero products and marketing campaigns.
Real-time personalization
Recommendations reflecting customers’ real-time intentions and interests
Products viewed together
The solution recommends products that are highly related to those a customer is currently viewing and that they are most likely to view next.
Products purchased together
A customer currently viewing a product will have products purchased by customers with similar purchasing behavior patterns recommended to them.
recommendation-022x
Various AI recommendation models
Different recommendation models are required depending on the customer decision journey. Selectively apply a variety of recommendation models depending on the location of exposure, from the landing page to the product detail page, or according to seasonal hero products and marketing campaigns.
recommendation-022x
Real-time personalization
Recommendations reflecting customers’ real-time intentions and interests
Products viewed together
The solution recommends products that are highly related to those a customer is currently viewing and that they are most likely to view next.
Products purchased together
A customer currently viewing a product will have products purchased by customers with similar purchasing behavior patterns recommended to them.
Recommendation
Easy model management
The more product types and numbers you have, the easier you need it to be to manage the recommendation model. A dashboard is provided to make it easy for marketers and operators to make changes to the recommendation model.
Scenario-specific model creation
Customized recommendation models for specific brands and product categories.
Model result preview
The suitability of recommendations for each customer is checked before the actual model is applied.
Easy model management
The more product types and numbers you have, the easier you need it to be to manage the recommendation model. A dashboard is provided to make it easy for marketers and operators to make changes to the recommendation model.
Recommendation
Scenario-specific model creation
Customized recommendation models for specific brands and product categories.
Model result preview
The suitability of recommendations for each customer is checked before the actual model is applied.
Performance analysis
Check the performance of the recommendation model with clear data. The dashboard provides a snapshot of the performance of the recommendation model, from its contribution to sales to the conversion rate (CVR).
Contribution analysis
Provides details of the contribution of the recommended model to sales, the AOV and the CVR.
Comparison by period and device
Provides performance comparison analysis through period and device classification.
Hourly and daily sales trend
Visualized data is provided for analysis of the sales effect, click-through rate (CTR) and CVR trends.
Performance analysis
Check the performance of the recommendation model with clear data. The dashboard provides a snapshot of the performance of the recommendation model, from its contribution to sales to the conversion rate (CVR).
Contribution analysis
Provides details of the contribution of the recommended model to sales, the AOV and the CVR.
Comparison by period and device
Provides performance comparison analysis through period and device classification.
Hourly and daily sales trend
Visualized data is provided for analysis of the sales effect, click-through rate (CTR) and CVR trends.
Data-driven user modeling,
Discover the hidden value
Customer analysis is performed based on not only structured data but also unstructured data that existing models were unable to utilize. The solution provides advanced recommendations, utilizing product names, keywords, colors and images, making sophisticated predictions possible even with extremely small volumes of data.
Fuel your drive for growth

E-commerce
Increased sales through customer data analysis and hyper-personalization

Finance
Recommends custimized financial products based on each customer’s spending habit
Unlock the power of your data
Unlock the power of your data