AI Dashboards: Scoring Models
The Scoring Models section allows you to customize how Tango.ad evaluates products, brands, and customer behavior. By configuring scoring rules and multipliers, you can improve AI recommendations, personalize campaigns, and prioritize high-value visitors based on your business goals.
For an overview of the AI configuration modules available in Tango.ad, see AI Dashboards Overview.
To open this page:
- Navigate to AI Dashboards.
- Click Configure on Scoring Models.
Overview
Scoring Models includes four configuration areas:
- Brand Booster – Increase the importance of specific brands.
- Category Booster – Apply score multipliers based on product categories or URLs.
- Shopping Insights – Adjust scoring based on customer purchase history and cart value.
- Metadata Manager – Configure custom metadata tags for future AI enhancements.
After making changes, click Save in the upper-right corner to apply your configuration.
For related AI configuration options, see AI Dashboards: Advanced AI Settings and AI Dashboards: Traits Mapping.
Brand Booster
The Brand Booster allows you to increase the AI score for products belonging to specific brands.
Each brand is assigned a multiplier.
Example
{
"Whyskas": 1.25
}
In this example:
- Products from the Whyskas brand receive a 1.25× score multiplier.
- Higher multipliers make products from that brand more influential in AI recommendations and personalization.
When to Use
Use Brand Booster when you want to:
- Promote strategic brands.
- Prioritize premium product lines.
- Increase visibility for partner or sponsored brands.
Category Booster
The Category Booster applies score multipliers based on page URLs, keywords, or product categories.
Three matching methods are available:
Exact Match
Matches specific URLs.
Example:
{
"tags": [
"lipedem",
"komprese"
],
"match": [
"https://example.com/article"
]
}
Regex Match
Uses regular expressions to match multiple URL patterns.
Example:
{
"tags": [
"lipedem",
"komprese"
],
"match": [
"(lipedem|lymfat|celulit|komprese)"
]
}
Partial Match
Matches pages containing selected keywords or text fragments.
This option is useful when many URLs share common naming patterns.
When to Use
Category Booster is useful for:
- Highlighting seasonal collections.
- Promoting selected product categories.
- Increasing interest scores for educational content.
- Assigning tags based on product or content topics.
For more information about how visitor interests can be used in analytics and personalization, see Analytics: Interests.
Shopping Insights
The Shopping Insights configuration adjusts AI scoring based on customer purchasing behavior.
It uses configurable multiplier buckets for:
- Revenue
- Number of completed orders
Revenue Buckets
Example:
"revenueBuckets": [
{
"min": 0,
"mult": 1
},
{
"min": 500,
"mult": 1.25
},
{
"min": 1000,
"mult": 1.45
},
{
"min": 2500,
"mult": 1.8
}
]
Visitors with higher lifetime spending receive larger score multipliers.
Order Count Buckets
Example:
"orderCountBuckets": [
{
"min": 2,
"mult": 1.08
},
{
"min": 5,
"mult": 1.18
}
]
Customers with more completed purchases receive additional weighting.
Benefits
Shopping Insights helps Tango.ad:
- Recognize loyal customers.
- Prioritize high-value shoppers.
- Personalize campaigns based on purchasing history.
- Improve AI recommendations using customer lifetime value.
For additional information about customer and purchase data, see Analytics Overview.
Metadata Manager
The Metadata Manager is reserved for custom metadata configuration.
Currently, the editor contains an empty JSON object:
{}
Future updates may allow you to:
- Define custom metadata fields.
- Store AI-related product attributes.
- Extend personalization using additional product information.
For information about using custom data fields throughout Tango.ad, see Meta Fields Overview.
Saving Changes
After updating any scoring configuration:
- Review your JSON configuration.
- Click Save in the upper-right corner.
- The updated scoring model will be applied to the selected website.
Best Practices
- Use modest multipliers to avoid overly biasing AI recommendations.
- Reserve Brand Boosters for brands that align with your business priorities.
- Use Category Boosters to highlight important product groups or content.
- Configure Shopping Insights to reward loyal customers based on lifetime value.
- Review scoring rules regularly to ensure they reflect current merchandising and marketing strategies.