AI Dashboards - Traits Mapping

The AI Analytics Hub lets you configure and manage behavioral scoring and conversion optimization with precision and control. This section is where you ensure that Powerpop correctly interprets your website or app data by mapping your customer’s dataLayer fields to Powerpop’sstandardeventstructure.

Important: Incorrect settings in this section may lead to inaccurate analytics.

Traits Mapping

Traits mapping ensures accurate analytics by linking your customer data to Powerpop’s event structure. This allows Powerpop to properly recognize key user actions and weight them in your conversion funnel.

Accessing Traits Mapping

Go to AI Dashboards → Traits Mapping.
Click the Configure traits button.

Once you click “Configure traits,” the Traits Dashboard will open.

Here you can:

See the full list of funnel events tracked in Powerpop.
Adjust the relative importance (weights) of each event.
Add or remove mappings if your dataLayer structure has changed.
Click Save changes to apply your configuration.

Example: A purchase event might be given a weight of 10, while a product view might be given 2.

Customer Websites & Predefined Mapping

Before diving into event details, you need to define where and how traits are applied.

Select Customer Websites

You can manage traits per website.
Example: If you manage multiple domains, choose DEF (or the relevant website) before setting mappings.
This ensures that traits are correctly aligned with the right data source.

Predefined Mapping Options

When configuring traits and events, you’ll be asked to choose a mapping approach:

Custom Mapping
- Define how Powerpop should interpret key user actions.
  Includes settings like:
  - Search query definition → parameter names used for search terms (q , search , term ).
  - Success page identifiers → URL keywords for conversions (thankyou , orderconfirmation ).
    GA4 Events
- Automatically align Powerpop’s events with Google Analytics 4 events.
- Ideal if your GA4 implementation is already reliable.
- ⚠ Choosing this option will prompt a confirmation:
  
  You are about to overwrite existing traits mapping for DEF. Are you sure you want to continue?
  
  Options: Cancel | Confirm

Events Mapping

Define how different user events are mapped to behavioral traits and interest scores calculation in Powerpop.

Powerpop provides a comprehensive default mapping of common user actions. Each event contains required fields plus optional custom parameters (cp1–cp5) for business-specific data.

Below is a sample JSON mapping:

{

"login": {

"fields": {
  "cp1": "customParameter1",
  "cp2": "customParameter2",
  "cp3": "customParameter3",
  "cp4": "customParameter4",
  "cp5": "customParameter5",
  "email": "data.e_mail",
  "method": "data.method",
  "source": "data.source",
  "consent": "data.consent",
  "customerId": "data.customer_id",
  "customerType": "data.customer_type"
}

Each event supports:

Core fields (e.g., value, currency, transactionId)
Custom parameters (cp1–cp5) for business-specific extensions
Item mappers:
- fullItem : includes SKU, brand, category, variant, etc.
- minimalItem : includes name, price, category, quantity.

Search Query Definition

Help Powerpop detect when a user is searching on your website. Define the parameter names used in your URLs to represent search terms.

Example configuration:

[
  "search",
  "q",
  "query",
  "s",
  "keyword",
  "term",
  "kw",
  "text",
  "query_text",
  "search_term",
  "lookup",
  "product_search"
]

Success Page Identifiers (Orders or Conversions)

Mark URLs that indicate a successful purchase or conversion. Add keywords or path segments from your order confirmation pages.

Example configuration:

[
  "thankyou",
  "orderconfirmation",
  "success"

]

By completing these configurations, you ensure that Powerpop can accurately track, interpret, and optimize customer behaviors - helping you unlock deeper insights and drive smarter marketing decisions.

Discover how scoring models use your mapped traits to evaluate visitor intent and predict conversions with greater accuracy: Next: AI Dashboards – Scoring Models