Overview

HawkSearch naturally accumulates user behavior data through its native Event Tracking capabilities. With AI Multipliers, you can automatically adjust which behaviors have more of an impact on content shown to a user and fine tune these values to maximize user interaction.

The final result of how much an item is boosted is calculated by the AI Multiplier set as well as the number of tracked events of the relevant type. Each AI Multiplier, as well as the different Event types it tracks, are listed below.

AI Multipliers

Learning Search Multiplier

See also:

• What is the Learning Search Multiplier?
• How does the Learning Search Multiplier work?
• How does the Learning Search Multiplier work with Visitor Targets?

The Learning Search Multiplier is a way to boost popular items. The popularity filter is calculated based on the number of times a particular item was clicked on across keyword searches and the position the item was displayed on the page at the time it was clicked on. This multiplier works with the Click event and the Search event based on a rolling 30-day history, which will help pushing recently searched and clicked products toward the top of your search results.

Relevant Events: Search Event, Click Event.

Personalized Multiplier

The Personalized Multiplier leverages the Personalized Strategy in Recommendation. This multiplier dynamically adjusts search results rankings based on a visitor's individual preferences and behavior. This helps in surfacing personally relevant items higher in the search result, potentially improving user engagement and conversion rates.

Relevant Events: Page Load Event (View), Sale Event.

How It Works

With the Personalized Multiplier, any products that overlap between the items detected by the strategy and items in the results will be boosted. With Recommendations and event tracking set up correctly, this AI Multiplier works immediately as it utilizes real-time data.

1. Personalized Recommendation: The system uses the Personalized Strategy to generate a set of recommended items unique to each visitor.
2. Search Result Overlap: When a user performs a search, the system identifies any overlap between the search results and the user's personalized recommended items.
3. Boosting Mechanism: Items that appear in both the search results and the user's personalized recommendations receive a boost value configured in the dashboard.

Orders Multiplier

The Orders Multiplier adds a boost for items that are frequently bought. This multiplier works with the Sale event, based on a rolling, 30-day history, which will help pushing recently sold products toward the top of your search results.

Relevant Events: Sale Event (based on Quantity Sold).

Importing Order Data

To enhance the effectiveness of Orders Multipliers from upon implementation and mitigate the cold start situation, there's an option to upload historical order data following the instructions documented in this article. This historical data provides an initial basis for the multiplier calculations.

Add2Carts Multiplier

The Add2Carts Multiplier adds a boost for items that are frequently added to the cart. This multiplier works with the Add to Cart event, based on a rolling, 30-day history, which will help pushing recently added to cart products toward the top of your search results.

Relevant Events: Add to Cart Event.

Search Learning Process

The following section describes how AI Multipliers leverage user behavior data to enhance the search experience. Upon initial setup, these three AI Multipliers requires at least one day to begin influencing the search results, following the completion of the daily summation process.

1. User Interaction Data Collected
   • Captures various events:
     • Learning Search: click event
     • Personalized: page load event, sale event
     • Orders: sale event
     • Add2Carts: add to cart event
2. Data Processing and Storage
   • Event data is processed in real-time
   • Information is stored in appropriate collections
3. Hourly Data Summation
   • Recent data is aggregated to identify short-term trends
4. Daily Data Summation
   • Comprehensive overnight summation into the main Summary collection
5. Search Index and Learning Updates
   • Periodic rebuild of search index to rebuild learning (automated or manual)
   • Utilizes the main summary to update learning data for all items that have received new interactions
6. Continuous Improvement Cycle
   • Updated search results and recommendations to reflect recent user behavior
   • Process repeats continuously, ensuring the system evolves with user preferences

This cycle enables HawkSearch to continuously refine search relevance, improve product recommendations, and enhance overall user experience based on actual user interactions.

Relationship Between Tracking Data and AI Multipliers

The below table summarizes each AI Multiplier and the Event it tracks.