View-Based Attribution Tracking for Indirect Influencer Impact
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Solution Overview
Problem
Influencer-based affiliate marketing systems primarily reward influencers based on click-based attribution, neglecting the indirect influence that drives users to purchase products or services over time, and fail to accurately attribute view-based influence on sales and promotion.
Innovation Solution
An attribution management system that tracks pixel traffic and user interactions on candidate posts, boosts posts with low user engagement, and identifies purchases initiated from these posts to calculate view-based attribution amounts for influencers using machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If click-based attribution is used to reward influencers, then direct sales performance is accurately measured, but indirect influence and view-based impact are ignored
Solution Approach 1:
The attribution model is segmented into multiple components: click-based attribution for direct conversions and view-based attribution for indirect influence. This segmentation allows the system to measure different types of influencer impact separately while combining them into a comprehensive reward calculation, resolving the contradiction between precise direct measurement and comprehensive coverage.
Solution Approach 2:
The system adds a temporal dimension to attribution by tracking purchases that occur after the viewing period. Instead of only measuring immediate click-to-purchase conversions, the system extends the attribution window to capture delayed conversions, thereby measuring indirect influence that manifests after the initial view.
2Productivity
If focus is given to actual sales, then direct conversion performance is rewarded, but indirect or view-based influence is ignored
Solution Approach 1:
The system performs preliminary tracking of view-based interactions and stores this information before the purchase decision is made. By pre-tracking views and storing user engagement data, the system preserves the indirect influence information that would otherwise be lost, enabling later attribution of credit to influencers whose posts preceded the purchase.
Solution Approach 2:
The system implements feedback loops that monitor purchase behavior and attribute it back to viewing interactions. When a purchase occurs, the system analyzes the feedback data to determine if it can be attributed to prior views of influencer content, thereby recovering and crediting the indirect influence that initially drove the purchase decision.
3Area of stationary object
If posts are boosted to increase reach, then visibility and audience exposure are improved, but correlation between boosted posts and purchases becomes difficult to identify
Solution Approach 1:
The system introduces an intermediary tracking mechanism that acts as a mediator between boosted posts and purchases. This intermediary layer captures and attributes purchase data specifically to boosted content, separating the measurement of boosted post performance from organic post performance. The intermediary tracking system manages the complexity by providing a dedicated pathway to trace and measure the correlation between boosted reach and resulting purchases.
Data Source
AI summary
An attribution management system for rewarding view-based attribution on an online platform includes an attribution server that retrieves from a business manager over a data communication network using an Application Programming Interface (API), a set of candidate posts. Pixel traffic from the set of candidate posts is tracked based on first party data and social media posts. User interaction, including clicks and views, is identified on the set of candidate posts. Candidate posts below a pre-determined threshold value are boosted using a predetermined number of user interactions. A source of the one or more boosted candidate posts is identified using tags. At least one purchase from the views on one or more boosted candidate posts is identified. An attribution amount for each owner of the one or more boosted candidate posts is determined based on the at least one purchase using the source identified by the tags.