Success Attribution via Historical Influence Scoring
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Solution Overview
Problem
Current attribution models, such as Last Touch, First Touch, and Linear, are not 100% accurate and fail to accurately assign credit for influencing user actions leading to a success event, as they rely on assumptions that may not hold true in all scenarios.
Innovation Solution
A method for success attribution using historical influence, where each user action is assigned a current success influence score based on its historical influence score as a proportion of the total historical influence scores of all participating actions, giving credit to actions that have historically shown greater influence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional attribution models (Last Touch, First Touch, Linear) are used to assign credit for success events, then the attribution process is simple and fast, but the accuracy of influence assessment is insufficient
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing historical influence scores for each user action before actual attribution is needed. When a success event occurs, the system retrieves these pre-computed scores and uses them to determine attribution weights, rather than calculating influence from scratch. This preliminary computation of historical data enables more accurate real-time attribution without adding complexity to the moment-of-need processing.
Solution Approach 2:
The system implements feedback by continuously updating historical influence scores based on observed user behavior patterns and success event outcomes. As more user actions and conversions are tracked, the historical influence scores are refined and adjusted, creating a feedback loop that progressively improves attribution accuracy. This feedback mechanism allows the system to learn from past data and adapt to changing user behaviors over time.
2Measurement precision
If equal credit is assigned to all user actions (Linear model), then the attribution process is simple, but it fails to account for varying influence levels of different actions
Solution Approach 1:
The system applies local quality by assigning different weights or credit values to different user actions based on their specific historical influence scores. Rather than treating all actions uniformly, the system evaluates each action type (e.g., search, click, view, purchase) and assigns localized credit proportions reflecting its actual contribution to conversions. This allows high-value actions to receive more credit while low-value actions receive less, creating a differentiated attribution approach.
Solution Approach 2:
The system changes parameters by dynamically adjusting attribution weights based on historical performance data. Instead of using fixed equal weights, the system modifies the credit allocation parameters according to observed user behavior patterns, action types, and conversion histories. This parameter adaptation enables the system to reflect varying influence levels of different actions while maintaining a systematic attribution framework.
3Measurement precision
If historical influence data is collected and analyzed for all user actions, then attribution accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by pre-computing historical influence scores and storing them in an optimized format before attribution is needed. This advance preparation includes aggregating historical data, calculating baseline influence metrics, and organizing data for quick retrieval. When a success event occurs, the system retrieves these pre-processed scores rather than analyzing raw historical data from scratch, significantly reducing real-time processing time while maintaining attribution accuracy.
Solution Approach 2:
The system extracts only the essential historical influence scores needed for attribution from the broader historical data repository, rather than processing or analyzing all available historical data. By extracting and utilizing only the relevant pre-computed influence metrics, the system minimizes data processing requirements while still capturing the necessary information for accurate attribution. This selective extraction approach reduces computational overhead and processing time.
Data Source
AI summary
User actions prior to, and associated with, an online success event may be considered participating actions that may have influenced the user toward the success event. A previously measured success influence metric for the participating actions may be used to determine a historical influence score for each participating action leading up to the success event. Each participating action may be assigned a current success influence score based on that event's historical influence score as a percentage of a combined historical influence score of all the participating actions for the success event. Additionally, the assigned current success influence scores may be combined with the previously measured success influence metric for use as historical influence scores for success attribution regarding further instances of the success event.