Multi-Channel Attribution Service for Conversion Optimization
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Solution Overview
Problem
Message providers face challenges in accurately determining the contribution of individual channels to conversion events, leading to inefficient resource allocation in multi-channel communication campaigns, as channels have varying influences based on message content, recipient interactions, and prior exposures.
Innovation Solution
A multi-channel attribution service that profiles channels, messages, and recipients to predictively attribute credit for conversions by analyzing impression paths and calculating uplifts for each impression, allowing for optimized channel and message selection in message delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If message providers send messages through multiple channels to maximize conversion, then the reach and potential effectiveness increase, but the ability to accurately determine which channel contributes to conversion decreases
Solution Approach 1:
The system establishes attribution models and profiles channels, messages, and recipients before the multi-channel campaign executes. This preliminary setup includes defining conversion windows, establishing baseline metrics, and creating recipient profiles that track engagement patterns across channels, enabling accurate attribution even as messages propagate through multiple channels
Solution Approach 2:
The patent introduces an attribution service as an intermediary layer between message delivery and conversion measurement. This service acts as a mediator that receives data from multiple channels, applies attribution models, and determines credit allocation. The intermediary processes impression paths and converts raw data into actionable attribution insights, resolving the measurement problem
2Adaptability or versatility
If message providers allocate resources across multiple channels without accurate attribution data, then channel exploration and optimization opportunities increase, but resource allocation efficiency decreases
Solution Approach 1:
The system implements continuous feedback loops where attribution results from previous campaigns inform resource allocation decisions in subsequent campaigns. The attribution service provides feedback on channel performance, message effectiveness, and recipient responsiveness, enabling providers to reallocate resources dynamically. This feedback mechanism transforms initial exploration into optimized resource distribution over time
Solution Approach 2:
The patent enables dynamic adjustment of campaign parameters based on attribution insights. Message providers can modify budget allocations, channel mix, message content, and targeting parameters according to measured performance. The system changes parameters such as spend per channel, message frequency, and recipient segments to optimize resource efficiency while maintaining adaptability
3Reliability
If message providers send repeated messages across multiple channels to recipients, then the probability of conversion increases, but the diminishing returns and wasted resources increase
Solution Approach 1:
The system applies partial action by determining the optimal number of impressions and channels for each recipient based on attribution models and predicted uplift. Rather than uniformly messaging all recipients across all channels, the system selectively applies message impressions where they are likely to generate positive uplift. This prevents excessive messaging while maintaining sufficient exposure to achieve conversions
Data Source
AI summary
Systems and methods are described for selecting recipients, channels, and messages for delivery via multi-channel communications. A message may be delivered to a recipient via multiple delivery channels, and the recipient may engage in a conversion event or activity associated with the message. Each potential delivery of the message may be associated with an incremental probability of causing the conversion event. The incremental probabilities may be used to determine the channel and recipient with the highest incremental probability of causing a conversion event for a given message, the recipient and message having the highest incremental probability of causing a conversion event when delivered via a given channel, and other combinations. Profiles may be used to compare messages, channels, and recipients in order to predict incremental probabilities, and messaging resources may be allocated according to budgets, cost-benefit analyses, or other criteria.