Clustering Models for Bias-Reduced Campaign Effectiveness Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for measuring the effectiveness of marketing campaigns suffer from bias due to insufficient matching of attributes between test and control groups, leading to inaccurate results and increased sensitivity to noise in the data.
Innovation Solution
A clustering computer model is used to automatically divide test and control groups into clusters based on customer attributes, calculating a weight factor for each cluster to accurately measure the impact of the campaign by comparing performance within each cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional bucketing methods are used to divide test and control groups into subgroups based on attributes, then the matching between groups is improved, but the number of buckets exponentially increases with the number of attributes, causing each bucket to contain too few customers and amplifying noise in the data
Solution Approach 1:
The patent segments customers into clusters using a clustering computer model that processes multiple attributes simultaneously, creating meaningful subgroups that balance homogeneity within clusters and heterogeneity between clusters. This segmentation approach reduces the exponential explosion of buckets while maintaining matching quality.
Solution Approach 2:
The patent transforms the bucketing approach into a clustering approach by changing the parameter space representation. Instead of creating discrete buckets based on attribute ranges, the system uses a clustering algorithm that optimizes the assignment of customers to clusters based on their attribute profiles, effectively changing how the data is organized and represented.
2Measurement precision
If conventional bucketing methods divide customers into many subgroups based on attribute ranges, then attribute matching is improved, but the small number of customers in each bucket makes the measurement sensitive to noise and amplifies noise in the data
Solution Approach 1:
The patent changes the parameter space from discrete attribute ranges to a continuous clustering space. The clustering computer model processes attribute data and creates clusters that capture the semantic meaning of customer similarities, resulting in more stable and reliable measurements by reducing sensitivity to noise in the data.
3Ease of operation
If conventional bucketing methods are used to group customers by attribute ranges, then the matching process is simplified, but the method fails to capture the semantics of customer data and cannot accurately group customers with similar attributes
Solution Approach 1:
The patent replaces the mechanical bucketing system with a clustering computer model that uses algorithms to process and interpret customer attribute data. This substitution enables the system to capture the semantics of customer data and accurately group customers with similar attributes, going beyond simple range-based matching.
Solution Approach 2:
The patent transforms the matching process from a simple range-based mechanism to a semantic clustering mechanism. The clustering computer model processes attribute data and creates groups based on the semantic meaning of the attributes, enabling accurate grouping of customers with similar characteristics even when the attributes are complex or interconnected.
Data Source
AI summary
Disclosed herein are systems and methods that use test and control datasets to evaluate effectiveness of an event. An analytic server may retrieve a set of nodes comprising a subset of nodes corresponding to the test customers and a subset of nodes corresponding to the control customers. The analytic server may use a clustering method to generate a number of clusters for the set of nodes based on a set of attributes prior to the event. The nodes within each cluster have a set of matching attributes. The analytic server may calculate a weight factor for each cluster corresponding to a proportion of the test customers. The analytic server may compare the performance difference between the test customers and control customers within each cluster. Based on the performance difference within each cluster and the weight factor for each cluster, the analytic server may determine the effectiveness of the event.