Customer Propensity Scoring for ARPU Growth
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Solution Overview
Problem
Businesses face challenges in effectively increasing revenue from their existing customer base, as traditional marketing campaigns often fail to target the most responsive customers, leading to inefficient resource allocation and suboptimal revenue growth.
Innovation Solution
A method and system that utilize data analysis tools, including an optimized data mart, data mining, and user access modules, to identify revenue lagging products/services, predict customer propensity for increased revenue, and tailor marketing campaigns to maximize response and revenue growth by scoring and filtering customer lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If marketing campaigns are directed toward large numbers of customers through multiple channels, then customer reach and potential revenue opportunities increase, but campaign costs increase significantly
Solution Approach 1:
The patent segments the customer base into distinct groups based on predicted revenue propensity scores. Instead of treating all customers uniformly, the system divides them into high-propensity and low-propensity segments, allowing targeted marketing efforts focused on the most promising customers while reducing waste on unlikely responders.
Solution Approach 2:
The patent applies local quality by treating different customer segments with different marketing strategies and resource allocations. High-propensity customers receive targeted campaign efforts with optimized channel selection, while low-propensity customers are excluded or receive different treatment, ensuring resources are concentrated where they generate the most value.
2Loss of energy
If marketing campaigns target selective customer segments, then campaign costs are reduced, but revenue opportunity may be lost if the wrong segments are selected
Solution Approach 1:
The patent performs preliminary action by calculating predicted revenue propensity scores for all customers before launching the marketing campaign. This pre-screening process identifies which customers are most likely to respond positively and generate revenue, allowing the campaign to be targeted in advance rather than relying on trial-and-error approaches.
Solution Approach 2:
The patent incorporates feedback mechanisms by using historical campaign data and customer responses to refine and retrain the propensity prediction models. This continuous learning process improves the accuracy of customer segmentation over time, ensuring that future campaigns target the right segments with higher precision.
3Ease of operation
If traditional marketing campaigns are launched without predictive analytics, then implementation is simpler and faster, but resource allocation is inefficient and revenue growth is suboptimal
Solution Approach 1:
The patent introduces an intermediary layer - the predictive analytics platform with propensity scoring models - that sits between the marketing team and the customer database. This intermediary automatically performs complex analysis and generates targeted customer lists, freeing the marketing team from manual segmentation work while providing data-driven insights that improve campaign effectiveness.
Data Source
AI summary
A method and system that provide analytical tools for increasing average revenue per user (ARPU) allows users to design and execute marketing campaigns that target customers with a statistically significant likelihood of accepting a marketing campaign offer and generating the greatest increase in revenue. The method and system creates statistical models to determine an individual customer's propensity to respond positively to a campaign and propensity to generate increased revenue. The method and system scores the customers according to the customers' propensities, and uses the scoring results to select an optimal mix of customers to contact during the marking campaign.