Customer Activity Score Calculation for Utility Engagement
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Solution Overview
Problem
Utility providers lack insight into customer engagement with their online services, limiting their ability to effectively manage customer interactions and improve user experience.
Innovation Solution
A computer-implemented method and system for calculating a customer activity score (CAS) based on historic consumption data and behavior information, which provides a quantitative measure of engagement, allowing for customer segmentation and targeted communication strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If utility providers collect limited customer information, then data privacy and system simplicity are maintained, but customer engagement insight and service personalization capability deteriorate
Solution Approach 1:
The patent segments customer information into multiple dimensions including consumption behavior patterns, online system interaction frequency, program participation status, and communication response history. This segmentation allows the system to collect comprehensive engagement data across different channels while maintaining organizational simplicity and clarity in data structure.
Solution Approach 2:
The patent creates a unified customer profile system that serves multiple functions: tracking consumption patterns, monitoring online engagement, managing program enrollments, and personalizing communications. This multi-functional approach consolidates various data collection needs into a single integrated system, reducing overall complexity while improving customer insight.
2Measurement precision
If utility providers implement comprehensive customer tracking systems, then customer engagement insight improves, but system complexity and implementation cost increase
Solution Approach 1:
The patent merges multiple tracking functions into a unified customer profile system that consolidates consumption data, online interaction records, program participation information, and communication history. This consolidation improves measurement accuracy by providing a comprehensive view of customer engagement while reducing system complexity through integrated data management.
Solution Approach 2:
The patent introduces a customer profile as an intermediary layer that aggregates and standardizes data from multiple sources including consumption systems, online platforms, and program management systems. This intermediary structure enables precise engagement measurement without requiring direct complex integration between all underlying systems.
3Adaptability or versatility
If utility providers use detailed behavior data for customer segmentation, then service personalization and targeted communication improve, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments customers into distinct groups based on multiple behavioral dimensions including consumption patterns, online engagement levels, program participation, and communication preferences. This segmentation enables highly personalized service delivery and targeted communications while maintaining manageable complexity through structured classification categories.
4Measurement precision
If utility providers calculate comprehensive customer activity scores, then customer engagement understanding improves, but computational resources and processing time increase
Solution Approach 1:
The patent calculates customer activity scores using a selective subset of behavioral indicators that provide sufficient engagement measurement accuracy without requiring analysis of every available data point. This partial action approach maintains measurement precision while reducing computational burden and processing time.
Solution Approach 2:
The patent employs weighted parameters in the customer activity score calculation, where different behavioral indicators are assigned varying weights based on their significance to customer engagement. This parameter optimization allows the system to focus computational resources on the most impactful data points, improving calculation efficiency while maintaining measurement accuracy.
Data Source
AI summary
Aspects of the subject technology relate to methods and systems for calculating a customer activity score (CAS). In some aspects, a method of the subject technology includes steps including aggregating behavior information for each of a plurality of utility customers, the behavior information including historic consumption data for at least one consumable resource, and calculating, and using the behavior information, a customer activity score (CAS) for one or more of the utility customers. In some aspects, the method can also include steps for generating customer content for at least one of utility customers based on a corresponding CAS value. In some aspects, systems and computer-readable media are provided.