Developed model to identify utility customers at risk of seasonal defection from Time-of-Use (TOU) price plan
Company Size
1,000+
Region
- America
Country
- United States
Product
- Predictive Analytics Model
Tech Stack
- C5.0
- CHAID
- QUEST
- Logistic Regression
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Utilities
Applicable Functions
- Business Operation
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
The customer is a large public utility company based in the United States. They provide essential utility services to a vast number of residential customers. The company offers various price plans to its customers, including a Time-of-Use (TOU) price plan. However, they were facing a challenge in identifying which customers on the TOU price plan were likely to switch to an alternate price plan. The company needed a solution that could accurately predict this switching behavior and help them retain their customers.
The Challenge
The client, a large US public utility, was facing a challenge in identifying which customers on a Time-of-Use (TOU) price plan would switch to an alternate price plan. They needed to understand the factors that predict this switching behavior and create propensity scores for each of the current TOU price plan customers to identify their individual likelihood of switching to a different price plan. The goal was to correctly predict a likely switcher approximately 70% of the time.
The Solution
Blueocean Market Intelligence developed a predictive analytics model using the client’s data repository of residential customer information and the Meter Data Management System over a two-year period. Historical and customer demographic information was used to predict program participation and identify differences between program switchers and non-switchers. Simultaneous models were run using C5.0, CHAID, QUEST, and logistic regression. The final commissioned model had a high level of predictive accuracy of 70% for who was most likely to switch.
Operational Impact
Quantitative Benefit
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