C5i Case Studies Developed model to identify utility customers at risk of seasonal defection from Time-of-Use (TOU) price plan

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	Developed model to identify utility customers at risk of seasonal defection from Time-of-Use (TOU) price plan C5i

Developed model to identify utility customers at risk of seasonal defection from Time-of-Use (TOU) price plan

C5i

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Utilities
Applicable Functions	Business Operation
Use Cases	Predictive Replenishment
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Utilities

Applicable Functions

Business Operation

Use Cases

Predictive Replenishment

Services

Data Science Services

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.

About 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.

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.

Impact #1	Developed a model to predict which customers currently on a Time-of-Use (TOU) price plan will switch to an alternate price plan
Impact #2	Identified key factors driving the switching behavior
Impact #3	Established profiles comparing Switchers to Non-Switchers on characteristics such as energy usage, savings and monthly bill amount

Impact #1

Developed a model to predict which customers currently on a Time-of-Use (TOU) price plan will switch to an alternate price plan

Impact #2

Identified key factors driving the switching behavior

Impact #3

Established profiles comparing Switchers to Non-Switchers on characteristics such as energy usage, savings and monthly bill amount

Benefit #1	The final commissioned model had a high level of predictive accuracy of 70% for who was most likely to switch

Benefit #1

The final commissioned model had a high level of predictive accuracy of 70% for who was most likely to switch

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Overview

Developed model to identify utility customers at risk of seasonal defection from Time-of-Use (TOU) price plan

Operational Impact

Quantitative Benefit