C5i Case Studies Improved direct response program participation and reduced marketing costs with predictive modeling

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	Improved direct response program participation and reduced marketing costs with predictive modeling C5i

Improved direct response program participation and reduced marketing costs with predictive modeling

C5i

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Utilities
Applicable Functions	Sales & Marketing
Use Cases	Predictive Replenishment
Services	Data Science Services
Challenge	The client, a large US public utility, was looking to understand the key drivers that influence the propensity to enroll in their program. They wanted to identify which residential customers are the best prospects for the program and optimize campaign costs by targeting the best subset of prospective customers. The challenge was to develop a robust and scientific framework to decrease mailing costs, and increase the campaign response rate by targeting only the best prospective customers for select rate plans. Read More
About Customer	The customer is a large public utility company based in the United States. They offer various utility services to a wide range of residential customers. The company was looking to optimize their marketing efforts by identifying the best prospects for their program and targeting them specifically. This would not only increase the response rate for their campaigns but also decrease mailing costs. The company was seeking a solution that would provide a scientific framework for this optimization, using data analysis and predictive modeling. Read More
Solution	Blueocean Market Intelligence developed a model using historical billing and usage data for customers over a two-year period and data from a satisfaction survey in the same time frame. Historical data analysis was used to understand what usage patterns would help to predict program participation. Historical and customer demographic information was captured to predict program participation and identify differences between program participants and non-participants. Blueocean Market Intelligence also conducted secondary research and evaluated social media conversations to obtain additional context on what customers think about the rate plan. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Utilities

Applicable Functions

Sales & Marketing

Use Cases

Predictive Replenishment

Services

Data Science Services

Challenge

The client, a large US public utility, was looking to understand the key drivers that influence the propensity to enroll in their program. They wanted to identify which residential customers are the best prospects for the program and optimize campaign costs by targeting the best subset of prospective customers. The challenge was to develop a robust and scientific framework to decrease mailing costs, and increase the campaign response rate by targeting only the best prospective customers for select rate plans.

About Customer

The customer is a large public utility company based in the United States. They offer various utility services to a wide range of residential customers. The company was looking to optimize their marketing efforts by identifying the best prospects for their program and targeting them specifically. This would not only increase the response rate for their campaigns but also decrease mailing costs. The company was seeking a solution that would provide a scientific framework for this optimization, using data analysis and predictive modeling.

Solution

Blueocean Market Intelligence developed a model using historical billing and usage data for customers over a two-year period and data from a satisfaction survey in the same time frame. Historical data analysis was used to understand what usage patterns would help to predict program participation. Historical and customer demographic information was captured to predict program participation and identify differences between program participants and non-participants. Blueocean Market Intelligence also conducted secondary research and evaluated social media conversations to obtain additional context on what customers think about the rate plan.

Impact #1	Developed a robust and scientific framework to decrease mailing costs
Impact #2	Increased the campaign response rate by targeting only the best prospective customers for select rate plans
Impact #3	Identified differences between program participants and non-participants

Impact #1

Developed a robust and scientific framework to decrease mailing costs

Impact #2

Increased the campaign response rate by targeting only the best prospective customers for select rate plans

Impact #3

Identified differences between program participants and non-participants

Benefit #1	Response rate more than doubled
Benefit #2	Approximately 52% savings with respect to mailing-related campaign efforts
Benefit #3	60% accuracy of participation likelihood with the predictive model

Benefit #1

Response rate more than doubled

Benefit #2

Approximately 52% savings with respect to mailing-related campaign efforts

Benefit #3

60% accuracy of participation likelihood with the predictive model

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Overview

Improved direct response program participation and reduced marketing costs with predictive modeling

Operational Impact

Quantitative Benefit