Improved direct response program participation and reduced marketing costs with predictive modeling
Company Size
1,000+
Region
- America
Country
- United States
Product
- Predictive Modeling
Tech Stack
- Data Analysis
- Social Media Analysis
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Utilities
Applicable Functions
- Sales & Marketing
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 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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