Multivariate profiling to understand utility customers by level of engagement
Company Size
1,000+
Region
- America
Country
- United States
Product
- Engagement Scoring System
Tech Stack
- Predictive Modeling
- Decision Tree Approach
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Brand Awareness
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Utilities
Applicable Functions
- Sales & Marketing
Services
- Data Science Services
About The Customer
The customer in this case study is a large public utility company based in the United States. The company operates in the utilities industry, providing essential services to residential customers. The company was seeking to enhance its marketing efforts by developing a better understanding of its customers' level of engagement. The goal was to identify highly engaged customers and target them with new product offers, as well as find ways to increase interaction with less engaged customers and convert them into more engaged ones.
The Challenge
The client, a large US public utility, was looking to develop an engagement scoring system to profile highly engaged residential customers. This was intended to serve as input for their product/service marketing efforts. The challenge was to measure engagement based on the number and importance of interactions by the customer with the utility on four different dimensions. The goal was to establish a distinct segment of most engaged customers for targeted marketing of new product offers and ways to increase interaction with and convert less engaged customers.
The Solution
Blueocean Market Intelligence developed a framework to measure engagement based on the number and importance of interactions by the customer with the utility on four different dimensions. Engagement scores were first developed for each dimension based on several parameters, and then an overall engagement score was computed using weights developed through an analytic hierarchy process. Third-party data was used to profile the groups of customers based on demographic, attitudinal and behavioral variables, as well as customer transaction data available from the client. Finally, a profile-based predictive model was built using a decision tree approach to predict which customers were likely to have a high propensity of engagement with the utility, based on demographic, attitudinal, behavioural and historical transactional data.
Operational Impact
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