Start Date
12-13-2015
Description
Following decades of stability and comfortable margins, utility companies today face strong pressure from regulatory bodies and competitors. As a response to the market dynamics, many have initiated a transformation from a “provider” to a service company, yet realize that their customer insights that would be necessary to successfully develop and market new services are sparse. We argue that the required information is contained in consumption data that is available to utility companies. We demonstrate how data analytics and machine learning make sense out of such data and add value to organizations. Using datasets containing annual electricity consumption information of private households, we apply and test in field experiments a Support Vector Machines algorithm that predicts probabilities of individual costumers to sign up on an energy efficiency portal. We show that signup rates can be doubled and argue that classification tools provide customer insights at low cost and at scale.
Recommended Citation
Sodenkamp, Mariya; Kozlovskiy, Ilya; and Staake, Thorsten, "Gaining IS Business Value through Big Data Analytics: A Case Study of the Energy Sector" (2015). ICIS 2015 Proceedings. 14.
https://aisel.aisnet.org/icis2015/proceedings/DecisionAnalytics/14
Gaining IS Business Value through Big Data Analytics: A Case Study of the Energy Sector
Following decades of stability and comfortable margins, utility companies today face strong pressure from regulatory bodies and competitors. As a response to the market dynamics, many have initiated a transformation from a “provider” to a service company, yet realize that their customer insights that would be necessary to successfully develop and market new services are sparse. We argue that the required information is contained in consumption data that is available to utility companies. We demonstrate how data analytics and machine learning make sense out of such data and add value to organizations. Using datasets containing annual electricity consumption information of private households, we apply and test in field experiments a Support Vector Machines algorithm that predicts probabilities of individual costumers to sign up on an energy efficiency portal. We show that signup rates can be doubled and argue that classification tools provide customer insights at low cost and at scale.