Powering program engagement for low-to-moderate income customers.
The Income-Eligible Challenge
Today’s utilities have multiple ways to help their low-to-moderate income (LMI) customers save money, increase energy efficiency, and ensure energy security for themselves and their families during hot summers and cold winters.
But with a wide range of possible offers, products, rates and programs—and each with its own eligibility requirements and enrollment processes—how can modern utilities instantly know which is the best program for every customer, and increase LMI program enrollment at the same time?
The EnergySavvy Solution
EnergySavvy’s Utility Customer Experience (UCX) platform enables utilities to recommend the best offer to each income-eligible customer, using personalization based on utility-specific energy analytics and each customer’s personal journey.
The result is more relevant recommendations, higher program enrollment—and, ultimately,more stable, comfortable, and satisfied income-eligible customers.
“Mindless! [EnergySavvy] helps you help the customer, provides what they need without you even having to think about it.
— Contact Center Rep, 5 Million-Meter+ Eastern Utility
Solving Income-Eligible Challenges
EnergySavvy’s Utility Customer Experience (UCX) platform enables contact center leaders to develop prioritized, analytics-driven, next-best actions for each customer—making this data available to CSRs in real time and enabling them to better uncover and resolve customers’ underlying issues, to minimize or prevent future calls.
EnergySavvy’s UCX platform enables utilities to easily target each customer with highly relevant actions at each moment—providing customers a truly personalized and consistent utility experience, no matter the channel.
DSM / EE
EnergySavvy’s UCX platform and Workflow Automation solutions enable DSM/Energy Efficiency managers to drive greater and more personalized customer engagement, automate manual processes, view a single, always current version of the truth, and optimize program performance with near real-time data.