Consumers Energy - Propensity Models

  

PROJECT TITLE:  Consumers Energy’s Layered Responder-Cancel Model (Predictive/Propensity Modeling)

 

 

ANALYTICS AREAS

 

  1. Which of the following analytics areas apply to your project (see definitions at end of document)? Place an “X” in the appropriate boxes.

 

 

Implementation underway

 

 

Project completed

Future

 

Meter data analytics

_______

 

 

_______

_______

Credit and collections

_______

 

 

_______

_______

Call center optimization

_______

 

 

_______

_______

Fraud detection

_______

 

 

_______

_______

Campaign management

_______

 

 

___X___

_______

Customer segmentation

_______

 

 

___X___

_______

Pricing optimization

_______

 

 

_______

_______

Demand response programs

_______

 

 

_______

_______

Energy efficiency programs

_______

 

 

_______

_______

Distributed generation management programs, including electric vehicles

_______

 

 

_______

_______

Transformer management

_______

 

 

_______

_______

Substation equipment management

_______

 

 

_______

_______

Overall transmission & distribution management

_______

 

 

_______

_______

Outage management

_______

 

 

_______

_______

System modeling

_______

 

 

_______

_______

Power quality optimization

_______

 

 

_______

_______

Advanced distribution management

_______

 

 

_______

_______

Analytics for real-time network operations

_______

 

 

_______

_______

 

Other: 

 

 

PROJECT OVERVIEW

 

  1. In two or three sentences or a short paragraph, please describe this project (include business drivers, how project aligns with utility’s corporate strategy, project goals, results achieved/desired outcomes). The Consumers Energy Layered Responder-Cancel Model Project was designed to predict which of our customers would be the most likely to sign up for one of our programs through the Direct Mail channel and stay with the program for longer than one year. Using logistic regression methodology and past history for similar campaigns, we were able to identify the customers to target for current and future Direct Mail campaigns.



BUSINESS VALUE

 

  1. Describe actual and/or anticipated QUALITATIVE (e.g. better customer service) and QUANTITATIVE (e.g. ROI, cost savings, revenue increases) benefits of this project. Quantified savings were in the hundreds of thousands of dollars in Marketing ROI because we were able to only send mail to customers most likely to respond and stay with the program and did not have to use a “scattershot” approach and mail to everyone and hope for the best.

 

 


BUSINESS PROCESS IMPACTS

 

  1. List the key business processes impacted (or expected to be impacted) and describe the nature of those impacts. There were no impacts to any business process.  This project was designed to limit the number of customers to which we send product offerings instead of mailing to “all”.  All the business processes stay the same. 

 

 

 

TECHNOLOGY

 

  1. Identify the PRIMARY TECHNOLOGY COMPONENTS associated with this project including the technologies used (or expected to be used) for: 1) data collection/integration 2) data analysis 3) data presentation and 4) data storage. On the Consumers Energy side, there were extracts taken from our customer system of record (SAP) and records were dropped if a customer did not meet high-level eligibility criteria. All the remaining data was sent via secure FTP to a vendor called Virtual DBS.  Virtual DBS did all the “heavy lifting” and used their mathematicians and data scientists to produce the models and the presentations that explained what the models were intended to do.

 

 

  1. Identify the PRIMARY DATA INPUTS/ELEMENTS utilized and their sources. Various customer related data elements were utilized that came from our customer system of record, SAP. The primary elements were name and service address.




PEOPLE

 

  1. Identify the following individuals by job title (as applicable):

 

  • Business-side Project Leader: Carl VanDomelen

 

  • Technology-side Project Leader: John Dodd (Virtual DBS)

 

  • Executive Sponsor: Terrence Mierzwa

 

 

  1. Identify and describe the role of external providers (e.g. strategy, consultant, design, development, implementation, ongoing operations). Vendor Virtual DBS did all the modeling for this project. They consulted in the overall strategy and fully executed the design and development of the models.

 

 

 

  1. Describe the investments required to implement and provide ongoing support for this initiative.

 

  • Internal resources (identify type of internal resources, e.g. job titles and quantities of each (e.g. # of full time/# of part time, total FTEs): 1 part time business analyst was needed to pull the data from our customer system of records and apply high-level exclusion criteria. Vendor Virtual DBS did most of the heavy lifting in the design and creation of the models.  1 additional part time resource was used to socialize the outcome with various Consumers Energy stakeholder groups.

 

 

 

  • Technology investments (estimate/provide ranges for one time and recurring costs if possible or indicate that existing technologies were leveraged): We did not invest in any new technology for this project.  Any technology used on the Consumers Energy side was technology we already had in place.

 

 




BEST PRACTICIES/LESSONS LEARNED

 

  1. What advice would you share with those either planning or implementing a similar initiative (e.g. best practices, lessons learned, what to do, what not to do)? Firstly, Virtual DBS was (and still is) a great partner for us at Consumers Energy - I highly recommend them. We executed this project in 2016 and this was the first time we used any science in determining customers to target for products and services.  At first, there were many skeptics of the models and our different business groups went back to the old ways of doing things (i.e., mailing to “all”).  It took them a while to trust that our models would work better and would save them money.  Advice here is to make sure to keep your stakeholders in the loop as to what you’re doing and why you’re doing it and be aware that it may take time for people to gain trust in the new way of doing things.  Consistently demonstrating that your models will meet or exceed program targets will go a long way in earning people’s trust.

 

 

 


 

CONTACT INFORMATION:

Name:

Jeff Yankley

Title:

Director, Customer Intelligence & Analytics

Company name:

Consumers Energy

Email:

Jeffrey.yankley@cmsenergy.com

Phone:              

517-788-0361


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