1. Propensity Modeling and Targeted Marketing
John
Propensity Modeling and Targeted Marketing
Sarah Holder & John Mills

2. 7.2 million electric customers / 500,000 natural gas customers
About Duke Energy
150+ years of service
7.2 million electric customers / 500,000 natural gas customers
Fortune 250 company
$110+ billion in assets
6 States
Renewable and energy efficiency programs
Carolinas ranked top 15 U.S. solar power providers by Solar Electric Power Association
John

3. Customer Analytics Team
Who We Are
Joe Cunningham
John Mills
Sarah Holder
Sabrina Li
Marsha Frederick
Tom Pluer
What We Do
Customer Analytics
All retail customers
Residential
Commercial
Industrial
What do customers look like
What products do they buy
Targeted Product Promotions
Direct mail
Bill Inserts
Call center
John

4. What is a Marketing Campaign?
Product Managers
Determine the offer and qualifications
Campaign
List
Creative
Offer
Corporate Communications
Design creative images and concepts
Customer Analytics
Target Customers with Propensity Models
Help with Customer Profiling
Marketing
Collaborate with Product Managers, Corporate Communications, and Customer Anaytics to bring the three pieces together
Sarah

5. Targeting Campaigns: The Old Way vs. The New Way
Traditional Targeting:
Mail to everyone that qualifies
Relies on Business Knowledge and personal opinions
Use purchased Segmentation or Behavioral Data to select customer lists
Variables are selected based on historical campaign procedures (Income, Square Footage) to reduce list size.
Predictive Modeling:
Trust Correlations and Information Value to select variables of interest
Use Logistic Regression to score and select customers
Mail to a smaller subset of customers, with the expectation that sales will not suffer
Targeted customers are challenging to profile
Traditional – John Predictive - Sarah

6. Segmentation Profiles
Easy to explain
Messages can be created around these profiles
Mailing lists are selected using segments.
John

7. Propensity Modeling Outputs
A probability for every customer
There are no clear lines between “good” and “bad” prospects
Logistic Regression is confusing for Marketing
Challenging to profile “the best customers”
Customer ID
Income
Home Wire Participant
Life Stage Segment
Propensity for Bank Draft
34234
$100,000 Y
Mature
.25
95789
$50,000
Single
67577
$20,000
11334 N
Family
67809
$70,000
00987
$60,000
85761
Sarah addresses audience – Complex relationships

8. What is the Benefit of Predictive Modeling?
Reduce Acquisition Cost
Smaller campaign size with increased response rate
Improve Customer Satisfaction
Contacting customers with offers that interest them.
Ability for Portfolio Marketing
Only customers interested in program, not all eligible customers.
The addition of Propensity Models to the traditional campaign process has produced a 50%-200% improvement in direct mail response rates.
Sarah

9. Predictive Modeling Success
Using SAS 9.3 and JMP Pro
Predictive Modeling Success
John – real world successes – Impressive take rates - both are used as complementary tools

10. Use SAS to consolidate and clean data
SAS/SQL SAS Access
Gather customer data
SAS
Clean Data
Cluster Categories
Correlations
Build Models
JMP Pro
Explore Profiles
Scan Variables
Create Model Graphs
John How the tools are used

11. SAS for Large Data Processing
Transform
multiple
datasets into
one table
Analyze
Outliers
and
Missing
Values
Cluster
Categorical
Variables
into Groups
Remove
Predictor
Variables that
are highly
correlated
Check
for
Non-Linear
Relationships
SAS Dataset
Sarah

12. Predictive Modeling
JMP Demo
Sarah – her pc

13. Logistic Regression Outputs
JMP Demo
Basic Profiling
Appliance Recycling Customers
Variable Screening
Decision Tree
Logistic Regression Outputs
Lift Chart
Profiler
Talking points for jmp demo Sarah, John

14. Success Story: Duke Energy Progress Appliance Recycling
The Propensity Model was built using SAS
After discussing customer profiles with Marketing Team, target customer group is changed.
Historically, we focused on Empty Nesters.
We expanded our market to Younger Homeowners.
The leading message changed from a $50 Incentive Focus to “Free Pickup.”
Direct Mail Response Rates doubled (from 0.84% up to 1.77%) which cut the acquisition cost for the campaigns in half.
Participation Exceeded Goals while under budget.
The improved message grouped with the propensity model helped drive these results.
John Sarah – all of the work you just saw

15. Questions