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Propensity Modeling and Targeted Marketing
John Propensity Modeling and Targeted Marketing Sarah Holder & John Mills
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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
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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
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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
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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
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Segmentation Profiles
Easy to explain Messages can be created around these profiles Mailing lists are selected using segments. John
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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
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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
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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
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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
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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
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Predictive Modeling JMP Demo Sarah – her pc
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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
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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
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Questions
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