Better Customer Engagement

Transcription

1 Better Customer Engagement Increasing Marketing Success with Predictive Analytics An Intelligent Utility Reality Webcast 10/15 10/29 September 29 th, /28

2 The Panel Dennis Rominger Market Manager, Customer Solutions Puget Sound Energy Micah DeHenau Vice President, Analytics & Consulting Services Vertex

3 KATHLEEN WOLF DAVIS editor-in-chief Intelligent Utility brand (magazine, daily, online) intelligentutility.com Daily insights that go way beyond the everyday.

4 Today s Discussion WHAT IS PREDICTIVE ANALYTICS? PREDICTIVE ANALYTICS IN UTILITIES PUGET SOUND ENERGY SMARTER CUSTOMER ENGAGEMENT REVIEW OF KEY TAKEAWAYS

5 5 WHAT IS PREDICTIVE ANALYTICS?

6 Utility Business Value What is Predictive Analytics? Predicting Customer Behavior & Taking Action Predictive Analytics What s Going to Happen & How Should We Prepare? Prescriptive Analytics What Happened/Is Happening & What Should We Do About It? Descriptive Analytics What Happened? DATA WAREHOUSE OTHER DATA FOCUS GROUPS Complexity / Degree of Intelligence

7 What is Predictive Analytics? Leaders in Analytics We as utilities need to start thinking more like these analytics leaders so we can understand our customers better than they understand themselves

8 8 PREDICTIVE ANALYTICS IN UTILITIES

9 % of Enrollers Identified Predictive Analytics in Utilities Why it Works.. Based on historical strength of predictive data models for enrollment, predictive analytics can identify ~64% of potential responders within 20% of the customer population 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 49.93% 63.87% 74.38% 82.46% 88.72% 93.24% 96.06% 97.90% 99.25% % 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% % % of Population Percent of of respondents Respondants Identified (No Model)

10 Predictive Analytics in Utilities Utility Use Cases What should a utility predict? Why? Likelihood of a customer to default on their debt Increase payment response Increase speed to payment Reduce truck rolls Reduce agency assignments Likelihood of a customer to enroll in Energy Efficiency programs Likelihood of a customer to enroll in Demand Response programs Likelihood of a customer to enroll in e-billing Likelihood of a customer to enroll in AutoPay Increase program participation Reduce cost per enrollment Increase self service Likelihood of a customer to enroll in Web Self-Service Which customers are likely to leave and go to another provider Which customers are likely to be acquired Which assets are likely to fail Reduce attrition Reduce cost to retain Acquire more customers Reduce cost per acquisition Optimize maintenance scheduling Mitigate risks of asset failure

11 Predictive Analytics in Utilities Utility Case Studies Midwest Gas & Electric Utility Propensity to Pay Propensity to Leave Propensity to Acquire Propensity to Enroll Increased arrears dollars collected by 30% Decreased number of accounts assigned to agencies by 80% Reduced number of customers entering arrears by 17% Can identify customers using predictive scoring algorithm that is 270% more effective than random selection Key leaver characteristics identified and marketing strategies adjusted Appliance Protection Program Retail Program Predictive model 4X more effective than previous target marketing efforts (Generating 4X the acquisitions for the same marketing spend) Improved energy efficiency marketing enrollments by 70% Proved to maintain same level of energy efficiency program enrollment with 41% less direct marketing cost

12 12 PUGET SOUND ENERGY SMARTER CUSTOMER ENGAGEMENT

13 Introducing PSE Puget Sound Energy (PSE) Highlights: Located in Washington state Utility Customers: 1.1 million electric households and 770,000 natural gas households 4 million people reside within our 6,000-square-mile service area Home to some of America s most recognized businesses: Boeing Commercial Airplane Group Microsoft Amazon.com Starbucks Costco T-Mobile Expedia Revenues: $3.19 billion Assets: $10.8 billion Employees: 2,700 Customer Utility Rates: $0.098 per kwh and $1.14 per therm National leader in wind power - the 2 nd largest utility owner of wind energy facilities in the United States

14 Introducing PSE PSE Integrated Resource Plan

15 Introducing PSE PSE Integrated Resource Plan

16 Introducing PSE

17 Predictive Analytics & Energy Efficiency Marketing

18 Predictive Analytics & Energy Efficiency Marketing Total number of fields evaluated for possible use in models: 290 Total number of variables determined to be predictive: 69 Energy Advisor Interaction Data 37 Fields Billing History & Usage Data 82 Fields Usage & Temp Data 32 Fields Demographic & Household Data 110 Fields Attitudinal Data 9 Fields PSE Internal Rebate Data 20 Fields Modeling Dataset 290 Fields Each data dimension of the PSE customer profile was represented within the modeling dataset inputs

19 Predictive Analytics & Energy Efficiency Marketing Analysis conducted to determine the effect of marketing communications on response when compared to response from those who did not receive marketing over the same time period Approach Program Response Rate % More Effective Than Non- Participant Response % More Effective Than Random Selection Non-Participants.133% - - Marketing with Random Selection.291% 118% (2.2X) - Marketing with Targeted Selection.494% 270% (3.7X) 70%

20 Predictive Analytics & Energy Efficiency Marketing Increase in Program Participation with Same Marketing Budget Decrease in Marketing Communications Cost to Generate Same Level of Participation 70% Average Impact 41% Average Impact

21 Predictive Analytics & Energy Efficiency Marketing Upsell Campaign This campaign delivers exciting limited time offers for our customers based on previous energy efficient purchases. Each month delivers new opportunities for our customers to participate in energy efficient offers. 240,000 s distributed a month 40,000 postcards distributed a month 24% open rate vs. 20% industry average 3% click through rate vs. 2.3% industry average 208% increased refrigerator sales compared to previous month

22 Predictive Analytics & Energy Efficiency Marketing 62% increase year over year refrigerator recycling sign-ups first week of 33% increase year over year refrigerator recycling sign-ups for month of 138% increase year over year refrigerator recycling sign-ups first week of with message in first position

23 Predictive Analytics & Energy Efficiency Marketing 188,000 s sent out 29% open rate for first 25% open rate for follow-up 11% click through rate for first 7% click through rate for follow-up Over half of the customers redeemed their promotion code after they clicked through the

24 Energy Efficiency Marketing Energy Upgrades Campaign Objective & Messaging Reach customers who are not necessarily green -motivated Increase customer awareness of PSE s energy efficiency offerings Motivate customers to take action on PSE s many energy-efficiency programs; residential & business Save money and cut your bills by upgrading with PSE s energy efficiency programs!

25 Energy Efficiency Marketing 2015 Goals 23 Community Events 2 Game Day Blitzes 50,000 Golden Upgrade tickets distributed with 60% redeemed at the PSE Booth 80,000,000 impressions through paid & earned media, traditional & social EE Awareness & Savings measurable increase from 2014 to 2015

26 Energy Efficiency Marketing The Golden Upgrade Ticket

27 Energy Efficiency Marketing

28 Energy Efficiency Marketing

29 Energy Efficiency Marketing Upgraded Customer Social Media

30 Energy Efficiency Marketing Past, Present & Future for Predictive Analytics Past Accounts randomly selected for marketing campaigns No intel for the EE opportunity within various geographies No mechanism to target direct marketing and community events Present Marketing campaigns directed to highest propensity customer segments driving more effective and efficient campaigns Armed with intel to negotiate favorability with trade allies Future Targeted website offers based on highest propensity EE product Contact center upsell based on defined thresholds for likelihood to enroll Leveraging enterprise HANA investment for data processing

31 REVIEW OF KEY TAKEAWAYS 31

32 5 Key Takeaways 1) Advanced analytics are changing the world we live in, and smart companies are taking advantage 2) Utilities can take advantage too! Predictive analytics, when paired with smart customer engagement, can significantly improve performance 3) We can no longer rely on our traditional energy sources. Consumers will need to take a more active role in meeting energy demand, and utilities must help this process by pairing customers with the right programs 4) The applications go far beyond enrollment, and can be applied to drive improved credit & collections performance, optimize asset maintenance scheduling and even retain or acquire more customers 5) Predictive analytics doesn t need to to be a core capability. Utilities can take advantage through Analytics as a Service from Vertex

33 QUESTIONS? 33