The Elusive U,lity Customer: How Big Data & Analy,cs Connects U,li,es & Their Customers

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1 The Place Analy,cs Leaders Turn to for Answers Member.U(lityAnaly(cs.com The Elusive U,lity Customer: How Big & Analy,cs Connects U,li,es & Their Customers Mike Smith Vice President, U(lity Analy(cs Ins(tute November 5, 2014 Aus(n, Texas

2 Agenda Who & What is the U(lity Analy(cs Ins(tute? What is Big? What is Analy(cs? What have we seen so far? What lies ahead? Open Discussion and Q & A

3 Who & What is the U,lity Analy,cs Ins,tute? Membership- based organiza(on "The U,lity Analy,cs Ins,tute s mission is to accelerate the adop,on, advancement and u,liza,on of analy,cs, enabling u,li,es to sa,sfy their customers and operate more safely, reliably, sustainably and efficiently. Building the Community Research- based approach The Ins(tute s many ac(vi(es Events: U(lity Analy(cs Summit & U(lity Analy(cs Week Quarterly Webcasts Online Engagement We are a Division of Execu(ve Advisory Council Working Groups Get Involved!

4 U,lity Members The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com Investor Owned U,li,es Member.UtilityAnalytics.com The Place Analytics Leaders Turn to for Answers Public & Interna,onal Member- U,li,es Owned U,li,es

5 The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com Solu,on Provider Members The Place Analytics Leaders Turn to for Answers Member.UtilityAnalytics.com *Members added consistently 5

6 What is Big? What is Analy(cs? BIG DATA Big is all about seeing and understanding the rela(ons within and among pieces of informa(on that, un(l very recently, we struggled to fully grasp. that is so big and complex in its nature and in its intended use that conven(onal data management and applica(ons are insufficient. ANALYTICS Analy(cs are technologies and apps that enable u(li(es to transform massive, complex data into predic,ve ac(onable insights. This process includes four key areas: Collec(ng, managing, cleaning and storing data Extrac(ng and analyzing data Repor(ng analysis results Applying the results predic(vely

7 BIG DATA: A Revolu(on That Will Transform How We Live, Work, and Think By Viktor Mayer- Schonberger & Kenneth Cukier 7

8 Value Curve 8

9 2014 Customer Analy(cs Report 9

10 Primary focus areas for customer analy(cs 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Source: U*lity Analy*cs Ins*tute 2014 Customer Analy*cs Report. 10

11 Importance of other areas covered by customer analy(cs ini(a(ve Meter data analy(cs Energy efficiency programs Fraud detec(on Demand response programs Customer segmenta(on Credit and collec(ons Pricing op(miza(on Call center op(miza(on Campaign management Distributed genera(on management Source: U*lity Analy*cs Ins*tute 2014 Customer Analy*cs Report. 11

12 Importance of social media data for specific analy(cs efforts Customer outreach and educa(on Measuring customer sen(ment Outage communica(on/no(fica(on Program promo(on Bill payment/collec(ons Source: U*lity Analy*cs Ins*tute 2014 Customer Analy*cs Report. 12

13 Does your u(lity have a social media leader? What is the focus/goal(s) of building a social media capability? Will this be used predic(vely? Is there a measureable payback to social media? 13

14 Losing Money lots of it due to Bad Debt Developed a Customer Behavior Analy(cs Engine Included payment history, energy usage history, por(ng of third party data Results: Bad Debt reduced from $42 Million to $8 million Reduced New Write- offs by 20% 14

15 Building the Founda(on is not easy! The primary technology components supporbng this inibabve include: Smart meter network management system (like a meter data management system) UBlizaBon solware package AnalyBcs appliances to support data analybcs Approximately 11 servers Three databases storage devices 15

16 The project calls for data from eleven disparate data sources to flow through an ETL (extract, transform and load) tool into a large storage database. AnalyBcs will be run against that data and will be pushed out into a visualizabon database. The visualizabon database will support the graphical display of the informabon through a user interface that sits on top of the smart meter network management system. 16

17 What About The Enterprise? Let s talk About. 17

18 What About The Enterprise? Rela(onships Between Tradi(onally Siloed bases Customer Grid Asset 18

19 What About The Enterprise? Rela(onships Between Tradi(onally Siloed bases Customer Grid Asset Do we agree that there are specific behaviors that reside in each siloed database? 19

20 What About The Enterprise? Customer What if a DR program reached Cri(cal Mass and changed aggregate customer behavior? 20

21 What About The Enterprise? Wouldn t this change the behavior of the grid? Customer Grid 21

22 What About The Enterprise? Customer Grid Asset And the behavior of the assets on the grid? 22

23 What About The Enterprise? Weather Demographics Real Estate Customer Grid And how can third- party data add value? Asset 23

24 A Closing Thought There are a lot of assumpbons around the data, and UBlity X admits that they didn t do as well as they would have liked in terms of documenbng it and documenbng it in a way that people could understand. For example, if an employee asked quesbons about the data like where you are ge^ng the data or, what does this field mean he or she may get a data dump from IT, but it won t make sense to most people. UBliBes must build a bridge between the IT person with the deep data knowledge and the business person. 24

25 The Place Analytics Leaders Turn to for Answers UtilityAnalytics.com The Place Analytics Leaders Turn to for Answers Member.UtilityAnalytics.com Q & A Thanks! 25

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