Low Income Energy Efficiency (LIEE)

1. Low Income Energy Efficiency (LIEE) Segmentation and HUNA Research:Overview of Proposed Work Plan Public Meeting November 23, 2009

2. Research Objectives • The overarching research objectives by project study are: • Segmentation Study:Among customers eligible for LIEE programs: • Develop a segmentation solution of LIEE customers that is predictive of customer “need” for the LIEE program • Deliver an algorithm to each utility that will classify all their LIEE households into one of the final segments, providing enterprise-wide LIEE segmentation solutions. • Identify discrete, significant neighborhoods of LIEE-qualified customers for targeting purposes that go beyond the current approach • Distill utility customer targeting strategies customized to SCE and PG&E, as appropriate and factoring in each utility’s current method of targeting • Specify messages, products and outreach vehicles to help maximize program participation • High Usage Needs Assessment (HUNA) Study: • Among high usage customers, identify: • Energy inefficient practices and beliefs likely to contribute to unusually high energy usage • Energy-inefficient appliances, electronics and household characteristics likely to contribute to unusually high energy usage • Barriers to changing energy inefficient attitudes and behavior • Messages, information and strategies that are likely to be successful in reaching and communicating with high usage customers 1

3. Research Overview HPI proposes several components for the two studies:

4. LIEE Segmentation Study

5. Segmentation Approach • SCE and PG&E have basically three types of data available: • Internally derived data, such as energy usage, program participation, etc. • Externally derived data from 3rd party suppliers, such as household income, ethnicity, etc., and • Externally derived survey data (provided through this custom research). • From these three data sources, we must choose the variables upon which to base the segmentation.

6. Segmentation Variable Types TypeStrengthsWeaknesses Internally Derived Data • Highly accurate behavioral • Don’t explain why behaviors occur, information customer attitudes or motivations • Include geographic targeting • Insufficient for identifying qualified information low-income households 3rd-Party External Data • Include household income • Modeled, usually in reference to estimates national (not regional) patterns • Help explain energy usage • Don’t directly or fully explain (e.g., through dwelling why people behave as they do characteristics) • Provide some targeting variables (language spoken) Survey Data • Explain why people do • Available only for the sample what they do surveyed • Provide insights into motiva- • Attitudes are generally insufficient tions, which can assist in predictors of behaviors affecting internally derived • Difficult to project attitude variables, such as energy use segmentation results onto an entire database population without similar segmentation variables

7. Segmentation Variables - Assessment Source:Comments: Survey Data Alone • Relatively few attitudinal variables (from survey) likely to relate to internal database variables • Often difficulty/limited statistical success in integrating survey data solutions to entire database population • A challenge to develop algorithms that connect segmentation outcomes to customers – apart from the difficulties of determining effective messaging for such segments 3rd-Party Data Combined • Mixing data types to derive segments poses unique challenges With Internal and Survey Data (e.g., identifying variables, beyond those about energy usage, for use) • Commonly faces the downside risk that segments derived from survey and database variables result in indistinct and difficult-to- interpret segments Internally Derived • Can directly classify full database into segments with high accuracy Data, With Survey Data • Energy usage likely to be a dominant, differentiating variable Used to Profile Segments • 3rd-party data, if available, can help estimate “usage density,” etc. • Survey data would be used to differentiate segment needs and motivation; to develop positioning and messaging strategies

8. Segmentation Variables – Selection Which types of variables should we use? The answer usually depends on how the segmentation will be used e.g., Identify neighborhoods with high proportion of qualifying customers so resources can be allocated geographically; Determine customers’ primary barriers to participation once they have been identified and offered LIEE so program marketing campaigns can focus on these; etc.

9. Data Assessment HPI proposes to assess: • Residential customer data records for SCE and PG&E to determine whether or not appropriate, sufficient and desirable profiling/ segmentation variables exist in each utility’s database. • 3rd-party data already available in each utility’s database, and to the extent this falls short, externally available data.

10. Segmentation Variables Which variables, within types, are most relevant? 3rd Party Data: Housing Characteristics: Age Square footage Type Socio-Economic Information: Income Ethnicity Language Number of people in house- hold Owner/renter Survey Data: Awareness Knowledge Attitudes Interest in behaviors (self- reported) Demographics: age, gender, Socio-economics: income, education, ethnicity, language Housing characteristics: AC, square footage, type, age of home, condition Household: number in household, presence of children, elderly Internal Data: Electricity usage Gas usage Program participation Type of meter (e.g., TOU) Climate zone Service established date Bill payment: methods used Bill payment: programs (LPP) Bill payment history Number and type of contacts with utility Language Cost to serve Medical indicator Online usage: MyAccount, OBP Outage history Unique ID: name, address, city, zip, phone

11. Analysis – Segmentation Study Segmentation analysis: • Iterative • Based on factor analysis (for variable reduction) and clustering (for grouping together households) • Guided by the intended applications Profiling and messaging analysis: • Profiling the segments using survey (and other) variables • Identify “positive consequences” (benefits) sought and “negative consequences” (penalties or barriers) avoided across segments • Prioritizing segments and determining messaging for each segment

12. Focus Groups – Segmentation Study • HPI recommends focus groups to inform development of the LIEE Segmentation questionnaire: • 18 focus groups divided between SCE (7) and PG&E (11) • Conducted in a mix of temperate and non-temperate zones – 2 PG&E groups in northern “remote” locations • Conducted English and Spanish • About half (ten) will be completed as part of the questionnaire development process; the balance will be conducted after completion of the survey in order to probe for deeper understanding of results. • 12 recruits per session, with the expectation of 8-10 to show • Respondents drawn from CARE households, and LIEE-participant households • Post-quantitative survey focus groups can include customers based on segments as well • Customer sample provided by SCE and PG&E • Participants would be persons predominantly or equally responsible for making decisions about and paying the monthly electric bills for their households • 90 minutes long

13. Focus Groups – Segmentation Study Proposed Allocation of the 16 Segmentation Focus Groups

14. Quantitative Survey – Segmentation Study HPI proposes Segmentation Study quantitative interviews: • Conducted among heads of CARE-enrolled households that are predominantly or equally responsible for paying their monthly electric bills. • Customer sample provided by SCE and PG&E. • 3,000 total interviews. • 1,500 among SCE customers and 1,500 among PG&E customers. • Averaging 15 to 20 minutes in length. • (Likely) comprised of approximately 35% - 40% Hispanic, Chicano or Latino respondents. • About one-half, or 17-20%, are expected to prefer to be interviewed in Spanish. • Computer-aided telephone interviewing (CATI).

15. Completed Confidence Interviews Interval Utility (+/ - ) SCE PG&E Quantitative Survey – Segmentation Study HPI proposed to allocate the Segmentation Study interviews as follows: (n) 1,500 2.5% 1,500 2.5%

16. Focus Groups - HUNA Study • HPI proposes additional focus group session time and sessions, beyond levels needed for the LIEE Segmentation Study, to address HUNA topics: • A module of 30 minutes added to each LIEE Segmentation focus group. • Two additional HUNA sessions (beyond the 7 SCE groups proposed for the LIEE Segmentation Study), completely dedicated to HUNA topics. • Conducted among SCE customers (all other LIEE Segmentation Screening criteria remain unchanged.).

17. Focus Groups - HUNA Study HPI proposes to allocate the HUNA focus groups as follows:

18. Home Energy Audit - HUNA Study • HPI proposes Home Energy Audit telephone interviews followed by on-site visits: • Twenty 20 – 25 minute telephone interviews. • Respondents stratified to reflect the range of High Usage Households in SCE’s service territory, and temperate and non-temperate climate areas. • Capture self-reported data via telephone to preview data to be obtained by the final Quantitative Survey. • Validate the completeness and accuracy of self-reported data through on-site visits; observe current practices and barriers to changing energy behavior. • Results to inform final development of the HUNA questionnaire module.

19. Home Energy Audit Visits - HUNA Study Proposed Allocation of Home Energy Audit Visits Total visits 201030

20. Quantitative Survey - HUNA Study • HPI proposes to use the LIEE Segmentation Survey for “double duty,” having it also address HUNA issues among qualified SCE respondents. • Each LIEE survey will trigger a module of HUNA questions when an SCE respondent is qualified to answer. • However, to ensure that sufficient interviews are completed among SCE customers for purposes of the HUNA Study, we recommended augmenting the base of 1,500 SCE customer interviews needed for the LIEE Segmentation. • Using a conservative estimation, we would expect 20%, or about 300 of the total 1,500 SCE interviews, to be among high use customers in temperate climate zones. • We recommend augmenting that estimate of 300 with 200 additional interviews of high use customers in SCE temperate climate zones to achieve approximately 500 HUNA Study cases for analysis.

21. Quantitative Survey - HUNA Study Proposed HUNA Quantitative Interviews LIEE Segmentation Interviews Utilized (est.): 300 Augment sample 200 Total HUNA interviews 500

22. Methodology Summary Traditional: Proposed: • Analysis 1 (internal data segmentation) • Pre- Focus Groups • Telephone Survey • Analysis 2 (segmentation confirmation, profiling) • Post- Focus Groups (messaging) • Focus Groups • Telephone Survey • Analysis 1 (segmentation) • Analysis 2 (profiling and messaging) Additional questions or comments?