Retail Sales Allocation Presentation of Methodology DraftApril 12, 2012

Bob Stull & Justin Larson - PECI

Agenda

• Why did BPA contract this work?• Why retail allocations? • What are retail allocations?• Research components

– Stakeholder interviews, product profiles, retail profiles

• Anatomy of an allocation– 3 primary components

• Roadmap to results– Going from inputs to outputs

1

Why did BPA contract this work?

Stakeholders asked for an approach to allocations that is:

• Defensible to regulators and evaluators• Transparent and well-documented• Available to all types of utilities• Applicable to a wide array of uses• Flexible enough to adapt to specific needs

2

Retail Sales Allocation: Why• Upstream incentive models

are cost effective• Give small utilities access to

upstream models• Estimate leakage (prior to

evaluation)• Control savings adjustments

from evaluation• Choose most equitable locations

to include in programs• More targeted and/or effective

marketing

3

Retail Sales Allocation: What

What we know:•Some ## people are walking these aisles buying EE products•All ## are walking out the door and installing in a utility territory nearby

What we want to know:•How many of ## are going to which nearby utility territories

*graphics are for illustrative purposes only, and do not represent real market data

4

Retail Sales Allocation: What NOTProjections• Allocations do not predict sales volumes

Estimate Market Share• Allocations are impartial to competing stores

Predict stores’ consumer mix, not store choices• Allocations model the mix of people likely to be in a store, not which retailer a consumer will choose over another

5

Research Components

Considerations

Topography mixes it up• A distance ring is not good enough

6 mile ring drawn around a Walmart in Pasco, WA

*graphics are for illustrative purposes only, and do not represent real market data

6

Considerations

Distance is measured in time

Area in blue represents 10 minutes of drive time from the same store.

Notice all the populated areas that are within the ring but not within the drive time.

*graphics are for illustrative purposes only, and do not represent real market data

7

ConsiderationsSituation: Two grocers share a parking lot

Consideration: Would they have the same CFL allocation?

Answer: Maybe.• Different retailers have unique customer mixes

• Makeup of households within the trade area matters, not just how many there are

• Segmentation identifies core customers for a retailer

• Allocations may be different depending on if core customers are blue or red

*graphics are for illustrative purposes only, and do not represent real market data

8

ConsiderationsSituation: I would drive a lot farther to buy a large appliance than I would to buy a CFL.

*graphics are for illustrative purposes only, and do not represent real market data

9

Consideration: Shouldn’t the allocation methodology reflect this behavior?

Answer: Yes.• Some products naturally draw from larger trade areas

• One location may have multiple trade area definitions

ConsiderationsSituation: A single retailer has locations in dense urban areas as well as isolated small towns

Consideration: Will both stores have the same trade area?

Answer: No.• Urban density plays a role in allocations with regard to trade area sizing

10

ConsiderationsSituation: Utility territories don’t conform to any administrative geography.

Consideration: Isn’t there a lot of utility “overlap”?

Answer: Yes.• Utilities territories are not exclusive areas on a map• Utilities are matched to zips they serve• Methodology will model distribution of households in shared zip codes

11

Allocation Mechanics

How the parts fit together

Anatomy of an AllocationRetail Purchasing

Topographic

Geographic

Market Profile

Census

Demographic

Psychographic

ConsumerProfile

Incentive Redemption

Retail Intelligence Customer Intelligence

Consumer

Trade Area

Retailer Profile

PECI Market Intelligence

Utility Boundary Profile

Allocation ProfileCalculates % attributable to each utility

1

2

34

Location Scorecard

MosaicSegmentation

SimmonsGreen Aware

Profile

12

Roadmap to Results

Market Profiles

Create trade area assumptions• Compare purchase locations to install sites to establish

transactional drive times (program data)• Compare distance between locations within each chain in

relation to each other (no program data)• Create drive-time assumptions for retailers’ trade areas• 3 components to each trade area:

– Product Type– Retailer Type– Urban Density

13

1

Roadmap to Results

Retailer Profiles

Create Mosaic Segmentation Profiles for each retailer•Analyze segmentation within retail trade areas

14

2

Roadmap to Results

Location Scorecard

Count Households within trade area• By segment• By zip code

•Dominant Mosaic segments get double weighting •Simmons GreenAware profile segments get double weighting

15

3

Roadmap to Results

Allocation

Divide scores between utilities• Weight utility scores where there are multiple utilities• Weight utility scores for municipal

16

4

Retailer

10 Minute Trade Area

Zip Code Boundaries

• GreenAware Households

30

250

45 15

• Retail Profile Dominant Households

30

250

45 15350

300

5

10

• All Households

30

250

45 15350

300

5

10125

1000

1250

50

1800Utility A = 900Utility B = 900

165Utility C = 165

1395Utility B = 697.5Utility C = 697.5

70 Utility B = 70

Utility A (900 / 3430) = 26% Utility B (1667.5 / 3430) = 49%Utility C (862.5 / 3430) = 25%

Total Trade Area Score = 3430

17

Questions, comments, follow-up:

Sarah F. Moore, BPA Residential Sector Lead sfmoore@bpa.gov

Bob Stull, PECI Sr. Oversight bstull@peci.org

Justin Larson – PECI Business Intelligence Analyst jlarson@peci.org