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Retail Sales Allocation Presentation of Methodology Draft
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Transcript of Retail Sales Allocation Presentation of Methodology Draft
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 [email protected]
Bob Stull, PECI Sr. Oversight [email protected]
Justin Larson – PECI Business Intelligence Analyst [email protected]