Utility Service Area Database Issues
Electric Utility Service Area Databases provide critical
market information on customers within each electric utility service area
in the US. Service Area Databases include the same extensive firmographic
and psychographic information on individual customers provided in the
MAISY State-Level Databases (e.g., business type,
number of employees, income, demographics, equipment and building structure
detail, energy use including hourly load profiles) and can optionally
include electric bills and measures of cost of service and profit
for each customer record.
Electric bills reflect current utility rate structures and each customer's
monthly and/or hourly electricity use; customer-detailed cost-of-service
is determined from each customer's hourly electricity use profiles and both
hourly generating costs and capacity cost allocations. Customer profit shows
the difference between revenue derived from the customer through monthly
bills and the cost of actually providing electricity to the customer. Use
Service Area Databases to immediately calculate service area headroom,
size and profile individual markets, assess revenue and profit potentials,
analyze cost-of-service issues and evaluate competitive threats, opportunities
and strategies. Databases are available for all electric utility service
areas including investor-owned utilities, municipal utilities, public utility
districts, and co-op areas. Electric Utility Service Area Databases use the
standard MAISY chart-based drill-down software described above. The Service
Area Profiler can also incorporate dynamic load profiling
to determine monthly profit (or loss), revenue and cost of service for
both residential and commercial customers and customer segments whose settlement
is determined by distribution-utility dynamic load profiles and monthly meter
readings.
While deregulation typically occurs throughout an entire state, existing
rate structures, customer loyalty (or lack thereof), cost-of-service
characteristics of the distribution utility, weather characteristics and
other utility-specific factors distinguish each service area as its own
market area. MAISY Service Area Databases are a cost-effective and timely
source of information available for individual utility service area market
evaluations and marketing analysis.
Use Service Area Databases to immediately:
Utilities and other Energy service providers (ESPs) preparing for future
deregulation can use the Service Area Databases to develop effective marketing
strategies before competition for customers actually begins while ESPs already
operating in competitive markets can benchmark current marketing efforts
and refine marketing strategies to improve profits.
See Texas Utility Marketing Databases Updates
to see how MAISY products can be extended to meet state-specific competitive
rules.
Customer information has little value in regulated markets; consequently,
utility customer information sources are extremely limited. While traditional
customer list sources such as Donnelley and Dun and Bradstreet (D&B)
provide information on household income and demographics and on commercial
and industrial firm employment and other variables, these data sources don't
provide energy use information required to assess electricity sales profits.
Complicating factors even more, establishment records in traditional list
databases do not automatically correspond to utility customer data because
of multi-establishment, single-metered buildings (e.g., each of the fifty
companies in a medium-sized office building is an individual establishment
in the list database; however, only the owner of the building is a utility
customer ). Furthermore, customer counts provided by utilities (and reported
in Department of Energy publications) reflect the number of meters and not
the number of individual utility customers.
Customer information is critically important in competitive markets. MAISY
Utility Service Area Databases are a comprehensive source of customer-detailed
energy use, load profiles, firmographic and psychographic variables, revenue,
cost-of-service and profitability measures.
MAISY Service Area Databases, which are available for all electric utility
service areas (including municipal utilities, public utility districts,
and co-ops), provide statistically representative samples of customers for
each service area. Customer records are developed from MAISY State-Level
databases and from additional data collected on individual customers within
each service area. Electric bills reflect actual utility rate structures
and each customer's monthly and/or hourly electricity use; customer-detailed
cost-of-service is determined from each customer's hourly electricity use
profiles, actual utility or market-based hourly generating costs and fixed
capacity costs. Client-provided rate structures and cost-of-service data
can also be incorporated in the databases. Customer profit shows the
difference between revenue derived from the customer through monthly bills
and the cost of actually providing electricity to the customer.
Electric Utility Service Area Databases use the standard
MAISY chart-based drill-down software which answers
specific marketing questions with mouse-clicks on charts and permits users
to navigate through the state-level energy marketing and hourly load databases,
exploring customer characteristics and relationships. Information can be
viewed in chart or table form at any level of aggregation ranging from an
individual customer to all customers in a state or states.
Users can also apply MAISY Profiler software which
uses a "wizard approach" to assist in the analysis and evaluation of customers,
customer segments and the utility market. This software is an add-on system
designed to assist MAISY users with customer and market hourly load and
profitability analysis. The profiler runs with State-Level and Electric Utility
Service Area Databases and: (1) adds a "wizard approach" where a set
of sequential screens help users define desired market segment or customer
specifications, (2) automatically extracts and processes relevant
customer data from the databases and (3) presents the results in a special
Excel Workbook which integrates hourly loads, cost of service and rate
structures. Use the Profiler for applications ranging from strategic
market analysis to individual customer pricing and profitability evaluations.
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Provides immediate answers to critical utility market, market segment and
customer questions
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Provides the a cost-effective and timely source of utility customer information
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Provides customer-detailed revenue based on current utility rate structures
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Provides cost-of-service based on generation and capacity costs along with
customer hourly loads
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Supports bottom-line revenue, cost of service and profitability evaluations
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Provides firmographic and psychographic information compatible with traditional
marketing analysis and databases
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Point-and-click software facilitates exploration and analysis
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On-call telephone support
MAISY Utility Service Area Databases provide answers to a variety of difficult
strategic marketing questions. This section illustrates the advantages of
using detailed customer data in the MAISY Databases to conduct utility-level
analysis compared to the traditional application of aggregate customer-segment
load profiles. In this example we address essentially the same question from
the perspective of two different competitive market players: the distribution
utility facing competition and an outside ESP considering entering this utility
market.
Questions for the distribution utility:
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Which customer are high-profit customers and therefore most at risk to marketing
and price offers from new ESPs?
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Which customers will create a profit drag in a competitive market?
Questions for the ESP considering entry to the utility market:
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Does enough "headroom" exist to support a marketing initiative in the utility
service area?
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Which customers are high-profit customers and should be targeted?
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How much leeway exists among the target customers to support aggressive pricing?
To answer these questions Traditional utility service area analysis
applies aggregate load profiles for broad customer segments, cost of service
data and current utility rates to determine revenue and cost of service for
each customer segment. If a dozen customer-segment load profiles are available
for commercial sector analysis, for instance, results of revenue and cost
of service computations for each of the dozen segments will identify segments
with highest and lowest profits while the results across all twelve segments
will determine whether sufficient headroom exists to justify entry by new
ESPs. The results of this aggregate analysis are biased, however,
because customer segments load profiles "cancel-out" all variability in
individual customer revenue and cost of service that occur within the segment.
This aggregation error can be quite large, depending on the intra-segment
level of customer variability.
MAISY Utility Service Area Databases permit customer-detailed analysis
avoiding the aggregation error inherent in the traditional analysis.
The remainder of this section demonstrates the value of customer-detailed
analysis in addressing the questions posed above.
The Utility Service Area - Rates and COS
To limit analysis details we focus on commercial sector customers for a utility
whose service area encompasses thirty-five counties including two small
metropolitan areas and a little more than one million total customers (about
2.5 million people). Of course, each utility service area is unique in its
composition of customers, so quantitative results from this example analysis
should not be extrapolated to other areas.
Monthly electric bills are determined with the following commercial rate
structure applied to each commercial sector customer.
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Less than 5kW maximum monthly demand: $11/month customer charge: $0.05/kWh
for the first 1,000 kWh, $0.03/kWh for additional kWh
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Less than 300kW maximum monthly demand: $35/month customer charge; demand
charge of $4.00/kW, $0.018/kWh for all kWH
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300kW or greater maximum monthly demand: $350/month customer charge: demand
charge of $7.35/kW, $0.0035/kWh for all kWH
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With a fuel adjustment charge of $0.025kWh for all kWh
The average revenue across all commercial customers is $0.06/kWh.
MAISY Databases include cost of service (COS) developed individually for
each customer based on the cost of supplying electricity in each hour of
the day as determined from actual historical hourly generating cost and adding
a capacity cost assessment based on the customer's monthly peak demands during
each of five summer months. Alternative formulas can be used for capacity
cost allocations; however, the results shown here are consistent with algorithms
that assess capacity costs based on the contribution of the customer to capacity
requirements. For simplicity in the analysis that follows, we use the
distribution utility's COS to assess COS for the new ESP considering an entry
to the market. This representation is reasonable if the new ESP contracts
for power in the existing market; however, an ESP with its own portfolio
of supply options will apply its own COS data. Other items can also be added
to the COS data including metering, billing, marketing and other overhead
expenses.
The Utility Service Area - Customers, Sales and Revenue
The figure below shows commercial customers in the utility service area by
primary business activity. 58,763 commercial customers are located in the
35 counties of the service area (comparing similarly-sized service areas
in DOE tables typically shows 50 -100% more "commercial customers" for a
service area this size because the term "customer" in DOE tables is synonymous
with meters, not actual utility customers. Many commercial customers have
multiple meters and many of the meters reported as "customers " reflect minor
non-building-related meters (parking lot lighting, etc.). The profile shown
below is typical for a service area with a population of 2.5 million with
two small metropolitan areas. Office and retail customers represent 44 percent
(26,044) of all commercial customers in the service area.
The next chart shows the distribution of electricity use across primary business
types revealing the more electricity intensive activities of offices, retail,
malls, groceries, restaurants, hospitals and hotels.
Annual revenue, shown in the chart below, reflects annual kWh as well as
demand charges especially for restaurants which tend to have large peak demands
relative to annual kWh.
The charts above provide an overview of the service area; much more detailed
assessments can be developed with MAISY by generating charts and tables of
customer characteristics for any number of market segments within the primary
business types.
For the commercial sector as a whole, we know that billing revenue from all
customers is approximately the same as the cost of serving these customers
since current electricity rates have been designed based on customer-class
cost of service studies. This result is shown in the following chart where
total commercial customer revenue for the distribution utility (annrev) of
$493 million is nearly offset by cost-of-service (anncos) leaving a net profit
(annprf) of only $3.5 million or $60/ per customer ( a "normal rate of return"
is included in the cost of service data, so annprf reflects profit in addition
to that allowed in the regulated rate of return).
Traditional Evaluation with Customer-Segment Load Profiles
Traditional analysis applies customer segment load profiles to address the
questions posed at the beginning of this section. Sector-specific profit
or headroom is shown in the chart below based on twelve aggregate customer
segment load profiles developed from the representative sample of utility
customers for the service area. The aggregate segment load profiles were
applied to the same rate structures and cost of service data used in developing
profit for individual customers
To answer questions posed at the beginning of this section, the distribution
utility will designate the most profitable customer segments as "at risk"
and the least profitable as its "problem segments." New market entrants will
apply the same data, summing profit across all segments ($10 million) to
determine whether potential revenue sufficiency exceeds costs to justify
entering the market. If the new ESP decides to enter the market, it will
focus its marketing efforts on customer segments with the greatest profit.
As we will show below, these aggregate segment results and the strategies
that they suggest suffer from serious aggregation biases and lead to marketing
strategies that are counterproductive; that is marketing strategies that
tend to diminish commodity profits.
Customer-Detailed Analysis With MAISY Utility Service Area
Databases
With the MAISY Service Area Databases we have access to detailed customer
information for a statistically representative sample of customers in the
service area. A quick look at revenue, cost of service and profit shows that
the customer segment analysis of traditional market evaluations mask highly
variable profit margins across individual customers. The table below
shows revenue and profit just a few of the customers in this service area
along with the customer's primary business category.
Since the Service Area Databases provide individual customer profits, we
can develop accurate sector-specific profitability results directly from
the customer data without having to apply aggregate customer-segment load
profiles. We would not expect the aggregate load profiles to provide exactly
the same results as computing profit from the individual customers because
of aggregation bias inherent in the aggregate load profile process.* However,
if intra-segment customer variability is small compared to variability across
segments, the level of aggregation bias may be insignificant. The chart below
presenting aggregate results along with the true individual customer calculations
shows that aggregation bias is, in fact, quit significant. The true individual
customer calculations were developed by computing profit for each of the
1076 customers in the representative sample and summing the profit from these
individual customers within business types to get profits based on individual
customer loads. Comparing these true profits to the biased profits based
on customer segment averages reveals the inaccuracies of using the segment
profiles shown in the second chart.
As these results indicate, profitability computations based on aggregate
load shapes provide extremely poor estimates for all but one segment (assembly).
For offices (off) and warehouses (dwr) sector-specific profit has the wrong
sign. These results illustrate the inaccuracy of the segment-specific
profitability estimates. Estimation errors for eight of the twelve segments
are greater than 90 percent.
Summing profit for individual customers across sectors reveals a true total
profit potential of $3.5 million. However, segment-wide results reflect the
sum of profits, some positive and some negative, across customers within
each sector. Evaluation of the individual customer profit data shows that
the $3.5 million in profit for the entire commercial sector actually reflects
$22 million in profit and $18.5 million in losses for a net profit of
$3.5 million. The chart below shows profits for each customer quintile
where customers have been sorted from least profitable to most profitable.
As indicated with these results, potential customer profits and headroom
can not be reliably estimated with traditional aggregate customer segment
analysis.
Answering the distribution utility and new ESP questions accurately requires
analysis which includes revenue and cost of service at the customer level.
It is not necessary to have information on all customers in the service area
to conduct customer-detailed analysis; analysis of a representative sample
of customers is sufficient to develop accurate service area revenue, cost
of service, profitability and headroom assessments.
Customer Profitability Models and Individual Customer
Profit Scores
The critical tasks for the distribution utility and the new ESP are to
identify profitable and unprofitable customers and profit-based customer
segments to gain the competitive advantage inherent in the questions
raised at the beginning of this section. Customer profitability can easily
be associated with customer characteristics such as number of employees and
business type in order to match profitability analysis results with names
of actual customers in customer list databases (e.g., D&B, Donnelley).
These profitability relationships or models, which can be developed with
cross-tab results or simple statistical models, provide a reliable guide
to identifying profitable and unprofitable customers.
The chart below illustrates the ability of simple profitability models to
distinguish customer profitability. In this case a profitability regression
model was applied to MAISY utility customer data to determine how well the
model actually distinguished between profitable and unprofitable customers.
Actual profit is shown on the horizontal axis while predicted profit is shown
on the vertical axis. The diagonal 45 degree line shows all of the points
where actual profit equals predicted profit. Each dot on the chart shows
a customer's actual profit and the profit predicted for that customer with
the model. Those dots that lie closest to the diagonal line have been predicted
most accurately. For instance, the right-most dot in the upper-right quadrant
reveals an actual profit of $9,500 and a predicted profit of $10.000. All
of the dots that lie in this first quadrant reflect customers for whom the
model correctly predicted positive profits while the dots in the third quadrant
(lower-left) reflect customers for whom the model correctly predicted negative
profits.
Profitability relationships and models for other market segments are equally
effective in distinguishing customer profitability. Applying profitability
models to individual customer characteristics derived from commercially available
customer list sources provides profit estimates or profit scores which can
be used to identify the most profitable customers prior to their
acquisition.
Thus, MAISY Utility Service Area Databases permit
utilities and ESPs to more accurately answer the questions first posed in
this section without any of the aggregation bias inherent in traditional
aggregate load profiling approaches.
To recap:
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High-profit customers can be identified from the MAISY Utility Service Area
Databases which incorporate current utility rate structures and relevant
cost-of-service data.
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Customers who will create a profit drag in a competitive market can also
be identified and targeted with ESCO, incentive rates or other measures to
improve profitability.
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Headroom can be more accurately evaluated using MAISY Utility Service Area
Databases compared to the aggregate customer-segment load profiles.
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The potential for competitive pricing can be determined from from the MAISY
Utility Service Area Databases.
* Aggregation bias, in this instance, is a result of the fact that
customer revenue and cost of service are both nonlinear functions of hourly
loads. With nonlinear functions the value derived from an average input is
different than the average of the values derived from the individual inputs.
For example consider the function y = x2. With x values of 2,
4 and 10, we get an average x value of 5.33 which evaluates to 28.4 (i.e.,
5.332). Evaluating each of the values individually provides three
y values, 4, 16 and 100 whose average is 40.0. The aggregation bias in this
example is 29% ( (40-28.4)/40 ). The true profit or headroom figure is determined
by the profit provided by each customer; consequently, any discrepancy between
the results based on aggregate load profiles and the results based on individual
customer calculations reflects aggregation bias.
MAISY database records protect the identity of individual customers; however,
more than three hundred customer characteristics are provided to map information
from the databases on revenue, cost of service and profitability to customer
contacts and addresses from commercially available lists such as Dun and
Bradstreet, Donnelley, Claritas, Database America and others. Identify high-value
target markets that match rates to cost of service with the MAISY databases
and analysis software and then select a subset of the customer list data
that most closely represent the most desirable market segments. This selective
access of customer list information and marketing activities typically save
three to four times as much as the cost of the MAISY Utility Service Area
Databases.
Jackson Associates provides both telephone and inhouse support to MAISY clients
on this important marketing program implementation process.
MAISY Utility Service Area Databases are composed of a sample of utility
customers whose energy use and other characteristics statistically represent
the entire service area. Commercial sector databases (i.e. office , retail,
restaurant, etc) typically include 1,500 - 2,500 individual customer records
and residential sector databases include 800 - 1500 records. In addition
to all of the customer information available in the State-Level Energy Marketing
and Hourly Load Databases, Service Area Databases include electric bills,
cost of service and profitability measures for each customer record.
Construction of the Utility Service Area Databases includes the following
four steps:
Develop Information on All Commercial and Residential Customers Within
a Utility Service Area. The first step in the Service Area Database
determines the total number and composition of all commercial and residential
utility customers within the service area. The number of commercial customers
by business type, size category and age are determined from a variety of
sources including commerce department databases, directories, associations
and proprietary sources. Extensive information on residential dwelling unit
type and age, income, demographics, heating fuel and other variables are
derived from census data and updated with current population, housing information
and other data.
Develop Service-Area Customer Samples. Data on current service area
population characteristics developed in the preceding step are used to determine
which customers to draw from the State-Level Databases in a way that the
sample of customers statistically represents customers in the service area.
Sample sizes of 1,500 - 2,500 for commercial and 800-1,500 for residential
databases are developed to provide robust representational accuracy. In some
cases, the number of state-databases customers located within service area
boundaries is less than the target sample sizes; in this case, customers
in nearby-areas are "borrowed" for the service area database. The energy
use and hourly load characteristics of the borrowed customers are updated
, if necessary, with statistical/engineering models to reflect any climate
differences between the service area and the location of the "borrowed"
customer.. This adjustment process was developed and refined in our utility
database development projects conducted over the past 15 years. Convenience
stores in Raleigh "look" just like convenience stores in Charlotte, after
accounting for weather-sensitive end -use energy adjustments.
Compute Monthly Electric Bills, Cost-of-Service and Profit for Each Database
Customer. Electric bills reflect current utility rate structures applied
to each customer's monthly and/or hourly electricity use. Customer-detailed
cost-of-service is determined from each customer's hourly electricity use
profiles, actual utility or market-based hourly generating costs and fixed
capacity costs. Client-provided rates and cost-of-service can also be applied
during the database development process or after delivery of the Service
Area Databases.
Customer Profit is calculated as the difference between revenue derived from
the customer through monthly bills and the cost of actually providing electricity
to the customer.
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