Energy Market Prediction: Papers from the 2014 AAAI Fall Symposium
Diffusion into New Markets: Economic Returns
Required by Households to Adopt Rooftop Photovoltaics
Ben Sigrin, Easan Drury
National Renewable Energy Laboratory
Benjamin.Sigrin@NREL.gov
state-issued rebates for residential systems in the second
half of 2013 in the SCE and PG&E service territories, yet
installations have continued. The U.S. Federal Installation
Tax Credit, once an irreplaceable incentive for profitable
installations, is expected to decrease from 30% to 10% in
2016—and the industry will live on.
Yet installers and their industry are not completely in the
clear. Customers still need to be recruited, and costs for
acquiring customer are high, estimated at $0.49/W per
customer, or roughly 10 - 20% of a system’s costs (GTM
2013). In part this is because rooftop solar is an unproven
commodity for many households. Trusted contacts from
social networks (friends, family, coworkers, and
neighbors) combined with observations of existing systems
does much of the heavy lifting in convincing unsure
customers. In response, the industry has experimented with
a number of innovative advertising and marketing methods
to either recruit new leads or improve their conversation
rate for existing ones. These methods range from door-todoor canvasing, to partnerships with established retailers,
to purchasing customers leads wholesale from third party
aggregators (GTM 2013). All of these point to a continued
need for research that can help identify new market
segments, predict areas ripe for adoption, and test
effectiveness of marketing tactics (Davidson et al 2014).
Customer behavior has been a focus of recent research.
In this, the main framework is of the consumer as a
decision-maker, drawing on the behavioral economics,
Diffusion of Innovations, and Value-Based Norms
frameworks (Faiers and Neame 2006; Rogers 2003; Stern
et al 1999; Wilson & Dowlatabadi 2007) to understand the
economic, informational, social, and behavioral factors that
predict adoption trends. Some early insights from this field
are that social networks can help reduce customer
uncertainty (Bollinger and Gillingham 2012; Rai and
Robinson 2013) and that customers are motivated to adopt
for a variety of reasons—not economics or environmental
concerns alone (Schelly 2014; Zhai & Williams 2011).
Finally, that a number of barriers may exist which inhibit
adoption including high upfront costs, inadequate access to
financing options, lack of awareness of available products,
Abstract
While the U.S. residential solar market is growing quickly,
costs for acquiring customers are high--and this indicates the
value of efforts to identify new market segments and predict
areas ripe for adoption. To better understand how the next
wave of solar diffusion could occur, we explore the range of
economic thresholds that households without PV would
require to consider solar adoption, finding that these
households require more attractive payback times by 1-3
years to achieve comparable market share as current adopters.
In contrast, non-adopters indicate they would be satisfied
with equal or lower returns when the benefits of solar are
expressed in terms of their monthly bill savings—as is the
case for third-party owned systems. If true, this suggests that
the leasing model fundamentally inverts the assumption that
later adopters require higher economic benefits. Adopters,
both buyers and leasers, are compared to their non-adopting
peers across a range of demographic and attitudinal factors.
We find that leasers appear to be more highly influenced by
installer advertising (radio, TV) and marketing, while buyers
were more influenced by personal contacts. Environmental
concern, once a preeminent reason for adopting is decreasing
in relative importance, whereas lowering total electricity costs
and protecting one’s household from future increases in
prices are now the two more important reasons.
Understanding these dynamics, and how they are changing,
offers installers low-cost opportunities to attract new
customers and expand their market base.
Introduction
The U.S. residential solar market is expanding quickly,
with installed capacity more than doubling between 2012
and 2014 (SEIA 2014). Several trends point to a maturing
market—consolidation of market share among solar
installers, increasing access to low-cost capital-particularly from institutional funding sources, and
increased competition between market players. California,
the largest market for solar in the U.S. stopped issuing
Copyright © 2013, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
36
concerns about required system maintenance, and the risk
of PV negatively affecting home values (Hoen et al 2011;
Margolis & Zuboy 2006).
Third-party ownership, or leasing, has been instrumental
both in the market’s expansion and in mitigating some of
the barriers outlined above. Most current lease contracts
guarantee both production and operational and
maintenance of the system, thus reducing risk and hassle to
the consumer (Shih and Chou 2011). More importantly, the
leasing business fundamentally inverts the financial
proposition to the consumer by eliminating the need to take
on debt or make a potentially large up-front payment. As
many households do not have sufficient free cash to make
these payments, leasing has both grown the market and
attracted new demographics (Rai and Sigrin 2013; Drury et
al 2012).
To better understand how the next wave of solar
diffusion could occur, we fielded two surveys in 2013 in
the San Diego metro area to explore: i) demographic and
attitudinal variations within current adopter populations; ii)
differences between adopters and their non-adopting peers;
iii) the range of economic thresholds that households
without PV would require to consider solar adoption—and
how these compare to the historic returns PV adopters have
received.
Adopter Survey
The PV adopter survey was administered in Oct/Nov 2013
as an online survey using SurveyGizmo. The survey was in
the field for three weeks, and two reminders were sent at
the end of weeks one and two. Invitations to complete the
survey were emailed to 10,064 PV adopters in San Diego
County who had applied for California Solar Initiative
incentives from January 2007 through the first quarter of
2013. Of these, participation in individual sections of the
survey ranged from about 880 – 1,230. The final response
rate was approximately 15%, defined as the number of
fully or partially completed surveys divided by the number
of successfully-delivered solicitations.
To ensure representativeness of survey respondents to
the population of PV owners in San Diego, we looked at
two main factors: (1) whether the respondent pool
generally represented the breakdown between third-party
owned PV customers and host owned PV customers; (2)
whether respondents effectively represent adoption from
early years (pre-2009) as well as more recent years (20122013). For (1), we find 29.7% of survey respondents leased
compared to 30.6% of all PV adopters in San Diego (CSI
2014). For (2) we do find a small bias towards overrepresenting recent installations--28.8% of survey
respondents reported adopting in 2012 versus 25.3% of
actual installations in 2012, and 2.3% versus 1.5%,
respectively, for the first quarter of 2013.
Data
Two surveys of San Diego households were conducted in
2013 for: (1) homeowners that had adopted PV (n=1234)
and (2) homeowners that had not adopted PV (n=790). The
survey instruments were designed to elicit new data
exploring the factors that drive households to adopt PV,
including household-level motivations (e.g., wanting to
save money, wanting to lock in stable electricity costs,
etc.), adoption barriers (e.g., upfront costs, impacts on
home value, etc.), personal factors (e.g., political beliefs,
demographics), social network characteristics (e.g., how
many neighbors/friends have adopted), and access to
information. In addition the surveys explored the economic
thresholds that households would require to seriously
consider solar adoption (non-adopting households) or
seriously re-adopting solar again (solar adopting
households), allowing us to compare these self-reported
thresholds for adopters and non-adopters.
Several survey questions were tested in a series of three
focus groups composed of (1) PV adopters who owned
their systems, (2) PV adopters who had leased their
systems (or signed a power purchase agreement), and (3)
PV non-adopters. Responses from focus group participants
were used to clarify and improve the survey instrument.
Non-Adopter Survey
In additional to the PV adopter survey, we also fielded a
survey through Qualtrics for PV non-adopters. This survey
was sent to single-family homeowners in San Diego county
that had not adopted rooftop solar systems. The nonadopter survey was administered during March and April
2014. The sampling method is somewhat different than the
adopter survey in that responses were solicited until
reaching a pre-determined number of 790 completed
survey responses.
The non-adopter’s instrument used many of the same
questions from the PV adopters survey so that responses
could be compared across the populations of PV adopters
and non-adopters. These include demographics, relative
importance of factors in the adoption decision,
characteristic of the home, and economic thresholds that
would entice homeowners to seriously consider adopting
PV. The non-adopter survey also included additional
questions exploring any contacts that homeowners have
had with solar installers to control for exposure to the solar
industry.
For both the adopter and non-adopter surveys, the
sampling design was limited to homeowners – since these
are the households that benefit from installing PV – and we
did not intend for the sampled populations to be
37
representative of the underlying San Diego population.
However by controlling for homeownership, this allows us
to understand how PV adopters differ from their peers, and
thus which customer segments are more likely to adopt.
Results
Our first section of analysis focuses on understanding
motivations for adoption and identifying differences in the
characteristics of adopters that decide to lease versus buy-their motivations, how these have changed over time, and
demographic variations.
Motivations for adoption
Adopters were asked how important various factors were
in their decision to install solar panels. Figure 1 shows the
relative importance of multiple factors plotted
longitudinally from 2007 to 2013, after converting
categorical responses to a numeric scale (e.g. 1 = “Not at
all important”, through 5 = “Very Important”). Lowering
total electricity costs and protecting one’s household from
future increases in prices were rated as the two most
important factors, which affirms the importance of
economic factors in driving adoption decisions.
Compounding this, importance of economic factors
increases over time, whereas we find that environmental
concern decreases in relative importance. This indicates
installers should continually evaluate their marketing
strategies to stay competitive.
The focus groups of PV adopters in San Diego
highlighted the importance of events in stimulating initial
interest in rooftop PV. Among all adopters surveyed the
top five events leading them to seriously consider rooftop
solar systems were increasing electricity rates (32%),
planning for retirement (24%), talking to friends or family
members with solar (21%), direct marketing by solar
companies (16%), and planning a remodeling project
(11%)1.
The top two events reflect a common theme from survey
respondents of general concern over rising electricity costs
or economic concerns in general; influence from social
groups is also strong (Bollinger & Gillingham 2012; Rai
and Robinson 2013). A surprising result here was the
relative importance of retirement planning in the decision
to adopt rooftop solar systems. Prevalence of retirement
planning as a trigger indicates potential for retirees or nearretirees as a significant market segment.
Fig. 1: Evolution of important factors in adopting solar.
Differences in buy vs lease samples
Within the San Diego market, momentum in adoption
trends is heavily skewed towards third-party ownership
(leasing), as opposed to host-ownership (buying) which led
early adoption trends (CSI 2014). Because our survey
covers adoption from 2007 – 2013, it is demonstrative of
this shift—overall 317 adopters, or 26.3% leased their
system, whereas for adoption in 2012 -2013 only, leasing
comprises 52.2% of the sample. Therefore, it is instructive
to understand differences in the third-party owned sample
as compared to the host-owned as it reveals how customer
demographics are changing.
Customers adopting via host-ownership reported that
different situations or events prompted their initial interest
in installing solar panels as compared to third-party
adopters (Figure 2). Specifically, for leasers “recent
increases in prices”, “a conversation with a friend or
family”, and “direct marketing by a solar company” were
the three most likely events to prompt interest. By
comparison, “thinking about retirement planning and
conversations with friends or family” were the second and
third most likely events for buyers. In general, leasers
appear to be more highly influenced by installer
advertising (radio, TV) and marketing, whereas buyers
were more influenced by personal contacts.
1
Since respondents were allowed to indicate more than one event, the
percentages do not sum to 100%.
38
hypothesis that the mean of buyers’ responses equals that
of the leasers’. Since the income and education variables
were initially solicited as ordinal categorical measures,
they are first converted to numeric responses. For income,
the midpoint of the interval e.g. $125,000 for “$100,000 $150,000” is used; Education is converted to the number of
years of post-secondary instruction. For the remaining
categorical variables that cannot be ordered, we use a
Pearson Chi-Squared test to determine whether distribution
of responses differ between the market segments
We find somewhat mixed results (Table 1) with some
demographic and attitudinal differences between customers
from the two business models. Specifically, buyers are
found to have higher incomes by $13,000 (in $2012) on
average, though the result is not statistically significant.
Buyers, however, are older on average than leasers by
nearly two years and have nearly half a year of additional
post-secondary education than leasers--and both results
were significant. In addition, leasers were less likely to be
retired (38% of sample vs. 45%) and more likely to have
children living at home (37% vs 31%) though result are
only significant at a 90% CI (χ2 = 3.21, df = 1, p = 0.073)
and (χ2 = 2.97, df = 1, p = 0.085) respectively.
For factors that adopters indicated were important in
their decision to adopt PV, buyers rated “Lowering my
total electricity costs” as being the most important,
whereas “Protecting myself from future increases in
electricity prices” was the most important factor for leasers
(table 1). Aside from this difference, the two groups rated
the remaining factors with comparable magnitude of
importance.
Fig 2: Buy vs lease differences in events that prompted adoption
interest
Previous research by has reached different conclusions as
to the demographic differences between host-owned versus
third-party owned adopters. Drury et al (2011) found
demographic differences in PV adoption in Southern
Caifornia Edison’s service territory at the zip code level,
with adoption by leasers associated with areas with lower
mean incomes and educational levels. In contrast, Rai &
Sigrin (2013) found no significant difference between the
groups in the nascent Texas market when surveying
individual households. To test differences in the sample,
we conducted a series of Student’s t-test with the null
Table 1: Comparison of demographic and adoption factors for buyers and leasers
Ha: μbuy ≥ μlease
Unequal Var. Assumed
Buy
Mean
Lease
Mean
t
df
p-value
2-tailed
95% CI of Difference
Lower
Upper
Age (years)
59.7
58.0
2.21
471.0
0.027*
0.20
3.27
Edu (years post-secondary)
4.64
4.23
2.91
479.4
0.003**
0.13
0.67
Income ($1,000)
168.4
155.2
1.55
459.7
0.121
-3.50
30.0
Imp. of lower elec. costs
4.58
4.50
1.41
470.0
0.158
-0.03
0.20
Imp. of protect increase in elec. prices
4.43
4.58
-2.44
566.8
0.015*
-0.26
-0.02
Imp. of protect environment
3.89
3.78
1.36
506.1
0.173
-0.052
0.288
Imp. of increasing home value
3.20
3.03
1.90
488.6
0.058
-0.006
0.343
Imp. of home easier to sell
2.52
2.45
0.746
501.5
0.456
-0.114
0.255
39
use heuristics to identify potential customers i.e. through
ownership of energy-intensive appliances.
Concerns over high electricity bills, in addition to
concern about future rate changes is often highlighted as a
motivation for adopting solar—supported by our results,
particularly in California which has some of the highest
retail rates of the nation. In both surveys, households were
asked how they thought electricity rates would change over
next 5 years. We find that a majority of respondents in both
populations expect electricity costs to increase
substantially, and at a faster pace than the long-term
Consumer Price Index average (BLS 2014). There were
also significant differences in expectations between
groups. Specifically, near half of adopters (45.2%) expect
rates to increase by at least 30% over the next five years,
whereas only a quarter of non-adopters (25.2%) hold the
same opinion.
Interestingly, while non-adopters rated protecting their
households from future rate increases as the most
important factor they would consider if adopting solar,
their responses above imply that they do not think this is a
likely outcome. Adopters’ disproportionate concern over
rate increases, therefore, could either be an outcome of the
adoption process i.e. personal research, conversations with
installers or a prior opinion which spurred their initial
interest in adopting.
Both samples were tested to compare for differences in
the factors they considered important when adopting solar
(Table 2). As in the previous comparison, lowering one’s
bill and protection from future rate increases were
considered the two most important factors in the decision.
One insight is that the general populace considered the
importance of home value--increasing home value and
making it easier to sell, to be far more important than the
adopting sample. An explanation for this could be that
Differences in adopter vs non-adopter samples
As the U.S. residential rooftop photovoltaics (PV) market
matures, markets must necessarily diffuse into new
populations and locations to continue growing. A key
prediction from the Diffusion of Innovations literature is
that there are attitudinal and demographic differences
between early-adopting individuals and the rest that follow
them (Rogers 2003; Wilson & Dowlatabadi 2007). For
example, while early adopters are highly interested in the
novelty of new technology, the general populace requires a
clear degree of relative advantage between the old and new
technology. Next we examine attitudinal and demographic
differences between adopters (both buyers and leasers as
one sample) and non-adopting households.
Adopting households were found to have statisticallysignificantly demographic differences as compared to nonadopting households across an array of characteristics.
Adopters were found to have higher incomes by $50,100
on average, be more highly-educated, and live in larger
homes (Table 2). Adopters also expect to stay in their
current home by nearly 20 years longer than non-adopters
—a prerequisite for making a long-term investment in a
PV system.
For non-numeric factors, we again use a Pearson’s ChiSquared test for differences in distribution of responses.
Adopters were found to be significantly more likely to
have children living in the household ( χ2 = 30.79, df = 1,
p < 1e-05), with 32.5% of adopters reporting at least one
child lives in their household, as compared to 19.5% of
non-adopters. Interestingly, no difference was found in the
likelihood of being retired, with 43.0% of adopters retired
as compared to 42.7% of non-adopters. Adopters were also
more likely to have air-conditioning (77.1% vs 63.9%) or a
pool (37.3% vs 18.2%)—and both results were significant
at a 95% CI. These results support the notion that installers
Table 2: Comparison of demographic and adoption factors for solar adopters and non-adopters (general homeowners)
Ha: μadopt ≥ μnonadopt
Unequal Var. Assumed
Adopters
Mean
Non-Adopt
Mean
t
df
p-value
2-tailed
95% CI of Difference
Lower
Upper
Age (years)
59.1
57.6
2.42
1608
0.015*
0.20
3.27
Edu (years post-secondary)
4.54
4.15
4.07
1666
5.0e-5****
0.13
0.67
Income ($1,000)
164.9
114.8
10.4
1568
< 1e-5****
40.6
59.5
Exp. remain in house (years)
33.7
15.2
3.96
1076
7.9e-5****
9.39
27.79
Home size (sq. ft)
2676
2208
4.76
1229
< 1e-5****
275.0
660.8
Imp. of lower elec. costs
4.56
4.59
-0.72
1684
0.472
-0.10
0.047
Imp. of protect increase in elec. prices
4.47
4.46
0.33
1816
0.745
-0.06
0.09
Imp. of protect environment
3.86
3.92
-1.05
1807
0.294
-0.164
0.050
Imp. of increasing home value
3.15
3.88
-13.39
1845
< 1e-5****
-0.831
-0.619
Imp. of home easier to sell
2.50
3.64
-18.97
1780
< 1e-5****
-1.26
-1.021
40
adopters, having already researched solar, judge the risk to
their home to be manageable. Conversely, this suggests
that the general populace considers PV installation to pose
a potential risk to their home value (founded or otherwise).
Efforts to provide additional information therefore could
provide a low-cost opportunity to expand potential market
size.
Table 3: Economic metrics used to evaluate solar investment
Buyers
40.3%
Leasers
60.5%
Non-Adopters
43.4%
Payback time
29.5%
16.1%
41.8%
Rate of return
17.1%
9.8%
6.3%
Economic returns required for adoption
Net present value
2.2%
1.6%
3.5%
To understand how adopters and non-adopters evaluate the
economics of a residential PV system, both surveys
solicited a number of questions relating to the economic
thresholds that individuals would require to seriously
consider adopting solar for their home. Since adopters have
actually already adopted PV for their home, the question is
posed in two ways—the historic return they expected to
receive at the time of adoption, and the return they would
require to readopt. Non-adopters were asked a similar
question regarding the level of returns they would require
to seriously consider adopting solar.
First, respondents were asked to select the economic
metric they would/did use to evaluate whether solar panels
made economic sense for their household. Again, for
adopters this is a question about their previous evaluation,
but for non-adopters it is a hypothetical question—“If you
were seriously considering solar, how would you evaluate
whether solar panels made sense”. A majority of all
populations reported they would primarily use monthly bill
saving ($/month) (MBS) to evaluate solar economics
(Table 3), followed by payback period (years to investment
payoff). Other metrics were reported to be used, such as
net present value (NPV) and rate of return (RoR), though
they are used by a minority of households. Given the
variation in preference for different metrics—and that
these metric show different price thresholds for when a PV
investment becomes profitable (Drury et al 2011), this has
strong implications for the price at which a solar PV
system becomes appealing to different types of customers.
Previously, the consumer behavior literature has
suggested that residential customers primarily use a simple
payback time to evaluate a new technology (Rai and Sigrin
2013; Camerer et al. 2004; Kempton & Montgomery 1982;
Kirchler et al. 2008). However, with the strong growth of
third-party owned systems, we expected that leasing
customers are frequently being pitched PV systems based
on the monthly bill savings rather than a payback time.
Surprisingly, customers who bought PV systems are also
increasingly using monthly bill savings. Use of the MBS
metric is consistent with the importance respondents place
on
reducing
their
current
and
future
bills.
I would not
estimate economics
3.0%
4.6%
3.7%
Other
7.8%
7.2%
1.4%
Monthly bill
savings
Based on the metric respondents indicated they would
use, they are then asked a series of questions to evaluate
the minimum economic return they would require to
seriously consider adopting solar. As we assume most nonadopters have not substantially examined the potential
solar returns, their question requires more finesse.
Specifically, non-adopters are asked a series of questions
implying an increasing or decreasing attractiveness e.g “I
would seriously consider solar if the payback time was one
year or less”, “…two years or less”, etc. Permissible
responses are “Yes”, “Maybe”, “No”, or “I don’t know”.
One expects the respondent to indicate in the affirmative
for highly attractive returns, with a transition to “maybe”
and then “no” as returns become less attractive. The
respondent’s willingness-to-pay is taken as the average
value for which they indicate “maybe”. For quality control,
we discard all responses that imply a preference for lower
returns over higher ones as well non-ordinal responses; for
responses with no “maybe” response, the value is taken as
the transition from “yes” to “no”. In addition, respondents
were randomly assigned questions with either
incrementally increasing or decreasing returns; willingness
to pay was found invariant to the ordering of these
questions.
Economic thresholds are given in terms of the percent of
the sample that indicated they would be willing to
seriously consider solar at a given return or better (Figures
3- 4). Since the sample is small for the metrics other than
payback period or MBS, the analysis will focus on these
two metrics.
Among respondents that used payback time to evaluate
returns, non-adopters required more attractive paybacks by
1-3 years. That is, 50% of non-adopters would require a
payback of 6 years or less to seriously consider adopting,
whereas adopters would only require a 7.5 year payback.
Expectations converge for paybacks greater than 10 years
for both groups, where approximately 20% of all
respondents indicated they would consider adopting at a
10-year payback.
41
Fig 3: Customer willingness-to-adopt for given payback period or
better
Fig 4: Customer willingness-to-adopt for normalized monthly
bill savings
Differences in responses for the monthly bill savings
metric are opposite those of payback time, with nonadopters indicating they would be satisfied with lower
savings when using the MBS metric. For example, only
24.7% of adopters indicate they would consider adopting
with savings of $50/month, whereas 71.9% of nonadopters indicate that would at the same level of returns.
Because monthly bill savings scales with both system size
(larger systems offset more consumption) and the
customer’s consumption prior to adoption (larger bills
allow more potential for avoided cost), we normalized the
MBS values by each customer’s reported summer bill; for
adopters we use summer bills prior to adoption. Thus, the
transformed metric is now the MBS as a percentage of a
summer bill, or the fraction of avoided bill. Note that with
this normalization, savings can exceed 100% if the
respondent indicates they would only adopt if monthly
savings exceed their monthly bill.
Savings of roughly 15% of the average summer bill are
required to entice 10% of both populations. Thereafter,
between 20% and 90% of the summer bill, an additional
10% - 35% of the non-adopter population indicates they
would seriously consider adopting. For savings above
90%, the pattern reverses, with adopters more likely to
indicate they would adopt—though 85% of the potential
market has been saturated at this level of returns.
Differences in the adopter and non-adopter populations’
willingness to consider adoption for different metrics
offers an intriguing insight into how each group perceives
the relative benefits of adoption. If true, this suggests that
the leasing model fundamentally inverts the traditional
Diffusion of Innovations assumption that later adopters
require higher economic benefits. By framing the
proposition for adopting solar as a series of monthly
savings—as opposed to a large upfront payment, greater
portions of the general population could be enticed than if
projects’ returns were expressed in terms of the payback
time. Conversely, the results suggest that there are portions
of the general population that are either unaware of the
potential MBS returns available, or are prevented from
adopting for other reasons e.g. insufficient roof space,
HOA restrictions, or low electricity bills. If activated, these
groups could provide additional momentum to the growing
solar market as they indicate they would be willing to
adopt under current market conditions.
Conclusion
The U.S. residential solar market is growing quickly, and
to continue growing, it must expand into new populations.
In the San Diego market motivations for adopting are
evolving, with environmental concerns decreasing in
priority, replaced with greater interest in saving money
and, particularly, reducing exposure to higher future bills.
Customers leasing their systems now constitute a majority
of new installations in many national markets—and these
customers are more representative of the general
population than early adopters.
Looking to future market growth, there are substantial
demographic gaps between adopters and the general
populace. A key insight is that non-adopting households
are more concerned with the risk of solar negatively
impacting their home’s value—reducing this concern could
unlock additional market potential. Consistent with prior
42
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research, we find that the general populace would require
more attractive payback periods by 1 – 3 years than current
adopters to consider adopting. Surprisingly, the general
populace would be satisfied with lower savings when
adoption benefits are framed in terms of the monthly bill
savings. For installers seeking to lower customer
acquisition costs, framing the benefits of solar in this way
could be a successful tactic.
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