Flip sides of the same coin? A simple efficiency score versus energy bill savings information to drive consumers to choose more energy-efficient products

The product choices that consumers make when purchasing appliances, equipment and electronic gadgets that consume natural resources during their operational phase lock in consumption over their lifetime. Typical useful lifetimes for major domestic appliances range from 10 to 15 years, with shorter lifetimes for electronics and longer lifetimes for heating, ventilation and air conditioning (HVAC) equipment. Given the need to dramatically reduce greenhouse gas emissions in the immediate future, if we are to limit the average global temperature increase to between 1.5 and 2 °C, the billions of one-time purchasing decisions being made by consumers annually matter.

Televisions alone consume an astounding 7% of national purchased electricity in the USA (EIA 2015)—only slightly less than water heating (9.2%) or refrigeration (7.5%)—and TV and other consumer electronics’ electricity demand is expected to grow further (NRDC 2015). Fortunately, the best commercially available consumer products and residential HVAC equipment offer tremendous scope to reduce energy requirements below business as usual. Our data show that the most efficient TVs offered on the US market use 40% less than the market weighted average for new TVs (Arquit Niederberger 2016a). Influencing 30% of US product purchase decisions across four product categories (refrigerators, dishwashers, dryers, TVs) in a single year to super-efficient models with Enervee Scores¹ of 90+ would save 15,100 GWh (see Fig. 1), more energy than needed to meet the annual residential electricity demand of Los Angeles and Sacramento combined.

With such clear opportunities for both energy and financial savings, why is it that many energy-efficient consumer products continue to suffer from very low uptake? Product efficiency incentive schemes have been dominated by financial incentives, delivered in the form of rebates (CEE 2015), presumably seeking to overcome perceived financial barriers or inattention to energy efficiency. Yet policymakers and regulators are increasingly encouraging market transformation approaches that tackle underlying market barriers directly, recognizing that untargeted financial rebate schemes are fraught with free ridership—and poor uptake amongst the low-income population (Cluett et al. 2016), where financial barriers would be expected to be most relevant—as well as low cost-effectiveness, especially for product categories without significant incremental costs.

Underinvestment in energy efficiency is often attributed to imperfect information and, increasingly, rational inattention (a form of ‘bounded rationality’; Simon 1955). The idea behind rational inattention is that when information is costly to acquire, decision-makers sometimes choose to act upon incomplete information, rather than incurring the cost to become perfectly informed (Sallee 2014). Estimating the value of future energy costs that could be avoided by purchasing an efficient appliance is beyond the abilities or motivation of most consumers and requires data that are not readily available.

Consumers are more likely to be rationally inattentive if perceived effort costs are high, the range of unknown energy costs is low and in cases where the product that is their first choice is very different than other model options (e.g. a TV with 4 k resolution vs. a standard resolution TV). Attempting to overcome rational inattention by incentivizing consumers to pay attention is not welfare improving, since increased attention involves the real opportunity cost of taking the time to consider efficiency implications, which might be larger than the potential energy bill savings. Thus, eliminating barriers to information acquisition and processing may be a more promising approach than prodding consumers with price incentives (Sallee 2014).

This has been attempted with mandatory and voluntary government labels, but the literature suggests that labels have not been a silver bullet (Newell and Siikamäki 2013; Davis and Metcalf 2014) and may come with unintended consequences, such as dampening manufacturers’ and consumers’ motivation to offer and purchase products that exceed the qualification requirement (Houde 2014), or leading consumers to pay more for labelled products than justified by their associated energy savings (Newell and Siikamäki 2013; Houde 2014). A problem with existing labels that include energy information is that they are not accurate for individual consumers, as they are based on averages and usage assumptions that cannot be personalized (Newell and Siikamäki 2013). The consumption of HVAC equipment, for example, varies geographically, as do electricity tariffs (currently averaging $0.25/kWh for residential customers in Alaska and Hawaii, compared with under $0.11/kWh in 11 states²), and households exhibit wide variations in activity levels.

Together with client utilities across the USA and Europe, Enervee is addressing imperfect information and rational inattention consequences, through consumer marketplaces that can support targeted incentives,³ as needed, as well as other interventions, and which function across all product categories. Enervee (www.​enervee.​com) is a software-as-a-service (‘SaaS’) company, featuring utility-branded product efficiency marketplaces that allow consumers to review and select more efficient products via specific data points that relate both to the specific product and the specific use case for the consumer, and, in the USA at least, to proceed to more efficiently claim rebates on those new purchases, where offered by their utility.

Utilities that license the platform typically do not choose to sell products direct to consumer; rather, they leverage the marketplace to facilitate energy-smart purchase decisions and transactions between their customers and established and trusted market intermediaries, such as retailers. As such, Enervee Marketplace is an intermediate (or initial) step for consumers, acting as a decision aid, with a focus on energy performance attributes of all products. Prices and retailers are updated daily (or at sale times, intra-daily), to ensure consumers are given the most up-to-date information regarding purchase price and availability. To date (April 2017), Enervee is working with 11 utilities in North America and Europe (France and the UK), servicing approximately 47 million households.

Utility case study research from the USA suggests that consumers in Northern California generally wish to consider energy in their shopping decisions, but have difficulty obtaining the information they need (Binley et al. 2016), as illustrated by the information gaps presented in Fig. 2.

To understand the influence of imperfect information, Enervee is committed to a research agenda to study the effects of both information disclosure and the presentation style or framing of that information, on product choice and purchase decisions. This agenda brings together a number of extant research themes focused on eliciting prosocial behaviours from consumers. On the one hand, defaults (e.g. Johnson et al. 2002) as choice-architect-designed reference points have been shown to be effective in motivating consumers to make choices that improve both their and social welfare (Beshears et al. 2007), particularly where consumers perceive the ‘effort tax’ (Sunstein 2013) to override the default as too high. At the same time, however, defaults have to be credible and salient (Sunstein 2013); otherwise, they fail to provide a suitably accurate or relevant feedback mechanism to affect consumer choice change (Karlin et al. 2015). Energy savings from a more efficient specific appliance purchase, shown to the consumer in a dollar amount and predicated on salient default values (e.g. hours or use, years of ownership), may represent a good use of defaults to provide consumers with salient feedback regarding a modified, more prosocial, choice. Indeed, defaults within the energy consumption content have been proven effective (Sunstein and Reisch 2014).

Alternatively, behaviours may also be modified without the typical requirement for an aligned pre-decisional attitude. This can be achieved through nudges (Thaler and Sunstein 2008), which focus on eliciting the target behaviour. Nudges can leverage mental shortcuts, or heuristics (e.g. Kahneman et al. 1991) that are used by individuals in specific decision contexts, characterized by peripheral (Petty and Cacioppo 1986), impulsive (Strack et al. 2006) or ‘hot’ (Peters et al. 2006) choice styles, or they can exploit inherent biases (Tversky and Kahneman 1974) that individuals struggle to override, e.g. present bias (Tversky and Kahneman 1974) or mental accounting effects (Thaler 1985). As such, whilst information is undoubtedly important in guiding consumers toward more efficient choices—which includes timely and salient feedback regarding those choices based on relevant and credible defaults, for example—there are also opportunities to secure efficient purchases through what could be considered less ‘cognition-heavy methods’, leveraging heuristics or biases. More specifically, a gaze heuristic could be used to encourage consumers to ‘simply’ look for the highest value on a scale (e.g. an energy scale with a 0–100 range).

This agenda draws on both observational data from consumer marketplaces live in the field, as well as randomized controlled trials, using bespoke versions of marketplaces under test conditions. Both approaches deliver the benefit of being based on observed choices, revealed preferences and behaviours in the marketplace, rather than the more typical self-stated responses that remain highly susceptible, in this specific context, to social desirability bias (Fisher 1993).

This paper reports results from four distinct but linked studies, relying on a combination of observational data (study 1) and data collected from a series of randomized controlled trials (studies 2–4). Study 1 analyses observational data from live utility consumer marketplaces to ascertain consumer willingness to handle two key energy efficiency data points which are unique to Enervee’s Marketplace—a relative product energy efficiency ranking (energy score) and associated energy bill savings (energy savings)—as a precursor to using these data points to steer their purchasing decisions. Study 2 then tests the influence of these specific energy efficiency factors on revealed consumer preferences via a randomized controlled trial. Study 3 repeats these tests, but with a specific consumer sample to reveal product preferences amongst financially constrained consumers. Finally, study 4 tests whether buying context—a rushed purchase due to a broken appliance versus a planned purchase as part of a general remodelling program—influences both consumer preferences for energy-efficient products and the effectiveness of the energy score and energy savings to steer consumers toward more efficient purchases.

This paper reports results from a series of observational and experimental studies, designed and run in concert, to explore and then test the effectiveness of two distinct, but related, pieces of energy efficiency information (energy score and energy savings). Study 1 focused on granular observational data relating to consumer behaviour within an online consumer product marketplace. Specifically, it was found that marketplace visitors sorted more frequently by these two energy attributes than they did by retail price and that the energy score was the most frequently used.

Studies 2 through 4 then created a series of RCT experiments to further test and isolate any effects of these pieces of information, as well as to identify any shifts in attitudinal response toward the platform, or toward a pro-environmental position more broadly. Building on the finding that marketplace visitors rely more frequently on the energy score than on other product attributes to sort when researching products, the results from study 2 showed that providing consumers with the energy score also has a significant and positive effect on the energy efficiency of products selected. The effect of the energy score was then replicated for low-income respondents in study 3 and was also seen in study 4, regardless of buying context.

In parallel, our research considered the effect of providing energy bill savings information. Study 2 did not support the hypothesis that providing consumers with energy bill savings data affects product selection. At first glance, this appears disappointing. However, it is important to remember that within the experimental design, this data point was presented as a generic saving (which did not account for individual washing machine usage rates, local energy and water tariffs or the fuel used by each household for water heating). This was clearly a limitation in the study design but, at the same time, points to supporting the need to further test the potential of personalized savings. In addition, we gained further insights on the salience of energy bill savings from the subsequent studies.

Unlike the general population in study 2, study 3 showed that low-income respondents select more efficient washer models when they are provided with the estimated bill savings, as well as with the product energy scores. Financial sums and figures might generally be more salient for shoppers struggling to make ends meet. In our discussion of the study 4 results, we posit that the salience of the bill savings information may have been increased for the wider population, just by providing an enriched buying context can lead to higher face validity for the respondents.

The fact that there was no interaction effect between the two energy attributes in study 3 suggests that they function in different ways to elicit the desired response. Our tentative suggestion that the simple-to-process energy score elicits a hot/impulsive decision style, whilst the cognitively more complex energy bill savings information prompts a reflective/cool decision style, was supported by study 4, which explored the effect of purchasing context. Consumers faced with the need to quickly replace a broken appliance (distressed purchase) selected significantly less efficient washer models than those in the planned purchase condition, who had plenty of time to research their choices. However, the energy score continued to be effective across both buying conditions. Moreover, the three-way interaction effect points to buying context influencing decision-making style. Whilst this feels intuitively correct, study 4 offers early empirical support for this view.

The effectiveness of a relative energy efficiency score in encouraging energy-efficient consumer buying behaviour and preference was supported by all four studies. These robust results make a strong case for leveraging heuristics-based nudges to drive energy-efficient purchasing behaviour at scale. Interestingly, where heuristics-based interventions run the continual risk of failing once novelty disappears, the elegance of an energy score is that it need only influence a one-time purchase decision, which ‘bakes in’ relative energy savings over the lifetime of that product.

Harnessing big data to integrate a relative energy efficiency score throughout the modern consumer product shopping journey has great potential to eliminate market inefficiencies and thereby transform markets with minimal intervention, no reduction in choice and a sustained energy gain. It is an approach that aligns well with ambitious energy efficiency goals and strategies, as well as the emerging role of utilities as facilitators of transactions between customers and distributed energy resource providers. Our findings suggest new opportunities to empower households, including low- and moderate-income households, to shop resource smart.

We have reported on the integration of a relative energy efficiency score in a growing number of consumer-facing, utility-branded product efficiency marketplaces (currently serving over 47million households in the USA and Europe), and the score is also deployed by retailers, manufacturers and publishers, enhancing our ability to reach shoppers when and where they make their purchase decisions. Overall, the studies provide intriguing and robust insights to inform the continued development of cost-effective and scalable interventions to drive more energy-efficient consumer product choices. It is the clear view of the authors that the potential of an intuitive energy score to affect such choices—without financial incentives or the need for explicit education and argument development—is both sizeable and realizable.

The authors recognize several limitations of these studies and identify multiple directions for further research. Firstly, these studies focused on one specific product category; more product categories should be tested. Second, and as already noted, the energy bill savings presented were static and generic. Further studies would seek to identify effects from variable and customizable versions, which are already enabled on ‘live’ utility marketplaces. Related to this limitation, it is recognized that energy savings benefits can be presented in a range of formats, which may make effective use of established psychological accounting theory (cf. Thaler 1985). That is to say, presenting consumers with an energy saving may not be as effective as presenting the consumer with a total cost of ownership, for example. Whilst using the same underlying data structure, the marketplace could look to make this information more salient through alternative frames. To this end, Enervee’s non-experimental Marketplace does indeed give consumers the choice to toggle between alternative views (savings versus total costs), although it is recognized that consumers on the platform currently have to, in effect, self-select the most salient presentation of the information. Further studies to better understand how different defaults influence resultant choices would further contribute to the potential effectiveness of the platform as an intervention.

Third, it is also recognized that the platform—under these experimental conditions—did not recognize that many such purchasing decisions within the household are made, or at least influenced, by a multitude of actors (Mum, Dad, children, etc.), or are extremely contingent on timing and context. Further and more elaborate studies should be undertaken to better understand the context- and identity-driven influences (cf. Champniss et al. 2015) in such purchases. Related to this point, it may be argued that the effectiveness of the platform to influence within ‘hot’ or impulsive decision contexts is questionable. This is for the simple reason that if your washing machine breaks down and you need to replace it in a hurry, you may not use an online platform for this service. Whilst the authors recognize this point, they tentatively argue that online retailers have made considerable strides in the last year alone to make the online purchase of major appliances both efficient and trustworthy. According to the NPD Group, online sales of major home appliances in the USA increased by 38% in 2016 over 2015,⁶ which the authors would propose is an indication of growing consumer convenience and trust in online purchasing for these categories and, by association, a broadening of valid contexts within which to buy online, which can include rushed or ‘hot’ purchases. Anecdotally, this is further supported by the impressive customer experience offered now by retailers such as Amazon and AO.​com (the UK), who offer to deliver and install major appliances within 24h, with no-quibble and hassle-free return options. These developments are contributing to a rash of physical appliance store closures. Consequently, the authors would argue that such a buying environment is increasingly relevant and viable for less planned and more rushed purchases.

Fourth, it is recognized that as a result of consumers not being able to actually purchase products on the ‘marketplace’, there may be moderating effects when the consumer then clicks through to the retailer site, i.e. intentions to buy energy-efficient products may be disrupted in that final step. The authors certainly acknowledge this as a limitation, but attempts are made within the non-experimental version of the platform, through minimizing the contact time and friction that a consumer may experience when leaving the marketplace site and landing on the retailer site. This includes linking directly to the product page of the retailer, whereupon the consumer can immediately place the product in their basket and proceed to checkout. In addition, Enervee is exploring placing the ‘buy’ button from the retailer within the marketplace platform, which would further reduce the friction and potentially mitigate for any possible retailer mediation effect. Moreover, the authors are confident that intentions per se should be maintained, due to the selection of the energy-efficient product on the marketplace representing a pre-commitment (Ariely and Wertenbroch 2002) to buy energy-efficient, and a desire to avoid subsequent cognitive dissonance (Festinger 1954) that may occur through deviating from that more efficient decision. This assumes of course that there are no perceived barriers to enacting that intention of course (Theory of Planned Behaviour (TPB); Ajzen 1991), and the authors recognize this caveat. Affiliate purchase data across categories confirm that actual marketplace-influenced purchases are double-digit more efficient (and less costly) than the average product available.

Finally, it is acknowledged that such behavioural, market-based interventions undertaken by utilities are not a panacea; rather, they complement other strategies, such as mandatory performance standards that have been effective in eliminating the worst products from the market. Nonetheless, the energy score—and the data behind it—can support the efforts of other actors to transform markets (Arquit Niederberger et al. 2015). The energy score offers a dynamic benchmark for the best commercially available technology to stimulate continual product innovation by manufacturers (Binley et al. 2016). Since the score is granular and dynamic, manufacturers can showcase efficiency innovation and derive a marketing and brand advantage from offering products that are ‘better than good’ (i.e. that sit toward the top end of the highest existing energy label for consumers). Similarly, the score can enable retailers to promote efficient products in-store and online. And daily consumer product market data can help policymakers analyse efficiency trends, ratchet up the stringency of standards in step with the pace of innovation, ensure compliance with product standards (Binley et al. 2016) and devise strategies to stimulate innovation at the cutting edge of efficiency. In concert, leading utilities, regulators, governments, retailers and manufacturers are leveraging consumer product market data and using targeted, closed-loop marketing to usher in a new era of energy-smart shopping that is poised to play a critical role in achieving residential energy efficiency targets (Arquit Niederberger 2016b; Arquit Niederberger et al. 2015).