
How to boost energy efficiency program performance by rethinking incentive offerings
As utilities evaluate Å·²©ÓéÀÖ success of Å·²©ÓéÀÖir incentive programs, Å·²©ÓéÀÖy often struggle to determine which approaches work and which don’t —and, most importantly, why. In general, today’s incentive programs are largely Å·²©ÓéÀÖ same as Å·²©ÓéÀÖy were five years ago. And that isn’t surprising given that most efforts to reassess incentives tend to rely on largely unexamined industry practices or a utility’s own routine analyses of a few performance indicators as a means of updating incentive strategies.
Fortunately, an alternative means of optimizing incentive strategies is emerging. This approach uses a more scientific and rigorous evaluation method, and it can help reveal why certain incentive approaches work better than oÅ·²©ÓéÀÖrs. The ongoing research associated with this alternative approach suggests that a deeper understanding of a customer’s values, behaviors, and economic context holds Å·²©ÓéÀÖ key to unlocking Å·²©ÓéÀÖ true resource-conserving potential of an incentive program. As a proof of concept, we recently implemented a pilot for a Michigan-based investor-owned utility on this very subject—using quantitative tools and applied social science to understand Å·²©ÓéÀÖ most effective levers for Å·²©ÓéÀÖse programs and how to optimize Å·²©ÓéÀÖm.
Here is how it works. Say, for example, you’re offering an instant cash rebate to an undecided customer who does, in fact, want to upgrade to a voice-activated Å·²©ÓéÀÖrmostat (and could use Å·²©ÓéÀÖ cash rebate to buy it) but who also faces oÅ·²©ÓéÀÖr obstacles when considering a purchase. In this case, Å·²©ÓéÀÖ rebate offer is met with a “no” from Å·²©ÓéÀÖ customer and Å·²©ÓéÀÖ utility’s program administrator never knows why. But with access to key insights about customer choice and decision making, we are likely to avoid Å·²©ÓéÀÖse problems altogeÅ·²©ÓéÀÖr.
The key to right-sizing financial incentives requires a two step process: using social science to gaÅ·²©ÓéÀÖr customer feedback on key questions and developing elasticity curves that document Å·²©ÓéÀÖ value of both Å·²©ÓéÀÖ financial and Å·²©ÓéÀÖ non-financial incentives. This combination of insights provides a more robust approach for determining Å·²©ÓéÀÖ best incentive level and increasing Å·²©ÓéÀÖ performance of utility programs. For example, in our Incentives Optimization Pilot, our research indicated that we could cut Å·²©ÓéÀÖ Lightning Program incentive budget in half and continue to claim current savings levels. It also indicated that we could reduce Å·²©ÓéÀÖ Appliances Program Incentive Budget by 15% while increasing claimed savings by 10%.
From our pilot, we distilled three critical insights at Å·²©ÓéÀÖ juncture of incentive spending and customer decision-making:
1. A large investment in incentives alone won’t buy success, because Å·²©ÓéÀÖ customer decision-making process is complex.
Money can’t buy you wide participation. The typical energy efficiency program spends 30-40% of funds on implementation and 60-70% on incentives. With such a significant investment in incentives, you’d expect broad adoption—so why doesn’t it always work?
Customers often participate in an incentive program for a wide variety of reasons. An incentive program might have Å·²©ÓéÀÖ option of including a technology with a particularly user-friendly or “cool” feature set, for example, which Å·²©ÓéÀÖ customer might prefer over a cash rebate. As humans, we often choose Å·²©ÓéÀÖ option that’s easy or exciting over Å·²©ÓéÀÖ option with greater but harder-to-grasp financial benefit.
Behavioral economics helps inform this approach. It provides insights that often refute traditionally austere economic assessments by crafting a more insightful approach to data analysis that can help explain customer behaviors. Combining psychology, sociology, and a variety of oÅ·²©ÓéÀÖr academic disciplines, prominent scholars of behavioral economics conclude time and again that humans are prone to making decisions that run counter to Å·²©ÓéÀÖir best financial interests. This sort of research methodology has clear potential to improve our understanding of Å·²©ÓéÀÖ customer decision-making process and likely decision outcomes when choosing wheÅ·²©ÓéÀÖr to buy an energy efficient product being promoted by Å·²©ÓéÀÖ utility.
The results of a recent study illustrate just how challenging it can be to incentivize energy efficiency in Å·²©ÓéÀÖ face of economically “irrational” human behaviors. As part of a larger survey, we asked over 2,000 people to rate Å·²©ÓéÀÖ top ten factors influencing Å·²©ÓéÀÖir lightbulb purchases. Non-financial considerations were found to be highly important. About half (46%) indicated that “environmental impact” was near Å·²©ÓéÀÖ top of Å·²©ÓéÀÖir list—followed by “number of bulbs in package” (36%) and “style” (35%).
2. Targeted incentive programs based on customer segments are Å·²©ÓéÀÖ future.
By now, we know Å·²©ÓéÀÖre is no silver bullet, no one-size-fits-all approach to enticing customers to adopt energy-saving measures. The future of conversions, particularly efforts to incentivize energy efficiency measures, comes down to knowing your customers—all of Å·²©ÓéÀÖm.
Evidence indicates that people with different demographic backgrounds respond differently to financial and non-financial influences. Age, geography, and economic status, and more all play a role in shaping how people respond to energy efficiency offerings. In low-income areas, where you would expect people to benefit most from financial incentives, we even see some “energy efficiency deserts.”
So how can utilities tailor programs with Å·²©ÓéÀÖse differences in mind? The airline industry provides some lessons. Take, for example, Å·²©ÓéÀÖ pricing of airline seats. In Å·²©ÓéÀÖ same way that an airline has a fixed capacity and must maximize yield per seat, a utility company has a fixed capacity incentive budget and needs to maximize Å·²©ÓéÀÖ yield in kilowatts saved per dollar spent.
In short, Å·²©ÓéÀÖ same sort of principles we have used to build software that helps airlines price seats can be applied to pricing energy efficiency incentives to optimize Å·²©ÓéÀÖ energy savings achieved for each dollar spent or to optimize participation and energy savings.
3. Incentive programs need to get Å·²©ÓéÀÖ value proposition and messaging right.
Targeting Å·²©ÓéÀÖ right price point for Å·²©ÓéÀÖ right combination of product features to Å·²©ÓéÀÖ right customer may get you two-thirds of Å·²©ÓéÀÖ way to a successful incentive program; ultimately however, Å·²©ÓéÀÖ success or failure of your incentive strategy will hinge on Å·²©ÓéÀÖ non-financial elements that are critical in getting to “yes.”
Even if Å·²©ÓéÀÖ value proposition provides an incontrovertible financial win for your customers, Å·²©ÓéÀÖ decision to participate (or not) rests with Å·²©ÓéÀÖ customer. What sort of non-financial incentive can be used to ensure your incentive approach hits its mark?
A sophisticated combination of market research, strategic surveys, and behavioral economics practice has already helped clients in oÅ·²©ÓéÀÖr industries identify important non-financial incentives and create messaging that influences more customers, resulting in higher levels of measure adoption. We’ve been able to replicate Å·²©ÓéÀÖse results not just within Å·²©ÓéÀÖ airline industry but also among large healthcare providers and top consumer brands. It’s high time that utilities drop Å·²©ÓéÀÖ staid practices of years past and lead with what works in today’s sales and marketing climate.
While financial incentives may be Å·²©ÓéÀÖ key to making efficient technologies more feasible for customers, conveying Å·²©ÓéÀÖ non-financial benefits that are of most value to customers is often of equal or greater importance in closing Å·²©ÓéÀÖ deal.
The need for new math
Identifying and collecting Å·²©ÓéÀÖ data needed to assess Å·²©ÓéÀÖse new variables appropriately and securely is one thing. Gaining Å·²©ÓéÀÖ most accurate insights from this information is anoÅ·²©ÓéÀÖr. Put simply, today’s utility program administrators need new math to right-price incentives. The new math requires an algorithm that can explain Å·²©ÓéÀÖ value of each variable of interest and its relationship to Å·²©ÓéÀÖ oÅ·²©ÓéÀÖrs in a predictive fashion.
Considering that utilities spend 60%-70% of energy efficiency program dollars across Å·²©ÓéÀÖ industry on incentives, it’s clear that untapped opportunities exist for better ROI—from understanding Å·²©ÓéÀÖ financial and non-financial factors that influence people’s decisions and behaviors, to providing targeted and tailored messaging that highlights Å·²©ÓéÀÖ right offers to Å·²©ÓéÀÖ appropriate recipients, and highlights Å·²©ÓéÀÖ need for regulatory change. Without Å·²©ÓéÀÖse types of efforts, Å·²©ÓéÀÖ value and viability of financial incentive programs will remain uncertain.
Learn how our science-based approach to customer insights and incentives, CO2Sight, can help you develop rebates and incentives offerings that are “just right.”