Building Better Programs with Demand-Side Analytics

By Jeff Woodward on

This post was originally published on Tendril’s website. Tendril is now Uplight.

“Lighting programs are on the outs–what will take their place?”
“I’d love to improve my programs, but it’s hard to bring all of our stakeholders along.”
“Here comes another last minute change from our regulators…”

I’ve heard multiple versions of the above from numerous utilities within the past few years. What I’ve learned is that utilities are hungry to develop better, more cost effective demand-side management programs, and to executive them more quickly and intelligently.

However, there is a severe gap in wanting to do this and actually being able to do so.

It’s a counterintuitive problem: utilities have the desire to offer better programs to their customers. They understand the benefits–better ROI, new revenue streams, happy regulators and rapidly meeting efficiency and demand management goals. They also have mass amounts of data–arguably more than any other industry–about who their customers are, what they care about and how their homes use energy. So using that data to build targeted, cost-effective programs and reap the benefits thereof should be easy, right?

Unfortunately within most utilities, accessing anything close to a valuable data insight relies too heavily on complex analysis and disparate data sources, which require a trained IT professional to access. This barrier hinders program administrators from accessing insights quickly and easily enough to leverage them in program design and planning.

Too many times have we seen DSM programs go awry from issues that could have been solved with data analytics. Take for example, the northeast utility that created rebate programs for high efficiency gas heating and water heating equipment for their northern and southern territories. The only factor taken into account during planning was the population density of each region. Since the southern region’s population is significantly higher than the northern, the goals and budget established for the south were much higher.

As it turned out, the southern program ended at 36% of its savings goal with inadequate acquisition numbers despite vigorous outreach and education efforts. Alternatively, the north region ended its program at 403% of its goal and having spent over four times its allotted budget.

Had this utility considered other factors such as differing climates, incomes and housing infrastructure–all of which can be achieved with demand-side management analytics–this severe miscalculation in planning could have been avoided.

Across the pond, utilities in Europe have embraced the notorious “Be more like Amazon” mindset and have started harnessing the power of data analytics. For instance, Endesa, a leading electricity dealer and second-largest gas vendor in Spain, implemented an analytics solution to help the company segment and better understand its customers to streamline campaign management. This helped Endesa reduce churn by 50% in two years, reduce customer acquisition costs by 50% and improve cross-selling. Endesa also realized a 70% reduction in the time required to design new campaigns and generate target customer lists. These types of benefits can and should be captured by US utilities using demand-side management analytics.

By being able to pinpoint customer preferences and proclivities to participate in specific programs, demand-side management analytics will help utilities get a lot better at developing and planning the right programs, communicating to regulators who the programs will benefit and marketing the right communications to the right customers.There are several ways utilities can access demand-side management analytics today. Business intelligence systems are widely available, though may offer more complexity than is needed. Some utilities are even bringing on their own in-house data scientists to mine their data mounds, but that can be more costly and hard to do when competing against the Googles and Amazons of the world to attract top talent.

At Tendril, we’ve made self-serve demand-side management analytics available to our utility partners. With Tendril Home Energy Analytics, DSM groups have a 360-degree view of energy consumers at their fingertips. It taps directly into the Tendril Platform to search and analyze hundreds of thousands of data points on each household to derive insights on the profile and energy use of the home and occupant, the products and services they are likely to purchase and how they will respond to promotions and recommendations. These types of insights are invaluable in the planning, execution and regulatory communications of DSM programs.

I recently gave a talk on the need for DSM data analytics at this year’s E Source forum. For more information, you can reach me at