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Finding the needles

For years prior to joining DNV GL, I worked in marketing analytics for the IT and retail industries. The IT industry especially was facing a challenge, much like what the energy efficiency industry is experiencing today. Firms like IBM had developed solutions for their largest customers (Fortune 500) but realized they also needed to reach smaller firms, which are more diverse and have less capital than their larger counterparts. They turned to marketing analytics to help build up business intelligence and identify and target customers for their solutions.

My role was to integrate vast amounts of transactional, demographic, and marketing data to identify the customers that were most likely to respond to marketing campaigns and direct mail. The techniques I used drove our marketing initiatives and helped develop sales strategies by defining customer segments. My work helped to develop propensity models to drive circulation planning of a well-known consumer goods retailer, helped a large enterprise resource planning software company realign their sales and marketing strategy around key vertical markets, and conducted trade-area analysis to inform how direct mail efforts drive business to different retail stores.

My experience with the IT industry was quite similar to what I now see in the energy efficiency industry: it too has scraped the top layer off the cake with solutions targeting the largest customers, primarily lighting in this case. Utilities and implementers now need to find deeper savings and opportunities from mid-size and small businesses and residential customers, but they are harder to reach and need different solutions and approaches than large customers. With larger customers, it is easier to reach out to them directly, since they are fewer in number; with small and midsized businesses or residential customers, there are too many to address them one on one. To effectively reach these customers we need a more data-driven approach.

Utilities have a wealth of information about their customers in their consumption, program tracking, marketing, and demographic data sets, but these data are usually not set up to analyze marketing activities. Building on years of experience with all these types of data, and bringing in advanced data analytic capabilities, DNV GL created a system that does this.  Our process pulls this information together, messy as it is, and flips it around so it can be used to analyze the effectiveness of marketing and outreach activities.

RAMP (Rapid Assessment Management Program) is a new DNV GL service that allows us to isolate utility customers who represent the greatest opportunities for increased participation and/or savings.  One way to think about this is finding needles in a haystack: it’s much easier to do if you have a tool, like a metal detector, to tell you where the needles are. RAMP uses data analytics to help utilities pull these customers out, rather than scouring the entire haystack.

RAMP starts with a data integration process to standardize information from different sources—energy consumption, program participation, and geographic data–and link them together. The data standardization process allows us to link data together based on geographic information, which is often all utilities receive from vendors who offer energy efficiency services.  We can tie address information to account data allowing for better program tracking and opening the door to target marketing.  The data standardization can become even more powerful when we use the links to tie in data from public and private sources outside the utility.

Next, we work with clients to help set up marketing and outreach activities that can be measured, and ensure those marketing metrics are being captured.  Then we set up processes for tracking the metrics over time, including reporting tools that can break down activities and responses by customer segment.   For utilities who want to take an even more targeted approach to managing their marketing and outreach, we can develop response models to help identify customers who are most likely to respond.  We work together with the utility’s program marketing and program design teams, making recommendations for changing activities to maximize participation.  For example, past RAMP projects have shown that audit-based programs are more effective at stimulating interest of non-participants to engage in further energy efficiency work than for reactivating past participants.  Another study isolated the lift in participation associated with offering customers a free package of CFLs.

We restructure utility data to describe prospective participants in terms of their consumption, program history, demographic/firmographic, and marketing profiles at any point in time relative to all other points in the customer’s history.  Once we define each customer in this way, we can analyze customers’ responsiveness to marketing or outreach activities or program changes that occur at a particular point in time.  We can then isolate, model and/or report on which customers were more, or less, responsive to the changes.  In this way, RAMP can help find the needles in the haystack. The RAMP data structure can be used in several ways: to assess marketing effectiveness, or to use modeled results to drive marketing and outreach efforts, and even program design to maximize effectiveness.

This type of data analytics can greatly enhance a utility’s ability to track and inform the success of a wide range of initiatives, including marketing campaigns, time-of-use rates, and energy efficiency programs. The ability to model likely participants can reduce marketing costs and increase overall response rates to marketing by isolating those customers who have the greatest likelihood of response, or to target different outreach strategies to different segments.  At a time when programs are looking for ways to improve operational efficiency, shaping the outreach strategies based on a granular understanding of customer response can provide the greatest return on marketing dollars.

Learn more about our marketing analytics services by visiting our knowledge hub or viewing our on-demand webinar.

2 Comments Add your comment
Noel Stevens Noel Stevens says:

This is precisely the type of question our marketing analytics service is designed to answer. Using information from customers’ consumption, energy efficiency program tracking, and a utility’s past marketing efforts, we can estimate the likelihood of each customer to respond to marketing efforts. We can also model or assume a savings level for participants to identify the expected savings. This can readily be monetized to provide the value of expected savings from each customer. If the utility tracks costs to market to each customer, we can isolate a break-even point above which it is profitable to continue marketing and below which it is not.

Jeanne Clinton says:

This blog would be enhanced with some illustrative info on
1) costs to support these systems and the translation info effective cost per “needle” prospect; and
2) opining on scale of system needed, e.g. does it make most zense to do this for a specific program (implementer), per utility, or possibly on some statewide scale?

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