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Lifestyle Segmentation – An Unexpected Aid to Advanced Measurement and Validation

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Lifestyle Segmentation – An Unexpected Aid to Advanced Measurement and Validation

By Dave Chambers

At the national AESP conference earlier this year, attendees were openly questioning whether there were any more efficiencies left to be squeezed out of demand-side management (DSM). Given energy producers have already implemented many of the obvious conservation programs, some fear that the work needed to secure additional efficiencies will be too difficult and expensive to be worthwhile.

It’s a reasonable concern, but a quick survey of common M&V 2.0 methodologies shows it to be unfounded. There are additional cost effective ways to encourage greater residential program participation in conservation programs. Last May, EnergySavvy’s Tim Guiterman outlined one approach in an AESP webinar on Advanced M&V. In the presentation, Guiterman notes that projections of future energy savings among residential program participants can be made more accurate when participants are viewed as a handful of key segments instead of a monolithic whole.

Segmentation is simple to implement and has a solid track record for delivering results. One way to put Guiterman’s idea to work would be to incorporate segmentation with the smart meter data, weather data and other elements of your measurement and validation 2.0 process (M&V). Developing distinct offers and communications to a targeted audience is one of the most effective ways to get consumers to buy into energy savings programs. Here is a look at the benefits of this approach and how it can help producers squeeze even more out of their demand-side management programs.

First, let’s summarize the key benefits of incorporating lifestyle segmentation into M&V 2.0:

1. More Focused Objectives Equal Greater Stakeholder Buy-in. An effective segmentation system brings with it the ability to animate each key market segment with human and cultural characteristics, allowing all stakeholders to understand key behavioral differences (program adoption and participation, for example) as well as prevailing attitudes, values and beliefs.

2. More Granular Forecasts of Energy Savings. Adding lifestyle market segments to an Advanced M&V analysis matrix provides additional insights into key behaviors and better forecasts over time.


Why Use Lifestyle Segmentation?

Every statistician, analyst and data scientist wants to create his/her own approach to segmenting a customer file, or to apply AI/machine learning to the latest business problem. These are satisfying exercises at the outset, often made frustrating as delays ensue and costs rise before an effective solution is delivered.

By contrast, commercial lifestyle segmentation systems are less expensive, time-tested and fully integrated with a broad syndicate of related databases. These datasets provide segment-level insights on everything from energy behavior to social values, media preferences, social media habits, propensity for technology adoption, brand preferences and favorite activities.

Implementing Lifestyle Segmentation into M&V 2.0

For those not familiar with the process of incorporating lifestyle segmentation into M&V 2.0, it is helpful to know that every U.S. Block Group and ZIP+4 (and every Canadian 6-digit postal code) have been classified into a lifestyle segment (about 70 segments, depending on the system used). This allows every customer address to be privately assigned to a specific segment and then illuminated with data from a syndicate of public surveys such as the Claritas Energy Track Survey, MRI Media Usage survey or the U.S. Social Values data from Environics Research, to name a few.

Here is how this approach could be used to engage residential customers who have not responded to a previous DSM program.

Step One: Build the Profile

The following chart shows a residential file coded by PRIZM Premier lifestyle code (01 – 68, in socio-economic order, left to right). Those with an index above 120 provide our starting point. From these, we look for similarities in socio-economic, urbanicity (AKA Social Groups), family status (AKA Lifestage) and other key characteristics to be used in creating custom Target Groups with similar behaviors and characteristics.

Step Two: Identify Target Groups

In this case, our target groups are aggregations of similar segments that have not proven responsive to a DSM program. The segments are aggregated into these groups by cultural and life stage commonalities and in so doing, often provide insight into whether a single communication program can adequately appeal to all target groups or if more tailored messages are required.

If you look at all segments with an index above 110 you will notice two distinct groups: mid-to-downscale young (pre/early families) and a downscale/middle/upscale mature group. Using data visualization tools we can break these groups into smaller sub segments:

Condense your high-indexing segments into custom sub-segments with similar socio-economic and/or life stages, as shown here.

 

Since the family segments have previously been responsive to DSM programs, we will only use the young and mature segments to form target groups in this example. They are shown on the chart with the mature group split into three, as groups 2-4, according to their socio-economic score. Even if this split doesn’t justify a separate message, it’s worth tracking on the back end to see if socio-economic factors impact adoption.

Our subsegments constitute 49% of the market – the key focus for a customized communication addressing the concerns of each group.

This visualization shows each group by the size of the file (X axis, increasing in size as you move left to right) and the height of the index (Y axis). The four target groups account for 37.6% of the customer file and 49.1% of the qualifying subset.

Step 3: Personification

Each of the four target groups are then analyzed using a multitude of syndicated sources resulting in a broad understanding of market behaviors, attitudes and media habits. These insights may suggest amending the target groups in a process that can iterate several times. When complete, the marketer understands key aspects of each target group, which aids in communicating program objectives across all stakeholders, who will want to know:

- Who they are

- Where to find them

- What they buy

- How they think/what they believe

- What they do

The next step is to use these insights to determine how many messages and offers will be required to appeal to each target group, which is a conversation that involves budget authorities, agency partners and evaluators.

Step 4: Integrate M&V 2.0

The following steps are, arguably, more complex than the initial three and might seem to demand more space than allowed for here. But they have been well covered in previous articles and webinars, so allow me to summarize:

- Launch the DSM campaign to the selected target groups as well as a random control group from among the non-targeted segments.

- For each participant, pull historic daily usage data from their smart meter and combine with daily weather data for the same time period, identifying correlations by segment or target group.

- Apply these correlations to project seasonal savings for each participant (meter) using a response rate for each segment or target group.

Although it may seem as if past DSM efforts have nearly exhausted all possible efficiencies, the use of residential segmentation systems allows us to get more juice from the orange, so to speak. And as we explore the possibility of adoption among previous non-responders many of them will be found in what our industry typically defines as “the hard to reach segments”.

Lifestyle segmentation systems help answer the “why” behind these hard to reach segments, indicating whether they’re put off by technology, financially challenged or lack sufficient time or presence in the home.

Or it could be the residents you are targeting prefer communications that use their own language and cultural references. But that’s a topic for a different article.

Additional resources

For more on Advanced M&V, I highly recommend this AESP webinar by Abadi, Guiterman & Perkins. This wide-ranging Gridium interview of Mark Shainian on NMEC (Normalized Metered Energy Consumption) is also worth you time.

Dave Chambers leads the U.S. Energy Practice for Environics Analytics. Environics Analytics helps organizations turn data into insight, strategy and results to solve their key business challenges.

This article was contributed by the AESP Marketing Topic Committee.

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