Editor’s Note: This is the second installment of a three-part series discussing how Big Data can be exploited to enrich market segmentations.
Special programs that motivate customer behaviors are a common component of customer experience management. Examples include loyalty rewards, medical adherence and low-income bill payment assistance programs. Big Data can be particularly helpful—as part of a larger plan—to identify target segments and define strategic actions to support special programs. The underlying idea is to use customer and/or third party data to help identify customers who are likely to benefit from and be responsive to your brand’s special program.
A Smart Example from the Energy Industry
Consider the energy industry, which is motivated to move customers onto energy efficiency programs. Electric utilities have historically had access to monthly usage data, as measured by in-home meters or by employees who travel door-to-door to record the information from meter dials. With the adoption of wireless technologies, traditional meters are being replaced by smart meters that monitor and record electrical consumption at intervals as frequent as 15 minutes and report it electronically to the utility. This creates a Big Data source that provides more discrete measures of usage (i.e., usage amount per hour) versus the gross monthly measure that was available in the past. Armed with this level of information, we can create segments that better describe when and how people use energy.
Now, let’s think about what we could do if we combine some data sources to create our segments:
- We start with smart meter data that describes when and how people use energy
- We pair it up with weather data from the National Climate Data Center (NCDC) to understand the relationship between usage and weather events
- We add survey data to layer on attitudes about our topic of interest: energy efficiency, renewable energy and reactions to efficiency program concepts and elements
- We layer on third party data (such as socio-economic status and life stage from the Census or lifestyle interest from Acxiom) to help us understand how macro forces might interact with attitudes toward energy efficiency and explore specific issues related to these macro forces, as well as gain targetability
Using these combined traditional and emerging data sources, we can create a unified segmentation and use it to score all customers within the utility’s footprint for their likelihood to benefit from and sign up for our special program. Armed with this intelligence, we can create pilot programs for moving people onto energy efficiency programs. For example, we can find and create motivating messages and tactics for:
- Segments that are interested in energy efficiency programs.
- Segments that would benefit from the program if they change their behavior.
- Segments that are already acting in the way these programs want them to (e.g., lower usage during peak hours) and offer them access to the program so that they can realize benefits and become advocates.
Benefits of a Blended Approach
Because we’ve used an array of behavioral (smart meter data), third party (Census, Acxiom, NCDC) and primary (survey) data, we can create motivating messages and benefits to optimize the success of our pilot program. And we can monitor results in terms of how many people sign up for the program, whether their usage patterns shift in the desired direction and how many customers effect a large enough change to earn a benefit (such as a rebate).
The same basic approach can be used in other industries as well, where the advent of Big Data has sharpened the lens on customer behaviors that are being captured. By combining measured customer behaviors with other data—such as third party population data and attitudes—we help our marketing teams target and capture customer interest.
Our previous post in this series explored how Big Data can help identify adoption patterns in support of a product launch. Our final post will focus on how Big Data can be used to extend the life of your segmentation. Contact me or Ray Reno to discuss how to weave Big Data into your segmentation work or browse the many segmentation posts on this blog.
Special thanks to Dr. Raymond Reno for his contributions to this post.