Targeted Messages = Greater Value

By increasing the relevance of individual and targeted messaging, the value of the data will be improved.
Published: Thu 14 May 2015

Customer satisfaction is at the heart of loyalty and this is critical in competitive markets such as the energy industry, says Nancy Hersh, Vice President of Analytics at Opower, who co-presented in a recent webinar, Understanding the Energy Customer: Lessons from the world's largest energy usage analytics program.

Hersch explains that the energy industry should take advantage of the escalating quantity of consumer data and their energy consumption habits, thanks to smart meters and digital interaction data. But, this data must be used to create value for customers and the utilities that serve them.

Personalization-an essential engagement tool

According to Hersh, people crave personalization and companies are tapping into this market opportunity. The successes are evident-for instance, Coke’s “share a Coke” campaign has boosted sales.

“It’s fun and makes people feel special. But, for me, personalization is only the tip of the iceberg. Targeting different customers with different (and appropriate) content is important. Even marketing time is critical. This is where the true value lies. All customers are different and should be treated as such. If more care is taken when it comes to targeted personalization, this will translate into a real business value which will mean long term customer loyalty. Customers will feel that the company really understands their needs.”

While utilities have “upped their game” with regards to marketing, they are still not taking advantage of the real value of targeted personalization, explains Hersch. She points out that utilities continue to send the same messages to the same customers without any regard for individual customer needs and preferences. “There is a huge opportunity to close that gap,” she says.

Customer segmentation

It is important not to treat all customers the same way since they all have different needs and expectations based on personal preferences and consumption habits. It is critical that data is used effectively for accurate customer segmentation.

Hersh believes that in order to reach customers with relevant information, two points should be considered:

1.Profile data-demographic and psychographic information

2.Behaviour data- energy use profile, digital activity levels, energy efficiency actions,

In terms of statistics, Hersch explains that across the globe, 50% of utilities have some type of segmentation-the other half is undecided. “Most segmentation approaches rely a great deal on customer profile data. Very few in the energy industry use behaviour data at all which is unfortunate since it is far more predictive of future actions than profile data is. Future actions can be predicted from past actions and data should be readily available since it is available on all customers because it’s based on everyday consumption. Profile data can’t help you predict future actions as accurately.”

Meaningful and valuable data for customers and utilities

Hersh suggests that utilities start with the basics. For instance, a customer billing analysis can be used to work out what type of consumer he or she is. Based on these analyses, different messages will be sent out to different groups of people. Even if readings are done only twice a year, patterns can still be picked up on and turned into meaningful messages for customers.

Since customer bases are large, utilities have to rely on technology to supply more complex data. For instance, smart meter data reveals more complex data and makes a quicker analysis. Machine learning certainly automates the process of data assimilation and analysis. AMI archetypes can help pinpoint trends in usage and give utilities more insight in order to make better and more accurate decisions.

Says Hersh, “I see the future geared towards personalizing every customer touch point. Targeting is important for the “moments that matter” in the customer lifecycle. For instance, when someone moves home. By increasing the relevance of individual messages, the value of the data will be greatly improved.”