We just get back from the 2013 Chartwell Utility Market Research Summit in sunny Phoenix, AZ.
All the utility attendees were on their game to discuss multiple solutions to improve customer satisfaction. We kicked off the meeting with several discussions that encompassed the following themes.
Segmentation came up several times as a great research approach with the mantra “We Must Know More”.
Most companies are looking “inward” at operations, programs, etc., and not out to the customer. But segmentation allows a utility to have a 360-degree single view of the customer and a more holistic view overall.
Segmentation also provides a wealth of info on attitudes, values, motivations and can help at all stages of engagement, such as with your messaging, campaigns, budget, and pre-pay billing.
Precise targeting is key. The C&I accounts are also part of the segmentation as one utility did segment their accounts to help with energy efficiency targets.
If you’re not measuring customer experience and improving satisfaction then your rate increase case is in jeopardy.
As utilities benchmark their touch points, some had new discoveries. For example, customers like telemarketing calls vs. emails, some like hard copy letters and newsletters vs. email. And message testing came up as a must for the rate increases, along with outage maps, mobile apps, and web testing.
The utilities that are building teams to strategically improve JD Power scores are finding that perception (in some cases outages that happened 5 years ago) is reality, and customers think that the utilities should “know me by my energy usage”
Market researchers have to lead and educate their internal clients about big data. What is it? Why is it important? To start, big data gets beyond the quantitative and qualitative; it's proactive and focuses on advanced analytics down to the individual to personalize services, products, and programs.
Certain trends continue to grow, such as mobile apps (which utilize point of sale/interactive research), social media, insight communities (which help flush out the story behind the data), and especially, predictive analytics. Combining customer data with the attitudinal allows utility companies to build crucial predictive models.