Marketing leaders in the utility sector are facing pressure to "personalize the customer journey" with AI. The intent is good: to use predictive analytics to target the right customer with the right program at exactly the right time. The problem is the growing disconnect between the money spent on AI marketing tools and the value reaching the customer.
In many cases, the technology is driving the marketing strategy, but it should be the other way around.
There’s a problem with a “technology-first” marketing strategy. When a company invests in a marketing automation platform without a clear operational roadmap, the result is an expensive layer of technology sitting on fragmented data and inconsistent messaging. The conversation becomes centered around “how do we use this tool?” when it should be focused on “what behavior are we trying to influence?” Outreach is shaped around what the technology can do, not what customers need.
The strategy must exist first. Technology comes second.
To keep strategy in the driver's seat, consider the following:
Most utilities are already sitting on plenty of customer data, like billing history, usage patterns, support calls, and app activity. That's usually enough to check whether a marketing idea is worth pursuing before spending money on a new system. For example, if you think customers will sign up for paperless billing after a surprise high bill, you can look back at people who already enrolled and see what happened right before they did. If a clear pattern shows up, the idea is worth building on. If it doesn't, a new AI platform won’t change that.
AI works best when the "operational path" is already paved. Before automating a campaign, make sure the business can handle the results. If a predictive model drives a 20% increase in program interest, is the back-office equipped to process enrollments? If AI finds 5,000 customers who'd benefit from budget billing before a seasonal spike, but signing them up is still a slow, manual process, the technology won’t really help. Build campaigns around what you want customers to do, not just because a feature happens to exist.
A business case for customer insights should be tied to tangible results (e.g. increased program adoption, higher self-service rates, improved cost-to-serve). An email with a 40% open rate sounds impressive, but if calls didn't go down and no one enrolled in anything, it didn't really do much. Optimize for what matters, not what’s easy to measure.
Practical AI in marketing should be an operational tool. When it's done right, it helps organizations do something specific and useful to improve the customer experience. AI can absolutely move utility marketing from reactive to proactive. It just requires the right strategy from the get-go. When strategy leads, AI can be used most effectively and drive real outcomes.
At Trenegy, we help utilities align processes and technology to support long-term changes like AI-powered customer service. To chat more about this, emailinfo@trenegy.com.
Trenegy is a non-traditional consulting firm based in Houston, Texas.
