The challenge
An energy company wanted to use its operational data to drive customer engagement. They had water quality data from across their service territory — pH levels, turbidity measurements, contamination indicators — but no way to turn that data into something customers would actually use.
The initial brief was simple: build something with this data. The hard part was figuring out what "something" meant.
The engagement
We embedded with the team in early 2025. Before writing any code, we spent three weeks understanding the business: the customer service workflows, the regulatory environment, the competitive landscape, and — critically — what customers actually cared about.
What followed was five distinct product iterations, each one informed by the failures of the last.
Iteration 1: Leak detection
Our first approach used operational data to identify potential infrastructure issues before they became emergencies. Technically sound. Emotionally flat. Homeowners don't think about their water mains. They think about their families.
Iteration 2: Bacteria screening
We pivoted to contamination detection. Faster than lab testing, technically impressive. Also terrifying. Telling a homeowner about potential bacterial contamination creates panic, not engagement.
Iteration 3: Lab testing
A softer approach — proactive water quality testing positioned as a health checkup. The problem was speed. Lab results take days. By the time results arrived, customers had forgotten they asked.
Iteration 4: Real-time monitoring
Closer. Continuous water quality data, accessible through a simple interface. But raw data — pH levels, turbidity numbers — means nothing to someone who just wants to know if their water is safe for their dog.
Iteration 5: Personalized risk context
The version that worked. Real-time water quality data, personalized by ZIP code, with risk context tailored to what people actually care about: pets, infants, and personal health. Instead of metrics, it delivers meaning.
The result
The system now processes 50,000+ calls per month with a 90% reduction in manual review. The underlying data pipeline is essentially the same technology from iteration one. What changed was the framing, the personalization, and the emotional context.
Lessons
- Emotional resonance beats technical accuracy. Every version was technically sound. Only the one that connected emotionally succeeded.
- Five pivots require trust. The client funded all five iterations because we were embedded enough to have earned that patience.
- The technology was never the hard part. The same pipeline powers all five versions. The challenge was understanding what to build, not how to build it.
- Speed matters more than precision. Real-time feedback, even if less precise than lab results, wins on engagement every time.