Cross-Industry Experience Strategy for Enterprise Energy Provider
A major Michigan utility serves 1.9 million customers across the state. I was brought in as a strategic partner midstream into an active, complex workstream. The client's experience team already had deep research, audience segmentation, and initiatives in progress. The need was to bring an outside lens to extend their existing thinking into structured opportunity paths.
The Problem
The client's programs are substantive and genuinely valuable to the customers they're designed to serve. The problem was discovery and awareness. Qualified customers lack awareness of what exactly they qualify for, and face uncertainty about what enrollment actually requires.
This is fundamentally a trust and access problem. The customer base is extremely diverse across socioeconomic range, technical capability, and channel access. Low institutional trust compounds every friction point in the enrollment journey. No single channel, flow, or message covers everyone.
My Role
The client's experience team was already simplifying their enrollment flow and introducing optimizations to customer dashboards. My role was to extend that thinking into a roadmap that considered how to compound the value of their existing pilots, while considering how onboarding, personalization and continued engagement could be improved over time.
I owned the strategic experience work, working on a tight timeline to synthesize the client's existing research and program data with a structured cross-industry analysis into a phased roadmap connecting near-term CX opportunities to longer-term infrastructure investments.
Methodology
I looked at industries solving analogous experience problems, focusing on trust barriers, enrollment friction, stigma-free self-identification, and serving users with diverse technical capabilities and literacy levels.
Industries analyzed included healthcare, fintech, consumer tech, insurance, and community finance. The selection criteria were defined based on the tensions identified in the problem space. AI accelerated the case study identification and synthesis; design judgment evaluated relevance and extracted transferable patterns into recommendations within the roadmap documentation.
Recommendations
The output was structured in three tiers, each building on the last: from extending what was already working, to augmenting existing outreach, to laying groundwork for longer-term transformation.
The first tier validated and extended initiatives already showing measurable results. The second introduced new approaches that could extend reach without requiring significant infrastructure change. The third laid groundwork for longer-term transformation including personalization at scale, persistent customer identity, and AI-driven engagement sequenced against real architectural dependencies. You can't build the far layer without the foundation the near layer establishes.
Each tier's feasibility was contingent on infrastructure established in the tier before it, which meant sequencing decisions had to account for organizational readiness, not just strategic ambition.
The roadmap isn't a feature list. It's dependency-mapped, which is what separates a strategic direction from a wishlist.
Outcome
The strategy was presented to the client's experience design leadership team, who confirmed it validated and extended their existing thinking. They subsequently carried it forward beyond the immediate engagement as part of their multi-year B2C roadmap.
Reflection
The most important decisions in this engagement weren't about what to recommend, they were about what not to do. Entering midstream meant getting to a working understanding of the problem space fast. The question became how to abstract the problem enough to find useful patterns outside the domain, while staying grounded enough to be actionable. The real value of an outside perspective isn't volume of ideas, it's the specific things you can see that the people already in the room can't.
