Case study · Client Experience Platform
Asurion Client Experience Platform
Led design on the first launch of a new platform that helps internal teams and clients monitor service program performance.
The problem
Asurion's internal client teams and enterprise partners both struggled to make sense of program performance data.
Account teams spent enormous effort just standing up reporting: every new client meant building a fresh PowerBI dashboard (often two, one internal, one client-facing), with a steady stream of one-off requests to customize them further. Weekly, monthly, and quarterly business reviews required manual analysis from multiple people who then had to align on a client-friendly narrative before anyone could present it. Core metrics often moved undetected without proactive monitoring in place.
Clients, meanwhile, had no single source of truth. The data existed, but getting the story behind the numbers meant waiting on internal teams, sitting through scheduled reviews, and asking follow-up questions that should've been self-serve.
We needed a scalable way to help both audiences — internal teams and clients — assess and communicate program performance without starting from scratch for each account.
What we learned talking to both sides
I talked to external client partners and the internal account teams who work with them every day, to get the full picture from both sides of the relationship. A few things became clear fast:
Data isn't the problem. Context is.
Partners had plenty of numbers, but no way to know if they were good, bad, or worth acting on.
Without proactive monitoring, issues went unnoticed.
Knowing when to act was the hardest part, and nothing was built to support that.
The tools weren't keeping up.
Staying on top of performance meant manually listening to calls and digging through reports — reactive by default, and slow.
Tribal knowledge carried too much weight.
The data alone rarely told the full story. Real-world context from real people was always required, and no one-size-fits-all approach worked across programs at different phases of maturity.
Design process
Navigating ambiguity was the biggest challenge here. With dozens of issues for different potential user personas surfacing from discovery, it was difficult to figure out where to focus first. Here are a few ways I helped the team figure out the right starting point to learn from in the wild.
Prototyping to align the team
I explored several early concepts and used them to gather feedback from stakeholders and users, aligning the team around a focused MVP that set a solid foundation while leaving room to grow.
Data visualization and plain-language design
I dug into how to visualize performance data and explain it in plain language — exploring different approaches for both internal and client-facing users, since each group needed a different level of context and framing.
Stepping outside my lane
Our content designer was pulled onto another initiative partway through the project, so I picked up content work using the direction she'd already set, supplementing it with AI-generated drafts I then tailored to fit our system and voice.
This became especially important for our AI chat feature, built on Databricks' Genie. I supported the content instructions and the prompt suggestions that help users understand how to engage with the tool, which was aimed at replacing the old workflow of asking a data analyst to pull a chart or segment metrics with something users could just ask for directly. At one point this was slated as a phase 2 feature; I pushed to keep it in scope for the alpha so we could start learning from real usage sooner rather than later.
What we've learned so far
We launched the MVP in Q3 2026 to validate with a core group of internal users on one account team. We're pressure-testing how the platform fits into their existing workflows and where it creates real opportunities to reduce manual effort.
A few things we're watching for:
- Are they asking the AI chat questions of their data that would otherwise require analyst time?
- Are they setting up proactive alerts to stay on top of movement without manually checking a dashboard?
- Does the plain-language AI summary on the homepage help them understand recent performance at a glance?
From here, the plan is to launch to the client and validate whether the platform delivers the same value externally, specifically whether clients can get the answers they need without emailing their Asurion contact.
We're also pressure-testing the platform with additional clients in parallel. The goal is to scale to all clients in 2027, evolving this into the single source of truth for Asurion program performance for both internal account teams and clients alike.