Investigating & resolving customer orders at a glance
Unifying features from seven disparate systems to create a better employee experience, cut resolution times in half, and launch a predictive triage algorithm
Client a global logistics company
Roles interaction designer, user researcher, presenter
Duration 3 months
Traits #artificial intelligence #employee experience #product design #web app
Note: Details of this case study removed for client privacy. Contact me for more on this and other projects.
“Where is my order?”
49 times out of 50, our client’s customers don’t have to ask this question. But sometimes one of their tens of thousands of orders gets lost or misdirected, leading to customer frustration and an average four-day investigation and resolution period. Our mission: to cut down resolution time, improve their employee experience, and become more proactive in addressing problems with orders.
Finding the sweet spot between “too little” and “too much” information
Conducted over a dozen on-site user interviews and usability test of a prior concept, taking note of unspoken insights and their physical workspaces
Laid out the seven legacy systems they currently use side-by-side to compare data and construct the story of a problematic order
Observed how they communicate with their customers and asked how they wish they could
Plotted the many different types of data (timestamps, location data, images, facility information, order details, customer details, and more) along the axis that best helped the investigative process: time
Shaved off irrelevant or cryptic order information, while making details accessible with a single click
Worked with our developers to construct a predictive algorithm based on historical data, and designed the presentation of that data to point the agent in a helpful direction without being intimidating or implying false accuracy
Challenges converting raw data into actions
Surfacing the right information in the best contexts
Communicating algorithmic insights to the agent without removing agency or implying false accuracy
Helping our main stakeholder build client-side support for the product against bureaucracy and complacency
A domino-effect of better experiences
We were able to combine seven different antiquated resources into one focused, flexible tool that presented information contextually, proactively provided causation insights with an ~80% successful algorithm, dramatically improved the employee experience, and cut resolution times from four days to less than two.
This project directly led to at least four more streams of work with the client, including a 2.0 version of the tool and a downscaled version used by the entire company.
(Header image by Ivan Bandura.)